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Sample records for mcculloch-pitts artificial neurons

  1. An algebraic interpretation of PSP composition.

    Science.gov (United States)

    Vaucher, G

    1998-01-01

    The introduction of time in artificial neurons is a delicate problem on which many groups are working. Our approach combines some properties of biological models and the algebraic properties of McCulloch and Pitts artificial neuron (AN) (McCulloch and Pitts, 1943) to produce a new model which links both characteristics. In this extended artificial neuron, postsynaptic potentials (PSPs) are considered as numerical elements, having two degrees of freedom, on which the neuron computes operations. Modelled in this manner, a group of neurons can be seen as a computer with an asynchronous architecture. To formalize the functioning of this computer, we propose an algebra of impulses. This approach might also be interesting in the modelling of the passive electrical properties in some biological neurons.

  2. The Widrow-Hoff algorithm for McCulloch-Pitts type neurons.

    Science.gov (United States)

    Hui, S; Zak, S H

    1994-01-01

    We analyze the convergence properties of the Widrow-Hoff delta rule applied to McCulloch-Pitts type neurons. We give sufficiency conditions under which the learning parameters converge and conditions under which the learning parameters diverge. In particular, we analyze how the learning rate affects the convergence of the learning parameters.

  3. 134 Application des réseaux de neurones formels pour la prévision ...

    African Journals Online (AJOL)

    BEEMG

    [10] - W. JAMES, „„The Principles of Psychology‟‟. Vol. 1 (1890) 689. [11] - W. S. McCulloch et W. Pitts, „„A logical calculus ideas immanent in nervous activity‟‟. Bulletin of. Mathematical Biophysics, Vol. 5 (1943) 115-133. [12] - D. HEBB, „„The Organization of Behavior: A Neuropsychological Theory‟‟(1949) 378.

  4. A computational paradigm for dynamic logic-gates in neuronal activity

    Directory of Open Access Journals (Sweden)

    Amir eGoldental

    2014-04-01

    Full Text Available In 1943 McCulloch and Pitts suggested that the brain is composed of reliable logic-gates similar to the logic at the core of today's computers. This framework had a limited impact on neuroscience, since neurons exhibit far richer dynamics. Here we propose a new experimentally corroborated paradigm in which the truth tables of the brain's logic-gates are time dependent, i.e. dynamic logic-gates (DLGs. The truth tables of the DLGs depend on the history of their activity and the stimulation frequencies of their input neurons. Our experimental results are based on a procedure where conditioned stimulations were enforced on circuits of neurons embedded within a large-scale network of cortical cells in-vitro. We demonstrate that the underlying biological mechanism is the unavoidable increase of neuronal response latencies to ongoing stimulations, which imposes a non-uniform gradual stretching of network delays. The limited experimental results are confirmed and extended by simulations and theoretical arguments based on identical neurons with a fixed increase of the neuronal response latency per evoked spike. We anticipate our results to lead to better understanding of the suitability of this computational paradigm to account for the brain's functionalities and will require the development of new systematic mathematical methods beyond the methods developed for traditional Boolean algebra.

  5. Biological formal counterparts of logical machines

    Energy Technology Data Exchange (ETDEWEB)

    Moreno-diaz, R; Hernandez Guarch, F

    1983-01-01

    The significance of the McCulloch-Pitts formal neural net theory (1943) is still nowadays frequently misunderstood, and their basic units are wrongly considered as factual models for neurons. As a consequence, the whole original theory and its later addenda are unreasonably criticized for their simplicity. But, as it was proved then and since, the theory is after the modular neurophysiological counterpart of logical machines, so that it actually provides biologically plausible models for automata, turing machines, etc., and not vice versa. In its true context, no theory has surpassed its proposals. In McCulloch and Pitts memoriam and for the sake of future theoretical research, the authors stress this important historical point, including also some recent results on the neurophysiological counterparts of modular arbitrary probabilistic automata. 16 references.

  6. Genetics Home Reference: Pitt-Hopkins syndrome

    Science.gov (United States)

    ... 1 link) PubMed OMIM (1 link) PITT-HOPKINS SYNDROME Sources for This Page Amiel J, Rio M, de Pontual L, Redon R, Malan V, Boddaert N, Plouin P, Carter NP, Lyonnet S, Munnich A, Colleaux L. Mutations in TCF4, ... a severe epileptic encephalopathy associated with autonomic dysfunction. ...

  7. Artificial spatiotemporal touch inputs reveal complementary decoding in neocortical neurons.

    Science.gov (United States)

    Oddo, Calogero M; Mazzoni, Alberto; Spanne, Anton; Enander, Jonas M D; Mogensen, Hannes; Bengtsson, Fredrik; Camboni, Domenico; Micera, Silvestro; Jörntell, Henrik

    2017-04-04

    Investigations of the mechanisms of touch perception and decoding has been hampered by difficulties in achieving invariant patterns of skin sensor activation. To obtain reproducible spatiotemporal patterns of activation of sensory afferents, we used an artificial fingertip equipped with an array of neuromorphic sensors. The artificial fingertip was used to transduce real-world haptic stimuli into spatiotemporal patterns of spikes. These spike patterns were delivered to the skin afferents of the second digit of rats via an array of stimulation electrodes. Combined with low-noise intra- and extracellular recordings from neocortical neurons in vivo, this approach provided a previously inaccessible high resolution analysis of the representation of tactile information in the neocortical neuronal circuitry. The results indicate high information content in individual neurons and reveal multiple novel neuronal tactile coding features such as heterogeneous and complementary spatiotemporal input selectivity also between neighboring neurons. Such neuronal heterogeneity and complementariness can potentially support a very high decoding capacity in a limited population of neurons. Our results also indicate a potential neuroprosthetic approach to communicate with the brain at a very high resolution and provide a potential novel solution for evaluating the degree or state of neurological disease in animal models.

  8. Development, cognition, and behaviour in Pitt-Hopkins

    NARCIS (Netherlands)

    Van Balkom, I.D.; Vuijk, P.J.; Franssens, M.; Hoek, H.W.; Hennekam, R.C.

    2012-01-01

    Aim The aim of the study was to collect detailed data on behavioural, adaptive, and psychological functioning in 10 individuals with Pitt-Hopkins syndrome (PTHS), with specific attention to manifestations of autism spectrum disorder (ASD). Method The participants (four females, six males), residing

  9. A Neuron- and a Synapse Chip for Artificial Neural Networks

    DEFF Research Database (Denmark)

    Lansner, John; Lehmann, Torsten

    1992-01-01

    A cascadable, analog, CMOS chip set has been developed for hardware implementations of artificial neural networks (ANN's):I) a neuron chip containing an array of neurons with hyperbolic tangent activation functions and adjustable gains, and II) a synapse chip (or a matrix-vector multiplier) where...

  10. Rapid Integration of Artificial Sensory Feedback during Operant Conditioning of Motor Cortex Neurons.

    Science.gov (United States)

    Prsa, Mario; Galiñanes, Gregorio L; Huber, Daniel

    2017-02-22

    Neuronal motor commands, whether generating real or neuroprosthetic movements, are shaped by ongoing sensory feedback from the displacement being produced. Here we asked if cortical stimulation could provide artificial feedback during operant conditioning of cortical neurons. Simultaneous two-photon imaging and real-time optogenetic stimulation were used to train mice to activate a single neuron in motor cortex (M1), while continuous feedback of its activity level was provided by proportionally stimulating somatosensory cortex. This artificial signal was necessary to rapidly learn to increase the conditioned activity, detect correct performance, and maintain the learned behavior. Population imaging in M1 revealed that learning-related activity changes are observed in the conditioned cell only, which highlights the functional potential of individual neurons in the neocortex. Our findings demonstrate the capacity of animals to use an artificially induced cortical channel in a behaviorally relevant way and reveal the remarkable speed and specificity at which this can occur. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  11. Hr Pitt mängib hr Smithi / Timo Diener

    Index Scriptorium Estoniae

    Diener, Timo

    2005-01-01

    Brad Pitt actionkomöödias "Hr ja pr Smith" ("Mr and Mrs Smith") : režissöör Doug Liman : Ameerika Ühendriigid 2005. Filmi ümber puhkenud skandaalist ning näitleja plaanitavatest filmirollidest aastal 2006

  12. Notes from the Lost Property Department. Bridget Pitt. Cape Town ...

    African Journals Online (AJOL)

    as a loss of motor-skills and memory, and changes in behaviour. Bridget Pitt's Notes from the Lost Property Department (2015) is primarily concerned with the ways in which these invisible wounds cause dis- junctions in personal identity and fissures in relationships. ... together her cracked sense of self, all the time keeping ...

  13. Temperature profiles from MBT casts from the MCCULLOCH from Ocean Weather Station E (OWS-E) in the North Atlantic Ocean from 1967-09-24 to 1967-10-21 (NODC Accession 6700547)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Bathythermograph data were collected from the MCCULLOCH within a 1-mile radius of Ocean Weather Station E (3500N 04800W) and in transit. Data were collected by the...

  14. Artificial Induction of Associative Olfactory Memory by Optogenetic and Thermogenetic Activation of Olfactory Sensory Neurons and Octopaminergic Neurons in Drosophila Larvae.

    Science.gov (United States)

    Honda, Takato; Lee, Chi-Yu; Honjo, Ken; Furukubo-Tokunaga, Katsuo

    2016-01-01

    The larval brain of Drosophila melanogaster provides an excellent system for the study of the neurocircuitry mechanism of memory. Recent development of neurogenetic techniques in fruit flies enables manipulations of neuronal activities in freely behaving animals. This protocol describes detailed steps for artificial induction of olfactory associative memory in Drosophila larvae. In this protocol, the natural reward signal is substituted by thermogenetic activation of octopaminergic neurons in the brain. In parallel, the odor signal is substituted by optogenetic activation of a specific class of olfactory receptor neurons. Association of reward and odor stimuli is achieved with the concomitant application of blue light and heat that leads to activation of both sets of neurons in living transgenic larvae. Given its operational simplicity and robustness, this method could be utilized to further our knowledge on the neurocircuitry mechanism of memory in the fly brain.

  15. Artificial neuron-glia networks learning approach based on cooperative coevolution.

    Science.gov (United States)

    Mesejo, Pablo; Ibáñez, Oscar; Fernández-Blanco, Enrique; Cedrón, Francisco; Pazos, Alejandro; Porto-Pazos, Ana B

    2015-06-01

    Artificial Neuron-Glia Networks (ANGNs) are a novel bio-inspired machine learning approach. They extend classical Artificial Neural Networks (ANNs) by incorporating recent findings and suppositions about the way information is processed by neural and astrocytic networks in the most evolved living organisms. Although ANGNs are not a consolidated method, their performance against the traditional approach, i.e. without artificial astrocytes, was already demonstrated on classification problems. However, the corresponding learning algorithms developed so far strongly depends on a set of glial parameters which are manually tuned for each specific problem. As a consequence, previous experimental tests have to be done in order to determine an adequate set of values, making such manual parameter configuration time-consuming, error-prone, biased and problem dependent. Thus, in this paper, we propose a novel learning approach for ANGNs that fully automates the learning process, and gives the possibility of testing any kind of reasonable parameter configuration for each specific problem. This new learning algorithm, based on coevolutionary genetic algorithms, is able to properly learn all the ANGNs parameters. Its performance is tested on five classification problems achieving significantly better results than ANGN and competitive results with ANN approaches.

  16. Temperature profiles from MBT casts from the MCCULLOCH from Ocean Weather Station E (OWS-E) and H)OWS-H) in the North Atlantic Ocean from 1969-08-08 to 1969-09-09 (NODC Accession 7000051)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Bathythermograph data were collected from the MCCULLOCH within a 1-mile radius of Ocean Weather Station E (3500N 04800W), H (3800N 07100W), and in transit. Data were...

  17. Introduction to Concepts in Artificial Neural Networks

    Science.gov (United States)

    Niebur, Dagmar

    1995-01-01

    This introduction to artificial neural networks summarizes some basic concepts of computational neuroscience and the resulting models of artificial neurons. The terminology of biological and artificial neurons, biological and machine learning and neural processing is introduced. The concepts of supervised and unsupervised learning are explained with examples from the power system area. Finally, a taxonomy of different types of neurons and different classes of artificial neural networks is presented.

  18. Introduction : sur les traces de Julian Pitt-Rivers en Andalousie

    Directory of Open Access Journals (Sweden)

    Antoinette Molinié

    2008-07-01

    Full Text Available Je voudrais avant tout remercier Françoise Pitt-Rivers de l’honneur et de la confiance faits au Laboratoire d’ethnologie et de sociologie comparative en attribuant à la Bibliothèque Éric-de-Dampierre et à sa directrice Marie-Dominique Mouton la responsabilité des archives de son époux. Cette acquisition a permis d’ouvrir des perspectives nouvelles à la construction d’un véritable objet ethnologique à partir d’archives ethnographiques, soit le projet essentiel de notre programme d’aci. Nous a...

  19. Artificial Astrocytes Improve Neural Network Performance

    Science.gov (United States)

    Porto-Pazos, Ana B.; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

    2011-01-01

    Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function. PMID:21526157

  20. Artificial astrocytes improve neural network performance.

    Directory of Open Access Journals (Sweden)

    Ana B Porto-Pazos

    Full Text Available Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN and artificial neuron-glia networks (NGN to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

  1. Artificial astrocytes improve neural network performance.

    Science.gov (United States)

    Porto-Pazos, Ana B; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

    2011-04-19

    Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

  2. Phenotype and natural history in 101 individuals with Pitt-Hopkins syndrome through an internet questionnaire system

    NARCIS (Netherlands)

    de Winter, Channa F.; Baas, Melanie; Bijlsma, Emilia K.; van Heukelingen, John; Routledge, Sue; Hennekam, Raoul C. M.

    2016-01-01

    Pitt-Hopkins syndrome (PTHS; MIM# 610954) is a genetically determined entity mainly caused by mutations in TransCription Factor 4 (TCF4). We have developed a new way to collect information on (ultra-)rare disorders through a web-based database which we call 'waihonapedia' (waihona [meaning treasure

  3. A Hybrid Fuzzy Time Series Approach Based on Fuzzy Clustering and Artificial Neural Network with Single Multiplicative Neuron Model

    Directory of Open Access Journals (Sweden)

    Ozge Cagcag Yolcu

    2013-01-01

    Full Text Available Particularly in recent years, artificial intelligence optimization techniques have been used to make fuzzy time series approaches more systematic and improve forecasting performance. Besides, some fuzzy clustering methods and artificial neural networks with different structures are used in the fuzzification of observations and determination of fuzzy relationships, respectively. In approaches considering the membership values, the membership values are determined subjectively or fuzzy outputs of the system are obtained by considering that there is a relation between membership values in identification of relation. This necessitates defuzzification step and increases the model error. In this study, membership values were obtained more systematically by using Gustafson-Kessel fuzzy clustering technique. The use of artificial neural network with single multiplicative neuron model in identification of fuzzy relation eliminated the architecture selection problem as well as the necessity for defuzzification step by constituting target values from real observations of time series. The training of artificial neural network with single multiplicative neuron model which is used for identification of fuzzy relation step is carried out with particle swarm optimization. The proposed method is implemented using various time series and the results are compared with those of previous studies to demonstrate the performance of the proposed method.

  4. Urban-rural differences in excess mortality among high-poverty populations: evidence from the Harlem Household Survey and the Pitt County, North Carolina Study of African American Health.

    Science.gov (United States)

    Geronimus, Arline T; Colen, Cynthia G; Shochet, Tara; Ingber, Lori Barer; James, Sherman A

    2006-08-01

    Black youth residing in high-poverty areas have dramatically lower probabilities of surviving to age 65 if they are urban than if they are rural. Chronic disease deaths contribute heavily. We begin to probe the reasons using the Harlem Household Survey (HHS) and the Pitt County, North Carolina Study of African American Health (PCS). We compare HHS and PCS respondents on chronic disease rates, health behaviors, social support, employment, indicators of health care access, and health insurance. Chronic disease profiles do not favor Pitt County. Smoking uptake is similar across samples, but PCS respondents are more likely to quit. Indicators of access to health care and private health insurance are more favorable in Pitt County. Findings suggest rural mortality is averted through secondary or tertiary prevention, not primary. Macroeconomic and health system changes of the past 20 years may have left poor urban Blacks as medically underserved as poor rural Blacks.

  5. Widening the clinical spectrum of Pitt-Rogers-Danks/Wolf-Hirschhorn syndromes

    Directory of Open Access Journals (Sweden)

    Juliana F. Mazzeu

    2007-03-01

    Full Text Available Chromosomal rearrangements involving partial deletion of the short arm of chromosome 4 and partial duplication of the short arm of chromosome 8 have been described both in Pitt-Rogers-Danks syndrome (PRDS and Wolf-Hirschhorn syndrome (WHS, the former being considered a milder phenotype of the latter. We describe a patient with partial deletion of chromosome 4 and partial duplication of chromosome 8 documented by array-comparative genomic hybridization (Array-CGH. In addition to the typical features of PRDS, the patient exhibited some clinical signs (genital hypoplasia, radioulnar synostosis and mesomelic limb shortness infrequently, or never previously, reported in PRDS. These findings broaden the spectrum of anomalies generally associated with these syndromes.

  6. Application of a NAPL partitioning interwell tracer test (PITT) to support DNAPL remediation at the Sandia National Laboratories/New Mexico chemical waste landfill

    International Nuclear Information System (INIS)

    Studer, J.E.; Mariner, P.; Jin, M.

    1996-01-01

    Chlorinated solvents as dense non-aqueous phase liquid (DNAPL) are present at a large number of hazardous waste sites across the U.S. and world. DNAPL is difficult to detect in the subsurface, much less characterize to any degree of accuracy. Without proper site characterization, remedial decisions are often difficult to make and technically effective, cost-efficient remediations are even more difficult to obtain. A new non-aqueous phase liquid (NAPL) characterization technology that is superior to conventional technologies has been developed and applied at full-scale. This technology, referred to as the Partitioning Interwell Tracer Test (PITT), has been adopted from oil-field practices and tailored to environmental application in the vadose and saturated zones. A PITT has been applied for the first time at full-scale to characterize DNAPL in the vadose zone. The PITT was applied in December 1995 beneath two side-by-side organic disposal pits at Sandia National Laboratories/New Mexico (SNL/NM) RCRA Interim Status Chemical Waste Landfill (CWL), located in Albuquerque, New Mexico. DNAPL, consisting of a mixture of chlorinated solvents, aromatic hydrocarbons, and PCE oils, is known to exist in at least one of the two buried pits. The vadose zone PITT was conducted by injecting a slug of non-partitioning and NAPL-partitioning tracers into and through a zone of interest under a controlled forced gradient. The forced gradient was created by a balanced extraction of soil gas at a location 55 feet from the injector. The extracted gas stream was sampled over time to define tracer break-through curves. Soil gas sampling ports from multilevel monitoring installations were sampled to define break-through curves at specific locations and depths. Analytical instrumentation such as gas chromatographs and a photoacoustical analyzers operated autonomously, were used for tracer detection

  7. Spiking Neurons for Analysis of Patterns

    Science.gov (United States)

    Huntsberger, Terrance

    2008-01-01

    Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern-analysis and pattern-recognition computational systems. These neurons are represented by a mathematical model denoted the state-variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets. Spiking neurons imitate biological neurons more closely than do the neurons of traditional artificial neural networks. A spiking neuron includes a central cell body (soma) surrounded by a tree-like interconnection network (dendrites). Spiking neurons are so named because they generate trains of output pulses (spikes) in response to inputs received from sensors or from other neurons. They gain their speed advantage over traditional neural networks by using the timing of individual spikes for computation, whereas traditional artificial neurons use averages of activity levels over time. Moreover, spiking neurons use the delays inherent in dendritic processing in order to efficiently encode the information content of incoming signals. Because traditional artificial neurons fail to capture this encoding, they have less processing capability, and so it is necessary to use more gates when implementing traditional artificial neurons in electronic circuitry. Such higher-order functions as dynamic tasking are effected by use of pools (collections) of spiking neurons interconnected by spike-transmitting fibers. The SVM includes adaptive thresholds and submodels of transport of ions (in imitation of such transport in biological

  8. Pitt-Hopkins syndrome: report of a case with a TCF4 gene mutation

    Directory of Open Access Journals (Sweden)

    Orsini Alessandro

    2010-02-01

    Full Text Available Abstract Aims We will discuss the clinical and genetic diagnosis of a child with severe psychomotor delay, who at 3 years of age presented with paroxysms of hyperpnea-apnea and seizures unrelated to breathing anomalies. Methods The child underwent genetic (karyotype, FISH telomeres and neuroradiological (cranial CT and MRI tests, which proved to be normal. He came under our clinical observation at 3 years and 5 months of age. Due to severe psychomotor delay and facial dysmorphisms we completed the genetic investigations based on his clinical feature and analysis of the available literature. Results The presence of severe mental retardation associated with anomalous breathing pattern may suggest the Joubert and Rett syndrome, however these were excluded on the basis of clinical and genetic examination. Angelman syndrome, suspected for facial dysmorphisms and absent language, was also excluded because of the presence of a normal pattern of methylation at SNRPN locus. Another possible diagnosis was the Pitt-Hopkins Syndrome (PHS, characterized by severe mental retardation, breathing anomalies (paroxisms of hyperpnea-apnea, dysmorphisms and sometimes epilepsy. Haploinsufficiency of TCF4 gene located at 18q21.2 region has been recently identified as causative of this syndrome. In our patient the research of TCF4 mutation by the Institute of Human Genetics, University Hospital Erlangen (Germany, showed a de novo mutation. Conclusions The diagnosis of Pitt-Hopkins syndrome, an underdiagnosed cause of mental retardation, was based on clinical and genetic findings. Searching for TCF4 mutations is highly recommended when others overlapping syndromes was excluded. At our knowledge our patient is the first italian case of PHS diagnosed at molecular level.

  9. Osteopathic Manipulative Treatment Limits Chronic Constipation in a Child with Pitt-Hopkins Syndrome

    Directory of Open Access Journals (Sweden)

    Alessandro Aquino

    2017-01-01

    Full Text Available Pitt-Hopkins Syndrome (PTHS is a rare genetic disorder caused by insufficient expression of the TCF4 gene. Children with PTHS typically present with gastrointestinal disorders and early severe chronic constipation is frequently found (75%. Here we describe the case of a PTHS male 10-year-old patient with chronic constipation in whom Osteopathic Manipulative Treatment (OMT resulted in improved bowel functions, as assessed by the diary, the QPGS-Form A Section C questionnaire, and the Paediatric Bristol Stool Form Scale. The authors suggested that OMT may be a valid tool to improve the defecation frequency and reduce enema administration in PTHS patients.

  10. Artificial neuron operations and spike-timing-dependent plasticity using memristive devices for brain-inspired computing

    Science.gov (United States)

    Marukame, Takao; Nishi, Yoshifumi; Yasuda, Shin-ichi; Tanamoto, Tetsufumi

    2018-04-01

    The use of memristive devices for creating artificial neurons is promising for brain-inspired computing from the viewpoints of computation architecture and learning protocol. We present an energy-efficient multiplier accumulator based on a memristive array architecture incorporating both analog and digital circuitries. The analog circuitry is used to full advantage for neural networks, as demonstrated by the spike-timing-dependent plasticity (STDP) in fabricated AlO x /TiO x -based metal-oxide memristive devices. STDP protocols for controlling periodic analog resistance with long-range stability were experimentally verified using a variety of voltage amplitudes and spike timings.

  11. Cross-sectional association between perceived discrimination and hypertension in African-American men and women: the Pitt County Study.

    Science.gov (United States)

    Roberts, Calpurnyia B; Vines, Anissa I; Kaufman, Jay S; James, Sherman A

    2008-03-01

    Few studies have examined the impact of the frequency of discrimination on hypertension risk. The authors assessed the cross-sectional associations between frequency of perceived racial and nonracial discrimination and hypertension among 1,110 middle-aged African-American men (n = 393) and women (n = 717) participating in the 2001 follow-up of the Pitt County Study (Pitt County, North Carolina). Odds ratios were estimated using gender-specific unconditional weighted logistic regression with adjustment for relevant confounders and the frequency of discrimination. More than half of the men (57%) and women (55%) were hypertensive. The prevalences of perceived racial discrimination, nonracial discrimination, and no discrimination were 57%, 29%, and 13%, respectively, in men and 42%, 43%, and 15%, respectively, in women. Women recounting frequent nonracial discrimination versus those reporting no exposure to discrimination had the highest odds of hypertension (adjusted odds ratio = 2.34, 95% confidence interval: 1.09, 5.02). A nonsignificant inverse odds ratio was evident in men who perceived frequent exposure to racial or nonracial discrimination in comparison with no exposure. A similar association was observed for women reporting perceived racial discrimination. These results indicate that the type and frequency of discrimination perceived by African-American men and women may differentially affect their risk of hypertension.

  12. Computational Models of Neuron-Astrocyte Interactions Lead to Improved Efficacy in the Performance of Neural Networks

    Science.gov (United States)

    Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B.

    2012-01-01

    The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem. PMID:22649480

  13. Single neuron computation

    CERN Document Server

    McKenna, Thomas M; Zornetzer, Steven F

    1992-01-01

    This book contains twenty-two original contributions that provide a comprehensive overview of computational approaches to understanding a single neuron structure. The focus on cellular-level processes is twofold. From a computational neuroscience perspective, a thorough understanding of the information processing performed by single neurons leads to an understanding of circuit- and systems-level activity. From the standpoint of artificial neural networks (ANNs), a single real neuron is as complex an operational unit as an entire ANN, and formalizing the complex computations performed by real n

  14. The principles of artificial neural network information processing

    International Nuclear Information System (INIS)

    Dai, Ru-Wei

    1993-01-01

    In this article, the basic structure of an artificial neuron is first introduced. In addition, principles of artificial neural network as well as several important artificial neural models such as Perceptron, Back propagation model, Hopfield net, and ART model are briefly discussed and analyzed. Finally, the application of artificial neural network for Chinese Character Recognition is also given. (author)

  15. The principles of artificial neural network information processing

    International Nuclear Information System (INIS)

    Dai, Ru-Wei

    1993-01-01

    In this article, the basic structure of an artificial neuron is first introduced. In addition, principles of artificial neural network as well as several important artificial neural models such as perception, back propagation model, Hopfield net, and ART model are briefly discussed and analyzed. Finally the application of artificial neural network for Chinese character recognition is also given. (author)

  16. A neuron-astrocyte transistor-like model for neuromorphic dressed neurons.

    Science.gov (United States)

    Valenza, G; Pioggia, G; Armato, A; Ferro, M; Scilingo, E P; De Rossi, D

    2011-09-01

    Experimental evidences on the role of the synaptic glia as an active partner together with the bold synapse in neuronal signaling and dynamics of neural tissue strongly suggest to investigate on a more realistic neuron-glia model for better understanding human brain processing. Among the glial cells, the astrocytes play a crucial role in the tripartite synapsis, i.e. the dressed neuron. A well-known two-way astrocyte-neuron interaction can be found in the literature, completely revising the purely supportive role for the glia. The aim of this study is to provide a computationally efficient model for neuron-glia interaction. The neuron-glia interactions were simulated by implementing the Li-Rinzel model for an astrocyte and the Izhikevich model for a neuron. Assuming the dressed neuron dynamics similar to the nonlinear input-output characteristics of a bipolar junction transistor, we derived our computationally efficient model. This model may represent the fundamental computational unit for the development of real-time artificial neuron-glia networks opening new perspectives in pattern recognition systems and in brain neurophysiology. Copyright © 2011 Elsevier Ltd. All rights reserved.

  17. Impairment of different protein domains causes variable clinical presentation within Pitt-Hopkins syndrome and suggests intragenic molecular syndromology of TCF4.

    Science.gov (United States)

    Bedeschi, Maria Francesca; Marangi, Giuseppe; Calvello, Maria Rosaria; Ricciardi, Stefania; Leone, Francesca Pia Chiara; Baccarin, Marco; Guerneri, Silvana; Orteschi, Daniela; Murdolo, Marina; Lattante, Serena; Frangella, Silvia; Keena, Beth; Harr, Margaret H; Zackai, Elaine; Zollino, Marcella

    2017-11-01

    Pitt-Hopkins syndrome is a neurodevelopmental disorder characterized by severe intellectual disability and a distinctive facial gestalt. It is caused by haploinsufficiency of the TCF4 gene. The TCF4 protein has different functional domains, with the NLS (nuclear localization signal) domain coded by exons 7-8 and the bHLH (basic Helix-Loop-Helix) domain coded by exon 18. Several alternatively spliced TCF4 variants have been described, allowing for translation of variable protein isoforms. Typical PTHS patients have impairment of at least the bHLH domain. To which extent impairment of the remaining domains contributes to the final phenotype is not clear. There is recent evidence that certain loss-of-function variants disrupting TCF4 are associated with mild ID, but not with typical PTHS. We describe a frameshift-causing partial gene deletion encompassing exons 4-6 of TCF4 in an adult patient with mild ID and nonspecific facial dysmorphisms but without the typical features of PTHS, and a c.520C > T nonsense variant within exon 8 in a child presenting with a severe phenotype largely mimicking PTHS, but lacking the typical facial dysmorphism. Investigation on mRNA, along with literature review, led us to suggest a preliminary phenotypic map of loss-of-function variants affecting TCF4. An intragenic phenotypic map of loss-of-function variants in TCF4 is suggested here for the first time: variants within exons 1-4 and exons 4-6 give rise to a recurrent phenotype with mild ID not in the spectrum of Pitt-Hopkins syndrome (biallelic preservation of both the NLS and bHLH domains); variants within exons 7-8 cause a severe phenotype resembling PTHS but in absence of the typical facial dysmorphism (impairment limited to the NLS domain); variants within exons 9-19 cause typical Pitt-Hopkins syndrome (impairment of at least the bHLH domain). Understanding the TCF4 molecular syndromology can allow for proper nosology in the current era of whole genomic investigations. Copyright

  18. Behavioral plasticity through the modulation of switch neurons.

    Science.gov (United States)

    Vassiliades, Vassilis; Christodoulou, Chris

    2016-02-01

    A central question in artificial intelligence is how to design agents capable of switching between different behaviors in response to environmental changes. Taking inspiration from neuroscience, we address this problem by utilizing artificial neural networks (NNs) as agent controllers, and mechanisms such as neuromodulation and synaptic gating. The novel aspect of this work is the introduction of a type of artificial neuron we call "switch neuron". A switch neuron regulates the flow of information in NNs by selectively gating all but one of its incoming synaptic connections, effectively allowing only one signal to propagate forward. The allowed connection is determined by the switch neuron's level of modulatory activation which is affected by modulatory signals, such as signals that encode some information about the reward received by the agent. An important aspect of the switch neuron is that it can be used in appropriate "switch modules" in order to modulate other switch neurons. As we show, the introduction of the switch modules enables the creation of sequences of gating events. This is achieved through the design of a modulatory pathway capable of exploring in a principled manner all permutations of the connections arriving on the switch neurons. We test the model by presenting appropriate architectures in nonstationary binary association problems and T-maze tasks. The results show that for all tasks, the switch neuron architectures generate optimal adaptive behaviors, providing evidence that the switch neuron model could be a valuable tool in simulations where behavioral plasticity is required. Copyright © 2015 Elsevier Ltd. All rights reserved.

  19. Operant Conditioning: A Minimal Components Requirement in Artificial Spiking Neurons Designed for Bio-Inspired Robot’s Controller

    Directory of Open Access Journals (Sweden)

    André eCyr

    2014-07-01

    Full Text Available We demonstrate the operant conditioning (OC learning process within a basic bio-inspired robot controller paradigm, using an artificial spiking neural network (ASNN with minimal component count as artificial brain. In biological agents, OC results in behavioral changes that are learned from the consequences of previous actions, using progressive prediction adjustment triggered by reinforcers. In a robotics context, virtual and physical robots may benefit from a similar learning skill when facing unknown environments with no supervision. In this work, we demonstrate that a simple ASNN can efficiently realise many OC scenarios. The elementary learning kernel that we describe relies on a few critical neurons, synaptic links and the integration of habituation and spike-timing dependent plasticity (STDP as learning rules. Using four tasks of incremental complexity, our experimental results show that such minimal neural component set may be sufficient to implement many OC procedures. Hence, with the described bio-inspired module, OC can be implemented in a wide range of robot controllers, including those with limited computational resources.

  20. Spin orbit torque based electronic neuron

    Energy Technology Data Exchange (ETDEWEB)

    Sengupta, Abhronil, E-mail: asengup@purdue.edu; Choday, Sri Harsha; Kim, Yusung; Roy, Kaushik [School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana 47907 (United States)

    2015-04-06

    A device based on current-induced spin-orbit torque (SOT) that functions as an electronic neuron is proposed in this work. The SOT device implements an artificial neuron's thresholding (transfer) function. In the first step of a two-step switching scheme, a charge current places the magnetization of a nano-magnet along the hard-axis, i.e., an unstable point for the magnet. In the second step, the SOT device (neuron) receives a current (from the synapses) which moves the magnetization from the unstable point to one of the two stable states. The polarity of the synaptic current encodes the excitatory and inhibitory nature of the neuron input and determines the final orientation of the magnetization. A resistive crossbar array, functioning as synapses, generates a bipolar current that is a weighted sum of the inputs. The simulation of a two layer feed-forward artificial neural network based on the SOT electronic neuron shows that it consumes ∼3× lower power than a 45 nm digital CMOS implementation, while reaching ∼80% accuracy in the classification of 100 images of handwritten digits from the MNIST dataset.

  1. Spin orbit torque based electronic neuron

    International Nuclear Information System (INIS)

    Sengupta, Abhronil; Choday, Sri Harsha; Kim, Yusung; Roy, Kaushik

    2015-01-01

    A device based on current-induced spin-orbit torque (SOT) that functions as an electronic neuron is proposed in this work. The SOT device implements an artificial neuron's thresholding (transfer) function. In the first step of a two-step switching scheme, a charge current places the magnetization of a nano-magnet along the hard-axis, i.e., an unstable point for the magnet. In the second step, the SOT device (neuron) receives a current (from the synapses) which moves the magnetization from the unstable point to one of the two stable states. The polarity of the synaptic current encodes the excitatory and inhibitory nature of the neuron input and determines the final orientation of the magnetization. A resistive crossbar array, functioning as synapses, generates a bipolar current that is a weighted sum of the inputs. The simulation of a two layer feed-forward artificial neural network based on the SOT electronic neuron shows that it consumes ∼3× lower power than a 45 nm digital CMOS implementation, while reaching ∼80% accuracy in the classification of 100 images of handwritten digits from the MNIST dataset

  2. Sweet taste and nutrient value subdivide rewarding dopaminergic neurons in Drosophila.

    Science.gov (United States)

    Huetteroth, Wolf; Perisse, Emmanuel; Lin, Suewei; Klappenbach, Martín; Burke, Christopher; Waddell, Scott

    2015-03-16

    Dopaminergic neurons provide reward learning signals in mammals and insects [1-4]. Recent work in Drosophila has demonstrated that water-reinforcing dopaminergic neurons are different to those for nutritious sugars [5]. Here, we tested whether the sweet taste and nutrient properties of sugar reinforcement further subdivide the fly reward system. We found that dopaminergic neurons expressing the OAMB octopamine receptor [6] specifically convey the short-term reinforcing effects of sweet taste [4]. These dopaminergic neurons project to the β'2 and γ4 regions of the mushroom body lobes. In contrast, nutrient-dependent long-term memory requires different dopaminergic neurons that project to the γ5b regions, and it can be artificially reinforced by those projecting to the β lobe and adjacent α1 region. Surprisingly, whereas artificial implantation and expression of short-term memory occur in satiated flies, formation and expression of artificial long-term memory require flies to be hungry. These studies suggest that short-term and long-term sugar memories have different physiological constraints. They also demonstrate further functional heterogeneity within the rewarding dopaminergic neuron population. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  3. The characteristic patterns of neuronal avalanches in mice under anesthesia and at rest: An investigation using constrained artificial neural networks

    Science.gov (United States)

    Knöpfel, Thomas; Leech, Robert

    2018-01-01

    Local perturbations within complex dynamical systems can trigger cascade-like events that spread across significant portions of the system. Cascades of this type have been observed across a broad range of scales in the brain. Studies of these cascades, known as neuronal avalanches, usually report the statistics of large numbers of avalanches, without probing the characteristic patterns produced by the avalanches themselves. This is partly due to limitations in the extent or spatiotemporal resolution of commonly used neuroimaging techniques. In this study, we overcome these limitations by using optical voltage (genetically encoded voltage indicators) imaging. This allows us to record cortical activity in vivo across an entire cortical hemisphere, at both high spatial (~30um) and temporal (~20ms) resolution in mice that are either in an anesthetized or awake state. We then use artificial neural networks to identify the characteristic patterns created by neuronal avalanches in our data. The avalanches in the anesthetized cortex are most accurately classified by an artificial neural network architecture that simultaneously connects spatial and temporal information. This is in contrast with the awake cortex, in which avalanches are most accurately classified by an architecture that treats spatial and temporal information separately, due to the increased levels of spatiotemporal complexity. This is in keeping with reports of higher levels of spatiotemporal complexity in the awake brain coinciding with features of a dynamical system operating close to criticality. PMID:29795654

  4. Artificial Intelligence and brain.

    Science.gov (United States)

    Shapshak, Paul

    2018-01-01

    From the start, Kurt Godel observed that computer and brain paradigms were considered on a par by researchers and that researchers had misunderstood his theorems. He hailed with displeasure that the brain transcends computers. In this brief article, we point out that Artificial Intelligence (AI) comprises multitudes of human-made methodologies, systems, and languages, and implemented with computer technology. These advances enhance development in the electron and quantum realms. In the biological realm, animal neurons function, also utilizing electron flow, and are products of evolution. Mirror neurons are an important paradigm in neuroscience research. Moreover, the paradigm shift proposed here - 'hall of mirror neurons' - is a potentially further productive research tactic. These concepts further expand AI and brain research.

  5. Training Spiking Neural Models Using Artificial Bee Colony

    Science.gov (United States)

    Vazquez, Roberto A.; Garro, Beatriz A.

    2015-01-01

    Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, several bioinspired algorithms have been proposed in the last years for solving a broad range of optimization problems, including those related to the field of artificial neural networks (ANNs). Artificial bee colony (ABC) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source. In this paper, we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems. Finally, the proposed approach is tested on several pattern recognition problems. It is important to remark that to realize the powerfulness of this type of model only one neuron will be used. In addition, we analyze how the performance of these models is improved using this kind of learning strategy. PMID:25709644

  6. Parvalbumin+ Neurons and Npas1+ Neurons Are Distinct Neuron Classes in the Mouse External Globus Pallidus.

    Science.gov (United States)

    Hernández, Vivian M; Hegeman, Daniel J; Cui, Qiaoling; Kelver, Daniel A; Fiske, Michael P; Glajch, Kelly E; Pitt, Jason E; Huang, Tina Y; Justice, Nicholas J; Chan, C Savio

    2015-08-26

    Compelling evidence suggests that pathological activity of the external globus pallidus (GPe), a nucleus in the basal ganglia, contributes to the motor symptoms of a variety of movement disorders such as Parkinson's disease. Recent studies have challenged the idea that the GPe comprises a single, homogenous population of neurons that serves as a simple relay in the indirect pathway. However, we still lack a full understanding of the diversity of the neurons that make up the GPe. Specifically, a more precise classification scheme is needed to better describe the fundamental biology and function of different GPe neuron classes. To this end, we generated a novel multicistronic BAC (bacterial artificial chromosome) transgenic mouse line under the regulatory elements of the Npas1 gene. Using a combinatorial transgenic and immunohistochemical approach, we discovered that parvalbumin-expressing neurons and Npas1-expressing neurons in the GPe represent two nonoverlapping cell classes, amounting to 55% and 27% of the total GPe neuron population, respectively. These two genetically identified cell classes projected primarily to the subthalamic nucleus and to the striatum, respectively. Additionally, parvalbumin-expressing neurons and Npas1-expressing neurons were distinct in their autonomous and driven firing characteristics, their expression of intrinsic ion conductances, and their responsiveness to chronic 6-hydroxydopamine lesion. In summary, our data argue that parvalbumin-expressing neurons and Npas1-expressing neurons are two distinct functional classes of GPe neurons. This work revises our understanding of the GPe, and provides the foundation for future studies of its function and dysfunction. Until recently, the heterogeneity of the constituent neurons within the external globus pallidus (GPe) was not fully appreciated. We addressed this knowledge gap by discovering two principal GPe neuron classes, which were identified by their nonoverlapping expression of the

  7. Parvalbumin+ Neurons and Npas1+ Neurons Are Distinct Neuron Classes in the Mouse External Globus Pallidus

    Science.gov (United States)

    Hernández, Vivian M.; Hegeman, Daniel J.; Cui, Qiaoling; Kelver, Daniel A.; Fiske, Michael P.; Glajch, Kelly E.; Pitt, Jason E.; Huang, Tina Y.; Justice, Nicholas J.

    2015-01-01

    Compelling evidence suggests that pathological activity of the external globus pallidus (GPe), a nucleus in the basal ganglia, contributes to the motor symptoms of a variety of movement disorders such as Parkinson's disease. Recent studies have challenged the idea that the GPe comprises a single, homogenous population of neurons that serves as a simple relay in the indirect pathway. However, we still lack a full understanding of the diversity of the neurons that make up the GPe. Specifically, a more precise classification scheme is needed to better describe the fundamental biology and function of different GPe neuron classes. To this end, we generated a novel multicistronic BAC (bacterial artificial chromosome) transgenic mouse line under the regulatory elements of the Npas1 gene. Using a combinatorial transgenic and immunohistochemical approach, we discovered that parvalbumin-expressing neurons and Npas1-expressing neurons in the GPe represent two nonoverlapping cell classes, amounting to 55% and 27% of the total GPe neuron population, respectively. These two genetically identified cell classes projected primarily to the subthalamic nucleus and to the striatum, respectively. Additionally, parvalbumin-expressing neurons and Npas1-expressing neurons were distinct in their autonomous and driven firing characteristics, their expression of intrinsic ion conductances, and their responsiveness to chronic 6-hydroxydopamine lesion. In summary, our data argue that parvalbumin-expressing neurons and Npas1-expressing neurons are two distinct functional classes of GPe neurons. This work revises our understanding of the GPe, and provides the foundation for future studies of its function and dysfunction. SIGNIFICANCE STATEMENT Until recently, the heterogeneity of the constituent neurons within the external globus pallidus (GPe) was not fully appreciated. We addressed this knowledge gap by discovering two principal GPe neuron classes, which were identified by their nonoverlapping

  8. Captain Nemo/Lt-General Pitt Rivers and Cleopatra’s Needle — A Story of Flagships

    Directory of Open Access Journals (Sweden)

    Christopher Evans

    2005-11-01

    Full Text Available Recently re-reading Verne’s 20,000 Leagues Beneath the Sea for our children I was struck by the marked similarities between the novel’s elusive protagonist, Captain Nemo, and the renowned later 19th century British archaeologist, Lt.-General Pitt Rivers. Could they have been the same person? How could something so seemingly blatant have gone unnoticed? These questions are, of course, only raised in a spirit of academic tongue-in-check. Yet, in an ethos of ‘learning through amusement’ (itself directly relevant to the themes of this study, exploring the parallels between these two ‘heroic’ individuals provides insights into the nature of 19th century science, Victorian edification and disciplinary institutionalisation (e.g. Levine 1986. This eclectic contribution will, moreover, be introduced with the third component of its headline title – Cleopatra’s Needle – as this provides an appropriately quasinautical parable on the project of 19th century archaeology and the problem of ‘deep time’ (Murray 1993.

  9. Doubly stochastic Poisson processes in artificial neural learning.

    Science.gov (United States)

    Card, H C

    1998-01-01

    This paper investigates neuron activation statistics in artificial neural networks employing stochastic arithmetic. It is shown that a doubly stochastic Poisson process is an appropriate model for the signals in these circuits.

  10. COMPUTER DYNAMICS SIMULATION OF DRUG DEPENDENCE THROUGH ARTIFICIAL NEURONAL NETWORK: PEDAGOGICAL AND CLINICAL IMPLICATIONS

    Directory of Open Access Journals (Sweden)

    G. SANTOS

    2008-05-01

    Full Text Available To develop and to evaluate the efficiency of a software able to simulate a virtual patient at different stages of addition was the main goal and challenge of this work. We developed the software in Borland™ Delphi  5®  programming language. Techniques of artificial intelligence, neuronal networks and expert systems, were responsible for modeling the neurobiological structures and mechanisms of the interaction with the drugs used. Dynamical simulation and  hypermedia were designed to increase the software’s interactivity which was able to show graphical information from virtual instrumentation and from realistic functional magnetic resonance imaging display. Early, the program was designed to be used by undergraduate students to improve their neurophysiologic learn, based not only in an interaction of membrane receptors with drugs, but in such a large behavioral simulation. The experimental manipulation of the software was accomplished by: i creating a virtual patient from a normal mood to a behavioral addiction, increasing gradatively: alcohol, opiate or cocaine doses. ii designing an approach to treat the patient, to get total or partial remission of behavioral disorder by combining psychopharmacology and psychotherapy. Integration of dynamic simulation with hypermedia and artificial intelligence has been able to point out behavioral details as tolerance, sensitization and level of addiction to drugs of abuse and so on, turned into a potentially useful tool in the development of teaching activities on several ways, such as education as well clinical skills, in which it could assist patients, families and health care to improve and test their knowledge and skills about different faces supported by drugs dependency. Those features are currently under investigation.

  11. The straintronic spin-neuron

    International Nuclear Information System (INIS)

    Biswas, Ayan K; Bandyopadhyay, Supriyo; Atulasimha, Jayasimha

    2015-01-01

    In artificial neural networks, neurons are usually implemented with highly dissipative CMOS-based operational amplifiers. A more energy-efficient implementation is a ‘spin-neuron’ realized with a magneto-tunneling junction (MTJ) that is switched with a spin-polarized current (representing weighted sum of input currents) that either delivers a spin transfer torque or induces domain wall motion in the soft layer of the MTJ to mimic neuron firing. Here, we propose and analyze a different type of spin-neuron in which the soft layer of the MTJ is switched with mechanical strain generated by a voltage (representing weighted sum of input voltages) and term it straintronic spin-neuron. It dissipates orders of magnitude less energy in threshold operations than the traditional current-driven spin neuron at 0 K temperature and may even be faster. We have also studied the room-temperature firing behaviors of both types of spin neurons and find that thermal noise degrades the performance of both types, but the current-driven type is degraded much more than the straintronic type if both are optimized for maximum energy-efficiency. On the other hand, if both are designed to have the same level of thermal degradation, then the current-driven version will dissipate orders of magnitude more energy than the straintronic version. Thus, the straintronic spin-neuron is superior to current-driven spin neurons. (paper)

  12. [Artificial intelligence in psychiatry-an overview].

    Science.gov (United States)

    Meyer-Lindenberg, A

    2018-06-18

    Artificial intelligence and the underlying methods of machine learning and neuronal networks (NN) have made dramatic progress in recent years and have allowed computers to reach superhuman performance in domains that used to be thought of as uniquely human. In this overview, the underlying methodological developments that made this possible are briefly delineated and then the applications to psychiatry in three domains are discussed: precision medicine and biomarkers, natural language processing and artificial intelligence-based psychotherapeutic interventions. In conclusion, some of the risks of this new technology are mentioned.

  13. An experimental biomimetic platform for artificial olfaction.

    Directory of Open Access Journals (Sweden)

    Corrado Di Natale

    Full Text Available Artificial olfactory systems have been studied for the last two decades mainly from the point of view of the features of olfactory neuron receptor fields. Other fundamental olfaction properties have only been episodically considered in artificial systems. As a result, current artificial olfactory systems are mostly intended as instruments and are of poor benefit for biologists who may need tools to model and test olfactory models. Herewith, we show how a simple experimental approach can be used to account for several phenomena observed in olfaction. An artificial epithelium is formed as a disordered distributed layer of broadly selective color indicators dispersed in a transparent polymer layer. The whole epithelium is probed with colored light, imaged with a digital camera and the olfactory response upon exposure to an odor is the change of the multispectral image. The pixels are treated as olfactory receptor neurons, whose optical properties are used to build a convergence classifier into a number of mathematically defined artificial glomeruli. A non-homogenous exposure of the test structure to the odours gives rise to a time and spatial dependence of the response of the different glomeruli strikingly similar to patterns observed in the olfactory bulb. The model seems to mimic both the formation of glomeruli, the zonal nature of olfactory epithelium, and the spatio-temporal signal patterns at the glomeruli level. This platform is able to provide a readily available test vehicle for chemists developing optical indicators for chemical sensing purposes and for biologists to test models of olfactory system organization.

  14. Applications of artificial intelligence technology to wastewater treatment fields in China

    Institute of Scientific and Technical Information of China (English)

    QING Xiao-xia; WANG Bo; MENG De-tao

    2005-01-01

    Current applications of artificial intelligence technology to wastewater treatment in China are summarized. Wastewater treatment plants use expert system mainly in the operation decision-making and fault diagnosis of system operation, use artificial neuron network for system modeling, water quality forecast and soft measure, and use fuzzy control technology for the intelligence control of wastewater treatment process. Finally, the main problems in applying artificial intelligence technology to wastewater treatment in China are analyzed.

  15. Performance of networks of artificial neurons: The role of clustering

    International Nuclear Information System (INIS)

    Kim, Beom Jun

    2004-01-01

    The performance of the Hopfield neural network model is numerically studied on various complex networks, such as the Watts-Strogatz network, the Barabasi-Albert network, and the neuronal network of Caenorhabditis elegans. Through the use of a systematic way of controlling the clustering coefficient, with the degree of each neuron kept unchanged, we find that the networks with the lower clustering exhibit much better performance. The results are discussed in the practical viewpoint of application, and the biological implications are also suggested

  16. Leaky Integrate-and-Fire Neuron Circuit Based on Floating-Gate Integrator

    Science.gov (United States)

    Kornijcuk, Vladimir; Lim, Hyungkwang; Seok, Jun Yeong; Kim, Guhyun; Kim, Seong Keun; Kim, Inho; Choi, Byung Joon; Jeong, Doo Seok

    2016-01-01

    The artificial spiking neural network (SNN) is promising and has been brought to the notice of the theoretical neuroscience and neuromorphic engineering research communities. In this light, we propose a new type of artificial spiking neuron based on leaky integrate-and-fire (LIF) behavior. A distinctive feature of the proposed FG-LIF neuron is the use of a floating-gate (FG) integrator rather than a capacitor-based one. The relaxation time of the charge on the FG relies mainly on the tunnel barrier profile, e.g., barrier height and thickness (rather than the area). This opens up the possibility of large-scale integration of neurons. The circuit simulation results offered biologically plausible spiking activity (circuit was subject to possible types of noise, e.g., thermal noise and burst noise. The simulation results indicated remarkable distributional features of interspike intervals that are fitted to Gamma distribution functions, similar to biological neurons in the neocortex. PMID:27242416

  17. Prediction of Availability Indicator of Water Pipes Using Artificial Intelligence

    Directory of Open Access Journals (Sweden)

    Kutyłowska Małgorzata

    2017-01-01

    Full Text Available The paper presents the results of artificial neural networks application to the availability indicator prediction. The forecasted results indicate that artificial networks may be used to model the reliability level of the water supply systems. The network was trained using 147 and 173 operational data from one Polish medium-sized city (distribution pipes and house connections, respectively. 50% of all data was chosen for learning, 25% for testing and 25% for validation. In prognosis phase, the best created network used 100% of 114 and 133 values for testing. Following functions were used to activate neurons in hidden and output layers: linear, logistic, hyperbolic tangent, exponential. The learning of the artificial network was performed using following input parameters: material, total length, diameter. In the optimal models hyperbolic tangent was chosen to activate the hidden and output neurons in modeling the availability indicator of house connections during 68 epochs of training. Hidden and output neurons were activated (20 epochs of learning respectively by hyperbolic tangent and linear function during the prediction of availability indicator of distribution pipes. The maximum relative errors in learning and prognosis step were equal to 0.10% and 1.20% as well as 0.27% and 1.15% for distribution pipes and house connections, respectively.

  18. Modeling the Development of Goal-Specificity in Mirror Neurons.

    Science.gov (United States)

    Thill, Serge; Svensson, Henrik; Ziemke, Tom

    2011-12-01

    Neurophysiological studies have shown that parietal mirror neurons encode not only actions but also the goal of these actions. Although some mirror neurons will fire whenever a certain action is perceived (goal-independently), most will only fire if the motion is perceived as part of an action with a specific goal. This result is important for the action-understanding hypothesis as it provides a potential neurological basis for such a cognitive ability. It is also relevant for the design of artificial cognitive systems, in particular robotic systems that rely on computational models of the mirror system in their interaction with other agents. Yet, to date, no computational model has explicitly addressed the mechanisms that give rise to both goal-specific and goal-independent parietal mirror neurons. In the present paper, we present a computational model based on a self-organizing map, which receives artificial inputs representing information about both the observed or executed actions and the context in which they were executed. We show that the map develops a biologically plausible organization in which goal-specific mirror neurons emerge. We further show that the fundamental cause for both the appearance and the number of goal-specific neurons can be found in geometric relationships between the different inputs to the map. The results are important to the action-understanding hypothesis as they provide a mechanism for the emergence of goal-specific parietal mirror neurons and lead to a number of predictions: (1) Learning of new goals may mostly reassign existing goal-specific neurons rather than recruit new ones; (2) input differences between executed and observed actions can explain observed corresponding differences in the number of goal-specific neurons; and (3) the percentage of goal-specific neurons may differ between motion primitives.

  19. William Pitt, the Bank of England, and the 1797 Suspension of Specie Payments: Central Bank War Finance During the Napoleonic Wars

    Directory of Open Access Journals (Sweden)

    Scott N. Duryea

    2010-05-01

    Full Text Available Modern military engagements are made possible by a state’s ability to easily acquire revenue. By either taking the money from its citizens via taxation, borrowing funds through bonds or loans from private financiers or other governments, or inflating the currency by issuing bank notes without the backing of specie or another commodity, Western governments wield enough power over money and banking to fund any venture. British involvement in the Napoleonic Wars was no exception to the rule. This paper examines the role of the British government, including William Pitt and Parliament, and the Bank of England in manipulating the currency, by borrowing, taxing, and issuing Bank notes to fund the war with Napoleonic France in the late eighteenth and early nineteenth centuries.

  20. CNTNAP2 and NRXN1 are mutated in autosomal-recessive Pitt-Hopkins-like mental retardation and determine the level of a common synaptic protein in Drosophila

    DEFF Research Database (Denmark)

    Zweier, Christiane; de Jong, Eiko K; Zweier, Markus

    2009-01-01

    , phenotypically overlapping with Pitt-Hopkins syndrome. With a frequency of at least 1% in our cohort of 179 patients, recessive defects in CNTNAP2 appear to significantly contribute to severe MR. Whereas the established synaptic role of NRXN1 suggests that synaptic defects contribute to the associated...... protein can reorganize synaptic morphology and induce increased density of active zones, the synaptic domains of neurotransmitter release. Moreover, both Nrx-I and Nrx-IV determine the level of the presynaptic active-zone protein bruchpilot, indicating a possible common molecular mechanism in Nrx...

  1. Integrated neuron circuit for implementing neuromorphic system with synaptic device

    Science.gov (United States)

    Lee, Jeong-Jun; Park, Jungjin; Kwon, Min-Woo; Hwang, Sungmin; Kim, Hyungjin; Park, Byung-Gook

    2018-02-01

    In this paper, we propose and fabricate Integrate & Fire neuron circuit for implementing neuromorphic system. Overall operation of the circuit is verified by measuring discrete devices and the output characteristics of the circuit. Since the neuron circuit shows asymmetric output characteristic that can drive synaptic device with Spike-Timing-Dependent-Plasticity (STDP) characteristic, the autonomous weight update process is also verified by connecting the synaptic device and the neuron circuit. The timing difference of the pre-neuron and the post-neuron induce autonomous weight change of the synaptic device. Unlike 2-terminal devices, which is frequently used to implement neuromorphic system, proposed scheme of the system enables autonomous weight update and simple configuration by using 4-terminal synapse device and appropriate neuron circuit. Weight update process in the multi-layer neuron-synapse connection ensures implementation of the hardware-based artificial intelligence, based on Spiking-Neural- Network (SNN).

  2. Dynamically stable associative learning: a neurobiologically based ANN and its applications

    Science.gov (United States)

    Vogl, Thomas P.; Blackwell, Kim L.; Barbour, Garth; Alkon, Daniel L.

    1992-07-01

    Most currently popular artificial neural networks (ANN) are based on conceptions of neuronal properties that date back to the 1940s and 50s, i.e., to the ideas of McCullough, Pitts, and Hebb. Dystal is an ANN based on current knowledge of neurobiology at the cellular and subcellular level. Networks based on these neurobiological insights exhibit the following advantageous properties: (1) A theoretical storage capacity of bN non-orthogonal memories, where N is the number of output neurons sharing common inputs and b is the number of distinguishable (gray shade) levels. (2) The ability to learn, store, and recall associations among noisy, arbitrary patterns. (3) A local synaptic learning rule (learning depends neither on the output of the post-synaptic neuron nor on a global error term), some of whose consequences are: (4) Feed-forward, lateral, and feed-back connections (as well as time-sensitive connections) are possible without alteration of the learning algorithm; (5) Storage allocation (patch creation) proceeds dynamically as associations are learned (self- organizing); (6) The number of training set presentations required for learning is small (different expressions and/or corrupted by noise, and on reading hand-written digits (98% accuracy) and hand-printed Japanese Kanji (90% accuracy) is demonstrated.

  3. Deep Learning with Dynamic Spiking Neurons and Fixed Feedback Weights.

    Science.gov (United States)

    Samadi, Arash; Lillicrap, Timothy P; Tweed, Douglas B

    2017-03-01

    Recent work in computer science has shown the power of deep learning driven by the backpropagation algorithm in networks of artificial neurons. But real neurons in the brain are different from most of these artificial ones in at least three crucial ways: they emit spikes rather than graded outputs, their inputs and outputs are related dynamically rather than by piecewise-smooth functions, and they have no known way to coordinate arrays of synapses in separate forward and feedback pathways so that they change simultaneously and identically, as they do in backpropagation. Given these differences, it is unlikely that current deep learning algorithms can operate in the brain, but we that show these problems can be solved by two simple devices: learning rules can approximate dynamic input-output relations with piecewise-smooth functions, and a variation on the feedback alignment algorithm can train deep networks without having to coordinate forward and feedback synapses. Our results also show that deep spiking networks learn much better if each neuron computes an intracellular teaching signal that reflects that cell's nonlinearity. With this mechanism, networks of spiking neurons show useful learning in synapses at least nine layers upstream from the output cells and perform well compared to other spiking networks in the literature on the MNIST digit recognition task.

  4. The connectivity Pitt, and how to avoid it

    Science.gov (United States)

    Durham, Brian

    2017-04-01

    UK government's newly announced intention to reconstitute its railway industry offers a chance to revisit an issue of hydrological connectivity. Following increased flooding nationally documented by the Pitt Review 2008, major industries have sought to improve resilience: this is especially relevant to the railway where a single company holds an infrastructure portfolio with major flood plain embankments and consequent drainage implications. At Oxford, following repeated railway flooding it was proposed in 2015 to raise a section of track. The flooding occurred at a location where the rail embankment intersects with a road embankment. The proposed mitigation flow matches the sectional area of track-raising measured in the plane of the road embankment, yet the applicant proposed its mitigation culvert in the plane of the rail embankment which already had original (I K Brunel, 1840s) culverts four times the Environment Agency's target flow for the entire flood plain. In terms of impact on neighbouring properties the company's Flood Risk Assessment (FRA) could be read in two ways, one being relatively benign (10-20mm rise), the other being an order of magnitude larger in locations where it was possible to relate proposed water level to individual properties, with implications in one case for an estate of 180 houses built to a nominal 100-year return flood standard. Assessment of impact was made harder by confidentiality. This might have been transparent to the regulating authorities, but in the event the local press attributed the award of planning permission to the company's claim that it had a fall-back position, i.e. it could raise its track without offering flood mitigation. Was this true? When the company proposed a local engineering operation in 2010 it published four pages from its Act of Parliament of 1843, one of which included a drainage term not relevant in that operation. Drainage duly became relevant five years later with the track raising discussed here

  5. Introducing Pitt-Hopkins syndrome-associated mutations of TCF4 to Drosophila daughterless

    Directory of Open Access Journals (Sweden)

    Laura Tamberg

    2015-12-01

    Full Text Available Pitt-Hopkins syndrome (PTHS is caused by haploinsufficiency of Transcription factor 4 (TCF4, one of the three human class I basic helix-loop-helix transcription factors called E-proteins. Drosophila has a single E-protein, Daughterless (Da, homologous to all three mammalian counterparts. Here we show that human TCF4 can rescue Da deficiency during fruit fly nervous system development. Overexpression of Da or TCF4 specifically in adult flies significantly decreases their survival rates, indicating that these factors are crucial even after development has been completed. We generated da transgenic fruit fly strains with corresponding missense mutations R578H, R580W, R582P and A614V found in TCF4 of PTHS patients and studied the impact of these mutations in vivo. Overexpression of wild type Da as well as human TCF4 in progenitor tissues induced ectopic sensory bristles and the rough eye phenotype. By contrast, overexpression of DaR580W and DaR582P that disrupt DNA binding reduced the number of bristles and induced the rough eye phenotype with partial lack of pigmentation, indicating that these act dominant negatively. Compared to the wild type, DaR578H and DaA614V were less potent in induction of ectopic bristles and the rough eye phenotype, respectively, suggesting that these are hypomorphic. All studied PTHS-associated mutations that we introduced into Da led to similar effects in vivo as the same mutations in TCF4 in vitro. Consequently, our Drosophila models of PTHS are applicable for further studies aiming to unravel the molecular mechanisms of this disorder.

  6. An FPGA Platform for Real-Time Simulation of Spiking Neuronal Networks.

    Science.gov (United States)

    Pani, Danilo; Meloni, Paolo; Tuveri, Giuseppe; Palumbo, Francesca; Massobrio, Paolo; Raffo, Luigi

    2017-01-01

    In the last years, the idea to dynamically interface biological neurons with artificial ones has become more and more urgent. The reason is essentially due to the design of innovative neuroprostheses where biological cell assemblies of the brain can be substituted by artificial ones. For closed-loop experiments with biological neuronal networks interfaced with in silico modeled networks, several technological challenges need to be faced, from the low-level interfacing between the living tissue and the computational model to the implementation of the latter in a suitable form for real-time processing. Field programmable gate arrays (FPGAs) can improve flexibility when simple neuronal models are required, obtaining good accuracy, real-time performance, and the possibility to create a hybrid system without any custom hardware, just programming the hardware to achieve the required functionality. In this paper, this possibility is explored presenting a modular and efficient FPGA design of an in silico spiking neural network exploiting the Izhikevich model. The proposed system, prototypically implemented on a Xilinx Virtex 6 device, is able to simulate a fully connected network counting up to 1,440 neurons, in real-time, at a sampling rate of 10 kHz, which is reasonable for small to medium scale extra-cellular closed-loop experiments.

  7. Label-free visualization of ultrastructural features of artificial synapses via cryo-EM.

    Science.gov (United States)

    Gopalakrishnan, Gopakumar; Yam, Patricia T; Madwar, Carolin; Bostina, Mihnea; Rouiller, Isabelle; Colman, David R; Lennox, R Bruce

    2011-12-21

    The ultrastructural details of presynapses formed between artificial substrates of submicrometer silica beads and hippocampal neurons are visualized via cryo-electron microscopy (cryo-EM). The silica beads are derivatized by poly-d-lysine or lipid bilayers. Molecular features known to exist at presynapses are clearly present at these artificial synapses, as visualized by cryo-EM. Key synaptic features such as the membrane contact area at synaptic junctions, the presynaptic bouton containing presynaptic vesicles, as well as microtubular structures can be identified. This is the first report of the direct, label-free observation of ultrastructural details of artificial synapses.

  8. Genetic algorithms used to optimize an artificial neural network design used in neutron spectrometry; Algoritmos geneticos utilizados para optimizar un diseno de red neuronal artificial usado en espectrometria de neutrones

    Energy Technology Data Exchange (ETDEWEB)

    Arteaga A, T.; Ortiz R, J. M.; Vega C, H. R., E-mail: tarcicio70@yahoo.co.uk [Universidad Autonoma de Zacatecas, Av. Lopez Velarde 117, 98600 Zacatecas, Zac. (Mexico)

    2016-10-15

    Artificial neural networks (Ann) are widely used; it which consist of an input layer, one or more hidden layers and an output layer; these layers contain neurons and each has connections called weights, where the knowledge are allowed and let to Ann solve problems proposed. These Ann is used to reconstruction of the energy spectrum of neutrons from count rates and develop Bonner sphere neutron dosimetry. Currently, we have developed Ann with high performance and generalization ability. Determine your optimal architecture is usually a difficult task, an exhaustive search of all possible combinations of parameters is rarely possible further training of the neural network with random initial weights can cause two major drawbacks: it can stuck in local minima or converge very slowly. In this project it will be used Genetic Algorithms (Ga); which are based on the principle or analogy of evolution through natural selection and has shown to be very effective in optimizing complex search functions and large spaces or to find a near optimal overall efficiency. The aim is to decrease the architecture in number of hidden neurons and therefore the total number of connections is reducing. The benefits obtained by optimizing the network are that the number of connections would be considerably smaller and thus the computational complexity, hardware integration, resources will be lower such that will allow to be even more viable implemented. To use the Ga three problems must be solve: 1) coding the problem into chromosomes. 2) Construct a fitness function. 3) Proper selection of genetic operators; crossover, selection, mutation. As a result, the scientific knowledge obtained can to be applied to similar problems having a reference parameters used and their impact on the optimization would to be generated. It concluded that the input layer and output are subject to the problem; the Ga propose the optimal number of neurons in the hidden layer without losing the quality of the

  9. PERSPECTIVE: Electrical activity enhances neuronal survival and regeneration

    Science.gov (United States)

    Corredor, Raul G.; Goldberg, Jeffrey L.

    2009-10-01

    The failure of regeneration in the central nervous system (CNS) remains an enormous scientific and clinical challenge. After injury or in degenerative diseases, neurons in the adult mammalian CNS fail to regrow their axons and reconnect with their normal targets, and furthermore the neurons frequently die and are not normally replaced. While significant progress has been made in understanding the molecular basis for this lack of regenerative ability, a second approach has gained momentum: replacing lost neurons or lost connections with artificial electrical circuits that interface with the nervous system. In the visual system, gene therapy-based 'optogenetics' prostheses represent a competing technology. Now, the two approaches are converging, as recent data suggest that electrical activity itself, via the molecular signaling pathways such activity stimulates, is sufficient to induce neuronal survival and regeneration, particularly in retinal ganglion cells. Here, we review these data, discuss the effects of electrical activity on neurons' molecular signaling pathways and propose specific mechanisms by which exogenous electrical activity may be acting to enhance survival and regeneration.

  10. Microfluidic Neurons, a New Way in Neuromorphic Engineering?

    Directory of Open Access Journals (Sweden)

    Timothée Levi

    2016-08-01

    Full Text Available This article describes a new way to explore neuromorphic engineering, the biomimetic artificial neuron using microfluidic techniques. This new device could replace silicon neurons and solve the issues of biocompatibility and power consumption. The biological neuron transmits electrical signals based on ion flow through their plasma membrane. Action potentials are propagated along axons and represent the fundamental electrical signals by which information are transmitted from one place to another in the nervous system. Based on this physiological behavior, we propose a microfluidic structure composed of chambers representing the intra and extracellular environments, connected by channels actuated by Quake valves. These channels are equipped with selective ion permeable membranes to mimic the exchange of chemical species found in the biological neuron. A thick polydimethylsiloxane (PDMS membrane is used to create the Quake valve membrane. Integrated electrodes are used to measure the potential difference between the intracellular and extracellular environments: the membrane potential.

  11. Suitability assessment of artificial neural network to approximate surface subsidence due to rock mass drainage

    Directory of Open Access Journals (Sweden)

    Ryszard Hejmanowski

    2015-01-01

    Full Text Available Based on the previous studies conducted by the authors, a new approach was proposed, namely the tools of artificial intelligence. One of neural networks is a multilayer perceptron network (MLP, which has already found applications in many fields of science. Sequentially, a series of calculations was made for different MLP neural network configuration and the best of them was selected. Mean square error (MSE and the correlation coefficient R were adopted as the selection criterion for the optimal network. The obtained results were characterized with a considerable dispersion. With an increase in the amount of hidden neurons, the MSE of the network increased while the correlation coefficient R decreased. Similar conclusions were drawn for the network with a small number of hidden neurons. The analysis allowed to select a network composed of 24 neurons as the best one for the issue under question. The obtained final answers of artificial neural network were presented in a histogram as differences between the calculated and expected value.

  12. Application of ANNS in tube CHF prediction: effect on neuron number in hidden layer

    International Nuclear Information System (INIS)

    Han, L.; Shan, J.; Zhang, B.

    2004-01-01

    Prediction of the Critical Heat Flux (CHF) for upward flow of water in uniformly heated vertical round tube is studied with Artificial Neuron Networks (ANNs) method utilizing different neuron number in hidden layers. This study is based on thermal equilibrium conditions. The neuron number in hidden layers is chosen to vary from 5 to 30 with the step of 5. The effect due to the variety of the neuron number in hidden layers is analyzed. The analysis shows that the neuron number in hidden layers should be appropriate, too less will affect the prediction accuracy and too much may result in abnormal parametric trends. It is concluded that the appropriate neuron number in two hidden layers should be [15 15]. (authors)

  13. Altered olfactory processing of stress-related body odors and artificial odors in patients with panic disorder.

    Science.gov (United States)

    Wintermann, Gloria-Beatrice; Donix, Markus; Joraschky, Peter; Gerber, Johannes; Petrowski, Katja

    2013-01-01

    Patients with Panic Disorder (PD) direct their attention towards potential threat, followed by panic attacks, and increased sweat production. Onés own anxiety sweat odor influences the attentional focus, and discrimination of threat or non-threat. Since olfactory projection areas overlap with neuronal areas of a panic-specific fear network, the present study investigated the neuronal processing of odors in general and of stress-related sweat odors in particular in patients with PD. A sample of 13 patients with PD with/ without agoraphobia and 13 age- and gender-matched healthy controls underwent an fMRI investigation during olfactory stimulation with their stress-related sweat odors (TSST, ergometry) as well as artificial odors (peach, artificial sweat) as non-fearful non-body odors. The two groups did not differ with respect to their olfactory identification ability. Independent of the kind of odor, the patients with PD showed activations in fronto-cortical areas in contrast to the healthy controls who showed activations in olfaction-related areas such as the amygdalae and the hippocampus. For artificial odors, the patients with PD showed a decreased neuronal activation of the thalamus, the posterior cingulate cortex and the anterior cingulate cortex. Under the presentation of sweat odor caused by ergometric exercise, the patients with PD showed an increased activation in the superior temporal gyrus, the supramarginal gyrus, and the cingulate cortex which was positively correlated with the severity of the psychopathology. For the sweat odor from the anxiety condition, the patients with PD showed an increased activation in the gyrus frontalis inferior, which was positively correlated with the severity of the psychopathology. The results suggest altered neuronal processing of olfactory stimuli in PD. Both artificial odors and stress-related body odors activate specific parts of a fear-network which is associated with an increased severity of the psychopathology.

  14. Altered olfactory processing of stress-related body odors and artificial odors in patients with panic disorder.

    Directory of Open Access Journals (Sweden)

    Gloria-Beatrice Wintermann

    Full Text Available Patients with Panic Disorder (PD direct their attention towards potential threat, followed by panic attacks, and increased sweat production. Onés own anxiety sweat odor influences the attentional focus, and discrimination of threat or non-threat. Since olfactory projection areas overlap with neuronal areas of a panic-specific fear network, the present study investigated the neuronal processing of odors in general and of stress-related sweat odors in particular in patients with PD.A sample of 13 patients with PD with/ without agoraphobia and 13 age- and gender-matched healthy controls underwent an fMRI investigation during olfactory stimulation with their stress-related sweat odors (TSST, ergometry as well as artificial odors (peach, artificial sweat as non-fearful non-body odors.The two groups did not differ with respect to their olfactory identification ability. Independent of the kind of odor, the patients with PD showed activations in fronto-cortical areas in contrast to the healthy controls who showed activations in olfaction-related areas such as the amygdalae and the hippocampus. For artificial odors, the patients with PD showed a decreased neuronal activation of the thalamus, the posterior cingulate cortex and the anterior cingulate cortex. Under the presentation of sweat odor caused by ergometric exercise, the patients with PD showed an increased activation in the superior temporal gyrus, the supramarginal gyrus, and the cingulate cortex which was positively correlated with the severity of the psychopathology. For the sweat odor from the anxiety condition, the patients with PD showed an increased activation in the gyrus frontalis inferior, which was positively correlated with the severity of the psychopathology.The results suggest altered neuronal processing of olfactory stimuli in PD. Both artificial odors and stress-related body odors activate specific parts of a fear-network which is associated with an increased severity of the

  15. On the number of preganglionic neurones driving human postganglionic sympathetic neurones: a comparison of modelling and empirical data

    Directory of Open Access Journals (Sweden)

    Vaughan G Macefield

    2011-12-01

    Full Text Available Postganglionic sympathetic axons in awake healthy human subjects, regardless of their identity as muscle vasoconstrictor, cutaneous vasoconstrictor or sudomotor neurones, discharge with a low firing probability (~30%, generate low firing rates (~0.5 Hz and typically fire only once per cardiac interval. The purpose of the present study was to use modelling of spike trains in an attempt to define the number of preganglionic neurones that drive an individual postganglionic neurone. Artificial spike trains were generated in 1-3 preganglionic neurones converging onto a single postganglionic neurone. Each preganglionic input fired with a mean interval distribution of either 1000, 1500, 2000, 2500 or 3000 ms and the standard deviation varied between 0.5, 1.0 and 2.0 x the mean interval; the discharge frequency of each preganglionic neurone exhibited positive skewness and kurtosis. Of the 45 patterns examined, the mean discharge properties of the postganglionic neurone could only be explained by it being driven by, on average, two preganglionic neurones firing with a mean interspike interval of 2500 ms and SD of 5000 ms. The mean firing rate resulting from this pattern was 0.22 Hz, comparable to that of spontaneously active muscle vasoconstrictor neurones in healthy subjects (0.40 Hz. Likewise, the distribution of the number of spikes per cardiac interval was similar between the modelled and actual data: 0 spikes (69.5 vs 66.6 %, 1 spike (25.6 vs 21.2 %, 2 spikes (4.3 vs 6.4 %, 3 spikes (0.5 vs 1.7 % and 4 spikes (0.1 vs 0.7 %. Although some features of the firing patterns could be explained by the postganglionic neurone being driven by a single preganglionic neurone, none of the emulated firing patterns generated by the firing of three preganglionic neurones matched the discharge of the real neurones. These modelling data indicate that, on average, human postganglionic sympathetic neurones are driven by two preganglionic inputs.

  16. How do auditory cortex neurons represent communication sounds?

    Science.gov (United States)

    Gaucher, Quentin; Huetz, Chloé; Gourévitch, Boris; Laudanski, Jonathan; Occelli, Florian; Edeline, Jean-Marc

    2013-11-01

    A major goal in auditory neuroscience is to characterize how communication sounds are represented at the cortical level. The present review aims at investigating the role of auditory cortex in the processing of speech, bird songs and other vocalizations, which all are spectrally and temporally highly structured sounds. Whereas earlier studies have simply looked for neurons exhibiting higher firing rates to particular conspecific vocalizations over their modified, artificially synthesized versions, more recent studies determined the coding capacity of temporal spike patterns, which are prominent in primary and non-primary areas (and also in non-auditory cortical areas). In several cases, this information seems to be correlated with the behavioral performance of human or animal subjects, suggesting that spike-timing based coding strategies might set the foundations of our perceptive abilities. Also, it is now clear that the responses of auditory cortex neurons are highly nonlinear and that their responses to natural stimuli cannot be predicted from their responses to artificial stimuli such as moving ripples and broadband noises. Since auditory cortex neurons cannot follow rapid fluctuations of the vocalizations envelope, they only respond at specific time points during communication sounds, which can serve as temporal markers for integrating the temporal and spectral processing taking place at subcortical relays. Thus, the temporal sparse code of auditory cortex neurons can be considered as a first step for generating high level representations of communication sounds independent of the acoustic characteristic of these sounds. This article is part of a Special Issue entitled "Communication Sounds and the Brain: New Directions and Perspectives". Copyright © 2013 Elsevier B.V. All rights reserved.

  17. Application of artificial intelligence to the management of urological cancer.

    Science.gov (United States)

    Abbod, Maysam F; Catto, James W F; Linkens, Derek A; Hamdy, Freddie C

    2007-10-01

    Artificial intelligence techniques, such as artificial neural networks, Bayesian belief networks and neuro-fuzzy modeling systems, are complex mathematical models based on the human neuronal structure and thinking. Such tools are capable of generating data driven models of biological systems without making assumptions based on statistical distributions. A large amount of study has been reported of the use of artificial intelligence in urology. We reviewed the basic concepts behind artificial intelligence techniques and explored the applications of this new dynamic technology in various aspects of urological cancer management. A detailed and systematic review of the literature was performed using the MEDLINE and Inspec databases to discover reports using artificial intelligence in urological cancer. The characteristics of machine learning and their implementation were described and reports of artificial intelligence use in urological cancer were reviewed. While most researchers in this field were found to focus on artificial neural networks to improve the diagnosis, staging and prognostic prediction of urological cancers, some groups are exploring other techniques, such as expert systems and neuro-fuzzy modeling systems. Compared to traditional regression statistics artificial intelligence methods appear to be accurate and more explorative for analyzing large data cohorts. Furthermore, they allow individualized prediction of disease behavior. Each artificial intelligence method has characteristics that make it suitable for different tasks. The lack of transparency of artificial neural networks hinders global scientific community acceptance of this method but this can be overcome by neuro-fuzzy modeling systems.

  18. Encoding of natural and artificial stimuli in the auditory midbrain

    Science.gov (United States)

    Lyzwa, Dominika

    How complex acoustic stimuli are encoded in the main center of convergence in the auditory midbrain is not clear. Here, the representation of neural spiking responses to natural and artificial sounds across this subcortical structure is investigated based on neurophysiological recordings from the mammalian midbrain. Neural and stimulus correlations of neuronal pairs are analyzed with respect to the neurons' distance, and responses to different natural communication sounds are discriminated. A model which includes linear and nonlinear neural response properties of this nucleus is presented and employed to predict temporal spiking responses to new sounds. Supported by BMBF Grant 01GQ0811.

  19. Building functional networks of spiking model neurons.

    Science.gov (United States)

    Abbott, L F; DePasquale, Brian; Memmesheimer, Raoul-Martin

    2016-03-01

    Most of the networks used by computer scientists and many of those studied by modelers in neuroscience represent unit activities as continuous variables. Neurons, however, communicate primarily through discontinuous spiking. We review methods for transferring our ability to construct interesting networks that perform relevant tasks from the artificial continuous domain to more realistic spiking network models. These methods raise a number of issues that warrant further theoretical and experimental study.

  20. Perceptron-like computation based on biologically-inspired neurons with heterosynaptic mechanisms

    Science.gov (United States)

    Kaluza, Pablo; Urdapilleta, Eugenio

    2014-10-01

    Perceptrons are one of the fundamental paradigms in artificial neural networks and a key processing scheme in supervised classification tasks. However, the algorithm they provide is given in terms of unrealistically simple processing units and connections and therefore, its implementation in real neural networks is hard to be fulfilled. In this work, we present a neural circuit able to perform perceptron's computation based on realistic models of neurons and synapses. The model uses Wang-Buzsáki neurons with coupling provided by axodendritic and axoaxonic synapses (heterosynapsis). The main characteristics of the feedforward perceptron operation are conserved, which allows to combine both approaches: whereas the classical artificial system can be used to learn a particular problem, its solution can be directly implemented in this neural circuit. As a result, we propose a biologically-inspired system able to work appropriately in a wide range of frequencies and system parameters, while keeping robust to noise and error.

  1. Application of artificial neural network for heat transfer in porous cone

    Science.gov (United States)

    Athani, Abdulgaphur; Ahamad, N. Ameer; Badruddin, Irfan Anjum

    2018-05-01

    Heat transfer in porous medium is one of the classical areas of research that has been active for many decades. The heat transfer in porous medium is generally studied by using numerical methods such as finite element method; finite difference method etc. that solves coupled partial differential equations by converting them into simpler forms. The current work utilizes an alternate method known as artificial neural network that mimics the learning characteristics of neurons. The heat transfer in porous medium fixed in a cone is predicted using backpropagation neural network. The artificial neural network is able to predict this behavior quite accurately.

  2. Signals and Circuits in the Purkinje Neuron

    Directory of Open Access Journals (Sweden)

    Ze'ev R Abrams

    2011-09-01

    Full Text Available Purkinje neurons in the cerebellum have over 100,000 inputs organized in an orthogonal geometry, and a single output channel. As the sole output of the cerebellar cortex layer, their complex firing pattern has been associated with motor control and learning. As such they have been extensively modeled and measured using tools ranging from electrophysiology and neuroanatomy, to dynamic systems and artificial intelligence methods. However, there is an alternative approach to analyze and describe the neuronal output of these cells using concepts from Electrical Engineering, particularly signal processing and digital/analog circuits. By viewing the Purkinje neuron as an unknown circuit to be reverse-engineered, we can use the tools that provide the foundations of today’s integrated circuits and communication systems to analyze the Purkinje system at the circuit level. We use Fourier transforms to analyze and isolate the inherent frequency modes in the Purkinje neuron and define 3 unique frequency ranges associated with the cells’ output. Comparing the Purkinje neuron to a signal generator that can be externally modulated adds an entire level of complexity to the functional role of these neurons both in terms of data analysis and information processing, relying on Fourier analysis methods in place of statistical ones. We also re-describe some of the recent literature in the field, using the nomenclature of signal processing. Furthermore, by comparing the experimental data of the past decade with basic electronic circuitry, we can resolve the outstanding controversy in the field, by recognizing that the Purkinje neuron can act as a multivibrator circuit.

  3. Spider Silk as Guiding Biomaterial for Human Model Neurons

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    Frank Roloff

    2014-01-01

    Full Text Available Over the last years, a number of therapeutic strategies have emerged to promote axonal regeneration. An attractive strategy is the implantation of biodegradable and nonimmunogenic artificial scaffolds into injured peripheral nerves. In previous studies, transplantation of decellularized veins filled with spider silk for bridging critical size nerve defects resulted in axonal regeneration and remyelination by invading endogenous Schwann cells. Detailed interaction of elongating neurons and the spider silk as guidance material is unknown. To visualize direct cellular interactions between spider silk and neurons in vitro, we developed an in vitro crossed silk fiber array. Here, we describe in detail for the first time that human (NT2 model neurons attach to silk scaffolds. Extending neurites can bridge gaps between single silk fibers and elongate afterwards on the neighboring fiber. Culturing human neurons on the silk arrays led to an increasing migration and adhesion of neuronal cell bodies to the spider silk fibers. Within three to four weeks, clustered somata and extending neurites formed ganglion-like cell structures. Microscopic imaging of human neurons on the crossed fiber arrays in vitro will allow for a more efficient development of methods to maximize cell adhesion and neurite growth on spider silk prior to transplantation studies.

  4. Solving Constraint Satisfaction Problems with Networks of Spiking Neurons.

    Science.gov (United States)

    Jonke, Zeno; Habenschuss, Stefan; Maass, Wolfgang

    2016-01-01

    Network of neurons in the brain apply-unlike processors in our current generation of computer hardware-an event-based processing strategy, where short pulses (spikes) are emitted sparsely by neurons to signal the occurrence of an event at a particular point in time. Such spike-based computations promise to be substantially more power-efficient than traditional clocked processing schemes. However, it turns out to be surprisingly difficult to design networks of spiking neurons that can solve difficult computational problems on the level of single spikes, rather than rates of spikes. We present here a new method for designing networks of spiking neurons via an energy function. Furthermore, we show how the energy function of a network of stochastically firing neurons can be shaped in a transparent manner by composing the networks of simple stereotypical network motifs. We show that this design approach enables networks of spiking neurons to produce approximate solutions to difficult (NP-hard) constraint satisfaction problems from the domains of planning/optimization and verification/logical inference. The resulting networks employ noise as a computational resource. Nevertheless, the timing of spikes plays an essential role in their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines) and Gibbs sampling.

  5. Solving constraint satisfaction problems with networks of spiking neurons

    Directory of Open Access Journals (Sweden)

    Zeno eJonke

    2016-03-01

    Full Text Available Network of neurons in the brain apply – unlike processors in our current generation ofcomputer hardware – an event-based processing strategy, where short pulses (spikes areemitted sparsely by neurons to signal the occurrence of an event at a particular point intime. Such spike-based computations promise to be substantially more power-efficient thantraditional clocked processing schemes. However it turned out to be surprisingly difficult todesign networks of spiking neurons that can solve difficult computational problems on the levelof single spikes (rather than rates of spikes. We present here a new method for designingnetworks of spiking neurons via an energy function. Furthermore we show how the energyfunction of a network of stochastically firing neurons can be shaped in a quite transparentmanner by composing the networks of simple stereotypical network motifs. We show that thisdesign approach enables networks of spiking neurons to produce approximate solutions todifficult (NP-hard constraint satisfaction problems from the domains of planning/optimizationand verification/logical inference. The resulting networks employ noise as a computationalresource. Nevertheless the timing of spikes (rather than just spike rates plays an essential rolein their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines and Gibbs sampling.

  6. The effect of correlated neuronal firing and neuronal heterogeneity on population coding accuracy in guinea pig inferior colliculus.

    Directory of Open Access Journals (Sweden)

    Oran Zohar

    Full Text Available It has been suggested that the considerable noise in single-cell responses to a stimulus can be overcome by pooling information from a large population. Theoretical studies indicated that correlations in trial-to-trial fluctuations in the responses of different neurons may limit the improvement due to pooling. Subsequent theoretical studies have suggested that inherent neuronal diversity, i.e., the heterogeneity of tuning curves and other response properties of neurons preferentially tuned to the same stimulus, can provide a means to overcome this limit. Here we study the effect of spike-count correlations and the inherent neuronal heterogeneity on the ability to extract information from large neural populations. We use electrophysiological data from the guinea pig Inferior-Colliculus to capture inherent neuronal heterogeneity and single cell statistics, and introduce response correlations artificially. To this end, we generate pseudo-population responses, based on single-cell recording of neurons responding to auditory stimuli with varying binaural correlations. Typically, when pseudo-populations are generated from single cell data, the responses within the population are statistically independent. As a result, the information content of the population will increase indefinitely with its size. In contrast, here we apply a simple algorithm that enables us to generate pseudo-population responses with variable spike-count correlations. This enables us to study the effect of neuronal correlations on the accuracy of conventional rate codes. We show that in a homogenous population, in the presence of even low-level correlations, information content is bounded. In contrast, utilizing a simple linear readout, that takes into account the natural heterogeneity, even of neurons preferentially tuned to the same stimulus, within the neural population, one can overcome the correlated noise and obtain a readout whose accuracy grows linearly with the size of

  7. Dynamic artificial neural networks with affective systems.

    Directory of Open Access Journals (Sweden)

    Catherine D Schuman

    Full Text Available Artificial neural networks (ANNs are processors that are trained to perform particular tasks. We couple a computational ANN with a simulated affective system in order to explore the interaction between the two. In particular, we design a simple affective system that adjusts the threshold values in the neurons of our ANN. The aim of this paper is to demonstrate that this simple affective system can control the firing rate of the ensemble of neurons in the ANN, as well as to explore the coupling between the affective system and the processes of long term potentiation (LTP and long term depression (LTD, and the effect of the parameters of the affective system on its performance. We apply our networks with affective systems to a simple pole balancing example and briefly discuss the effect of affective systems on network performance.

  8. A Subset of Serotonergic Neurons Evokes Hunger in Adult Drosophila.

    Science.gov (United States)

    Albin, Stephanie D; Kaun, Karla R; Knapp, Jon-Michael; Chung, Phuong; Heberlein, Ulrike; Simpson, Julie H

    2015-09-21

    Hunger is a complex motivational state that drives multiple behaviors. The sensation of hunger is caused by an imbalance between energy intake and expenditure. One immediate response to hunger is increased food consumption. Hunger also modulates behaviors related to food seeking such as increased locomotion and enhanced sensory sensitivity in both insects and vertebrates. In addition, hunger can promote the expression of food-associated memory. Although progress is being made, how hunger is represented in the brain and how it coordinates these behavioral responses is not fully understood in any system. Here, we use Drosophila melanogaster to identify neurons encoding hunger. We found a small group of neurons that, when activated, induced a fed fly to eat as though it were starved, suggesting that these neurons are downstream of the metabolic regulation of hunger. Artificially activating these neurons also promotes appetitive memory performance in sated flies, indicating that these neurons are not simply feeding command neurons but likely play a more general role in encoding hunger. We determined that the neurons relevant for the feeding effect are serotonergic and project broadly within the brain, suggesting a possible mechanism for how various responses to hunger are coordinated. These findings extend our understanding of the neural circuitry that drives feeding and enable future exploration of how state influences neural activity within this circuit. Copyright © 2015 Elsevier Ltd. All rights reserved.

  9. An excitatory paraventricular nucleus to AgRP neuron circuit that drives hunger.

    Science.gov (United States)

    Krashes, Michael J; Shah, Bhavik P; Madara, Joseph C; Olson, David P; Strochlic, David E; Garfield, Alastair S; Vong, Linh; Pei, Hongjuan; Watabe-Uchida, Mitsuko; Uchida, Naoshige; Liberles, Stephen D; Lowell, Bradford B

    2014-03-13

    Hunger is a hard-wired motivational state essential for survival. Agouti-related peptide (AgRP)-expressing neurons in the arcuate nucleus (ARC) at the base of the hypothalamus are crucial to the control of hunger. They are activated by caloric deficiency and, when naturally or artificially stimulated, they potently induce intense hunger and subsequent food intake. Consistent with their obligatory role in regulating appetite, genetic ablation or chemogenetic inhibition of AgRP neurons decreases feeding. Excitatory input to AgRP neurons is important in caloric-deficiency-induced activation, and is notable for its remarkable degree of caloric-state-dependent synaptic plasticity. Despite the important role of excitatory input, its source(s) has been unknown. Here, through the use of Cre-recombinase-enabled, cell-specific neuron mapping techniques in mice, we have discovered strong excitatory drive that, unexpectedly, emanates from the hypothalamic paraventricular nucleus, specifically from subsets of neurons expressing thyrotropin-releasing hormone (TRH) and pituitary adenylate cyclase-activating polypeptide (PACAP, also known as ADCYAP1). Chemogenetic stimulation of these afferent neurons in sated mice markedly activates AgRP neurons and induces intense feeding. Conversely, acute inhibition in mice with caloric-deficiency-induced hunger decreases feeding. Discovery of these afferent neurons capable of triggering hunger advances understanding of how this intense motivational state is regulated.

  10. Neurons other than motor neurons in motor neuron disease.

    Science.gov (United States)

    Ruffoli, Riccardo; Biagioni, Francesca; Busceti, Carla L; Gaglione, Anderson; Ryskalin, Larisa; Gambardella, Stefano; Frati, Alessandro; Fornai, Francesco

    2017-11-01

    Amyotrophic lateral sclerosis (ALS) is typically defined by a loss of motor neurons in the central nervous system. Accordingly, morphological analysis for decades considered motor neurons (in the cortex, brainstem and spinal cord) as the neuronal population selectively involved in ALS. Similarly, this was considered the pathological marker to score disease severity ex vivo both in patients and experimental models. However, the concept of non-autonomous motor neuron death was used recently to indicate the need for additional cell types to produce motor neuron death in ALS. This means that motor neuron loss occurs only when they are connected with other cell types. This concept originally emphasized the need for resident glia as well as non-resident inflammatory cells. Nowadays, the additional role of neurons other than motor neurons emerged in the scenario to induce non-autonomous motor neuron death. In fact, in ALS neurons diverse from motor neurons are involved. These cells play multiple roles in ALS: (i) they participate in the chain of events to produce motor neuron loss; (ii) they may even degenerate more than and before motor neurons. In the present manuscript evidence about multi-neuronal involvement in ALS patients and experimental models is discussed. Specific sub-classes of neurons in the whole spinal cord are reported either to degenerate or to trigger neuronal degeneration, thus portraying ALS as a whole spinal cord disorder rather than a disease affecting motor neurons solely. This is associated with a novel concept in motor neuron disease which recruits abnormal mechanisms of cell to cell communication.

  11. Emergent properties of interacting populations of spiking neurons.

    Science.gov (United States)

    Cardanobile, Stefano; Rotter, Stefan

    2011-01-01

    Dynamic neuronal networks are a key paradigm of increasing importance in brain research, concerned with the functional analysis of biological neuronal networks and, at the same time, with the synthesis of artificial brain-like systems. In this context, neuronal network models serve as mathematical tools to understand the function of brains, but they might as well develop into future tools for enhancing certain functions of our nervous system. Here, we present and discuss our recent achievements in developing multiplicative point processes into a viable mathematical framework for spiking network modeling. The perspective is that the dynamic behavior of these neuronal networks is faithfully reflected by a set of non-linear rate equations, describing all interactions on the population level. These equations are similar in structure to Lotka-Volterra equations, well known by their use in modeling predator-prey relations in population biology, but abundant applications to economic theory have also been described. We present a number of biologically relevant examples for spiking network function, which can be studied with the help of the aforementioned correspondence between spike trains and specific systems of non-linear coupled ordinary differential equations. We claim that, enabled by the use of multiplicative point processes, we can make essential contributions to a more thorough understanding of the dynamical properties of interacting neuronal populations.

  12. Restoring the encoding properties of a stochastic neuron model by an exogenous noise

    Science.gov (United States)

    Paffi, Alessandra; Camera, Francesca; Apollonio, Francesca; d'Inzeo, Guglielmo; Liberti, Micaela

    2015-01-01

    Here we evaluate the possibility of improving the encoding properties of an impaired neuronal system by superimposing an exogenous noise to an external electric stimulation signal. The approach is based on the use of mathematical neuron models consisting of stochastic HH-like circuit, where the impairment of the endogenous presynaptic inputs is described as a subthreshold injected current and the exogenous stimulation signal is a sinusoidal voltage perturbation across the membrane. Our results indicate that a correlated Gaussian noise, added to the sinusoidal signal can significantly increase the encoding properties of the impaired system, through the Stochastic Resonance (SR) phenomenon. These results suggest that an exogenous noise, suitably tailored, could improve the efficacy of those stimulation techniques used in neuronal systems, where the presynaptic sensory neurons are impaired and have to be artificially bypassed. PMID:25999845

  13. Restoring the encoding properties of a stochastic neuron model by an exogenous noise

    Directory of Open Access Journals (Sweden)

    Alessandra ePaffi

    2015-05-01

    Full Text Available Here we evaluate the possibility of improving the encoding properties of an impaired neuronal system by superimposing an exogenous noise to an external electric stimulation signal. The approach is based on the use of mathematical neuron models consisting of stochastic HH-like circuit, where the impairment of the endogenous presynaptic inputs is described as a subthreshold injected current and the exogenous stimulation signal is a sinusoidal voltage perturbation across the membrane. Our results indicate that a correlated Gaussian noise, added to the sinusoidal signal can significantly increase the encoding properties of the impaired system, through the Stochastic Resonance (SR phenomenon. These results suggest that an exogenous noise, suitably tailored, could improve the efficacy of those stimulation techniques used in neuronal systems, where the presynaptic sensory neurons are impaired and have to be artificially bypassed.

  14. A microsensor array for quantification of lubricant contaminants using a back propagation artificial neural network

    International Nuclear Information System (INIS)

    Zhu, Xiaoliang; Du, Li; Zhe, Jiang; Liu, Bendong

    2016-01-01

    We present a method based on an electrochemical sensor array and a back propagation artificial neural network for detection and quantification of four properties of lubrication oil, namely water (0, 500 ppm, 1000 ppm), total acid number (TAN) (13.1, 13.7, 14.4, 15.6 mg KOH g −1 ), soot (0, 1%, 2%, 3%) and sulfur content (1.3%, 1.37%, 1.44%, 1.51%). The sensor array, consisting of four micromachined electrochemical sensors, detects the four properties with overlapping sensitivities. A total set of 36 oil samples containing mixtures of water, soot, and sulfuric acid with different concentrations were prepared for testing. The sensor array’s responses were then divided to three sets: training sets (80% data), validation sets (10%) and testing sets (10%). Several back propagation artificial neural network architectures were trained with the training and validation sets; one architecture with four input neurons, 50 and 5 neurons in the first and second hidden layer, and four neurons in the output layer was selected. The selected neural network was then tested using the four sets of testing data (10%). Test results demonstrated that the developed artificial neural network is able to quantitatively determine the four lubrication properties (water, TAN, soot, and sulfur content) with a maximum prediction error of 18.8%, 6.0%, 6.7%, and 5.4%, respectively, indicting a good match between the target and predicted values. With the developed network, the sensor array could be potentially used for online lubricant oil condition monitoring. (paper)

  15. Control Neuronal Combinado para Generar Espectros de Oleajes

    Directory of Open Access Journals (Sweden)

    Luis P. Sánchez Fernández

    2013-10-01

    Full Text Available Resumen: Se presenta un método novedoso para controlar la obtención de espectros de energía de oleajes, de gran utilidad en los laboratorios de investigación y desarrollo de Hidráulica Marítima. El elemento final de control o manipulador es un motor eléctrico lineal conectado a un servo-control el cual es supervisado mediante una computadora. El algoritmo de control automático se realiza con un esquema neuronal combinado, compuesto por una red neuronal artificial “feed-forward” y un controlador proporcional integral. El sistema computacional implementado incluye características de autoaprendizaje, materializado en el re-entrenamiento en línea de la red neuronal lo cual hace posible adaptarse a cambios en los parámetros del “proceso controlado” y a perturbaciones, altamente influyentes en el espectro de energía que impacta una obra hidráulica objeto de estudio. Abstract: A novel method is presented to control the generation of wave energy spectrum, useful in research and development laboratories of Maritime Hydraulic. The final control element is a linear electric motor connected to a servo-control which is monitored by a computer. The automatic control algorithm is performed with a combined neural scheme. It consists of an artificial neural network “feed-forward” and a proportional integral controller. The computer system includes self-learning based on an online training of the neural network. It makes possible to adapt to changes in the parameters of the “controlled process” and disturbances that impact the studied hydraulic work. Palabras clave: Control, neuronal, oleaje, espectros, hidráulica, Keywords: Control, neural, wave, spectrums, hydraulic

  16. [Hardware Implementation of Numerical Simulation Function of Hodgkin-Huxley Model Neurons Action Potential Based on Field Programmable Gate Array].

    Science.gov (United States)

    Wang, Jinlong; Lu, Mai; Hu, Yanwen; Chen, Xiaoqiang; Pan, Qiangqiang

    2015-12-01

    Neuron is the basic unit of the biological neural system. The Hodgkin-Huxley (HH) model is one of the most realistic neuron models on the electrophysiological characteristic description of neuron. Hardware implementation of neuron could provide new research ideas to clinical treatment of spinal cord injury, bionics and artificial intelligence. Based on the HH model neuron and the DSP Builder technology, in the present study, a single HH model neuron hardware implementation was completed in Field Programmable Gate Array (FPGA). The neuron implemented in FPGA was stimulated by different types of current, the action potential response characteristics were analyzed, and the correlation coefficient between numerical simulation result and hardware implementation result were calculated. The results showed that neuronal action potential response of FPGA was highly consistent with numerical simulation result. This work lays the foundation for hardware implementation of neural network.

  17. Artificial neural network modeling and optimization of ultrahigh pressure extraction of green tea polyphenols.

    Science.gov (United States)

    Xi, Jun; Xue, Yujing; Xu, Yinxiang; Shen, Yuhong

    2013-11-01

    In this study, the ultrahigh pressure extraction of green tea polyphenols was modeled and optimized by a three-layer artificial neural network. A feed-forward neural network trained with an error back-propagation algorithm was used to evaluate the effects of pressure, liquid/solid ratio and ethanol concentration on the total phenolic content of green tea extracts. The neural network coupled with genetic algorithms was also used to optimize the conditions needed to obtain the highest yield of tea polyphenols. The obtained optimal architecture of artificial neural network model involved a feed-forward neural network with three input neurons, one hidden layer with eight neurons and one output layer including single neuron. The trained network gave the minimum value in the MSE of 0.03 and the maximum value in the R(2) of 0.9571, which implied a good agreement between the predicted value and the actual value, and confirmed a good generalization of the network. Based on the combination of neural network and genetic algorithms, the optimum extraction conditions for the highest yield of green tea polyphenols were determined as follows: 498.8 MPa for pressure, 20.8 mL/g for liquid/solid ratio and 53.6% for ethanol concentration. The total phenolic content of the actual measurement under the optimum predicated extraction conditions was 582.4 ± 0.63 mg/g DW, which was well matched with the predicted value (597.2mg/g DW). This suggests that the artificial neural network model described in this work is an efficient quantitative tool to predict the extraction efficiency of green tea polyphenols. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

  18. An Investigation on the Role of Spike Latency in an Artificial Olfactory System

    Directory of Open Access Journals (Sweden)

    Corrado eDi Natale

    2011-12-01

    Full Text Available Experimental studies have shown that the reactions to external stimuli may appear only few hundreds of milliseconds after the physical interaction of the stimulus with the proper receptor. This behavior suggests that neurons transmit the largest meaningful part of their signal in the first spikes, and than that the spike latency is a good descriptor of the information content in biological neural networks. In this paper this property has been investigated in an artificial sensorial system where a single layer of spiking neurons is trained with the data generated by an artificial olfactory platform based on a large array of chemical sensors. The capability to discriminate between distinct chemicals and mixtures of them was studied with spiking neural networks endowed with and without lateral inhibitions and considering as output feature of the network both the spikes latency and the average firing rate. Results show that the average firing rate of the output spikes sequences shows the best separation among the experienced vapors, however the latency code is able in a shorter time to correctly discriminate all the tested volatile compounds. This behavior is qualitatively similar to those recently found in natural olfaction, and noteworthy it provides practical suggestions to tail the measurement conditions of artificial olfactory systems defining for each specific case a proper measurement time.

  19. Lateral Information Processing by Spiking Neurons: A Theoretical Model of the Neural Correlate of Consciousness

    Directory of Open Access Journals (Sweden)

    Marc Ebner

    2011-01-01

    Full Text Available Cognitive brain functions, for example, sensory perception, motor control and learning, are understood as computation by axonal-dendritic chemical synapses in networks of integrate-and-fire neurons. Cognitive brain functions may occur either consciously or nonconsciously (on “autopilot”. Conscious cognition is marked by gamma synchrony EEG, mediated largely by dendritic-dendritic gap junctions, sideways connections in input/integration layers. Gap-junction-connected neurons define a sub-network within a larger neural network. A theoretical model (the “conscious pilot” suggests that as gap junctions open and close, a gamma-synchronized subnetwork, or zone moves through the brain as an executive agent, converting nonconscious “auto-pilot” cognition to consciousness, and enhancing computation by coherent processing and collective integration. In this study we implemented sideways “gap junctions” in a single-layer artificial neural network to perform figure/ground separation. The set of neurons connected through gap junctions form a reconfigurable resistive grid or sub-network zone. In the model, outgoing spikes are temporally integrated and spatially averaged using the fixed resistive grid set up by neurons of similar function which are connected through gap-junctions. This spatial average, essentially a feedback signal from the neuron's output, determines whether particular gap junctions between neurons will open or close. Neurons connected through open gap junctions synchronize their output spikes. We have tested our gap-junction-defined sub-network in a one-layer neural network on artificial retinal inputs using real-world images. Our system is able to perform figure/ground separation where the laterally connected sub-network of neurons represents a perceived object. Even though we only show results for visual stimuli, our approach should generalize to other modalities. The system demonstrates a moving sub-network zone of

  20. Three-Dimensional Human iPSC-Derived Artificial Skeletal Muscles Model Muscular Dystrophies and Enable Multilineage Tissue Engineering

    Directory of Open Access Journals (Sweden)

    Sara Martina Maffioletti

    2018-04-01

    Full Text Available Summary: Generating human skeletal muscle models is instrumental for investigating muscle pathology and therapy. Here, we report the generation of three-dimensional (3D artificial skeletal muscle tissue from human pluripotent stem cells, including induced pluripotent stem cells (iPSCs from patients with Duchenne, limb-girdle, and congenital muscular dystrophies. 3D skeletal myogenic differentiation of pluripotent cells was induced within hydrogels under tension to provide myofiber alignment. Artificial muscles recapitulated characteristics of human skeletal muscle tissue and could be implanted into immunodeficient mice. Pathological cellular hallmarks of incurable forms of severe muscular dystrophy could be modeled with high fidelity using this 3D platform. Finally, we show generation of fully human iPSC-derived, complex, multilineage muscle models containing key isogenic cellular constituents of skeletal muscle, including vascular endothelial cells, pericytes, and motor neurons. These results lay the foundation for a human skeletal muscle organoid-like platform for disease modeling, regenerative medicine, and therapy development. : Maffioletti et al. generate human 3D artificial skeletal muscles from healthy donors and patient-specific pluripotent stem cells. These human artificial muscles accurately model severe genetic muscle diseases. They can be engineered to include other cell types present in skeletal muscle, such as vascular cells and motor neurons. Keywords: skeletal muscle, pluripotent stem cells, iPS cells, myogenic differentiation, tissue engineering, disease modeling, muscular dystrophy, organoids

  1. Artificial intelligence in hematology.

    Science.gov (United States)

    Zini, Gina

    2005-10-01

    Artificial intelligence (AI) is a computer based science which aims to simulate human brain faculties using a computational system. A brief history of this new science goes from the creation of the first artificial neuron in 1943 to the first artificial neural network application to genetic algorithms. The potential for a similar technology in medicine has immediately been identified by scientists and researchers. The possibility to store and process all medical knowledge has made this technology very attractive to assist or even surpass clinicians in reaching a diagnosis. Applications of AI in medicine include devices applied to clinical diagnosis in neurology and cardiopulmonary diseases, as well as the use of expert or knowledge-based systems in routine clinical use for diagnosis, therapeutic management and for prognostic evaluation. Biological applications include genome sequencing or DNA gene expression microarrays, modeling gene networks, analysis and clustering of gene expression data, pattern recognition in DNA and proteins, protein structure prediction. In the field of hematology the first devices based on AI have been applied to the routine laboratory data management. New tools concern the differential diagnosis in specific diseases such as anemias, thalassemias and leukemias, based on neural networks trained with data from peripheral blood analysis. A revolution in cancer diagnosis, including the diagnosis of hematological malignancies, has been the introduction of the first microarray based and bioinformatic approach for molecular diagnosis: a systematic approach based on the monitoring of simultaneous expression of thousands of genes using DNA microarray, independently of previous biological knowledge, analysed using AI devices. Using gene profiling, the traditional diagnostic pathways move from clinical to molecular based diagnostic systems.

  2. Evaluation of extra virgin olive oil stability by artificial neural network.

    Science.gov (United States)

    Silva, Simone Faria; Anjos, Carlos Alberto Rodrigues; Cavalcanti, Rodrigo Nunes; Celeghini, Renata Maria dos Santos

    2015-07-15

    The stability of extra virgin olive oil in polyethylene terephthalate bottles and tinplate cans stored for 6 months under dark and light conditions was evaluated. The following analyses were carried out: free fatty acids, peroxide value, specific extinction at 232 and 270 nm, chlorophyll, L(∗)C(∗)h color, total phenolic compounds, tocopherols and squalene. The physicochemical changes were evaluated by artificial neural network (ANN) modeling with respect to light exposure conditions and packaging material. The optimized ANN structure consists of 11 input neurons, 18 hidden neurons and 5 output neurons using hyperbolic tangent and softmax activation functions in hidden and output layers, respectively. The five output neurons correspond to five possible classifications according to packaging material (PET amber, PET transparent and tinplate can) and light exposure (dark and light storage). The predicted physicochemical changes agreed very well with the experimental data showing high classification accuracy for test (>90%) and training set (>85). Sensitivity analysis showed that free fatty acid content, peroxide value, L(∗)Cab(∗)hab(∗) color parameters, tocopherol and chlorophyll contents were the physicochemical attributes with the most discriminative power. Copyright © 2015 Elsevier Ltd. All rights reserved.

  3. Spin-neurons: A possible path to energy-efficient neuromorphic computers

    Energy Technology Data Exchange (ETDEWEB)

    Sharad, Mrigank; Fan, Deliang; Roy, Kaushik [School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana 47907 (United States)

    2013-12-21

    Recent years have witnessed growing interest in the field of brain-inspired computing based on neural-network architectures. In order to translate the related algorithmic models into powerful, yet energy-efficient cognitive-computing hardware, computing-devices beyond CMOS may need to be explored. The suitability of such devices to this field of computing would strongly depend upon how closely their physical characteristics match with the essential computing primitives employed in such models. In this work, we discuss the rationale of applying emerging spin-torque devices for bio-inspired computing. Recent spin-torque experiments have shown the path to low-current, low-voltage, and high-speed magnetization switching in nano-scale magnetic devices. Such magneto-metallic, current-mode spin-torque switches can mimic the analog summing and “thresholding” operation of an artificial neuron with high energy-efficiency. Comparison with CMOS-based analog circuit-model of a neuron shows that “spin-neurons” (spin based circuit model of neurons) can achieve more than two orders of magnitude lower energy and beyond three orders of magnitude reduction in energy-delay product. The application of spin-neurons can therefore be an attractive option for neuromorphic computers of future.

  4. Predicting the effects of magnesium oxide nanoparticles and temperature on the thermal conductivity of water using artificial neural network and experimental data

    Science.gov (United States)

    Afrand, Masoud; Hemmat Esfe, Mohammad; Abedini, Ehsan; Teimouri, Hamid

    2017-03-01

    The current paper first presents an empirical correlation based on experimental results for estimating thermal conductivity enhancement of MgO-water nanofluid using curve fitting method. Then, artificial neural networks (ANNs) with various numbers of neurons have been assessed by considering temperature and MgO volume fraction as the inputs variables and thermal conductivity enhancement as the output variable to select the most appropriate and optimized network. Results indicated that the network with 7 neurons had minimum error. Eventually, the output of artificial neural network was compared with the results of the proposed empirical correlation and those of the experiments. Comparisons revealed that ANN modeling was more accurate than curve-fitting method in the predicting the thermal conductivity enhancement of the nanofluid.

  5. Emergent properties of interacting populations of spiking neurons

    Directory of Open Access Journals (Sweden)

    Stefano eCardanobile

    2011-12-01

    Full Text Available Dynamic neuronal networks are a key paradigm of increasing importance in brain research, concerned with the functional analysis of biological neuronal networks and, at the same time, with the synthesis of artificial brain-like systems. In this context, neuronal network models serve as mathematical tools to understand the function of brains, but they might as well develop into future tools for enhancing certain functions of our nervous system.Here, we discuss our recent achievements in developing multiplicative point processes into a viable mathematical framework for spiking network modeling. The perspective is that the dynamic behavior of these neuronal networks on the population level is faithfully reflected by a set of non-linear rate equations, describing all interactions on this level. These equations, in turn, are similar in structure to the Lotka-Volterra equations, well known by their use in modeling predator-prey relationships in population biology, but abundant applications to economic theory have also been described.We present a number of biologically relevant examples for spiking network function, which can be studied with the help of the aforementioned correspondence between spike trains and specific systems of non-linear coupled ordinary differential equations. We claim that, enabled by the use of multiplicative point processes, we can make essential contributions to a more thorough understanding of the dynamical properties of neural populations.

  6. Archeological and Historic Cultural Resources Inventory for a Proposed Flood Control Project at Devils Lake, Ramsey County, North Dakota.

    Science.gov (United States)

    1983-10-01

    Chautauqua Association signed a contract with J.H. McCulloch to secure a right-of- way for the Chautauqua Railway. McCulloch received a 30 year franchise in...excavation at Bakery of U.S. military fort Palo Duro St. Park, TX; Test excavation at historic dugout Sea Rim State Park, TX; Archaeological survey San

  7. Contributions of intrinsic motor neuron properties to the production of rhythmic motor output in the mammalian spinal cord

    DEFF Research Database (Denmark)

    Kiehn, O; Kjaerulff, O; Tresch, M C

    2000-01-01

    Motor neurons are endowed with intrinsic and conditional membrane properties that may shape the final motor output. In the first half of this paper we present data on the contribution of I(h), a hyperpolarization-activated inward cation current, to phase-transition in motor neurons during rhythmic...... firing. Motor neurons were recorded intracellularly during locomotion induced with a mixture of N-methyl-D-aspartate (NMDA) and serotonin, after pharmacological blockade of I(h). I(h) was then replaced by using dynamic clamp, a computer program that allows artificial conductances to be inserted into real...... neurons. I(h) was simulated with biophysical parameters determined in voltage clamp experiments. The data showed that electronic replacement of the native I(h) caused a depolarization of the average membrane potential, a phase-advance of the locomotor drive potential, and increased motor neuron spiking...

  8. Using neural networks for prediction of nuclear parameters

    Energy Technology Data Exchange (ETDEWEB)

    Pereira Filho, Leonidas; Souto, Kelling Cabral, E-mail: leonidasmilenium@hotmail.com, E-mail: kcsouto@bol.com.br [Instituto Federal de Educacao, Ciencia e Tecnologia do Rio de Janeiro (IFRJ), Rio de Janeiro, RJ (Brazil); Machado, Marcelo Dornellas, E-mail: dornemd@eletronuclear.gov.br [Eletrobras Termonuclear S.A. (GCN.T/ELETRONUCLEAR), Rio de Janeiro, RJ (Brazil). Gerencia de Combustivel Nuclear

    2013-07-01

    Dating from 1943, the earliest work on artificial neural networks (ANN), when Warren Mc Cullock and Walter Pitts developed a study on the behavior of the biological neuron, with the goal of creating a mathematical model. Some other work was done until after the 80 witnessed an explosion of interest in ANNs, mainly due to advances in technology, especially microelectronics. Because ANNs are able to solve many problems such as approximation, classification, categorization, prediction and others, they have numerous applications in various areas, including nuclear. Nodal method is adopted as a tool for analyzing core parameters such as boron concentration and pin power peaks for pressurized water reactors. However, this method is extremely slow when it is necessary to perform various core evaluations, for example core reloading optimization. To overcome this difficulty, in this paper a model of Multi-layer Perceptron (MLP) artificial neural network type backpropagation will be trained to predict these values. The main objective of this work is the development of Multi-layer Perceptron (MLP) artificial neural network capable to predict, in very short time, with good accuracy, two important parameters used in the core reloading problem - Boron Concentration and Power Peaking Factor. For the training of the neural networks are provided loading patterns and nuclear data used in cycle 19 of Angra 1 nuclear power plant. Three models of networks are constructed using the same input data and providing the following outputs: 1- Boron Concentration and Power Peaking Factor, 2 - Boron Concentration and 3 - Power Peaking Factor. (author)

  9. Using neural networks for prediction of nuclear parameters

    International Nuclear Information System (INIS)

    Pereira Filho, Leonidas; Souto, Kelling Cabral; Machado, Marcelo Dornellas

    2013-01-01

    Dating from 1943, the earliest work on artificial neural networks (ANN), when Warren Mc Cullock and Walter Pitts developed a study on the behavior of the biological neuron, with the goal of creating a mathematical model. Some other work was done until after the 80 witnessed an explosion of interest in ANNs, mainly due to advances in technology, especially microelectronics. Because ANNs are able to solve many problems such as approximation, classification, categorization, prediction and others, they have numerous applications in various areas, including nuclear. Nodal method is adopted as a tool for analyzing core parameters such as boron concentration and pin power peaks for pressurized water reactors. However, this method is extremely slow when it is necessary to perform various core evaluations, for example core reloading optimization. To overcome this difficulty, in this paper a model of Multi-layer Perceptron (MLP) artificial neural network type backpropagation will be trained to predict these values. The main objective of this work is the development of Multi-layer Perceptron (MLP) artificial neural network capable to predict, in very short time, with good accuracy, two important parameters used in the core reloading problem - Boron Concentration and Power Peaking Factor. For the training of the neural networks are provided loading patterns and nuclear data used in cycle 19 of Angra 1 nuclear power plant. Three models of networks are constructed using the same input data and providing the following outputs: 1- Boron Concentration and Power Peaking Factor, 2 - Boron Concentration and 3 - Power Peaking Factor. (author)

  10. Artificial Neural Network-Based System for PET Volume Segmentation

    Directory of Open Access Journals (Sweden)

    Mhd Saeed Sharif

    2010-01-01

    Full Text Available Tumour detection, classification, and quantification in positron emission tomography (PET imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs, as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.

  11. Short term wind speed forecasting in La Venta, Oaxaca, Mexico, using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Cadenas, Erasmo [Facultad de Ingenieria Mecanica, Universidad Michoacana de San Nicolas de Hidalgo, Santiago Tapia No. 403, Centro, 5000, Mor., Mich. (Mexico); Rivera, Wilfrido [Centro de Ivestigacion en Energia, Universidad Nacional Autonoma de Mexico, Apartado Postal 34, Temixco 62580, Morelos (Mexico)

    2009-01-15

    In this paper the short term wind speed forecasting in the region of La Venta, Oaxaca, Mexico, applying the technique of artificial neural network (ANN) to the hourly time series representative of the site is presented. The data were collected by the Comision Federal de Electricidad (CFE) during 7 years through a network of measurement stations located in the place of interest. Diverse configurations of ANN were generated and compared through error measures, guaranteeing the performance and accuracy of the chosen models. First a model with three layers and seven neurons was chosen, according to the recommendations of diverse authors, nevertheless, the results were not sufficiently satisfactory so other three models were developed, consisting of three layers and six neurons, two layers and four neurons and two layers and three neurons. The simplest model of two layers, with two input neurons and one output neuron, was the best for the short term wind speed forecasting, with mean squared error and mean absolute error values of 0.0016 and 0.0399, respectively. The developed model for short term wind speed forecasting showed a very good accuracy to be used by the Electric Utility Control Centre in Oaxaca for the energy supply. (author)

  12. Application of artificial neural networks to identify equilibration in computer simulations

    Science.gov (United States)

    Leibowitz, Mitchell H.; Miller, Evan D.; Henry, Michael M.; Jankowski, Eric

    2017-11-01

    Determining which microstates generated by a thermodynamic simulation are representative of the ensemble for which sampling is desired is a ubiquitous, underspecified problem. Artificial neural networks are one type of machine learning algorithm that can provide a reproducible way to apply pattern recognition heuristics to underspecified problems. Here we use the open-source TensorFlow machine learning library and apply it to the problem of identifying which hypothetical observation sequences from a computer simulation are “equilibrated” and which are not. We generate training populations and test populations of observation sequences with embedded linear and exponential correlations. We train a two-neuron artificial network to distinguish the correlated and uncorrelated sequences. We find that this simple network is good enough for > 98% accuracy in identifying exponentially-decaying energy trajectories from molecular simulations.

  13. Obituary: Ronald Eugene Pitts, 1949-2008

    Science.gov (United States)

    MacConnell, D. Jack

    2009-01-01

    Ronald Pitts, systems engineer in the Commanding Branch of the Space Telescope Science Institute and long-time Computer Sciences Corporation employee, died suddenly of a stroke on 4 May 2008 at his home in Laurel, Maryland. He was a dedicated scientist-engineer, husband, father, volunteer, and cherished friend to many. Ron was born on 19 January 1949 in Tucson, Arizona, and was raised, along with his sister Suzanne, on his parents' turkey farm outside Tucson. He picked up practical knowledge from his father, Vernon, and became a competent amateur electrician and plumber, skills he kept honed and used throughout his life. His mother, Ruth (Stephens), was a nurse and taught him compassion and patience and encouraged his inquisitive mind. Ron attended public schools and enrolled at the University of Arizona, graduating with a B. S. in Astronomy in 1971. Being from a family of modest means, he put himself through school working summers and part-time at a large copper mine south of town. Ron enrolled in the graduate astronomy program at the Ohio State University [OSU] in the fall of 1971 where he was a first-year fellowship student. During his second and third years, he was the Perkins Assistant, taking spectra for the very exacting but appreciative Philip Keenan who once remarked to another faculty member that Ron was the best observer he ever had. Later, in 1980, Ron was co-author with Keenan on "Revised MK Spectral Types for G, K, and M stars" and again in 1985 in a study of supergiants in open clusters. He met his future wife, Patricia Moore, also a graduate student in the department, and they were wed in 1973. Ron was also partially supported during his early OSU years by an NSF grant to Robert Wing, writing parts of Wing's photometric reduction code and observing on the program at Kitt Peak and Flagstaff in the summer of 1974. Wing remembers him as being very competent and pleasant to work with. Ron's thesis topic was "Oscillator Strengths for Neutral Iron and

  14. Switching between solid solution and two-phase regimes in the Li1-xFe1-yMnyPO4 cathode materials during lithium (de)insertion: combined PITT, in situ XRPD and electron diffraction tomography study

    International Nuclear Information System (INIS)

    Drozhzhin, Oleg A.; Sumanov, Vasiliy D.; Karakulina, Olesia M.; Abakumov, Artem M.; Hadermann, Joke; Baranov, Andrey N.; Stevenson, Keith J.; Antipov, Evgeny V.

    2016-01-01

    The electrochemical properties and phase transformations during (de)insertion of Li + in LiFePO 4 , LiFe 0.9 Mn 0.1 PO 4 and LiFe 0.5 Mn 0.5 PO 4 are studied by means of galvanostatic cycling, potential intermittent titration technique (PITT) and in situ X-ray powder diffraction. Different modes of switching between the solid solution and two-phase regimes are revealed which are influenced by the Mn content in Li 1-x Fe 1-y Mn y PO 4 . Additionally, an increase in electrochemical capacity with the Mn content is observed at high rates of galvanostatic cycling (10C, 20C), which is in good agreement with the numerically estimated contribution of the solid solution mechanism determined from PITT data. The observed asymmetric behavior of the phase transformations in Li 1-x Fe 0.5 Mn 0.5 PO 4 during charge and discharge is discussed. For the first time, the crystal structures of electrochemically deintercalated Li 1-x Fe 0.5 Mn 0.5 PO 4 with different Li content – LiFe 0.5 Mn 0.5 PO 4 , Li 0.5 Fe 0.5 Mn 0.5 PO 4 and Li 0.1 Fe 0.5 Mn 0.5 PO 4 – are refined, including the occupancy factors of the Li position. This refinement is done using electron diffraction tomography data. The crystallographic analyses of Li 1-x Fe 0.5 Mn 0.5 PO 4 reveal that at x = 0.5 and 0.9 the structure retains the Pnma symmetry and the main motif of the pristine x = 0 structure without noticeable short range order effects.

  15. Inteligência artificial aplicada à Zootecnia Artificial intelligence in Animal Science

    Directory of Open Access Journals (Sweden)

    Ernane José Xavier Costa

    2009-07-01

    Full Text Available Os sistemas biológicos são surpreendentemente flexíveis pra processar informação proveniente do mundo real. Alguns organismos biológicos possuem uma unidade central de processamento denominada de cérebro. O cérebro humano consiste de 10(11 neurônios e realiza processamento inteligente de forma exata e subjetiva. A Inteligência Artificial (IA tenta trazer para o mundo da computação digital a heurística dos sistemas biológicos de várias maneiras, mas, ainda resta muito para que isso seja concretizado. No entanto, algumas técnicas como Redes neurais artificiais e lógica fuzzy tem mostrado efetivas para resolver problemas complexos usando a heurística dos sistemas biológicos. Recentemente o numero de aplicação dos métodos da IA em sistemas zootécnicos tem aumentado significativamente. O objetivo deste artigo é explicar os princípios básicos da resolução de problemas usando heurística e demonstrar como a IA pode ser aplicada para construir um sistema especialista para resolver problemas na área de zootecnia.Biological systems are surprising flexible in processing information in the real world. Some biological organisms have a central unit processing named brain. The human's brain, consisting of 10(11 neurons, realizes intelligent information processing based on exact and commonsense reasoning. Artificial intelligence (AI has been trying to implement biological intelligence in computers in various ways, but is still far from real one. Therefore, there are approaches like Symbolic AI, Artificial Neural Network and Fuzzy system that partially successful in implementing heuristic from biological intelligence. Many recent applications of these approaches show an increased interest in animal science research. The main goal of this article is to explain the principles of heuristic problem-solving approach and to demonstrate how they can be applied to building knowledge-based systems for animal science problem solving.

  16. A screen for constituents of motor control and decision making in Drosophila reveals visual distance-estimation neurons

    Science.gov (United States)

    Triphan, Tilman; Nern, Aljoscha; Roberts, Sonia F.; Korff, Wyatt; Naiman, Daniel Q.; Strauss, Roland

    2016-01-01

    Climbing over chasms larger than step size is vital to fruit flies, since foraging and mating are achieved while walking. Flies avoid futile climbing attempts by processing parallax-motion vision to estimate gap width. To identify neuronal substrates of climbing control, we screened a large collection of fly lines with temporarily inactivated neuronal populations in a novel high-throughput assay described here. The observed climbing phenotypes were classified; lines in each group are reported. Selected lines were further analysed by high-resolution video cinematography. One striking class of flies attempts to climb chasms of unsurmountable width; expression analysis guided us to C2 optic-lobe interneurons. Inactivation of C2 or the closely related C3 neurons with highly specific intersectional driver lines consistently reproduced hyperactive climbing whereas strong or weak artificial depolarization of C2/C3 neurons strongly or mildly decreased climbing frequency. Contrast-manipulation experiments support our conclusion that C2/C3 neurons are part of the distance-evaluation system. PMID:27255169

  17. Artificial Consciousness or Artificial Intelligence

    OpenAIRE

    Spanache Florin

    2017-01-01

    Artificial intelligence is a tool designed by people for the gratification of their own creative ego, so we can not confuse conscience with intelligence and not even intelligence in its human representation with conscience. They are all different concepts and they have different uses. Philosophically, there are differences between autonomous people and automatic artificial intelligence. This is the difference between intelligence and artificial intelligence, autonomous versus a...

  18. Out of time: a possible link between mirror neurons, autism and electromagnetic radiation.

    Science.gov (United States)

    Thornton, Ian M

    2006-01-01

    Recent evidence suggests a link between autism and the human mirror neuron system. In this paper, I argue that temporal disruption from the environment may play an important role in the observed mirror neuron dysfunction, leading in turn to the pattern of deficits associated with autism. I suggest that the developing nervous system of an infant may be particularly prone to temporal noise that can interfere with the initial calibration of brain networks such as the mirror neuron system. The most likely source of temporal noise in the environment is artificially generated electromagnetic radiation. To date, there has been little evidence that electromagnetic radiation poses a direct biological hazard. It is clear, however, that time-varying electromagnetic waves have the potential to temporally modulate the nervous system, particularly when populations of neurons are required to act together. This modulation may be completely harmless for the fully developed nervous system of an adult. For an infant, this same temporal disruption might act to severely delay or disrupt vital calibration processes.

  19. Evaluation of thermal conductivity of MgO-MWCNTs/EG hybrid nanofluids based on experimental data by selecting optimal artificial neural networks

    Science.gov (United States)

    Vafaei, Masoud; Afrand, Masoud; Sina, Nima; Kalbasi, Rasool; Sourani, Forough; Teimouri, Hamid

    2017-01-01

    In this paper, the thermal conductivity ratio of MgO-MWCNTs/EG hybrid nanofluids has been predicted by an optimal artificial neural network at solid volume fractions of 0.05%, 0.1%, 0.15%, 0.2%, 0.4% and 0.6% in the temperature range of 25-50 °C. In this way, at the first, thirty six experimental data was presented to determine the thermal conductivity ratio of the hybrid nanofluid. Then, four optimal artificial neural networks with 6, 8, 10 and 12 neurons in hidden layer were designed to predict the thermal conductivity ratio of the nanofluid. The comparison between four optimal ANN results and experimental showed that the ANN with 12 neurons in hidden layer was the best model. Moreover, the results obtained from the best ANN indicated the maximum deviation margin of 0.8%.

  20. The Pitt Innovation Challenge (PInCh): Driving Innovation in Translational Research Through an Incentive-Based, Problem-Focused Competition.

    Science.gov (United States)

    Fitzpatrick, Nicole Edgar; Maier, John; Yasko, Laurel; Mathias, David; Qua, Kacy; Wagner, Erika; Miller, Elizabeth; Reis, Steven E

    2017-05-01

    Translational research aims to move scientific discoveries across the biomedical spectrum from the laboratory to humans, and to ultimately transform clinical practice and public health policies. Despite efforts to accelerate translational research through national initiatives, several major hurdles remain. The authors created the Pitt Innovation Challenge (PInCh) as an incentive-based, problem-focused approach to solving identified clinical or public health problems at the University of Pittsburgh Clinical and Translational Science Institute in spring 2014. With input from a broad range of stakeholders, PInCh leadership arrived at the challenge question: How do we empower individuals to take control of their own health outcomes? The authors developed the PInCh's three-round proposal submission and review process as well as an online contest management tool to support the process. Ninety-two teams submitted video proposals in round one. Proposals included mobile applications (29; 32%), other information technology (19; 21%), and community program (22; 24%) solutions. Ten teams advanced to the final round, where three were awarded $100,000 to implement their solution over 12 months. In a 6-month follow-up survey, 6/11 (55%) team leaders stated the PInCh helped to facilitate connections outside their normal sphere of collaborators. Additional educational training sessions related to problem-focused research will be developed. The PInCh will be expanded to engage investment and industry communities to facilitate the translation of solutions to clinical practice via commercialization pathways. External organizations and other universities will be engaged to use the PInCh as a mechanism to fuel innovation in their spaces.

  1. Changes in Lymantria dispar protocerebral neurosecretory neurons after exposure to cadmium

    Directory of Open Access Journals (Sweden)

    Ilijin Larisa

    2011-01-01

    Full Text Available Gypsy moth 4th instar caterpillars were fed for 3 days with an artificial diet supplemented with increasing cadmium (Cd concentrations (0, 10, 30, 100 and 250 μg⁄g of dry food weight. Changes in the morphometric characteristics of A1’ dorso-medial and L2 dorso-lateral neurosecretory neurons (nsn were analyzed. In the A1’ nsn, Cd supplements led to an enhanced nuclear size, except in the group treated with 250 μg Cd⁄g in the form of dry food. The size of L2 type nsn was increased in the groups provided with 30 and 100 μg Cd⁄g, while no differences in the size of nuclei was detected in L2 neurons among the experimental groups.

  2. Natural and artificial intelligence misconceptions about brains and neural networks

    CERN Document Server

    de Callataÿ, A

    1992-01-01

    How does the mind work? How is data stored in the brain? How does the mental world connect with the physical world? The hybrid system developed in this book shows a radically new view on the brain. Briefly, in this model memory remains permanent by changing the homeostasis rebuilding the neuronal organelles. These transformations are approximately abstracted as all-or-none operations. Thus the computer-like neural systems become plausible biological models. This illustrated book shows how artificial animals with such brains learn invariant methods of behavior control from their repeated action

  3. Natural - synthetic - artificial!

    DEFF Research Database (Denmark)

    Nielsen, Peter E

    2010-01-01

    The terms "natural," "synthetic" and "artificial" are discussed in relation to synthetic and artificial chromosomes and genomes, synthetic and artificial cells and artificial life.......The terms "natural," "synthetic" and "artificial" are discussed in relation to synthetic and artificial chromosomes and genomes, synthetic and artificial cells and artificial life....

  4. Artificial neural networks for spatial distribution of fuel assemblies in reload of PWR reactors

    Energy Technology Data Exchange (ETDEWEB)

    Oliveira, Edyene; Castro, Victor F.; Velásquez, Carlos E.; Pereira, Claubia, E-mail: claubia@nuclear.ufmg.br [Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG (Brazil). Programa de Pós-Graduação em Ciências e Técnicas Nucleares

    2017-07-01

    An artificial neural network methodology is being developed in order to find an optimum spatial distribution of the fuel assemblies in a nuclear reactor core during reload. The main bounding parameter of the modelling was the neutron multiplication factor, k{sub ef{sub f}}. The characteristics of the network are defined by the nuclear parameters: cycle, burnup, enrichment, fuel type, and average power peak of each element. These parameters were obtained by the ORNL nuclear code package SCALE6.0. As for the artificial neural network, the ANN Feedforward Multi{sub L}ayer{sub P}erceptron with various layers and neurons were constructed. Three algorithms were used and tested: LM (Levenberg-Marquardt), SCG (Scaled Conjugate Gradient) and BayR (Bayesian Regularization). Artificial neural network have implemented using MATLAB 2015a version. As preliminary results, the spatial distribution of the fuel assemblies in the core using a neural network was slightly better than the standard core. (author)

  5. Artificial neural networks for spatial distribution of fuel assemblies in reload of PWR reactors

    International Nuclear Information System (INIS)

    Oliveira, Edyene; Castro, Victor F.; Velásquez, Carlos E.; Pereira, Claubia

    2017-01-01

    An artificial neural network methodology is being developed in order to find an optimum spatial distribution of the fuel assemblies in a nuclear reactor core during reload. The main bounding parameter of the modelling was the neutron multiplication factor, k ef f . The characteristics of the network are defined by the nuclear parameters: cycle, burnup, enrichment, fuel type, and average power peak of each element. These parameters were obtained by the ORNL nuclear code package SCALE6.0. As for the artificial neural network, the ANN Feedforward Multi L ayer P erceptron with various layers and neurons were constructed. Three algorithms were used and tested: LM (Levenberg-Marquardt), SCG (Scaled Conjugate Gradient) and BayR (Bayesian Regularization). Artificial neural network have implemented using MATLAB 2015a version. As preliminary results, the spatial distribution of the fuel assemblies in the core using a neural network was slightly better than the standard core. (author)

  6. NeuronBank: a tool for cataloging neuronal circuitry

    Directory of Open Access Journals (Sweden)

    Paul S Katz

    2010-04-01

    Full Text Available The basic unit of any nervous system is the neuron. Therefore, understanding the operation of nervous systems ultimately requires an inventory of their constituent neurons and synaptic connectivity, which form neural circuits. The presence of uniquely identifiable neurons or classes of neurons in many invertebrates has facilitated the construction of cellular-level connectivity diagrams that can be generalized across individuals within a species. Homologous neurons can also be recognized across species. Here we describe NeuronBank.org, a web-based tool that we are developing for cataloging, searching, and analyzing neuronal circuitry within and across species. Information from a single species is represented in an individual branch of NeuronBank. Users can search within a branch or perform queries across branches to look for similarities in neuronal circuits across species. The branches allow for an extensible ontology so that additional characteristics can be added as knowledge grows. Each entry in NeuronBank generates a unique accession ID, allowing it to be easily cited. There is also an automatic link to a Wiki page allowing an encyclopedic explanation of the entry. All of the 44 previously published neurons plus one previously unpublished neuron from the mollusc, Tritonia diomedea, have been entered into a branch of NeuronBank as have 4 previously published neurons from the mollusc, Melibe leonina. The ability to organize information about neuronal circuits will make this information more accessible, ultimately aiding research on these important models.

  7. Texture coarseness responsive neurons and their mapping in layer 2–3 of the rat barrel cortex in vivo

    Science.gov (United States)

    Garion, Liora; Dubin, Uri; Rubin, Yoav; Khateb, Mohamed; Schiller, Yitzhak; Azouz, Rony; Schiller, Jackie

    2014-01-01

    Texture discrimination is a fundamental function of somatosensory systems, yet the manner by which texture is coded and spatially represented in the barrel cortex are largely unknown. Using in vivo two-photon calcium imaging in the rat barrel cortex during artificial whisking against different surface coarseness or controlled passive whisker vibrations simulating different coarseness, we show that layer 2–3 neurons within barrel boundaries differentially respond to specific texture coarsenesses, while only a minority of neurons responded monotonically with increased or decreased surface coarseness. Neurons with similar preferred texture coarseness were spatially clustered. Multi-contact single unit recordings showed a vertical columnar organization of texture coarseness preference in layer 2–3. These findings indicate that layer 2–3 neurons perform high hierarchical processing of tactile information, with surface coarseness embodied by distinct neuronal subpopulations that are spatially mapped onto the barrel cortex. DOI: http://dx.doi.org/10.7554/eLife.03405.001 PMID:25233151

  8. Inteligência biológica versus inteligência artificial: uma abordagem crítica Biologic intelligence versus artificial intelligence: a critical approach

    Directory of Open Access Journals (Sweden)

    Wilson Luiz Sanvito

    1995-09-01

    Full Text Available Após considerações iniciais sobre inteligência, um estudo comparativo entre inteligência biológica e inteligência artificial é feito. Os especialistas em Inteligência Artificial são de opinião que inteligência é simplesmente uma matéria de manipulação de símbolos físicos. Neste sentido, o objetivo da Inteligência Artificial é entender como a inteligência cerebral funciona em termos de conceitos e técnicas de engenharia. De modo diverso os filósofos da ciência acreditam que os computadores podem ter uma sintaxe, porém não têm uma semântica. No presente trabalho é ressaltado que o complexo cérebro/mente constitui um sistema monolítico, que funciona com funções emergentes em vários níveis de organização hierárquica. Esses níveis hierárquicos não são redutíveis um ao outro. Eles são, no mínimo, três (neuronal, funcional e semântico e funcionam dentro de um plano interacional. Do ponto de vista epistemológico, o complexo cérebro/mente se utiliza de mecanismos lógicos e não-lógicos para lidar com os problemas do dia-a-dia. A lógica é necessária para o processo do pensamento, porém não é suficiente. Ênfase é dada aos mecanismos não-lógicos (lógica nebulosa, heurística, raciocínio intuitivo, os quais permitem à mente desenvolver estratégias para encontrar soluções.After brief considerations about intelligence, a comparative study between biologic and artificial intelligence is made. The specialists in Artificial Intelligence found that intelligence is purely a matter of physical symbol manipulation. The enterprise of Artificial Intelligence aims to understand what we might call Brain Intelligence in terms of concepts and techniques of engineering. However the philosophers believed that computer-machine can have syntax, but can never have semantics. In other words, that they can follow rules, such as those of arithmetic or grammar, but not understand what to us are meanings of symbols

  9. Artificial Synaptic Devices Based on Natural Chicken Albumen Coupled Electric-Double-Layer Transistors.

    Science.gov (United States)

    Wu, Guodong; Feng, Ping; Wan, Xiang; Zhu, Liqiang; Shi, Yi; Wan, Qing

    2016-03-24

    Recent progress in using biomaterials to fabricate functional electronics has got growing attention for the new generation of environmentally friendly and biocompatible electronic devices. As a kind of biological material with rich source, proteins are essential natural component of all organisms. At the same time, artificial synaptic devices are of great significance for neuromorphic systems because they can emulate the signal process and memory behaviors of biological synapses. In this report, natural chicken albumen with high proton conductivity was used as the coupling electrolyte film for organic/inorganic hybrid synaptic devices fabrication. Some important synaptic functions including paired-pulse facilitation, dynamic filtering, short-term to long-term memory transition and spatial summation and shunting inhibition were successfully mimicked. Our results are very interesting for biological friendly artificial neuron networks and neuromorphic systems.

  10. Modeling the thermotaxis behavior of C.elegans based on the artificial neural network.

    Science.gov (United States)

    Li, Mingxu; Deng, Xin; Wang, Jin; Chen, Qiaosong; Tang, Yun

    2016-07-03

    ASBTRACT This research aims at modeling the thermotaxis behavior of C.elegans which is a kind of nematode with full clarified neuronal connections. Firstly, this work establishes the motion model which can perform the undulatory locomotion with turning behavior. Secondly, the thermotaxis behavior is modeled by nonlinear functions and the nonlinear functions are learned by artificial neural network. Once the artificial neural networks have been well trained, they can perform the desired thermotaxis behavior. Last, several testing simulations are carried out to verify the effectiveness of the model for thermotaxis behavior. This work also analyzes the different performances of the model under different environments. The testing results reveal the essence of the thermotaxis of C.elegans to some extent, and theoretically support the research on the navigation of the crawling robots.

  11. Artificial Synaptic Devices Based on Natural Chicken Albumen Coupled Electric-Double-Layer Transistors

    Science.gov (United States)

    Wu, Guodong; Feng, Ping; Wan, Xiang; Zhu, Liqiang; Shi, Yi; Wan, Qing

    2016-03-01

    Recent progress in using biomaterials to fabricate functional electronics has got growing attention for the new generation of environmentally friendly and biocompatible electronic devices. As a kind of biological material with rich source, proteins are essential natural component of all organisms. At the same time, artificial synaptic devices are of great significance for neuromorphic systems because they can emulate the signal process and memory behaviors of biological synapses. In this report, natural chicken albumen with high proton conductivity was used as the coupling electrolyte film for organic/inorganic hybrid synaptic devices fabrication. Some important synaptic functions including paired-pulse facilitation, dynamic filtering, short-term to long-term memory transition and spatial summation and shunting inhibition were successfully mimicked. Our results are very interesting for biological friendly artificial neuron networks and neuromorphic systems.

  12. A hierarchical model for structure learning based on the physiological characteristics of neurons

    Institute of Scientific and Technical Information of China (English)

    WEI Hui

    2007-01-01

    Almost all applications of Artificial Neural Networks (ANNs) depend mainly on their memory ability.The characteristics of typical ANN models are fixed connections,with evolved weights,globalized representations,and globalized optimizations,all based on a mathematical approach.This makes those models to be deficient in robustness,efficiency of learning,capacity,anti-jamming between training sets,and correlativity of samples,etc.In this paper,we attempt to address these problems by adopting the characteristics of biological neurons in morphology and signal processing.A hierarchical neural network was designed and realized to implement structure learning and representations based on connected structures.The basic characteristics of this model are localized and random connections,field limitations of neuron fan-in and fan-out,dynamic behavior of neurons,and samples represented through different sub-circuits of neurons specialized into different response patterns.At the end of this paper,some important aspects of error correction,capacity,learning efficiency,and soundness of structural representation are analyzed theoretically.This paper has demonstrated the feasibility and advantages of structure learning and representation.This model can serve as a fundamental element of cognitive systems such as perception and associative memory.Key-words structure learning,representation,associative memory,computational neuroscience

  13. Responses of mirror neurons in area F5 to hand and tool grasping observation

    Science.gov (United States)

    Rochat, Magali J.; Caruana, Fausto; Jezzini, Ahmad; Escola, Ludovic; Intskirveli, Irakli; Grammont, Franck; Gallese, Vittorio; Rizzolatti, Giacomo

    2010-01-01

    Mirror neurons are a distinct class of neurons that discharge both during the execution of a motor act and during observation of the same or similar motor act performed by another individual. However, the extent to which mirror neurons coding a motor act with a specific goal (e.g., grasping) might also respond to the observation of a motor act having the same goal, but achieved with artificial effectors, is not yet established. In the present study, we addressed this issue by recording mirror neurons from the ventral premotor cortex (area F5) of two monkeys trained to grasp objects with pliers. Neuron activity was recorded during the observation and execution of grasping performed with the hand, with pliers and during observation of an experimenter spearing food with a stick. The results showed that virtually all neurons responding to the observation of hand grasping also responded to the observation of grasping with pliers and, many of them to the observation of spearing with a stick. However, the intensity and pattern of the response differed among conditions. Hand grasping observation determined the earliest and the strongest discharge, while pliers grasping and spearing observation triggered weaker responses at longer latencies. We conclude that F5 grasping mirror neurons respond to the observation of a family of stimuli leading to the same goal. However, the response pattern depends upon the similarity between the observed motor act and the one executed by the hand, the natural motor template. PMID:20577726

  14. Temperature, Humidity and Energy Consumption Forecasting in the Poultry Hall Using Artificial Neural Networknetwork

    Directory of Open Access Journals (Sweden)

    N Gholamrezaei

    2017-10-01

    Full Text Available Introduction Energy consumption management is one of the most important issues in poultry halls management. Considering the situation of poultry as one of the largest and most developed industries, it is needed to control growing condition based on world standards. The neural networks as one of the intelligent methods are applied in a lot of fields such as classification, pattern recognition, prediction and modeling of processes. To detect and classify several agricultural crops, a research was conducted based on texture and color feature. The highest classification accuracy for vegetables, grains and fruits with using artificial neural network were 80%, 86% and 70%. In this research, the ability to Multilayer Perceptron (MLP Neural Network in predicting energy consumption, temperature and humidity in different coordinate placement of electronic control unit sensors in the poultry house environment was examined. Materials and Methods The experiments were conducted in a poultry unit (3000 pieces that is located in Fars province, Marvdasht city, Ramjerd town, with dimensions of 32 meters long, 7 meters wide and 2.2 meters height. To determine the appropriate placement of the sensor, 60 different points in terms of length, width and height in poultry were selected. Initially, the data was divided into two datasets. 80 percent of total data as a training set and 20 percent of total data as a test set. From180 observations, 144 data were used to train network and 36 data were used to test the process. There are several criteria for evaluating predictive models that they are mainly based according to the difference between the predicted outputs and actual outputs. To evaluate the performance of the model, two statistical indexes, mean squared error (MSE and the coefficient of determination (R² were used. Results and Discussions In this study, to train artificial neural network for predicting the temperature, humidity and energy consumption, the

  15. Clustering predicts memory performance in networks of spiking and non-spiking neurons

    Directory of Open Access Journals (Sweden)

    Weiliang eChen

    2011-03-01

    Full Text Available The problem we address in this paper is that of finding effective and parsimonious patterns of connectivity in sparse associative memories. This problem must be addressed in real neuronal systems, so that results in artificial systems could throw light on real systems. We show that there are efficient patterns of connectivity and that these patterns are effective in models with either spiking or non-spiking neurons. This suggests that there may be some underlying general principles governing good connectivity in such networks. We also show that the clustering of the network, measured by Clustering Coefficient, has a strong linear correlation to the performance of associative memory. This result is important since a purely static measure of network connectivity appears to determine an important dynamic property of the network.

  16. Peripheral chemoreceptors tune inspiratory drive via tonic expiratory neuron hubs in the medullary ventral respiratory column network.

    Science.gov (United States)

    Segers, L S; Nuding, S C; Ott, M M; Dean, J B; Bolser, D C; O'Connor, R; Morris, K F; Lindsey, B G

    2015-01-01

    Models of brain stem ventral respiratory column (VRC) circuits typically emphasize populations of neurons, each active during a particular phase of the respiratory cycle. We have proposed that "tonic" pericolumnar expiratory (t-E) neurons tune breathing during baroreceptor-evoked reductions and central chemoreceptor-evoked enhancements of inspiratory (I) drive. The aims of this study were to further characterize the coordinated activity of t-E neurons and test the hypothesis that peripheral chemoreceptors also modulate drive via inhibition of t-E neurons and disinhibition of their inspiratory neuron targets. Spike trains of 828 VRC neurons were acquired by multielectrode arrays along with phrenic nerve signals from 22 decerebrate, vagotomized, neuromuscularly blocked, artificially ventilated adult cats. Forty-eight of 191 t-E neurons fired synchronously with another t-E neuron as indicated by cross-correlogram central peaks; 32 of the 39 synchronous pairs were elements of groups with mutual pairwise correlations. Gravitational clustering identified fluctuations in t-E neuron synchrony. A network model supported the prediction that inhibitory populations with spike synchrony reduce target neuron firing probabilities, resulting in offset or central correlogram troughs. In five animals, stimulation of carotid chemoreceptors evoked changes in the firing rates of 179 of 240 neurons. Thirty-two neuron pairs had correlogram troughs consistent with convergent and divergent t-E inhibition of I cells and disinhibitory enhancement of drive. Four of 10 t-E neurons that responded to sequential stimulation of peripheral and central chemoreceptors triggered 25 cross-correlograms with offset features. The results support the hypothesis that multiple afferent systems dynamically tune inspiratory drive in part via coordinated t-E neurons. Copyright © 2015 the American Physiological Society.

  17. Estimation of monthly wind power outputs of WECS with limited record period using artificial neural networks

    International Nuclear Information System (INIS)

    Tu, Yi-Long; Chang, Tsang-Jung; Chen, Cheng-Lung; Chang, Yu-Jung

    2012-01-01

    Highlights: ► ANN with short record training data is used to estimate power outputs in an existing station. ► The suitable numbers/parameters of input neurons for ANN are presented. ► Current wind speeds and previous power outputs are the most important input neurons. ► Choosing suitable input parameters is more important than choosing multiple parameters. - Abstract: For the brand new wind power industry, online recordings of wind power data are always in a relatively limited period. The aim of the study is to investigate the suitable numbers/parameters of input neurons for artificial neural networks under a short record of measured data. Measured wind speeds, wind directions (yaw angles) and power outputs with 10-min resolution at an existing wind power station, located at Jhongtun, Taiwan, are integrated to form three types of input neuron numbers and sixteen cases of input neurons. The first-10 days of each month in 2006 are used for data training to simulate the following 20-day power generation of the same month. The performance of various input neuron cases is evaluated. The simulated results show that using the first 10-day training data with adequate input neurons can estimate energy outputs well except the weak wind regime (May, June, and July). Among the input neuron parameters used, current wind speeds V(t) and previous power outputs P(t − 1) are the most important. Individually using one of them into input neurons can only provide satisfactory estimation. However, simultaneously using these two parameters into input neurons can give the best estimation. Thus, choosing suitable input parameters is more important than choosing multiple parameters.

  18. Artificial intelligence

    CERN Document Server

    Hunt, Earl B

    1975-01-01

    Artificial Intelligence provides information pertinent to the fundamental aspects of artificial intelligence. This book presents the basic mathematical and computational approaches to problems in the artificial intelligence field.Organized into four parts encompassing 16 chapters, this book begins with an overview of the various fields of artificial intelligence. This text then attempts to connect artificial intelligence problems to some of the notions of computability and abstract computing devices. Other chapters consider the general notion of computability, with focus on the interaction bet

  19. Multielectrode recordings from auditory neurons in the brain of a small grasshopper.

    Science.gov (United States)

    Bhavsar, Mit Balvantray; Heinrich, Ralf; Stumpner, Andreas

    2015-12-30

    Grasshoppers have been used as a model system to study the neuronal basis of insect acoustic behavior. Auditory neurons have been described from intracellular recordings. The growing interest to study population activity of neurons has been satisfied so far with artificially combining data from different individuals. We for the first time used multielectrode recordings from a small grasshopper brain. We used three 12μm tungsten wires (combined in a multielectrode) to record from local brain neurons and from a population of auditory neurons entering the brain from the thorax. Spikes of the recorded units were separated by sorting algorithms and spike collision analysis. The tungsten wires enabled stable recordings with high signal to noise ratio. Due to the tight temporal coupling of auditory activity to the stimulus spike collisions were frequent and collision analysis retrieved 10-15% of additional spikes. Marking the electrode position was possible using a fluorescent dye or electrocoagulation with high current. Physiological identification of units described from intracellular recordings was hard to achieve. 12μm tungsten wires gave a better signal to noise ratio than 15μm copper wires previously used in recordings from bees' brains. Recording the population activity of auditory neurons in one individual prevents interindividual and trial-to-trial variability which otherwise reduce the validity of the analysis. Double intracellular recordings have quite low success rate and therefore are rarely achieved and their stability is much lower than that of multielectrode recordings which allows sampling of data for 30min or more. Copyright © 2015 Elsevier B.V. All rights reserved.

  20. Medical imaging was boosted by the discovery of artificial radioactivity

    International Nuclear Information System (INIS)

    Demarthon, F.; Dupuy-Maury, F.; Donnars, O.

    2002-01-01

    This article draws the history of medical imaging since the discovery of artificial radioactivity in 1934. The author reviews the PET (positron emission tomography) and MRI (magnetic resonance imaging) technologies and presents the recent progress in neuro-sciences that have been made possible by using these 2 technologies. Brain imaging has allowed to show: - the impact of emotions on logical mental processes and on mental performances, - the management of memory in the brain of talented quick reckoners, - the degeneration of neurons, and - the link between autism and the presence of structural and functional anomalies in the brain. (A.C.)

  1. Artificial Consciousness or Artificial Intelligence

    Directory of Open Access Journals (Sweden)

    Spanache Florin

    2017-05-01

    Full Text Available Artificial intelligence is a tool designed by people for the gratification of their own creative ego, so we can not confuse conscience with intelligence and not even intelligence in its human representation with conscience. They are all different concepts and they have different uses. Philosophically, there are differences between autonomous people and automatic artificial intelligence. This is the difference between intelligence and artificial intelligence, autonomous versus automatic. But conscience is above these differences because it is neither conditioned by the self-preservation of autonomy, because a conscience is something that you use to help your neighbor, nor automatic, because one’s conscience is tested by situations which are not similar or subject to routine. So, artificial intelligence is only in science-fiction literature similar to an autonomous conscience-endowed being. In real life, religion with its notions of redemption, sin, expiation, confession and communion will not have any meaning for a machine which cannot make a mistake on its own.

  2. Prediction of hearing loss among the noise-exposed workers in a steel factory using artificial intelligence approach.

    Science.gov (United States)

    Aliabadi, Mohsen; Farhadian, Maryam; Darvishi, Ebrahim

    2015-08-01

    Prediction of hearing loss in noisy workplaces is considered to be an important aspect of hearing conservation program. Artificial intelligence, as a new approach, can be used to predict the complex phenomenon such as hearing loss. Using artificial neural networks, this study aims to present an empirical model for the prediction of the hearing loss threshold among noise-exposed workers. Two hundred and ten workers employed in a steel factory were chosen, and their occupational exposure histories were collected. To determine the hearing loss threshold, the audiometric test was carried out using a calibrated audiometer. The personal noise exposure was also measured using a noise dosimeter in the workstations of workers. Finally, data obtained five variables, which can influence the hearing loss, were used for the development of the prediction model. Multilayer feed-forward neural networks with different structures were developed using MATLAB software. Neural network structures had one hidden layer with the number of neurons being approximately between 5 and 15 neurons. The best developed neural networks with one hidden layer and ten neurons could accurately predict the hearing loss threshold with RMSE = 2.6 dB and R(2) = 0.89. The results also confirmed that neural networks could provide more accurate predictions than multiple regressions. Since occupational hearing loss is frequently non-curable, results of accurate prediction can be used by occupational health experts to modify and improve noise exposure conditions.

  3. Kappe neurons, a novel population of olfactory sensory neurons.

    Science.gov (United States)

    Ahuja, Gaurav; Bozorg Nia, Shahrzad; Zapilko, Veronika; Shiriagin, Vladimir; Kowatschew, Daniel; Oka, Yuichiro; Korsching, Sigrun I

    2014-02-10

    Perception of olfactory stimuli is mediated by distinct populations of olfactory sensory neurons, each with a characteristic set of morphological as well as functional parameters. Beyond two large populations of ciliated and microvillous neurons, a third population, crypt neurons, has been identified in teleost and cartilaginous fishes. We report here a novel, fourth olfactory sensory neuron population in zebrafish, which we named kappe neurons for their characteristic shape. Kappe neurons are identified by their Go-like immunoreactivity, and show a distinct spatial distribution within the olfactory epithelium, similar to, but significantly different from that of crypt neurons. Furthermore, kappe neurons project to a single identified target glomerulus within the olfactory bulb, mdg5 of the mediodorsal cluster, whereas crypt neurons are known to project exclusively to the mdg2 glomerulus. Kappe neurons are negative for established markers of ciliated, microvillous and crypt neurons, but appear to have microvilli. Kappe neurons constitute the fourth type of olfactory sensory neurons reported in teleost fishes and their existence suggests that encoding of olfactory stimuli may require a higher complexity than hitherto assumed already in the peripheral olfactory system.

  4. Development of Artificial Neural Network Model of Crude Oil Distillation Column

    Directory of Open Access Journals (Sweden)

    Ali Hussein Khalaf

    2016-02-01

    Full Text Available Artificial neural network in MATLAB simulator is used to model Baiji crude oil distillation unit based on data generated from aspen-HYSYS simulator. Thirteen inputs, six outputs and over 1487 data set are used to model the actual unit. Nonlinear autoregressive network with exogenous inputs (NARXand back propagation algorithm are used for training. Seventy percent of data are used for training the network while the remaining  thirty percent are used for testing  and validating the network to determine its prediction accuracy. One hidden layer and 34 hidden neurons are used for the proposed network with MSE of 0.25 is obtained. The number of neuron are selected based on less MSE for the network. The model founded to predict the optimal operating conditions for different objective functions within the training limit since ANN models are poor extrapolators. They are usually only reliable within the range of data that they had been trained for.

  5. Development of Artificial Neural Network Model of Crude Oil Distillation Column

    Directory of Open Access Journals (Sweden)

    Duraid F. Ahmed

    2016-02-01

    Full Text Available Artificial neural network in MATLAB simulator is used to model Baiji crude oil distillation unit based on data generated from aspen-HYSYS simulator. Thirteen inputs, six outputs and over 1487 data set are used to model the actual unit. Nonlinear autoregressive network with exogenous inputs (NARX and back propagation algorithm are used for training. Seventy percent of data are used for training the network while the remaining thirty percent are used for testing and validating the network to determine its prediction accuracy. One hidden layer and 34 hidden neurons are used for the proposed network with MSE of 0.25 is obtained. The number of neuron are selected based on less MSE for the network. The model founded to predict the optimal operating conditions for different objective functions within the training limit since ANN models are poor extrapolators. They are usually only reliable within the range of data that they had been trained for.

  6. Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications.

    Science.gov (United States)

    Pastur-Romay, Lucas Antón; Cedrón, Francisco; Pazos, Alejandro; Porto-Pazos, Ana Belén

    2016-08-11

    Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure-Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron-Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods.

  7. Kappe neurons, a novel population of olfactory sensory neurons

    OpenAIRE

    Ahuja, Gaurav; Nia, Shahrzad Bozorg; Zapilko, Veronika; Shiriagin, Vladimir; Kowatschew, Daniel; Oka, Yuichiro; Korsching, Sigrun I.

    2014-01-01

    Perception of olfactory stimuli is mediated by distinct populations of olfactory sensory neurons, each with a characteristic set of morphological as well as functional parameters. Beyond two large populations of ciliated and microvillous neurons, a third population, crypt neurons, has been identified in teleost and cartilaginous fishes. We report here a novel, fourth olfactory sensory neuron population in zebrafish, which we named kappe neurons for their characteristic shape. Kappe neurons ar...

  8. [The Identification of the Origin of Chinese Wolfberry Based on Infrared Spectral Technology and the Artificial Neural Network].

    Science.gov (United States)

    Li, Zhong; Liu, Ming-de; Ji, Shou-xiang

    2016-03-01

    The Fourier Transform Infrared Spectroscopy (FTIR) is established to find the geographic origins of Chinese wolfberry quickly. In the paper, the 45 samples of Chinese wolfberry from different places of Qinghai Province are to be surveyed by FTIR. The original data matrix of FTIR is pretreated with common preprocessing and wavelet transform. Compared with common windows shifting smoothing preprocessing, standard normal variation correction and multiplicative scatter correction, wavelet transform is an effective spectrum data preprocessing method. Before establishing model through the artificial neural networks, the spectra variables are compressed by means of the wavelet transformation so as to enhance the training speed of the artificial neural networks, and at the same time the related parameters of the artificial neural networks model are also discussed in detail. The survey shows even if the infrared spectroscopy data is compressed to 1/8 of its original data, the spectral information and analytical accuracy are not deteriorated. The compressed spectra variables are used for modeling parameters of the backpropagation artificial neural network (BP-ANN) model and the geographic origins of Chinese wolfberry are used for parameters of export. Three layers of neural network model are built to predict the 10 unknown samples by using the MATLAB neural network toolbox design error back propagation network. The number of hidden layer neurons is 5, and the number of output layer neuron is 1. The transfer function of hidden layer is tansig, while the transfer function of output layer is purelin. Network training function is trainl and the learning function of weights and thresholds is learngdm. net. trainParam. epochs=1 000, while net. trainParam. goal = 0.001. The recognition rate of 100% is to be achieved. It can be concluded that the method is quite suitable for the quick discrimination of producing areas of Chinese wolfberry. The infrared spectral analysis technology

  9. Artificial neural nets application in the cotton yarn industry

    Directory of Open Access Journals (Sweden)

    Gilberto Clóvis Antoneli

    2016-06-01

    Full Text Available The competitiveness in the yarn production sector has led companies to search for solutions to attain quality yarn at a low cost. Today, the difference between them, and thus the sector, is in the raw material, meaning processed cotton and its characteristics. There are many types of cotton with different characteristics due to its production region, harvest, storage and transportation. Yarn industries work with cotton mixtures, which makes it difficult to determine the quality of the yarn produced from the characteristics of the processed fibers. This study uses data from a conventional spinning, from a raw material made of 100% cotton, and presents a solution with artificial neural nets that determine the thread quality information, using the fibers’ characteristics values and settings of some process adjustments. In this solution a neural net of the type MultiLayer Perceptron with 11 entry neurons (8 characteristics of the fiber and 3 process adjustments, 7 output neurons (yarn quality and two types of training, Back propagation and Conjugate gradient descent. The selection and organization of the production data of the yarn industry of the cocamar® indústria de fios company are described, to apply the artificial neural nets developed. In the application of neural nets to determine yarn quality, one concludes that, although the ideal precision of absolute values is lacking, the presented solution represents an excellent tool to define yarn quality variations when modifying the raw material composition. The developed system enables a simulation to define the raw material percentage mixture to be processed in the plant using the information from the stocked cotton packs, thus obtaining a mixture that maintains the stability of the entire productive process.

  10. Vasculo-Neuronal Coupling: Retrograde Vascular Communication to Brain Neurons.

    Science.gov (United States)

    Kim, Ki Jung; Ramiro Diaz, Juan; Iddings, Jennifer A; Filosa, Jessica A

    2016-12-14

    Continuous cerebral blood flow is essential for neuronal survival, but whether vascular tone influences resting neuronal function is not known. Using a multidisciplinary approach in both rat and mice brain slices, we determined whether flow/pressure-evoked increases or decreases in parenchymal arteriole vascular tone, which result in arteriole constriction and dilation, respectively, altered resting cortical pyramidal neuron activity. We present evidence for intercellular communication in the brain involving a flow of information from vessel to astrocyte to neuron, a direction opposite to that of classic neurovascular coupling and referred to here as vasculo-neuronal coupling (VNC). Flow/pressure increases within parenchymal arterioles increased vascular tone and simultaneously decreased resting pyramidal neuron firing activity. On the other hand, flow/pressure decreases evoke parenchymal arteriole dilation and increased resting pyramidal neuron firing activity. In GLAST-CreERT2; R26-lsl-GCaMP3 mice, we demonstrate that increased parenchymal arteriole tone significantly increased intracellular calcium in perivascular astrocyte processes, the onset of astrocyte calcium changes preceded the inhibition of cortical pyramidal neuronal firing activity. During increases in parenchymal arteriole tone, the pyramidal neuron response was unaffected by blockers of nitric oxide, GABA A , glutamate, or ecto-ATPase. However, VNC was abrogated by TRPV4 channel, GABA B , as well as an adenosine A 1 receptor blocker. Differently to pyramidal neuron responses, increases in flow/pressure within parenchymal arterioles increased the firing activity of a subtype of interneuron. Together, these data suggest that VNC is a complex constitutive active process that enables neurons to efficiently adjust their resting activity according to brain perfusion levels, thus safeguarding cellular homeostasis by preventing mismatches between energy supply and demand. We present evidence for vessel-to-neuron

  11. A molecular toolbox for rapid generation of viral vectors to up- or down-regulate in vivo neuronal gene expression

    Directory of Open Access Journals (Sweden)

    Melanie D. White

    2011-07-01

    Full Text Available We introduce a molecular toolbox for manipulation of neuronal gene expression in vivo. The toolbox includes promoters, ion channels, optogenetic tools, fluorescent proteins and intronic artificial microRNAs. The components are easily assembled into adeno-associated virus (AAV or lentivirus vectors using recombination cloning. We demonstrate assembly of toolbox components into lentivirus and AAV vectors and use these vectors for in vivo expression of inwardly rectifying potassium channels (Kir2.1, Kir3.1 and Kir3.2 and an artificial microRNA targeted against the ion channel HCN1 (HCN1 miR. We show that AAV assembled to express HCN1 miR produces efficacious and specific in vivo knockdown of HCN1 channels. Comparison of in vivo viral transduction using HCN1 miR with mice containing a germ line deletion of HCN1 reveals similar physiological phenotypes in cerebellar Purkinje cells. The easy assembly and re-usability of the toolbox components, together with the ability to up- or down-regulate neuronal gene expression in vivo, may be useful for applications in many areas of neuroscience.

  12. Development of A-type allatostatin immunoreactivity in antennal lobe neurons of the sphinx moth Manduca sexta.

    Science.gov (United States)

    Utz, Sandra; Schachtner, Joachim

    2005-04-01

    The antennal lobe (AL) of the sphinx moth Manduca sexta is a well-established model system for studying mechanisms of neuronal development. To understand whether neuropeptides are suited to playing a role during AL development, we have studied the cellular localization and temporal expression pattern of neuropeptides of the A-type allatostatin family. Based on morphology and developmental appearance, we distinguished four types of AST-A-immunoreactive cell types. The majority of the cells were local interneurons of the AL (type Ia) which acquired AST-A immunostaining in a complex pattern consisting of three rising (RI-RIII) and two declining phases (DI, DII). Type Ib neurons consisted of two local neurons with large cell bodies not appearing before 7/8 days after pupal ecdysis (P7/P8). Types II and III neurons accounted for single centrifugal neurons, with type II neurons present in the larva and disappearing in the early pupa. The type III neuron did not appear before P7/P8. RI and RII coincided with the rises of the ecdysteroid hemolymph titer. Artificially shifting the pupal 20-hydroxyecdysone (20E) peak to an earlier developmental time point resulted in the precocious appearance of AST-A immunostaining in types Ia, Ib, and III neurons. This result supports the hypothesis that the pupal rise in 20E plays a role in AST-A expression during AL development. Because of their early appearance in newly forming glomeruli, AST-A-immunoreactive fibers could be involved in glomerulus formation. Diffuse AST-A labeling during early AL development is discussed as a possible signal providing information for ingrowing olfactory receptor neurons.

  13. Artificial skin and patient simulator comprising the artificial skin

    NARCIS (Netherlands)

    2011-01-01

    The invention relates to an artificial skin (10, 12, 14), and relates to a patient simulator (100) comprising the artificial skin. The artificial skin is a layered structure comprising a translucent cover layer (20) configured for imitating human or animal skin, and comprising a light emitting layer

  14. Power-law inter-spike interval distributions infer a conditional maximization of entropy in cortical neurons.

    Directory of Open Access Journals (Sweden)

    Yasuhiro Tsubo

    Full Text Available The brain is considered to use a relatively small amount of energy for its efficient information processing. Under a severe restriction on the energy consumption, the maximization of mutual information (MMI, which is adequate for designing artificial processing machines, may not suit for the brain. The MMI attempts to send information as accurate as possible and this usually requires a sufficient energy supply for establishing clearly discretized communication bands. Here, we derive an alternative hypothesis for neural code from the neuronal activities recorded juxtacellularly in the sensorimotor cortex of behaving rats. Our hypothesis states that in vivo cortical neurons maximize the entropy of neuronal firing under two constraints, one limiting the energy consumption (as assumed previously and one restricting the uncertainty in output spike sequences at given firing rate. Thus, the conditional maximization of firing-rate entropy (CMFE solves a tradeoff between the energy cost and noise in neuronal response. In short, the CMFE sends a rich variety of information through broader communication bands (i.e., widely distributed firing rates at the cost of accuracy. We demonstrate that the CMFE is reflected in the long-tailed, typically power law, distributions of inter-spike intervals obtained for the majority of recorded neurons. In other words, the power-law tails are more consistent with the CMFE rather than the MMI. Thus, we propose the mathematical principle by which cortical neurons may represent information about synaptic input into their output spike trains.

  15. Artificial Intelligence.

    Science.gov (United States)

    Information Technology Quarterly, 1985

    1985-01-01

    This issue of "Information Technology Quarterly" is devoted to the theme of "Artificial Intelligence." It contains two major articles: (1) Artificial Intelligence and Law" (D. Peter O'Neill and George D. Wood); (2) "Artificial Intelligence: A Long and Winding Road" (John J. Simon, Jr.). In addition, it contains two sidebars: (1) "Calculating and…

  16. Single-cell axotomy of cultured hippocampal neurons integrated in neuronal circuits.

    Science.gov (United States)

    Gomis-Rüth, Susana; Stiess, Michael; Wierenga, Corette J; Meyn, Liane; Bradke, Frank

    2014-05-01

    An understanding of the molecular mechanisms of axon regeneration after injury is key for the development of potential therapies. Single-cell axotomy of dissociated neurons enables the study of the intrinsic regenerative capacities of injured axons. This protocol describes how to perform single-cell axotomy on dissociated hippocampal neurons containing synapses. Furthermore, to axotomize hippocampal neurons integrated in neuronal circuits, we describe how to set up coculture with a few fluorescently labeled neurons. This approach allows axotomy of single cells in a complex neuronal network and the observation of morphological and molecular changes during axon regeneration. Thus, single-cell axotomy of mature neurons is a valuable tool for gaining insights into cell intrinsic axon regeneration and the plasticity of neuronal polarity of mature neurons. Dissociation of the hippocampus and plating of hippocampal neurons takes ∼2 h. Neurons are then left to grow for 2 weeks, during which time they integrate into neuronal circuits. Subsequent axotomy takes 10 min per neuron and further imaging takes 10 min per neuron.

  17. Artificial organs: recent progress in artificial hearing and vision.

    Science.gov (United States)

    Ifukube, Tohru

    2009-01-01

    Artificial sensory organs are a prosthetic means of sending visual or auditory information to the brain by electrical stimulation of the optic or auditory nerves to assist visually impaired or hearing-impaired people. However, clinical application of artificial sensory organs, except for cochlear implants, is still a trial-and-error process. This is because how and where the information transmitted to the brain is processed is still unknown, and also because changes in brain function (plasticity) remain unknown, even though brain plasticity plays an important role in meaningful interpretation of new sensory stimuli. This article discusses some basic unresolved issues and potential solutions in the development of artificial sensory organs such as cochlear implants, brainstem implants, artificial vision, and artificial retinas.

  18. Neurons are MHC class I-dependent targets for CD8 T cells upon neurotropic viral infection.

    Directory of Open Access Journals (Sweden)

    Grégoire Chevalier

    2011-11-01

    Full Text Available Following infection of the central nervous system (CNS, the immune system is faced with the challenge of eliminating the pathogen without causing significant damage to neurons, which have limited capacities of renewal. In particular, it was thought that neurons were protected from direct attack by cytotoxic T lymphocytes (CTL because they do not express major histocompatibility class I (MHC I molecules, at least at steady state. To date, most of our current knowledge on the specifics of neuron-CTL interaction is based on studies artificially inducing MHC I expression on neurons, loading them with exogenous peptide and applying CTL clones or lines often differentiated in culture. Thus, much remains to be uncovered regarding the modalities of the interaction between infected neurons and antiviral CD8 T cells in the course of a natural disease. Here, we used the model of neuroinflammation caused by neurotropic Borna disease virus (BDV, in which virus-specific CTL have been demonstrated as the main immune effectors triggering disease. We tested the pathogenic properties of brain-isolated CD8 T cells against pure neuronal cultures infected with BDV. We observed that BDV infection of cortical neurons triggered a significant up regulation of MHC I molecules, rendering them susceptible to recognition by antiviral CTL, freshly isolated from the brains of acutely infected rats. Using real-time imaging, we analyzed the spatio-temporal relationships between neurons and CTL. Brain-isolated CTL exhibited a reduced mobility and established stable contacts with BDV-infected neurons, in an antigen- and MHC-dependent manner. This interaction induced rapid morphological changes of the neurons, without immediate killing or impairment of electrical activity. Early signs of neuronal apoptosis were detected only hours after this initial contact. Thus, our results show that infected neurons can be recognized efficiently by brain-isolated antiviral CD8 T cells and

  19. Neuronal survival in the brain: neuron type-specific mechanisms

    DEFF Research Database (Denmark)

    Pfisterer, Ulrich Gottfried; Khodosevich, Konstantin

    2017-01-01

    Neurogenic regions of mammalian brain produce many more neurons that will eventually survive and reach a mature stage. Developmental cell death affects both embryonically produced immature neurons and those immature neurons that are generated in regions of adult neurogenesis. Removal of substantial...... numbers of neurons that are not yet completely integrated into the local circuits helps to ensure that maturation and homeostatic function of neuronal networks in the brain proceed correctly. External signals from brain microenvironment together with intrinsic signaling pathways determine whether...... for survival in a certain brain region. This review focuses on how immature neurons survive during normal and impaired brain development, both in the embryonic/neonatal brain and in brain regions associated with adult neurogenesis, and emphasizes neuron type-specific mechanisms that help to survive for various...

  20. Reduction in spontaneous firing of mouse excitatory layer 4 cortical neurons following visual classical conditioning

    Science.gov (United States)

    Bekisz, Marek; Shendye, Ninad; Raciborska, Ida; Wróbel, Andrzej; Waleszczyk, Wioletta J.

    2017-08-01

    The process of learning induces plastic changes in neuronal network of the brain. Our earlier studies on mice showed that classical conditioning in which monocular visual stimulation was paired with an electric shock to the tail enhanced GABA immunoreactivity within layer 4 of the monocular part of the primary visual cortex (V1), contralaterally to the stimulated eye. In the present experiment we investigated whether the same classical conditioning paradigm induces changes of neuronal excitability in this cortical area. Two experimental groups were used: mice that underwent 7-day visual classical conditioning and controls. Patch-clamp whole-cell recordings were performed from ex vivo slices of mouse V1. The slices were perfused with the modified artificial cerebrospinal fluid, the composition of which better mimics the brain interstitial fluid in situ and induces spontaneous activity. The neuronal excitability was characterized by measuring the frequency of spontaneous action potentials. We found that layer 4 star pyramidal cells located in the monocular representation of the "trained" eye in V1 had lower frequency of spontaneous activity in comparison with neurons from the same cortical region of control animals. Weaker spontaneous firing indicates decreased general excitability of star pyramidal neurons within layer 4 of the monocular representation of the "trained" eye in V1. Such effect could result from enhanced inhibitory processes accompanying learning in this cortical area.

  1. Detection of Pistachio Aflatoxin Using Raman Spectroscopy and Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    R Mohammadigol

    2015-03-01

    Full Text Available Pistachio contamination to aflatoxin has been known as a serious problem for pistachio exportation. With regards to the increasing demand for Raman spectroscopy to detect and classify different materials and also the current experimental and technical problems for measuring toxin (such as being expensive and time-consuming, the main objective of this study was to detect aflatoxin contamination in pistachio by using Raman spectroscopy technique and artificial neural networks. Three sets of samples were prepared: non-contaminated (healthy and contaminated samples with 20 and 100 ppb of the total aflatoxins (B1+B2+G1+G2. After spectral acquisition, considering to the results, spectral data were normalized and then principal components (PCs were extracted to reduce the data dimensions. For classification of the samples spectra, an artificial neural network was used with a feed forward back propagation algorithm for 4 inputs and 3 neurons in hidden layer. Mean overall accuracy was achieved to be 98 percent; therefore, non-liner Raman spectra data modeling by ANN for samples classification was successful.

  2. Estimating surface longwave radiative fluxes from satellites utilizing artificial neural networks

    Science.gov (United States)

    Nussbaumer, Eric A.; Pinker, Rachel T.

    2012-04-01

    A novel approach for calculating downwelling surface longwave (DSLW) radiation under all sky conditions is presented. The DSLW model (hereafter, DSLW/UMD v2) similarly to its predecessor, DSLW/UMD v1, is driven with a combination of Moderate Resolution Imaging Spectroradiometer (MODIS) level-3 cloud parameters and information from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim model. To compute the clear sky component of DSLW a two layer feed-forward artificial neural network with sigmoid hidden neurons and linear output neurons is implemented; it is trained with simulations derived from runs of the Rapid Radiative Transfer Model (RRTM). When computing the cloud contribution to DSLW, the cloud base temperature is estimated by using an independent artificial neural network approach of similar architecture as previously mentioned, and parameterizations. The cloud base temperature neural network is trained using spatially and temporally co-located MODIS and CloudSat Cloud Profiling Radar (CPR) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations. Daily average estimates of DSLW from 2003 to 2009 are compared against ground measurements from the Baseline Surface Radiation Network (BSRN) giving an overall correlation coefficient of 0.98, root mean square error (rmse) of 15.84 W m-2, and a bias of -0.39 W m-2. This is an improvement over an earlier version of the model (DSLW/UMD v1) which for the same time period has an overall correlation coefficient 0.97 rmse of 17.27 W m-2, and bias of 0.73 W m-2.

  3. Neurons with two sites of synaptic integration learn invariant representations.

    Science.gov (United States)

    Körding, K P; König, P

    2001-12-01

    Neurons in mammalian cerebral cortex combine specific responses with respect to some stimulus features with invariant responses to other stimulus features. For example, in primary visual cortex, complex cells code for orientation of a contour but ignore its position to a certain degree. In higher areas, such as the inferotemporal cortex, translation-invariant, rotation-invariant, and even view point-invariant responses can be observed. Such properties are of obvious interest to artificial systems performing tasks like pattern recognition. It remains to be resolved how such response properties develop in biological systems. Here we present an unsupervised learning rule that addresses this problem. It is based on a neuron model with two sites of synaptic integration, allowing qualitatively different effects of input to basal and apical dendritic trees, respectively. Without supervision, the system learns to extract invariance properties using temporal or spatial continuity of stimuli. Furthermore, top-down information can be smoothly integrated in the same framework. Thus, this model lends a physiological implementation to approaches of unsupervised learning of invariant-response properties.

  4. CARACTERIZACIÓN DE CAFÉ CEREZA EMPLEANDO TÉCNICAS DE VISIÓN ARTIFICIAL AN ARTIFICIAL VISION SYSTEM FOR CLASSIFICATION OF COFFEE BEANS

    Directory of Open Access Journals (Sweden)

    Zulma Liliana Sandoval Niño

    2007-12-01

    Full Text Available Se desarrolló un sistema de visión artificial para la clasificación de frutos de café en once categorías dependiendo de su estado de madurez. Para la descripción de la forma, el color y la textura de cada fruto de café se extrajeron 208 características. La reducción del conjunto de características de 208 a 9 se hizo con base en los resultados de dos métodos de selección de características, uno univariado y otro multivariado. Las características seleccionadas corresponden a 4 características de textura, 3 de color y 2 de forma. Este conjunto final de características se evaluó en dos técnicas de clasificación: Bayesiano y redes neuronales. Con el clasificador Bayesiano se obtuvo un error de clasificación del 5,43% y requirió un tiempo de clasificación de 5,5 ms, mientras que usando redes neuronales el error de clasificación fue de 7,46%, pero disminuyó el tiempo de clasificación a 0,8 ms.An artificial vision system for classification of coffee beans, in eleven categories, according to its state of maturity was developed. The description of the coffee beans was done by using 208 characteristics (form, color and texture characteristics. The reduction of the set of characteristics from 208 to 9 was done by using two methods of characteristic selection. The final set of characteristics is composed by 4 texture characteristics, 3 color characteristics and 2 shape characteristics. This final set was evaluated in two classifiers: The Bayesian and a neuronal networks classifier. The classification error obtained by the Bayesian classifier was 5,43%, it required 5,5 ms for the classification process, while the error obtained by neuronal networks classifier was 7,46% and the classification time decreased to 0,8 ms.

  5. Coherence resonance in globally coupled neuronal networks with different neuron numbers

    International Nuclear Information System (INIS)

    Ning Wei-Lian; Zhang Zheng-Zhen; Zeng Shang-You; Luo Xiao-Shu; Hu Jin-Lin; Zeng Shao-Wen; Qiu Yi; Wu Hui-Si

    2012-01-01

    Because a brain consists of tremendous neuronal networks with different neuron numbers ranging from tens to tens of thousands, we study the coherence resonance due to ion channel noises in globally coupled neuronal networks with different neuron numbers. We confirm that for all neuronal networks with different neuron numbers there exist the array enhanced coherence resonance and the optimal synaptic conductance to cause the maximal spiking coherence. Furthermoremore, the enhancement effects of coupling on spiking coherence and on optimal synaptic conductance are almost the same, regardless of the neuron numbers in the neuronal networks. Therefore for all the neuronal networks with different neuron numbers in the brain, relative weak synaptic conductance (0.1 mS/cm 2 ) is sufficient to induce the maximal spiking coherence and the best sub-threshold signal encoding. (interdisciplinary physics and related areas of science and technology)

  6. Progress in artificial vision through suprachoroidal retinal implants

    Science.gov (United States)

    Bareket, Lilach; Barriga-Rivera, Alejandro; Zapf, Marc Patrick; Lovell, Nigel H.; Suaning, Gregg J.

    2017-08-01

    Retinal implants have proven their ability to restore visual sensation to people with degenerative retinopathy, characterized by photoreceptor cell death and the retina’s inability to sense light. Retinal bionics operate by electrically stimulating the surviving neurons in the retina, thus triggering the transfer of visual sensory information to the brain. Suprachoroidal implants were first investigated in Australia in the 1950s. In this approach, the neuromodulation hardware is positioned between the sclera and the choroid, thus providing significant surgical and safety benefits for patients, with the potential to maintain residual vision combined with the artificial input from the device. Here we review the latest advances and state of the art devices for suprachoroidal prostheses, highlight future technologies and discuss challenges and perspectives towards improved rehabilitation of vision.

  7. Monitoring of operation with artificial intelligence methods; Betriebsueberwachung mit Verfahren der Kuenstlichen Intelligenz

    Energy Technology Data Exchange (ETDEWEB)

    Bruenninghaus, H. [DMT-Gesellschaft fuer Forschung und Pruefung mbH, Essen (Germany). Geschaeftsbereich Systemtechnik

    1999-03-11

    Taking the applications `early detection of fires` and `reduction of burst of messages` as an example, the usability of artificial intelligence (AI) methods in the monitoring of operation was checked in a R and D project. The contribution describes the concept, development and evaluation of solutions to the specified problems. A platform, which made it possible to investigate different AI methods (in particular artificial neuronal networks), had to be creaated as a basis for the project. At the same time ventilation data had to be acquired and processed by the networks for the classification. (orig.) [Deutsch] Am Beispiel der Anwendungsfaelle `Brandfrueherkennung` und `Meldungsschauerreduzierung` wurde im Rahmen eines F+E-Vorhabens die Einsetzbarkeit von Kuenstliche-Intelligenz-Methoden (KI) in der Betriebsueberwachung geprueft. Der Beitrag stellt Konzeption, Entwicklung und Bewertung von Loesungsansaetzen fuer die genannten Aufgabenstellungen vor. Als Grundlage fuer das Vorhaben wurden anhand KI-Methoden (speziell: Kuenstliche Neuronale Netze -KNN) auf der Grundlage gewonnener und aufbereiteter Wetterdaten die Beziehungen zwischen den Wettermessstellen im Laufe des Wetterwegs klassifiziert. (orig.)

  8. Proposal for an All-Spin Artificial Neural Network: Emulating Neural and Synaptic Functionalities Through Domain Wall Motion in Ferromagnets.

    Science.gov (United States)

    Sengupta, Abhronil; Shim, Yong; Roy, Kaushik

    2016-12-01

    Non-Boolean computing based on emerging post-CMOS technologies can potentially pave the way for low-power neural computing platforms. However, existing work on such emerging neuromorphic architectures have either focused on solely mimicking the neuron, or the synapse functionality. While memristive devices have been proposed to emulate biological synapses, spintronic devices have proved to be efficient at performing the thresholding operation of the neuron at ultra-low currents. In this work, we propose an All-Spin Artificial Neural Network where a single spintronic device acts as the basic building block of the system. The device offers a direct mapping to synapse and neuron functionalities in the brain while inter-layer network communication is accomplished via CMOS transistors. To the best of our knowledge, this is the first demonstration of a neural architecture where a single nanoelectronic device is able to mimic both neurons and synapses. The ultra-low voltage operation of low resistance magneto-metallic neurons enables the low-voltage operation of the array of spintronic synapses, thereby leading to ultra-low power neural architectures. Device-level simulations, calibrated to experimental results, was used to drive the circuit and system level simulations of the neural network for a standard pattern recognition problem. Simulation studies indicate energy savings by  ∼  100× in comparison to a corresponding digital/analog CMOS neuron implementation.

  9. Neuron-derived IgG protects neurons from complement-dependent cytotoxicity.

    Science.gov (United States)

    Zhang, Jie; Niu, Na; Li, Bingjie; McNutt, Michael A

    2013-12-01

    Passive immunity of the nervous system has traditionally been thought to be predominantly due to the blood-brain barrier. This concept must now be revisited based on the existence of neuron-derived IgG. The conventional concept is that IgG is produced solely by mature B lymphocytes, but it has now been found to be synthesized by murine and human neurons. However, the function of this endogenous IgG is poorly understood. In this study, we confirm IgG production by rat cortical neurons at the protein and mRNA levels, with 69.0 ± 5.8% of cortical neurons IgG-positive. Injury to primary-culture neurons was induced by complement leading to increases in IgG production. Blockage of neuron-derived IgG resulted in more neuronal death and early apoptosis in the presence of complement. In addition, FcγRI was found in microglia and astrocytes. Expression of FcγR I in microglia was increased by exposure to neuron-derived IgG. Release of NO from microglia triggered by complement was attenuated by neuron-derived IgG, and this attenuation could be reversed by IgG neutralization. These data demonstrate that neuron-derived IgG is protective of neurons against injury induced by complement and microglial activation. IgG appears to play an important role in maintaining the stability of the nervous system.

  10. [Artificial organs].

    Science.gov (United States)

    Raguin, Thibaut; Dupret-Bories, Agnès; Debry, Christian

    2017-01-01

    Research has been fighting against organ failure and shortage of donations by supplying artificial organs for many years. With the raise of new technologies, tissue engineering and regenerative medicine, many organs can benefit of an artificial equivalent: thanks to retinal implants some blind people can visualize stimuli, an artificial heart can be proposed in case of cardiac failure while awaiting for a heart transplant, artificial larynx enables laryngectomy patients to an almost normal life, while the diabetic can get a glycemic self-regulation controlled by smartphones with an artificial device. Dialysis devices become portable, as well as the oxygenation systems for terminal respiratory failure. Bright prospects are being explored or might emerge in a near future. However, the retrospective assessment of putative side effects is not yet sufficient. Finally, the cost of these new devices is significant even if the advent of three dimensional printers may reduce it. © 2017 médecine/sciences – Inserm.

  11. Daily Suspended Sediment Discharge Prediction Using Multiple Linear Regression and Artificial Neural Network

    Science.gov (United States)

    Uca; Toriman, Ekhwan; Jaafar, Othman; Maru, Rosmini; Arfan, Amal; Saleh Ahmar, Ansari

    2018-01-01

    Prediction of suspended sediment discharge in a catchments area is very important because it can be used to evaluation the erosion hazard, management of its water resources, water quality, hydrology project management (dams, reservoirs, and irrigation) and to determine the extent of the damage that occurred in the catchments. Multiple Linear Regression analysis and artificial neural network can be used to predict the amount of daily suspended sediment discharge. Regression analysis using the least square method, whereas artificial neural networks using Radial Basis Function (RBF) and feedforward multilayer perceptron with three learning algorithms namely Levenberg-Marquardt (LM), Scaled Conjugate Descent (SCD) and Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton (BFGS). The number neuron of hidden layer is three to sixteen, while in output layer only one neuron because only one output target. The mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2 ) and coefficient of efficiency (CE) of the multiple linear regression (MLRg) value Model 2 (6 input variable independent) has the lowest the value of MAE and RMSE (0.0000002 and 13.6039) and highest R2 and CE (0.9971 and 0.9971). When compared between LM, SCG and RBF, the BFGS model structure 3-7-1 is the better and more accurate to prediction suspended sediment discharge in Jenderam catchment. The performance value in testing process, MAE and RMSE (13.5769 and 17.9011) is smallest, meanwhile R2 and CE (0.9999 and 0.9998) is the highest if it compared with the another BFGS Quasi-Newton model (6-3-1, 9-10-1 and 12-12-1). Based on the performance statistics value, MLRg, LM, SCG, BFGS and RBF suitable and accurately for prediction by modeling the non-linear complex behavior of suspended sediment responses to rainfall, water depth and discharge. The comparison between artificial neural network (ANN) and MLRg, the MLRg Model 2 accurately for to prediction suspended sediment discharge (kg

  12. Differential effects of cocaine on histone posttranslational modifications in identified populations of striatal neurons.

    Science.gov (United States)

    Jordi, Emmanuelle; Heiman, Myriam; Marion-Poll, Lucile; Guermonprez, Pierre; Cheng, Shuk Kei; Nairn, Angus C; Greengard, Paul; Girault, Jean-Antoine

    2013-06-04

    Drugs of abuse, such as cocaine, induce changes in gene expression and epigenetic marks including alterations in histone posttranslational modifications in striatal neurons. These changes are thought to participate in physiological memory mechanisms and to be critical for long-term behavioral alterations. However, the striatum is composed of multiple cell types, including two distinct populations of medium-sized spiny neurons, and little is known concerning the cell-type specificity of epigenetic modifications. To address this question we used bacterial artificial chromosome transgenic mice, which express EGFP fused to the N-terminus of the large subunit ribosomal protein L10a driven by the D1 or D2 dopamine receptor (D1R, D2R) promoter, respectively. Fluorescence in nucleoli was used to sort nuclei from D1R- or D2R-expressing neurons and to quantify by flow cytometry the cocaine-induced changes in histone acetylation and methylation specifically in these two types of nuclei. The two populations of medium-sized spiny neurons displayed different patterns of histone modifications 15 min or 24 h after a single injection of cocaine or 24 h after seven daily injections. In particular, acetylation of histone 3 on Lys 14 and of histone 4 on Lys 5 and 12, and methylation of histone 3 on Lys 9 exhibited distinct and persistent changes in the two cell types. Our data provide insights into the differential epigenetic responses to cocaine in D1R- and D2R-positive neurons and their potential regulation, which may participate in the persistent effects of cocaine in these neurons. The method described should have general utility for studying nuclear modifications in different types of neuronal or nonneuronal cell types.

  13. On the Construction of Artificial Brains

    CERN Document Server

    Ramacher, Ulrich

    2009-01-01

    This book presents a first generation of artificial brains, using vision as sample application. An object recognition system is built, using neurons and synapses as exclusive building elements. The system contains a feature pyramid with 8 orientations and 5 resolution levels for 1000 objects and networks for binding of features into objects. This vision system can recognize objects robustly in the presence of changes in illumination, deformation, distance and pose (as long as object components remain visible). The neuro-synaptic network owes its functional power to the introduction of rapidly modifiable dynamic synapses. These give a network greater pattern recognition capabilities than are achievable with fixed connections. The spatio-temporal correlation structure of patterns is captured by a single synaptic differential equation in a universal way. The correlation can appear as synchronous neural firing, which signals the presence of a feature in a robust way, or binds features into objects. Although in th...

  14. Inhibitory neurons modulate spontaneous signaling in cultured cortical neurons: density-dependent regulation of excitatory neuronal signaling

    International Nuclear Information System (INIS)

    Serra, Michael; Guaraldi, Mary; Shea, Thomas B

    2010-01-01

    Cortical neuronal activity depends on a balance between excitatory and inhibitory influences. Culturing of neurons on multi-electrode arrays (MEAs) has provided insight into the development and maintenance of neuronal networks. Herein, we seeded MEAs with murine embryonic cortical/hippocampal neurons at different densities ( 1000 cells mm −2 ) and monitored resultant spontaneous signaling. Sparsely seeded cultures displayed a large number of bipolar, rapid, high-amplitude individual signals with no apparent temporal regularity. By contrast, densely seeded cultures instead displayed clusters of signals at regular intervals. These patterns were observed even within thinner and thicker areas of the same culture. GABAergic neurons (25% of total neurons in our cultures) mediated the differential signal patterns observed above, since addition of the inhibitory antagonist bicuculline to dense cultures and hippocampal slice cultures induced the signal pattern characteristic of sparse cultures. Sparsely seeded cultures likely lacked sufficient inhibitory neurons to modulate excitatory activity. Differential seeding of MEAs can provide a unique model for analyses of pertubation in the interaction between excitatory and inhibitory function during aging and neuropathological conditions where dysregulation of GABAergic neurons is a significant component

  15. Trans-generational desensitization and within-generational resensitization of a sucrose-best neuron in the polyphagous herbivore Helicoverpa armigera (Lepidoptera: Noctuidae).

    Science.gov (United States)

    Ma, Ying; Li, Jingjing; Tang, Qingbo; Zhang, Xuening; Zhao, Xincheng; Yan, Fengming; van Loon, Joop J A

    2016-12-14

    Dietary exposure of insects to a feeding deterrent substance for hours to days can induce habituation and concomitant desensitization of the response of peripheral gustatory neurons to such a substance. In the present study, larvae of the herbivore Helicoverpa armigera were fed on diets containing either a high, medium or low concentration of sucrose, a major feeding stimulant. The responsiveness of the sucrose-best neuron in the lateral sensilla styloconica on the galea was quantified. Results showed the response of the sucrose-best neuron exposed to high-sucrose diets decreased gradually over successive generations, resulting in complete desensitization in the 5 th and subsequent generations. However, the sensitivity was completely restored in the ninth generation after neonate larvae were exposed to low-sucrose diet. These findings demonstrate phenotypic plasticity and exclude inadvertent artificial selection for low sensitivity to sucrose. No significant changes were found in the sensitivity of caterpillars which experienced low- or medium-sucrose diets over the same generations. Such desensitization versus re-sensitization did not generalise to the phagosimulant myo-inositol-sensitive neuron or the feeding deterrent-sensitive neuron. Our results demonstrate that under conditions of high sucrose availability trans-generational desensitization of a neuron sensitive to this feeding stimulant becomes more pronounced whereas re-sensitization occurs within one generation.

  16. Information in small neuronal ensemble activity in the hippocampal CA1 during delayed non-matching to sample performance in rats

    Directory of Open Access Journals (Sweden)

    Takahashi Susumu

    2009-09-01

    Full Text Available Abstract Background The matrix-like organization of the hippocampus, with its several inputs and outputs, has given rise to several theories related to hippocampal information processing. Single-cell electrophysiological studies and studies of lesions or genetically altered animals using recognition memory tasks such as delayed non-matching-to-sample (DNMS tasks support the theories. However, a complete understanding of hippocampal function necessitates knowledge of the encoding of information by multiple neurons in a single trial. The role of neuronal ensembles in the hippocampal CA1 for a DNMS task was assessed quantitatively in this study using multi-neuronal recordings and an artificial neural network classifier as a decoder. Results The activity of small neuronal ensembles (6-18 cells over brief time intervals (2-50 ms contains accurate information specifically related to the matching/non-matching of continuously presented stimuli (stimulus comparison. The accuracy of the combination of neurons pooled over all the ensembles was markedly lower than those of the ensembles over all examined time intervals. Conclusion The results show that the spatiotemporal patterns of spiking activity among cells in the small neuronal ensemble contain much information that is specifically useful for the stimulus comparison. Small neuronal networks in the hippocampal CA1 might therefore act as a comparator during recognition memory tasks.

  17. Neurons from the adult human dentate nucleus: neural networks in the neuron classification.

    Science.gov (United States)

    Grbatinić, Ivan; Marić, Dušica L; Milošević, Nebojša T

    2015-04-07

    Topological (central vs. border neuron type) and morphological classification of adult human dentate nucleus neurons according to their quantified histomorphological properties using neural networks on real and virtual neuron samples. In the real sample 53.1% and 14.1% of central and border neurons, respectively, are classified correctly with total of 32.8% of misclassified neurons. The most important result present 62.2% of misclassified neurons in border neurons group which is even greater than number of correctly classified neurons (37.8%) in that group, showing obvious failure of network to classify neurons correctly based on computational parameters used in our study. On the virtual sample 97.3% of misclassified neurons in border neurons group which is much greater than number of correctly classified neurons (2.7%) in that group, again confirms obvious failure of network to classify neurons correctly. Statistical analysis shows that there is no statistically significant difference in between central and border neurons for each measured parameter (p>0.05). Total of 96.74% neurons are morphologically classified correctly by neural networks and each one belongs to one of the four histomorphological types: (a) neurons with small soma and short dendrites, (b) neurons with small soma and long dendrites, (c) neuron with large soma and short dendrites, (d) neurons with large soma and long dendrites. Statistical analysis supports these results (pneurons can be classified in four neuron types according to their quantitative histomorphological properties. These neuron types consist of two neuron sets, small and large ones with respect to their perykarions with subtypes differing in dendrite length i.e. neurons with short vs. long dendrites. Besides confirmation of neuron classification on small and large ones, already shown in literature, we found two new subtypes i.e. neurons with small soma and long dendrites and with large soma and short dendrites. These neurons are

  18. Neuronal variability during handwriting: lognormal distribution.

    Directory of Open Access Journals (Sweden)

    Valery I Rupasov

    Full Text Available We examined time-dependent statistical properties of electromyographic (EMG signals recorded from intrinsic hand muscles during handwriting. Our analysis showed that trial-to-trial neuronal variability of EMG signals is well described by the lognormal distribution clearly distinguished from the Gaussian (normal distribution. This finding indicates that EMG formation cannot be described by a conventional model where the signal is normally distributed because it is composed by summation of many random sources. We found that the variability of temporal parameters of handwriting--handwriting duration and response time--is also well described by a lognormal distribution. Although, the exact mechanism of lognormal statistics remains an open question, the results obtained should significantly impact experimental research, theoretical modeling and bioengineering applications of motor networks. In particular, our results suggest that accounting for lognormal distribution of EMGs can improve biomimetic systems that strive to reproduce EMG signals in artificial actuators.

  19. Neuronal Migration and Neuronal Migration Disorder in Cerebral Cortex

    OpenAIRE

    SUN, Xue-Zhi; TAKAHASHI, Sentaro; GUI, Chun; ZHANG, Rui; KOGA, Kazuo; NOUYE, Minoru; MURATA, Yoshiharu

    2002-01-01

    Neuronal cell migration is one of the most significant features during cortical development. After final mitosis, neurons migrate from the ventricular zone into the cortical plate, and then establish neuronal lamina and settle onto the outermost layer, forming an "inside-out" gradient of maturation. Neuronal migration is guided by radial glial fibers and also needs proper receptors, ligands, and other unknown extracellular factors, requests local signaling (e.g. some emitted by the Cajal-Retz...

  20. Contribution of synchronized GABAergic neurons to dopaminergic neuron firing and bursting.

    Science.gov (United States)

    Morozova, Ekaterina O; Myroshnychenko, Maxym; Zakharov, Denis; di Volo, Matteo; Gutkin, Boris; Lapish, Christopher C; Kuznetsov, Alexey

    2016-10-01

    In the ventral tegmental area (VTA), interactions between dopamine (DA) and γ-aminobutyric acid (GABA) neurons are critical for regulating DA neuron activity and thus DA efflux. To provide a mechanistic explanation of how GABA neurons influence DA neuron firing, we developed a circuit model of the VTA. The model is based on feed-forward inhibition and recreates canonical features of the VTA neurons. Simulations revealed that γ-aminobutyric acid (GABA) receptor (GABAR) stimulation can differentially influence the firing pattern of the DA neuron, depending on the level of synchronization among GABA neurons. Asynchronous activity of GABA neurons provides a constant level of inhibition to the DA neuron and, when removed, produces a classical disinhibition burst. In contrast, when GABA neurons are synchronized by common synaptic input, their influence evokes additional spikes in the DA neuron, resulting in increased measures of firing and bursting. Distinct from previous mechanisms, the increases were not based on lowered firing rate of the GABA neurons or weaker hyperpolarization by the GABAR synaptic current. This phenomenon was induced by GABA-mediated hyperpolarization of the DA neuron that leads to decreases in intracellular calcium (Ca 2+ ) concentration, thus reducing the Ca 2+ -dependent potassium (K + ) current. In this way, the GABA-mediated hyperpolarization replaces Ca 2+ -dependent K + current; however, this inhibition is pulsatile, which allows the DA neuron to fire during the rhythmic pauses in inhibition. Our results emphasize the importance of inhibition in the VTA, which has been discussed in many studies, and suggest a novel mechanism whereby computations can occur locally. Copyright © 2016 the American Physiological Society.

  1. Hindbrain Catecholamine Neurons Activate Orexin Neurons During Systemic Glucoprivation in Male Rats.

    Science.gov (United States)

    Li, Ai-Jun; Wang, Qing; Elsarelli, Megan M; Brown, R Lane; Ritter, Sue

    2015-08-01

    Hindbrain catecholamine neurons are required for elicitation of feeding responses to glucose deficit, but the forebrain circuitry required for these responses is incompletely understood. Here we examined interactions of catecholamine and orexin neurons in eliciting glucoprivic feeding. Orexin neurons, located in the perifornical lateral hypothalamus (PeFLH), are heavily innervated by hindbrain catecholamine neurons, stimulate food intake, and increase arousal and behavioral activation. Orexin neurons may therefore contribute importantly to appetitive responses, such as food seeking, during glucoprivation. Retrograde tracing results showed that nearly all innervation of the PeFLH from the hindbrain originated from catecholamine neurons and some raphe nuclei. Results also suggested that many catecholamine neurons project collaterally to the PeFLH and paraventricular hypothalamic nucleus. Systemic administration of the antiglycolytic agent, 2-deoxy-D-glucose, increased food intake and c-Fos expression in orexin neurons. Both responses were eliminated by a lesion of catecholamine neurons innervating orexin neurons using the retrogradely transported immunotoxin, anti-dopamine-β-hydroxylase saporin, which is specifically internalized by dopamine-β-hydroxylase-expressing catecholamine neurons. Using designer receptors exclusively activated by designer drugs in transgenic rats expressing Cre recombinase under the control of tyrosine hydroxylase promoter, catecholamine neurons in cell groups A1 and C1 of the ventrolateral medulla were activated selectively by peripheral injection of clozapine-N-oxide. Clozapine-N-oxide injection increased food intake and c-Fos expression in PeFLH orexin neurons as well as in paraventricular hypothalamic nucleus neurons. In summary, catecholamine neurons are required for the activation of orexin neurons during glucoprivation. Activation of orexin neurons may contribute to appetitive responses required for glucoprivic feeding.

  2. β1 integrin signaling promotes neuronal migration along vascular scaffolds in the post-stroke brain

    Directory of Open Access Journals (Sweden)

    Teppei Fujioka

    2017-02-01

    Full Text Available Cerebral ischemic stroke is a main cause of chronic disability. However, there is currently no effective treatment to promote recovery from stroke-induced neurological symptoms. Recent studies suggest that after stroke, immature neurons, referred to as neuroblasts, generated in a neurogenic niche, the ventricular-subventricular zone, migrate toward the injured area, where they differentiate into mature neurons. Interventions that increase the number of neuroblasts distributed at and around the lesion facilitate neuronal repair in rodent models for ischemic stroke, suggesting that promoting neuroblast migration in the post-stroke brain could improve efficient neuronal regeneration. To move toward the lesion, neuroblasts form chain-like aggregates and migrate along blood vessels, which are thought to increase their migration efficiency. However, the molecular mechanisms regulating these migration processes are largely unknown. Here we studied the role of β1-class integrins, transmembrane receptors for extracellular matrix proteins, in these migrating neuroblasts. We found that the neuroblast chain formation and blood vessel-guided migration critically depend on β1 integrin signaling. β1 integrin facilitated the adhesion of neuroblasts to laminin and the efficient translocation of their soma during migration. Moreover, artificial laminin-containing scaffolds promoted neuroblast chain formation and migration toward the injured area. These data suggest that laminin signaling via β1 integrin supports vasculature-guided neuronal migration to efficiently supply neuroblasts to injured areas. This study also highlights the importance of vascular scaffolds for cell migration in development and regeneration.

  3. BlastNeuron for Automated Comparison, Retrieval and Clustering of 3D Neuron Morphologies.

    Science.gov (United States)

    Wan, Yinan; Long, Fuhui; Qu, Lei; Xiao, Hang; Hawrylycz, Michael; Myers, Eugene W; Peng, Hanchuan

    2015-10-01

    Characterizing the identity and types of neurons in the brain, as well as their associated function, requires a means of quantifying and comparing 3D neuron morphology. Presently, neuron comparison methods are based on statistics from neuronal morphology such as size and number of branches, which are not fully suitable for detecting local similarities and differences in the detailed structure. We developed BlastNeuron to compare neurons in terms of their global appearance, detailed arborization patterns, and topological similarity. BlastNeuron first compares and clusters 3D neuron reconstructions based on global morphology features and moment invariants, independent of their orientations, sizes, level of reconstruction and other variations. Subsequently, BlastNeuron performs local alignment between any pair of retrieved neurons via a tree-topology driven dynamic programming method. A 3D correspondence map can thus be generated at the resolution of single reconstruction nodes. We applied BlastNeuron to three datasets: (1) 10,000+ neuron reconstructions from a public morphology database, (2) 681 newly and manually reconstructed neurons, and (3) neurons reconstructions produced using several independent reconstruction methods. Our approach was able to accurately and efficiently retrieve morphologically and functionally similar neuron structures from large morphology database, identify the local common structures, and find clusters of neurons that share similarities in both morphology and molecular profiles.

  4. The role of glycogen, glucose and lactate in neuronal activity during hypoxia in the hooded seal (Cystophora cristata) brain.

    Science.gov (United States)

    Czech-Damal, N U; Geiseler, S J; Hoff, M L M; Schliep, R; Ramirez, J-M; Folkow, L P; Burmester, T

    2014-09-05

    The brains of diving mammals are repeatedly exposed to hypoxic conditions during diving. Brain neurons of the hooded seal (Cystophora cristata) have been shown to be more hypoxia tolerant than those of mice, but the underlying mechanisms are not clear. Here we investigated the roles of different metabolic substrates for maintenance of neuronal activity and integrity, by comparing the in vitro spontaneous neuronal activity of brain slices from layer V of the visual cortex of hooded seals with those in mice (Mus musculus). Studies were conducted by manipulating the composition of the artificial cerebrospinal fluid (aCSF), containing either 10 mM glucose, or 20 mM lactate, or no external carbohydrate supply (aglycemia). Normoxic, hypoxic and ischemic conditions were applied. The lack of glucose or the application of lactate in the aCSF containing no glucose had little effect on the neuronal activity of seal neurons in either normoxia or hypoxia, while neurons from mice survived in hypoxia only few minutes regardless of the composition of the aCSF. We propose that seal neurons have higher intrinsic energy stores. Indeed, we found about three times higher glycogen stores in the seal brain (∼4.1 ng per μg total protein in the seal cerebrum) than in the mouse brain. Notably, in aCSF containing no glucose, seal neurons can tolerate 20 mM lactate while in mouse neuronal activity vanished after few minutes even in normoxia. This can be considered as an adaptation to long dives, during which lactate accumulates in the blood. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

  5. Effects of aspartame metabolites on astrocytes and neurons.

    Science.gov (United States)

    Rycerz, Karol; Jaworska-Adamu, Jadwiga Elżbieta

    2013-01-01

    Aspartame, a widespread sweetener used in many food products, is considered as a highly hazardous compound. Aspartame was discovered in 1965 and raises a lot of controversy up to date. Astrocytes are glial cells, the presence and functions of which are closely connected with the central nervous system (CNS). The aim of this article is to demonstrate the direct and indirect role of astrocytes participating in the harmful effects of aspartame metabolites on neurons. The artificial sweetener is broken down into phenylalanine (50%), aspartic acid (40%) and methanol (10%) during metabolism in the body. The excess of phenylalanine blocks the transport of important amino acids to the brain contributing to reduced levels of dopamine and serotonin. Astrocytes directly affect the transport of this amino acid and also indirectly by modulation of carriers in the endothelium. Aspartic acid at high concentrations is a toxin that causes hyperexcitability of neurons and is also a precursor of other excitatory amino acid - glutamates. Their excess in quantity and lack of astrocytic uptake induces excitotoxicity and leads to the degeneration of astrocytes and neurons. The methanol metabolites cause CNS depression, vision disorders and other symptoms leading ultimately to metabolic acidosis and coma. Astrocytes do not play a significant role in methanol poisoning due to a permanent consumption of large amounts of aspartame. Despite intense speculations about the carcinogenicity of aspartame, the latest studies show that its metabolite - diketopiperazine - is cancirogenic in the CNS. It contributes to the formation of tumors in the CNS such as gliomas, medulloblastomas and meningiomas. Glial cells are the main source of tumors, which can be caused inter alia by the sweetener in the brain. On the one hand the action of astrocytes during aspartame poisoning may be advantageous for neuro-protection while on the other it may intensify the destruction of neurons. The role of the glia in

  6. Artificial intelligence

    CERN Document Server

    Ennals, J R

    1987-01-01

    Artificial Intelligence: State of the Art Report is a two-part report consisting of the invited papers and the analysis. The editor first gives an introduction to the invited papers before presenting each paper and the analysis, and then concludes with the list of references related to the study. The invited papers explore the various aspects of artificial intelligence. The analysis part assesses the major advances in artificial intelligence and provides a balanced analysis of the state of the art in this field. The Bibliography compiles the most important published material on the subject of

  7. Labeling of neuronal differentiation and neuron cells with biocompatible fluorescent nanodiamonds.

    Science.gov (United States)

    Hsu, Tzu-Chia; Liu, Kuang-Kai; Chang, Huan-Cheng; Hwang, Eric; Chao, Jui-I

    2014-05-16

    Nanodiamond is a promising carbon nanomaterial developed for biomedical applications. Here, we show fluorescent nanodiamond (FND) with the biocompatible properties that can be used for the labeling and tracking of neuronal differentiation and neuron cells derived from embryonal carcinoma stem (ECS) cells. The fluorescence intensities of FNDs were increased by treatment with FNDs in both the mouse P19 and human NT2/D1 ECS cells. FNDs were taken into ECS cells; however, FNDs did not alter the cellular morphology and growth ability. Moreover, FNDs did not change the protein expression of stem cell marker SSEA-1 of ECS cells. The neuronal differentiation of ECS cells could be induced by retinoic acid (RA). Interestingly, FNDs did not affect on the morphological alteration, cytotoxicity and apoptosis during the neuronal differentiation. Besides, FNDs did not alter the cell viability and the expression of neuron-specific marker β-III-tubulin in these differentiated neuron cells. The existence of FNDs in the neuron cells can be identified by confocal microscopy and flow cytometry. Together, FND is a biocompatible and readily detectable nanomaterial for the labeling and tracking of neuronal differentiation process and neuron cells from stem cells.

  8. Heavy metals in locus ceruleus and motor neurons in motor neuron disease.

    Science.gov (United States)

    Pamphlett, Roger; Kum Jew, Stephen

    2013-12-12

    The causes of sporadic amyotrophic lateral sclerosis (SALS) and other types of motor neuron disease (MND) remain largely unknown. Heavy metals have long been implicated in MND, and it has recently been shown that inorganic mercury selectively enters human locus ceruleus (LC) and motor neurons. We therefore used silver nitrate autometallography (AMG) to look for AMG-stainable heavy metals (inorganic mercury and bismuth) in LC and motor neurons of 24 patients with MND (18 with SALS and 6 with familial MND) and in the LC of 24 controls. Heavy metals in neurons were found in significantly more MND patients than in controls when comparing: (1) the presence of any versus no heavy metal-containing LC neurons (MND 88%, controls 42%), (2) the median percentage of heavy metal-containing LC neurons (MND 9.5%, control 0.0%), and (3) numbers of individuals with heavy metal-containing LC neurons in the upper half of the percentage range (MND 75%, controls 25%). In MND patients, 67% of remaining spinal motor neurons contained heavy metals; smaller percentages were found in hypoglossal, nucleus ambiguus and oculomotor neurons, but none in cortical motor neurons. The majority of MND patients had heavy metals in both LC and spinal motor neurons. No glia or other neurons, including neuromelanin-containing neurons of the substantia nigra, contained stainable heavy metals. Uptake of heavy metals by LC and lower motor neurons appears to be fairly common in humans, though heavy metal staining in the LC, most likely due to inorganic mercury, was seen significantly more often in MND patients than in controls. The LC innervates many cell types that are affected in MND, and it is possible that MND is triggered by toxicant-induced interactions between LC and motor neurons.

  9. Heavy metals in locus ceruleus and motor neurons in motor neuron disease

    Science.gov (United States)

    2013-01-01

    Background The causes of sporadic amyotrophic lateral sclerosis (SALS) and other types of motor neuron disease (MND) remain largely unknown. Heavy metals have long been implicated in MND, and it has recently been shown that inorganic mercury selectively enters human locus ceruleus (LC) and motor neurons. We therefore used silver nitrate autometallography (AMG) to look for AMG-stainable heavy metals (inorganic mercury and bismuth) in LC and motor neurons of 24 patients with MND (18 with SALS and 6 with familial MND) and in the LC of 24 controls. Results Heavy metals in neurons were found in significantly more MND patients than in controls when comparing: (1) the presence of any versus no heavy metal-containing LC neurons (MND 88%, controls 42%), (2) the median percentage of heavy metal-containing LC neurons (MND 9.5%, control 0.0%), and (3) numbers of individuals with heavy metal-containing LC neurons in the upper half of the percentage range (MND 75%, controls 25%). In MND patients, 67% of remaining spinal motor neurons contained heavy metals; smaller percentages were found in hypoglossal, nucleus ambiguus and oculomotor neurons, but none in cortical motor neurons. The majority of MND patients had heavy metals in both LC and spinal motor neurons. No glia or other neurons, including neuromelanin-containing neurons of the substantia nigra, contained stainable heavy metals. Conclusions Uptake of heavy metals by LC and lower motor neurons appears to be fairly common in humans, though heavy metal staining in the LC, most likely due to inorganic mercury, was seen significantly more often in MND patients than in controls. The LC innervates many cell types that are affected in MND, and it is possible that MND is triggered by toxicant-induced interactions between LC and motor neurons. PMID:24330485

  10. Comparison of classifiers for decoding sensory and cognitive information from prefrontal neuronal populations.

    Directory of Open Access Journals (Sweden)

    Elaine Astrand

    Full Text Available Decoding neuronal information is important in neuroscience, both as a basic means to understand how neuronal activity is related to cerebral function and as a processing stage in driving neuroprosthetic effectors. Here, we compare the readout performance of six commonly used classifiers at decoding two different variables encoded by the spiking activity of the non-human primate frontal eye fields (FEF: the spatial position of a visual cue, and the instructed orientation of the animal's attention. While the first variable is exogenously driven by the environment, the second variable corresponds to the interpretation of the instruction conveyed by the cue; it is endogenously driven and corresponds to the output of internal cognitive operations performed on the visual attributes of the cue. These two variables were decoded using either a regularized optimal linear estimator in its explicit formulation, an optimal linear artificial neural network estimator, a non-linear artificial neural network estimator, a non-linear naïve Bayesian estimator, a non-linear Reservoir recurrent network classifier or a non-linear Support Vector Machine classifier. Our results suggest that endogenous information such as the orientation of attention can be decoded from the FEF with the same accuracy as exogenous visual information. All classifiers did not behave equally in the face of population size and heterogeneity, the available training and testing trials, the subject's behavior and the temporal structure of the variable of interest. In most situations, the regularized optimal linear estimator and the non-linear Support Vector Machine classifiers outperformed the other tested decoders.

  11. Imaging of intracranial neuronal and mixed neuronal-glial tumours

    International Nuclear Information System (INIS)

    Cui Shimin; Qin Jinxi; Zhang Leili; Liu Meili; Jin Song; Yan Shixin; Liu Li; Dai Weiying; Li Tao; Gao Man

    2001-01-01

    Objective: To investigate the characteristic clinical, imaging , and pathologic findings of intracranial neuronal and mixed neuronal-glial tumours. Methods: The imaging findings of surgery and pathobiology proved intracranial neuronal and mixed neuronal-glial tumours in 14 cases (7 male and 7 female, ranging in age from 6-56 years; mean age 33.8 years) were retrospectively analyzed. Results: Eight gangliogliomas were located in the frontal lobe (4 cases), temporal lobe (1 case), front- temporal lobe (2 cases), and pons (1 case). They appeared as iso-or low density on CT, iso-or low signal intensity on T 1 WI, and high signal intensity on T 2 WI on MR imaging. Two central neurocytomas were located in the supratentorial ventricles. Four desmoplastic gangliogliomas were seen as cystic masses, appearing as low signal intensity on T 1 WI and high signal intensity on T 2 WI. Conclusion: Intracranial neuronal and mixed neuronal-glial tumours had imaging characteristics. Combined with clinical history, it was possible to make a tendency preoperative diagnosis using CT or MR

  12. Intrinsically active and pacemaker neurons in pluripotent stem cell-derived neuronal populations.

    Science.gov (United States)

    Illes, Sebastian; Jakab, Martin; Beyer, Felix; Gelfert, Renate; Couillard-Despres, Sébastien; Schnitzler, Alfons; Ritter, Markus; Aigner, Ludwig

    2014-03-11

    Neurons generated from pluripotent stem cells (PSCs) self-organize into functional neuronal assemblies in vitro, generating synchronous network activities. Intriguingly, PSC-derived neuronal assemblies develop spontaneous activities that are independent of external stimulation, suggesting the presence of thus far undetected intrinsically active neurons (IANs). Here, by using mouse embryonic stem cells, we provide evidence for the existence of IANs in PSC-neuronal networks based on extracellular multielectrode array and intracellular patch-clamp recordings. IANs remain active after pharmacological inhibition of fast synaptic communication and possess intrinsic mechanisms required for autonomous neuronal activity. PSC-derived IANs are functionally integrated in PSC-neuronal populations, contribute to synchronous network bursting, and exhibit pacemaker properties. The intrinsic activity and pacemaker properties of the neuronal subpopulation identified herein may be particularly relevant for interventions involving transplantation of neural tissues. IANs may be a key element in the regulation of the functional activity of grafted as well as preexisting host neuronal networks.

  13. Injection of fully-defined signal mixtures: a novel high-throughput tool to study neuronal encoding and computations.

    Directory of Open Access Journals (Sweden)

    Vladimir Ilin

    Full Text Available Understanding of how neurons transform fluctuations of membrane potential, reflecting input activity, into spike responses, which communicate the ultimate results of single-neuron computation, is one of the central challenges for cellular and computational neuroscience. To study this transformation under controlled conditions, previous work has used a signal immersed in noise paradigm where neurons are injected with a current consisting of fluctuating noise that mimics on-going synaptic activity and a systematic signal whose transmission is studied. One limitation of this established paradigm is that it is designed to examine the encoding of only one signal under a specific, repeated condition. As a result, characterizing how encoding depends on neuronal properties, signal parameters, and the interaction of multiple inputs is cumbersome. Here we introduce a novel fully-defined signal mixture paradigm, which allows us to overcome these problems. In this paradigm, current for injection is synthetized as a sum of artificial postsynaptic currents (PSCs resulting from the activity of a large population of model presynaptic neurons. PSCs from any presynaptic neuron(s can be now considered as "signal", while the sum of all other inputs is considered as "noise". This allows us to study the encoding of a large number of different signals in a single experiment, thus dramatically increasing the throughput of data acquisition. Using this novel paradigm, we characterize the detection of excitatory and inhibitory PSCs from neuronal spike responses over a wide range of amplitudes and firing-rates. We show, that for moderately-sized neuronal populations the detectability of individual inputs is higher for excitatory than for inhibitory inputs during the 2-5 ms following PSC onset, but becomes comparable after 7-8 ms. This transient imbalance of sensitivity in favor of excitation may enhance propagation of balanced signals through neuronal networks. Finally, we

  14. Glutamate neurons are intermixed with midbrain dopamine neurons in nonhuman primates and humans

    Science.gov (United States)

    Root, David H.; Wang, Hui-Ling; Liu, Bing; Barker, David J.; Mód, László; Szocsics, Péter; Silva, Afonso C.; Maglóczky, Zsófia; Morales, Marisela

    2016-01-01

    The rodent ventral tegmental area (VTA) and substantia nigra pars compacta (SNC) contain dopamine neurons intermixed with glutamate neurons (expressing vesicular glutamate transporter 2; VGluT2), which play roles in reward and aversion. However, identifying the neuronal compositions of the VTA and SNC in higher mammals has remained challenging. Here, we revealed VGluT2 neurons within the VTA and SNC of nonhuman primates and humans by simultaneous detection of VGluT2 mRNA and tyrosine hydroxylase (TH; for identification of dopamine neurons). We found that several VTA subdivisions share similar cellular compositions in nonhuman primates and humans; their rostral linear nuclei have a high prevalence of VGluT2 neurons lacking TH; their paranigral and parabrachial pigmented nuclei have mostly TH neurons, and their parabrachial pigmented nuclei have dual VGluT2-TH neurons. Within nonhuman primates and humans SNC, the vast majority of neurons are TH neurons but VGluT2 neurons were detected in the pars lateralis subdivision. The demonstration that midbrain dopamine neurons are intermixed with glutamate or glutamate-dopamine neurons from rodents to humans offers new opportunities for translational studies towards analyzing the roles that each of these neurons play in human behavior and in midbrain-associated illnesses such as addiction, depression, schizophrenia, and Parkinson’s disease. PMID:27477243

  15. Life-long stability of neurons: a century of research on neurogenesis, neuronal death and neuron quantification in adult CNS.

    Science.gov (United States)

    Turlejski, Kris; Djavadian, Ruzanna

    2002-01-01

    In this chapter we provide an extensive review of 100 years of research on the stability of neurons in the mammalian brain, with special emphasis on humans. Although Cajal formulated the Neuronal Doctrine, he was wrong in his beliefs that adult neurogenesis did not occur and adult neurons are dying throughout life. These two beliefs became accepted "common knowledge" and have shaped much of neuroscience research and provided much of the basis for clinical treatment of age-related brain diseases. In this review, we consider adult neurogenesis from a historical and evolutionary perspective. It is concluded, that while adult neurogenesis is a factor in the dynamics of the dentate gyrus and olfactory bulb, it is probably not a major factor during the life-span in most brain areas. Likewise, the acceptance of neuronal death as an explanation for normal age-related senility is challenged with evidence collected over the last fifty years. Much of the problem in changing this common belief of dying neurons was the inadequacies of neuronal counting methods. In this review we discuss in detail implications of recent improvements in neuronal quantification. We conclude: First, age-related neuronal atrophy is the major factor in functional deterioration of existing neurons and could be slowed down, or even reversed by various pharmacological interventions. Second, in most cases neuronal degeneration during aging is a pathology that in principle may be avoided. Third, loss of myelin and of the white matter is more frequent and important than the limited neuronal death in normal aging.

  16. Artificial neural network-aided image analysis system for cell counting.

    Science.gov (United States)

    Sjöström, P J; Frydel, B R; Wahlberg, L U

    1999-05-01

    In histological preparations containing debris and synthetic materials, it is difficult to automate cell counting using standard image analysis tools, i.e., systems that rely on boundary contours, histogram thresholding, etc. In an attempt to mimic manual cell recognition, an automated cell counter was constructed using a combination of artificial intelligence and standard image analysis methods. Artificial neural network (ANN) methods were applied on digitized microscopy fields without pre-ANN feature extraction. A three-layer feed-forward network with extensive weight sharing in the first hidden layer was employed and trained on 1,830 examples using the error back-propagation algorithm on a Power Macintosh 7300/180 desktop computer. The optimal number of hidden neurons was determined and the trained system was validated by comparison with blinded human counts. System performance at 50x and lO0x magnification was evaluated. The correlation index at 100x magnification neared person-to-person variability, while 50x magnification was not useful. The system was approximately six times faster than an experienced human. ANN-based automated cell counting in noisy histological preparations is feasible. Consistent histology and computer power are crucial for system performance. The system provides several benefits, such as speed of analysis and consistency, and frees up personnel for other tasks.

  17. Artificial Leaf Based on Artificial Photosynthesis for Solar Fuel Production

    Science.gov (United States)

    2017-06-30

    collect light energy and separate charge for developing new types of nanobiodevices to construct ”artificial leaf” from solar to fuel. or Concept of...AFRL-AFOSR-JP-TR-2017-0054 Artificial Leaf Based on Artificial Photosynthesis for Solar Fuel Production Mamoru Nango NAGOYA INSTITUTE OF TECHNOLOGY...display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION. 1. REPORT DATE (DD-MM-YYYY)      30-06-2017 2

  18. Dynamical System Approach for Edge Detection Using Coupled FitzHugh-Nagumo Neurons.

    Science.gov (United States)

    Li, Shaobai; Dasmahapatra, Srinandan; Maharatna, Koushik

    2015-12-01

    The prospect of emulating the impressive computational capabilities of biological systems has led to considerable interest in the design of analog circuits that are potentially implementable in very large scale integration CMOS technology and are guided by biologically motivated models. For example, simple image processing tasks, such as the detection of edges in binary and grayscale images, have been performed by networks of FitzHugh-Nagumo-type neurons using the reaction-diffusion models. However, in these studies, the one-to-one mapping of image pixels to component neurons makes the size of the network a critical factor in any such implementation. In this paper, we develop a simplified version of the employed reaction-diffusion model in three steps. In the first step, we perform a detailed study to locate this threshold using continuous Lyapunov exponents from dynamical system theory. Furthermore, we render the diffusion in the system to be anisotropic, with the degree of anisotropy being set by the gradients of grayscale values in each image. The final step involves a simplification of the model that is achieved by eliminating the terms that couple the membrane potentials of adjacent neurons. We apply our technique to detect edges in data sets of artificially generated and real images, and we demonstrate that the performance is as good if not better than that of the previous methods without increasing the size of the network.

  19. Role of neuronal activity in regulating the structure and function of auditory neurons

    International Nuclear Information System (INIS)

    Born, D.E.

    1986-01-01

    The role of afferent activity in maintaining neuronal structure and function was investigated in second order auditory neurons in nucleus magnocellularis (NM) of the chicken. The cochlea provides the major excitatory input to NM neurons via the eighth nerve. Removal of the cochlea causes dramatic changes in NM neurons. To determine if the elimination of neuronal activity is responsible for the changes in NM seen after cochlea removal, tetrodotoxin was used block action potentials in the cochlear ganglion cells. Tetrodotoxin injections into the perilymph reliably blocked neuronal activity in the cochlear nerve and NM. Far field recordings of sound-evoked potentials revealed that responses returned within 6 hours. Changes in amino acid incorporation in NM neurons were measured by giving intracardiac injections of 3 H-leucine and preparing tissue for autoradiographic demonstration of incorporated amino acid. Grain counts over individual neurons revealed that a single injection of tetrodotoxin produced a 40% decrease in grain density in ipsilateral NM neurons. It is concluded that neuronal activity plays an important contribution to the maintenance of the normal properties of NM neurons

  20. NeuronMetrics: software for semi-automated processing of cultured neuron images.

    Science.gov (United States)

    Narro, Martha L; Yang, Fan; Kraft, Robert; Wenk, Carola; Efrat, Alon; Restifo, Linda L

    2007-03-23

    Using primary cell culture to screen for changes in neuronal morphology requires specialized analysis software. We developed NeuronMetrics for semi-automated, quantitative analysis of two-dimensional (2D) images of fluorescently labeled cultured neurons. It skeletonizes the neuron image using two complementary image-processing techniques, capturing fine terminal neurites with high fidelity. An algorithm was devised to span wide gaps in the skeleton. NeuronMetrics uses a novel strategy based on geometric features called faces to extract a branch number estimate from complex arbors with numerous neurite-to-neurite contacts, without creating a precise, contact-free representation of the neurite arbor. It estimates total neurite length, branch number, primary neurite number, territory (the area of the convex polygon bounding the skeleton and cell body), and Polarity Index (a measure of neuronal polarity). These parameters provide fundamental information about the size and shape of neurite arbors, which are critical factors for neuronal function. NeuronMetrics streamlines optional manual tasks such as removing noise, isolating the largest primary neurite, and correcting length for self-fasciculating neurites. Numeric data are output in a single text file, readily imported into other applications for further analysis. Written as modules for ImageJ, NeuronMetrics provides practical analysis tools that are easy to use and support batch processing. Depending on the need for manual intervention, processing time for a batch of approximately 60 2D images is 1.0-2.5 h, from a folder of images to a table of numeric data. NeuronMetrics' output accelerates the quantitative detection of mutations and chemical compounds that alter neurite morphology in vitro, and will contribute to the use of cultured neurons for drug discovery.

  1. Neuronal-glial trafficking

    International Nuclear Information System (INIS)

    Bachelard, H.S.

    2001-01-01

    Full text: The name 'glia' originates from the Greek word for glue, because astro glia (or astrocytes) were thought only to provide an anatomical framework for the electrically-excitable neurones. However, awareness that astrocytes perform vital roles in protecting the neurones, which they surround, emerged from evidence that they act as neuroprotective K + -sinks, and that they remove potentially toxic extracellular glutamate from the vicinity of the neurones. The astrocytes convert the glutamate to non-toxic glutamine which is returned to the neurones and used to replenish transmitter glutamate. This 'glutamate-glutamine cycle' (established in the 1960s by Berl and his colleagues) also contributes to protecting the neurones against a build-up of toxic ammonia. Glial cells also supply the neurones with components for free-radical scavenging glutathione. Recent studies have revealed that glial cells play a more positive interactive role in furnishing the neurones with fuels. Studies using radioactive 14 C, 13 C-MRS and 15 N-GCMS have revealed that glia produce alanine, lactate and proline for consumption by neurones, with increased formation of neurotransmitter glutamate. On neuronal activation the release of NH 4 + and glutamate from the neurones stimulates glucose uptake and glycolysis in the glia to produce more alanine, which can be regarded as an 'alanine-glutamate cycle' Use of 14 C-labelled precursors provided early evidence that neurotransmitter GABA may be partly derived from glial glutamine, and this has been confirmed recently in vivo by MRS isotopomer analysis of the GABA and glutamine labelled from 13 C-acetate. Relative rates of intermediary metabolism in glia and neurones can be calculated using a combination of [1- 13 C] glucose and [1,2- 13 C] acetate. When glutamate is released by neurones there is a net neuronal loss of TCA intermediates which have to be replenished. Part of this is derived from carboxylation of pyruvate, (pyruvate carboxylase

  2. Prediction of electricity and lpg consumption in a hotel using artificial neural networks

    International Nuclear Information System (INIS)

    Montero, L Reiners; Perez T, Carlos; Gongora L, Ever; Marrero, R Secundino

    2009-01-01

    This work was developed in order to improve the current tools for energy planning. This makes possible to predict electricity and LPG consumption in a tourist facility with accuracy higher than 90% by using Artificial Neuronal Networks (ANN) as fitting and predictive models. Local climatology and occupational patterns were used as entering variables for the models. Parametric modeling was performed as starting conditions and then improved with ANN. Matlab tools were used for calculations. The average deviation when predicting electricity consumption was 0.6% with a standard deviation of 4%. For LPG consumption the average deviation was less than 1% with a standard deviation of 1.3%.

  3. Comparison of logistic regression and artificial neural network in low back pain prediction: second national health survey.

    Science.gov (United States)

    Parsaeian, M; Mohammad, K; Mahmoudi, M; Zeraati, H

    2012-01-01

    The purpose of this investigation was to compare empirically predictive ability of an artificial neural network with a logistic regression in prediction of low back pain. Data from the second national health survey were considered in this investigation. This data includes the information of low back pain and its associated risk factors among Iranian people aged 15 years and older. Artificial neural network and logistic regression models were developed using a set of 17294 data and they were validated in a test set of 17295 data. Hosmer and Lemeshow recommendation for model selection was used in fitting the logistic regression. A three-layer perceptron with 9 inputs, 3 hidden and 1 output neurons was employed. The efficiency of two models was compared by receiver operating characteristic analysis, root mean square and -2 Loglikelihood criteria. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the logistic regression was 0.752 (0.004), 0.3832 and 14769.2, respectively. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the artificial neural network was 0.754 (0.004), 0.3770 and 14757.6, respectively. Based on these three criteria, artificial neural network would give better performance than logistic regression. Although, the difference is statistically significant, it does not seem to be clinically significant.

  4. Artificial Neural Network Modelling of the Energy Content of Municipal Solid Wastes in Northern Nigeria

    Directory of Open Access Journals (Sweden)

    M. B. Oumarou

    2017-12-01

    Full Text Available The study presents an application of the artificial neural network model using the back propagation learning algorithm to predict the actual calorific value of the municipal solid waste in major cities of the northern part of Nigeria, with high population densities and intense industrial activities. These cities are: Kano, Damaturu, Dutse, Bauchi, Birnin Kebbi, Gusau, Maiduguri, Katsina and Sokoto. Experimental data of the energy content and the physical characterization of the municipal solid waste serve as the input parameter in nature of wood, grass, metal, plastic, food remnants, leaves, glass and paper. Comparative studies were made by using the developed model, the experimental results and a correlation which was earlier developed by the authors to predict the energy content. While predicting the actual calorific value, the maximum error was 0.94% for the artificial neural network model and 5.20% by the statistical correlation. The network with eight neurons and an R2 = 0.96881 in the hidden layer results in a stable and optimum network. This study showed that the artificial neural network approach could successfully be used for energy content predictions from the municipal solid wastes in Northern Nigeria and other areas of similar waste stream and composition.

  5. Reconstruction of phrenic neuron identity in embryonic stem cell-derived motor neurons.

    Science.gov (United States)

    Machado, Carolina Barcellos; Kanning, Kevin C; Kreis, Patricia; Stevenson, Danielle; Crossley, Martin; Nowak, Magdalena; Iacovino, Michelina; Kyba, Michael; Chambers, David; Blanc, Eric; Lieberam, Ivo

    2014-02-01

    Air breathing is an essential motor function for vertebrates living on land. The rhythm that drives breathing is generated within the central nervous system and relayed via specialised subsets of spinal motor neurons to muscles that regulate lung volume. In mammals, a key respiratory muscle is the diaphragm, which is innervated by motor neurons in the phrenic nucleus. Remarkably, relatively little is known about how this crucial subtype of motor neuron is generated during embryogenesis. Here, we used direct differentiation of motor neurons from mouse embryonic stem cells as a tool to identify genes that direct phrenic neuron identity. We find that three determinants, Pou3f1, Hoxa5 and Notch, act in combination to promote a phrenic neuron molecular identity. We show that Notch signalling induces Pou3f1 in developing motor neurons in vitro and in vivo. This suggests that the phrenic neuron lineage is established through a local source of Notch ligand at mid-cervical levels. Furthermore, we find that the cadherins Pcdh10, which is regulated by Pou3f1 and Hoxa5, and Cdh10, which is controlled by Pou3f1, are both mediators of like-like clustering of motor neuron cell bodies. This specific Pcdh10/Cdh10 activity might provide the means by which phrenic neurons are assembled into a distinct nucleus. Our study provides a framework for understanding how phrenic neuron identity is conferred and will help to generate this rare and inaccessible yet vital neuronal subtype directly from pluripotent stem cells, thus facilitating subsequent functional investigations.

  6. Generative Artificial Intelligence : Philosophy and Theory of Artificial Intelligence

    NARCIS (Netherlands)

    van der Zant, Tijn; Kouw, Matthijs; Schomaker, Lambertus; Mueller, Vincent C.

    2013-01-01

    The closed systems of contemporary Artificial Intelligence do not seem to lead to intelligent machines in the near future. What is needed are open-ended systems with non-linear properties in order to create interesting properties for the scaffolding of an artificial mind. Using post-structuralistic

  7. Exploration of artificial neural network [ANN] to predict the electrochemical characteristics of lithium-ion cells

    Energy Technology Data Exchange (ETDEWEB)

    Parthiban, Thirumalai; Ravi, R.; Kalaiselvi, N. [Central Electrochemical Research Institute (CECRI), Karaikudi 630006 (India)

    2007-12-31

    CoO anode, as an alternate to the carbonaceous anodes of lithium-ion cells has been prepared and investigated for electrochemical charge-discharge characteristics for about 50 cycles. Artificial neural networks (ANNs), which are useful in estimating battery performance, has been deployed for the first time to forecast and to verify the charge-discharge behavior of lithium-ion cells containing CoO anode for a total of 50 cycles. In this novel approach, ANN that has one input layer with one neuron corresponding to one input variable, viz., cycles [charge-discharge cycles] and a hidden layer consisting of three neurons to produce their outputs to the output layer through a sigmoid function has been selected for the present investigation. The output layer consists of two neurons, representing the charge and discharge capacity, whose activation function is also the sigmoid transfer function. In this ever first attempt to exploit ANN as an effective theoretical tool to understand the charge-discharge characteristics of lithium-ion cells, an excellent agreement between the calculated and observed capacity values was found with CoO anodes with the best fit values corresponding to an error factor of <1%, which is the highlight of the present study. (author)

  8. Energy-efficient neural information processing in individual neurons and neuronal networks.

    Science.gov (United States)

    Yu, Lianchun; Yu, Yuguo

    2017-11-01

    Brains are composed of networks of an enormous number of neurons interconnected with synapses. Neural information is carried by the electrical signals within neurons and the chemical signals among neurons. Generating these electrical and chemical signals is metabolically expensive. The fundamental issue raised here is whether brains have evolved efficient ways of developing an energy-efficient neural code from the molecular level to the circuit level. Here, we summarize the factors and biophysical mechanisms that could contribute to the energy-efficient neural code for processing input signals. The factors range from ion channel kinetics, body temperature, axonal propagation of action potentials, low-probability release of synaptic neurotransmitters, optimal input and noise, the size of neurons and neuronal clusters, excitation/inhibition balance, coding strategy, cortical wiring, and the organization of functional connectivity. Both experimental and computational evidence suggests that neural systems may use these factors to maximize the efficiency of energy consumption in processing neural signals. Studies indicate that efficient energy utilization may be universal in neuronal systems as an evolutionary consequence of the pressure of limited energy. As a result, neuronal connections may be wired in a highly economical manner to lower energy costs and space. Individual neurons within a network may encode independent stimulus components to allow a minimal number of neurons to represent whole stimulus characteristics efficiently. This basic principle may fundamentally change our view of how billions of neurons organize themselves into complex circuits to operate and generate the most powerful intelligent cognition in nature. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  9. A single-neuron tracing study of arkypallidal and prototypic neurons in healthy rats.

    Science.gov (United States)

    Fujiyama, Fumino; Nakano, Takashi; Matsuda, Wakoto; Furuta, Takahiro; Udagawa, Jun; Kaneko, Takeshi

    2016-12-01

    The external globus pallidus (GP) is known as a relay nucleus of the indirect pathway of the basal ganglia. Recent studies in dopamine-depleted and healthy rats indicate that the GP comprises two main types of pallidofugal neurons: the so-called "prototypic" and "arkypallidal" neurons. However, the reconstruction of complete arkypallidal neurons in healthy rats has not been reported. Here we visualized the entire axonal arborization of four single arkypallidal neurons and six single prototypic neurons in rat brain using labeling with a viral vector expressing membrane-targeted green fluorescent protein and examined the distribution of axon boutons in the target nuclei. Results revealed that not only the arkypallidal neurons but nearly all of the prototypic neurons projected to the striatum with numerous axon varicosities. Thus, the striatum is a major target nucleus for pallidal neurons. Arkypallidal and prototypic GP neurons located in the calbindin-positive and calbindin-negative regions mainly projected to the corresponding positive and negative regions in the striatum. Because the GP and striatum calbindin staining patterns reflect the topographic organization of the striatopallidal projection, the striatal neurons in the sensorimotor and associative regions constitute the reciprocal connection with the GP neurons in the corresponding regions.

  10. Artificial life and Piaget.

    Science.gov (United States)

    Mueller, Ulrich; Grobman, K H.

    2003-04-01

    Artificial life provides important theoretical and methodological tools for the investigation of Piaget's developmental theory. This new method uses artificial neural networks to simulate living phenomena in a computer. A recent study by Parisi and Schlesinger suggests that artificial life might reinvigorate the Piagetian framework. We contrast artificial life with traditional cognitivist approaches, discuss the role of innateness in development, and examine the relation between physiological and psychological explanations of intelligent behaviour.

  11. Cerebellar Nuclear Neurons Use Time and Rate Coding to Transmit Purkinje Neuron Pauses.

    Science.gov (United States)

    Sudhakar, Shyam Kumar; Torben-Nielsen, Benjamin; De Schutter, Erik

    2015-12-01

    Neurons of the cerebellar nuclei convey the final output of the cerebellum to their targets in various parts of the brain. Within the cerebellum their direct upstream connections originate from inhibitory Purkinje neurons. Purkinje neurons have a complex firing pattern of regular spikes interrupted by intermittent pauses of variable length. How can the cerebellar nucleus process this complex input pattern? In this modeling study, we investigate different forms of Purkinje neuron simple spike pause synchrony and its influence on candidate coding strategies in the cerebellar nuclei. That is, we investigate how different alignments of synchronous pauses in synthetic Purkinje neuron spike trains affect either time-locking or rate-changes in the downstream nuclei. We find that Purkinje neuron synchrony is mainly represented by changes in the firing rate of cerebellar nuclei neurons. Pause beginning synchronization produced a unique effect on nuclei neuron firing, while the effect of pause ending and pause overlapping synchronization could not be distinguished from each other. Pause beginning synchronization produced better time-locking of nuclear neurons for short length pauses. We also characterize the effect of pause length and spike jitter on the nuclear neuron firing. Additionally, we find that the rate of rebound responses in nuclear neurons after a synchronous pause is controlled by the firing rate of Purkinje neurons preceding it.

  12. Cerebellar Nuclear Neurons Use Time and Rate Coding to Transmit Purkinje Neuron Pauses

    Science.gov (United States)

    Sudhakar, Shyam Kumar; Torben-Nielsen, Benjamin; De Schutter, Erik

    2015-01-01

    Neurons of the cerebellar nuclei convey the final output of the cerebellum to their targets in various parts of the brain. Within the cerebellum their direct upstream connections originate from inhibitory Purkinje neurons. Purkinje neurons have a complex firing pattern of regular spikes interrupted by intermittent pauses of variable length. How can the cerebellar nucleus process this complex input pattern? In this modeling study, we investigate different forms of Purkinje neuron simple spike pause synchrony and its influence on candidate coding strategies in the cerebellar nuclei. That is, we investigate how different alignments of synchronous pauses in synthetic Purkinje neuron spike trains affect either time-locking or rate-changes in the downstream nuclei. We find that Purkinje neuron synchrony is mainly represented by changes in the firing rate of cerebellar nuclei neurons. Pause beginning synchronization produced a unique effect on nuclei neuron firing, while the effect of pause ending and pause overlapping synchronization could not be distinguished from each other. Pause beginning synchronization produced better time-locking of nuclear neurons for short length pauses. We also characterize the effect of pause length and spike jitter on the nuclear neuron firing. Additionally, we find that the rate of rebound responses in nuclear neurons after a synchronous pause is controlled by the firing rate of Purkinje neurons preceding it. PMID:26630202

  13. Genetic variation in total number and locations of GnRH neurons identified using in situ hybridization in a wild-source population.

    Science.gov (United States)

    Kaugars, Katherine E; Rivers, Charlotte I; Saha, Margaret S; Heideman, Paul D

    2016-02-01

    The evolution of brain function in the regulation of physiology may depend in part upon the numbers and locations of neurons. Wild populations of rodents contain natural genetic variation in the inhibition of reproduction by winter-like short photoperiod, and it has been hypothesized that this functional variation might be due in part to heritable variation in the numbers or location of gonadotropin releasing hormone (GnRH) neurons. A naturally variable wild-source population of white-footed mice was used to develop lines artificially selected for or against mature gonads in short, winter-like photoperiods. We compared a selection line that is reproductively inhibited in short photoperiod (Responsive) to a line that is weakly inhibited by short photoperiod (Nonresponsive) for differences in counts of neurons identified using in situ hybridization for GnRH mRNA. There was no effect of photoperiod, but there were 60% more GnRH neurons in total in the Nonresponsive selection line than the Responsive selection line. The lines differed specifically in numbers of GnRH neurons in more anterior regions, whereas numbers of GnRH neurons in posterior areas were not statistically different between lines. We compare these results to those of an earlier study that used immunohistochemical labeling for GnRH neurons. The results are consistent with the hypothesis that the selection lines and natural source population contain significant genetic variation in the number and location of GnRH neurons. The variation in GnRH neurons may contribute to functional variation in fertility that occurs in short photoperiods in the laboratory and in the wild source population in winter. © 2015 Wiley Periodicals, Inc.

  14. Bifurcation of synchronous oscillations into torus in a system of two reciprocally inhibitory silicon neurons: Experimental observation and modeling

    International Nuclear Information System (INIS)

    Bondarenko, Vladimir E.; Cymbalyuk, Gennady S.; Patel, Girish; DeWeerth, Stephen P.; Calabrese, Ronald L.

    2004-01-01

    Oscillatory activity in the central nervous system is associated with various functions, like motor control, memory formation, binding, and attention. Quasiperiodic oscillations are rarely discussed in the neurophysiological literature yet they may play a role in the nervous system both during normal function and disease. Here we use a physical system and a model to explore scenarios for how quasiperiodic oscillations might arise in neuronal networks. An oscillatory system of two mutually inhibitory neuronal units is a ubiquitous network module found in nervous systems and is called a half-center oscillator. Previously we created a half-center oscillator of two identical oscillatory silicon (analog Very Large Scale Integration) neurons and developed a mathematical model describing its dynamics. In the mathematical model, we have shown that an in-phase limit cycle becomes unstable through a subcritical torus bifurcation. However, the existence of this torus bifurcation in experimental silicon two-neuron system was not rigorously demonstrated or investigated. Here we demonstrate the torus predicted by the model for the silicon implementation of a half-center oscillator using complex time series analysis, including bifurcation diagrams, mapping techniques, correlation functions, amplitude spectra, and correlation dimensions, and we investigate how the properties of the quasiperiodic oscillations depend on the strengths of coupling between the silicon neurons. The potential advantages and disadvantages of quasiperiodic oscillations (torus) for biological neural systems and artificial neural networks are discussed

  15. Neuron-to-neuron transmission of α-synuclein fibrils through axonal transport

    Science.gov (United States)

    Freundt, Eric C.; Maynard, Nate; Clancy, Eileen K.; Roy, Shyamali; Bousset, Luc; Sourigues, Yannick; Covert, Markus; Melki, Ronald; Kirkegaard, Karla; Brahic, Michel

    2012-01-01

    Objective The lesions of Parkinson's disease spread through the brain in a characteristic pattern that corresponds to axonal projections. Previous observations suggest that misfolded α-synuclein could behave as a prion, moving from neuron to neuron and causing endogenous α-synuclein to misfold. Here, we characterized and quantified the axonal transport of α-synuclein fibrils and showed that fibrils could be transferred from axons to second-order neurons following anterograde transport. Methods We grew primary cortical mouse neurons in microfluidic devices to separate soma from axonal projections in fluidically isolated microenvironments. We used live-cell imaging and immunofluorescence to characterize the transport of fluorescent α-synuclein fibrils and their transfer to second-order neurons. Results Fibrillar α-synuclein was internalized by primary neurons and transported in axons with kinetics consistent with slow component-b of axonal transport (fast axonal transport with saltatory movement). Fibrillar α-synuclein was readily observed in the cell bodies of second-order neurons following anterograde axonal transport. Axon-to-soma transfer appeared not to require synaptic contacts. Interpretation These results support the hypothesis that the progression of Parkinson's disease can be caused by neuron-to-neuron spread of α-synuclein aggregates and that the anatomical pattern of progression of lesions between axonally connected areas results from the axonal transport of such aggregates. That the transfer did not appear to be transsynaptic gives hope that α-synuclein fibrils could be intercepted by drugs during the extra-cellular phase of their journey. PMID:23109146

  16. Optimization of Melatonin Dissolution from Extended Release Matrices Using Artificial Neural Networking.

    Science.gov (United States)

    Martarelli, D; Casettari, L; Shalaby, K S; Soliman, M E; Cespi, M; Bonacucina, G; Fagioli, L; Perinelli, D R; Lam, J K W; Palmieri, G F

    2016-01-01

    Efficacy of melatonin in treating sleep disorders has been demonstrated in numerous studies. Being with short half-life, melatonin needs to be formulated in extended-release tablets to prevent the fast drop of its plasma concentration. However, an attempt to mimic melatonin natural plasma levels during night time is challenging. In this work, Artificial Neural Networks (ANNs) were used to optimize melatonin release from hydrophilic polymer matrices. Twenty-seven different tablet formulations with different amounts of hydroxypropyl methylcellulose, xanthan gum and Carbopol®974P NF were prepared and subjected to drug release studies. Using dissolution test data as inputs for ANN designed by Visual Basic programming language, the ideal number of neurons in the hidden layer was determined trial and error methodology to guarantee the best performance of constructed ANN. Results showed that the ANN with nine neurons in the hidden layer had the best results. ANN was examined to check its predictability and then used to determine the best formula that can mimic the release of melatonin from a marketed brand using similarity fit factor. This work shows the possibility of using ANN to optimize the composition of prolonged-release melatonin tablets having dissolution profile desired.

  17. Artificial sweeteners

    DEFF Research Database (Denmark)

    Raben, Anne Birgitte; Richelsen, Bjørn

    2012-01-01

    Artificial sweeteners can be a helpful tool to reduce energy intake and body weight and thereby risk for diabetes and cardiovascular diseases (CVD). Considering the prevailing diabesity (obesity and diabetes) epidemic, this can, therefore, be an important alternative to natural, calorie-containin......Artificial sweeteners can be a helpful tool to reduce energy intake and body weight and thereby risk for diabetes and cardiovascular diseases (CVD). Considering the prevailing diabesity (obesity and diabetes) epidemic, this can, therefore, be an important alternative to natural, calorie......-containing sweeteners. The purpose of this review is to summarize the current evidence on the effect of artificial sweeteners on body weight, appetite, and risk markers for diabetes and CVD in humans....

  18. Artificial cognition architectures

    CERN Document Server

    Crowder, James A; Friess, Shelli A

    2013-01-01

    The goal of this book is to establish the foundation, principles, theory, and concepts that are the backbone of real, autonomous Artificial Intelligence. Presented here are some basic human intelligence concepts framed for Artificial Intelligence systems. These include concepts like Metacognition and Metamemory, along with architectural constructs for Artificial Intelligence versions of human brain functions like the prefrontal cortex. Also presented are possible hardware and software architectures that lend themselves to learning, reasoning, and self-evolution

  19. Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system

    Energy Technology Data Exchange (ETDEWEB)

    Esen, Hikmet; Esen, Mehmet [Department of Mechanical Education, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey); Inalli, Mustafa [Department of Mechanical Engineering, Faculty of Engineering, Firat University, 23279 Elazig (Turkey); Sengur, Abdulkadir [Department of Electronic and Computer Science, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey)

    2008-07-01

    This article present a comparison of artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) applied for modelling a ground-coupled heat pump system (GCHP). The aim of this study is predicting system performance related to ground and air (condenser inlet and outlet) temperatures by using desired models. Performance forecasting is the precondition for the optimal design and energy-saving operation of air-conditioning systems. So obtained models will help the system designer to realize this precondition. The most suitable algorithm and neuron number in the hidden layer are found as Levenberg-Marquardt (LM) with seven neurons for ANN model whereas the most suitable membership function and number of membership functions are found as Gauss and two, respectively, for ANFIS model. The root-mean squared (RMS) value and the coefficient of variation in percent (cov) value are 0.0047 and 0.1363, respectively. The absolute fraction of variance (R{sup 2}) is 0.9999 which can be considered as very promising. This paper shows the appropriateness of ANFIS for the quantitative modeling of GCHP systems. (author)

  20. Attenuation of hypoxic current by intracellular applications of ATP regenerating agents in hippocampal CA1 neurons of rat brain slices.

    Science.gov (United States)

    Chung, I; Zhang, Y; Eubanks, J H; Zhang, L

    1998-10-01

    Hypoxia-induced outward currents (hyperpolarization) were examined in hippocampal CA1 neurons of rat brain slices, using the whole-cell recording technique. Hypoxic episodes were induced by perfusing slices with an artificial cerebrospinal fluid aerated with 5% CO2/95% N2 rather than 5% CO2/95% O2, for about 3 min. The hypoxic current was consistently and reproducibly induced in CA1 neurons dialysed with an ATP-free patch pipette solution. This current manifested as an outward shift in the holding current in association with increased conductance, and it reversed at -78 +/- 2.5 mV, with a linear I-V relation in the range of -100 to -40 mV. To provide extra energy resources to individual neurons recorded, agents were added to the patch pipette solution, including MgATP alone, MgATP + phosphocreatine + creatine kinase, or MgATP + creatine. In CA1 neurons dialysed with patch solutions including these agents, hypoxia produced small outward currents in comparison with those observed in CA1 neurons dialysed with the ATP-free solution. Among the above agents examined, whole-cell dialysis with MgATP + creatine was the most effective at decreasing the hypoxic outward currents. We suggest that the hypoxic hyperpolarization is closely related to energy metabolism in individual CA1 neurons, and that the energy supply provided by phosphocreatine metabolism may play a critical role during transient metabolic stress.

  1. A program for assisting automatic generation control of the ELETRONORTE using artificial neural network; Um programa para assistencia ao controle automatico de geracao da Eletronorte usando rede neuronal artificial

    Energy Technology Data Exchange (ETDEWEB)

    Brito Filho, Pedro Rodrigues de; Nascimento Garcez, Jurandyr do [Para Univ., Belem, PA (Brazil). Centro Tecnologico; Charone, Junior, Wady [Centrais Eletricas do Nordeste do Brasil S.A. (ELETRONORTE), Belem, PA (Brazil)

    1994-12-31

    This work presents an application of artificial neural network as a support to decision making in the automatic generation control (AGC) of the ELETRONORTE. It uses a software to auxiliary in the decisions in real time of the AGC. (author) 2 refs., 6 figs., 1 tab.

  2. Prediction by Artificial Neural Networks (ANN of the diffusivity, mass, moisture, volume and solids on osmotically dehydrated yacon (Smallantus sonchifolius

    Directory of Open Access Journals (Sweden)

    Julio Rojas Naccha

    2012-09-01

    Full Text Available The predictive ability of Artificial Neural Network (ANN on the effect of the concentration (30, 40, 50 y 60 % w/w and temperature (30, 40 y 50°C of fructooligosaccharides solution, in the mass, moisture, volume and solids of osmodehydrated yacon cubes, and in the coefficients of the water means effective diffusivity with and without shrinkage was evaluated. The Feedforward type ANN with the Backpropagation training algorithms and the Levenberg-Marquardt weight adjustment was applied, using the following topology: 10-5 goal error, 0.01 learning rate, 0.5 moment coefficient, 2 input neurons, 6 output neurons, one hidden layer with 18 neurons, 15 training stages and logsig-pureline transfer functions. The overall average error achieved by the ANN was 3.44% and correlation coefficients were bigger than 0.9. No significant differences were found between the experimental values and the predicted values achieved by the ANN and with the predicted values achieved by a statistical model of second-order polynomial regression (p > 0.95.

  3. NBLAST: Rapid, Sensitive Comparison of Neuronal Structure and Construction of Neuron Family Databases.

    Science.gov (United States)

    Costa, Marta; Manton, James D; Ostrovsky, Aaron D; Prohaska, Steffen; Jefferis, Gregory S X E

    2016-07-20

    Neural circuit mapping is generating datasets of tens of thousands of labeled neurons. New computational tools are needed to search and organize these data. We present NBLAST, a sensitive and rapid algorithm, for measuring pairwise neuronal similarity. NBLAST considers both position and local geometry, decomposing neurons into short segments; matched segments are scored using a probabilistic scoring matrix defined by statistics of matches and non-matches. We validated NBLAST on a published dataset of 16,129 single Drosophila neurons. NBLAST can distinguish neuronal types down to the finest level (single identified neurons) without a priori information. Cluster analysis of extensively studied neuronal classes identified new types and unreported topographical features. Fully automated clustering organized the validation dataset into 1,052 clusters, many of which map onto previously described neuronal types. NBLAST supports additional query types, including searching neurons against transgene expression patterns. Finally, we show that NBLAST is effective with data from other invertebrates and zebrafish. VIDEO ABSTRACT. Copyright © 2016 MRC Laboratory of Molecular Biology. Published by Elsevier Inc. All rights reserved.

  4. Artificial Disc Replacement

    Science.gov (United States)

    ... Spondylolisthesis BLOG FIND A SPECIALIST Treatments Artificial Disc Replacement (ADR) Patient Education Committee Jamie Baisden The disc ... Disc An artificial disc (also called a disc replacement, disc prosthesis or spine arthroplasty device) is a ...

  5. Direct projections from hypothalamic orexin neurons to brainstem cardiac vagal neurons.

    Science.gov (United States)

    Dergacheva, Olga; Yamanaka, Akihiro; Schwartz, Alan R; Polotsky, Vsevolod Y; Mendelowitz, David

    2016-12-17

    Orexin neurons are known to augment the sympathetic control of cardiovascular function, however the role of orexin neurons in parasympathetic cardiac regulation remains unclear. To test the hypothesis that orexin neurons contribute to parasympathetic control we selectively expressed channelrhodopsin-2 (ChR2) in orexin neurons in orexin-Cre transgenic rats and examined postsynaptic currents in cardiac vagal neurons (CVNs) in the dorsal motor nucleus of the vagus (DMV). Simultaneous photostimulation and recording in ChR2-expressing orexin neurons in the lateral hypothalamus resulted in reliable action potential firing as well as large whole-cell currents suggesting a strong expression of ChR2 and reliable optogenetic excitation. Photostimulation of ChR2-expressing fibers in the DMV elicited short-latency (ranging from 3.2ms to 8.5ms) postsynaptic currents in 16 out of 44 CVNs tested. These responses were heterogeneous and included excitatory glutamatergic (63%) and inhibitory GABAergic (37%) postsynaptic currents. The results from this study suggest different sub-population of orexin neurons may exert diverse influences on brainstem CVNs and therefore may play distinct functional roles in parasympathetic control of the heart. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

  6. Fuzzy logic and neural networks in artificial intelligence and pattern recognition

    Science.gov (United States)

    Sanchez, Elie

    1991-10-01

    With the use of fuzzy logic techniques, neural computing can be integrated in symbolic reasoning to solve complex real world problems. In fact, artificial neural networks, expert systems, and fuzzy logic systems, in the context of approximate reasoning, share common features and techniques. A model of Fuzzy Connectionist Expert System is introduced, in which an artificial neural network is designed to construct the knowledge base of an expert system from, training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the synaptic connections in an AND-OR structure: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through min-max fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feedforward network is described and first illustrated in a biomedical application (medical diagnosis assistance from inflammatory-syndromes/proteins profiles). Then, it is shown how this methodology can be utilized for handwritten pattern recognition (characters play the role of diagnoses): in a fuzzy neuron describing a number for example, the linguistic weights represent fuzzy sets on cross-detecting lines and the numerical weights reflect the importance (or weakness) of connections between cross-detecting lines and characters.

  7. On the number of neurons and time scale of integration underlying the formation of percepts in the brain.

    Science.gov (United States)

    Wohrer, Adrien; Machens, Christian K

    2015-03-01

    All of our perceptual experiences arise from the activity of neural populations. Here we study the formation of such percepts under the assumption that they emerge from a linear readout, i.e., a weighted sum of the neurons' firing rates. We show that this assumption constrains the trial-to-trial covariance structure of neural activities and animal behavior. The predicted covariance structure depends on the readout parameters, and in particular on the temporal integration window w and typical number of neurons K used in the formation of the percept. Using these predictions, we show how to infer the readout parameters from joint measurements of a subject's behavior and neural activities. We consider three such scenarios: (1) recordings from the complete neural population, (2) recordings of neuronal sub-ensembles whose size exceeds K, and (3) recordings of neuronal sub-ensembles that are smaller than K. Using theoretical arguments and artificially generated data, we show that the first two scenarios allow us to recover the typical spatial and temporal scales of the readout. In the third scenario, we show that the readout parameters can only be recovered by making additional assumptions about the structure of the full population activity. Our work provides the first thorough interpretation of (feed-forward) percept formation from a population of sensory neurons. We discuss applications to experimental recordings in classic sensory decision-making tasks, which will hopefully provide new insights into the nature of perceptual integration.

  8. Transgenic tools to characterize neuronal properties of discrete populations of zebrafish neurons.

    Science.gov (United States)

    Satou, Chie; Kimura, Yukiko; Hirata, Hiromi; Suster, Maximiliano L; Kawakami, Koichi; Higashijima, Shin-ichi

    2013-09-01

    The developing nervous system consists of a variety of cell types. Transgenic animals expressing reporter genes in specific classes of neuronal cells are powerful tools for the study of neuronal network formation. We generated a wide variety of transgenic zebrafish that expressed reporter genes in specific classes of neurons or neuronal progenitors. These include lines in which neurons of specific neurotransmitter phenotypes expressed fluorescent proteins or Gal4, and lines in which specific subsets of the dorsal progenitor domain in the spinal cord expressed fluorescent proteins. Using these, we examined domain organization in the developing dorsal spinal cord, and found that there are six progenitor domains in zebrafish, which is similar to the domain organization in mice. We also systematically characterized neurotransmitter properties of the neurons that are produced from each domain. Given that reporter gene expressions occurs in a wide area of the nervous system in the lines generated, these transgenic fish should serve as powerful tools for the investigation of not only the neurons in the dorsal spinal cord but also neuronal structures and functions in many other regions of the nervous system.

  9. Evidence for role of acid-sensing ion channels in nucleus ambiguus neurons: essential differences in anesthetized versus awake rats.

    Science.gov (United States)

    Brailoiu, G Cristina; Deliu, Elena; Altmann, Joseph B; Chitravanshi, Vineet; Brailoiu, Eugen

    2014-08-01

    Acid-sensing ion channels (ASIC) are widely expressed in several brain regions including medulla; their role in physiology and pathophysiology is incompletely understood. We examined the effect of acidic pH of 6.2 on the medullary neurons involved in parasympathetic cardiac control. Our results indicate that retrogradely labeled cardiac vagal neurons of nucleus ambiguus are depolarized by acidic pH. In addition, acidic saline of pH 6.2 increases cytosolic Ca(2+) concentration by promoting Ca(2+) influx in nucleus ambiguus neurons. In vivo studies indicate that microinjection of acidic artificial cerebrospinal fluid (pH 6.2) into the nucleus ambiguus decreases the heart rate in conscious rats, whereas it has no effect in anesthetized animals. Pretreatment with either amiloride or benzamil, two widely used ASIC blockers, abolishes both the in vitro and in vivo effects elicited by pH 6.2. Our findings support a critical role for ASIC in modulation of cardiac vagal tone and provide a potential mechanism for acidosis-induced bradycardia, while identifying important differences in the response to acidic pH between anesthetized and conscious rats.

  10. Adaptive Neuron Model: An architecture for the rapid learning of nonlinear topological transformations

    Science.gov (United States)

    Tawel, Raoul (Inventor)

    1994-01-01

    A method for the rapid learning of nonlinear mappings and topological transformations using a dynamically reconfigurable artificial neural network is presented. This fully-recurrent Adaptive Neuron Model (ANM) network was applied to the highly degenerate inverse kinematics problem in robotics, and its performance evaluation is bench-marked. Once trained, the resulting neuromorphic architecture was implemented in custom analog neural network hardware and the parameters capturing the functional transformation downloaded onto the system. This neuroprocessor, capable of 10(exp 9) ops/sec, was interfaced directly to a three degree of freedom Heathkit robotic manipulator. Calculation of the hardware feed-forward pass for this mapping was benchmarked at approximately 10 microsec.

  11. Morphine disinhibits glutamatergic input to VTA dopamine neurons and promotes dopamine neuron excitation.

    Science.gov (United States)

    Chen, Ming; Zhao, Yanfang; Yang, Hualan; Luan, Wenjie; Song, Jiaojiao; Cui, Dongyang; Dong, Yi; Lai, Bin; Ma, Lan; Zheng, Ping

    2015-07-24

    One reported mechanism for morphine activation of dopamine (DA) neurons of the ventral tegmental area (VTA) is the disinhibition model of VTA-DA neurons. Morphine inhibits GABA inhibitory neurons, which shifts the balance between inhibitory and excitatory input to VTA-DA neurons in favor of excitation and then leads to VTA-DA neuron excitation. However, it is not known whether morphine has an additional strengthening effect on excitatory input. Our results suggest that glutamatergic input to VTA-DA neurons is inhibited by GABAergic interneurons via GABAB receptors and that morphine promotes presynaptic glutamate release by removing this inhibition. We also studied the contribution of the morphine-induced disinhibitory effect on the presynaptic glutamate release to the overall excitatory effect of morphine on VTA-DA neurons and related behavior. Our results suggest that the disinhibitory action of morphine on presynaptic glutamate release might be the main mechanism for morphine-induced increase in VTA-DA neuron firing and related behaviors.

  12. Classic cadherin expressions balance postnatal neuronal positioning and dendrite dynamics to elaborate the specific cytoarchitecture of the mouse cortical area.

    Science.gov (United States)

    Egusa, Saki F; Inoue, Yukiko U; Asami, Junko; Terakawa, Youhei W; Hoshino, Mikio; Inoue, Takayoshi

    2016-04-01

    A unique feature of the mammalian cerebral cortex is in its tangential parcellation via anatomical and functional differences. However, the cellular and/or molecular machinery involved in cortical arealization remain largely unknown. Here we map expression profiles of classic cadherins in the postnatal mouse barrel field of the primary somatosensory area (S1BF) and generate a novel bacterial artificial chromosome transgenic (BAC-Tg) mouse line selectively illuminating nuclei of cadherin-6 (Cdh6)-expressing layer IV barrel neurons to confirm that tangential cellular assemblage of S1BF is established by postnatal day 5 (P5). When we electroporate the cadherins expressed in both barrel neurons and thalamo-cortical axon (TCA) terminals limited to the postnatal layer IV neurons, S1BF cytoarchitecture is disorganized with excess elongation of dendrites at P7. Upon delivery of dominant negative molecules for all classic cadherins, tangential cellular positioning and biased dendritic arborization of barrel neurons are significantly altered. These results underscore the value of classic cadherin-mediated sorting among neuronal cell bodies, dendrites and TCA terminals in postnatally elaborating the S1BF-specific tangential cytoarchitecture. Additionally, how the "protocortex" machinery affects classic cadherin expression profiles in the process of cortical arealization is examined and discussed. Copyright © 2015 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

  13. Artificial life and life artificialization in Tron

    Directory of Open Access Journals (Sweden)

    Carolina Dantas Figueiredo

    2012-12-01

    Full Text Available Cinema constantly shows the struggle between the men and artificial intelligences. Fiction, and more specifically fiction films, lends itself to explore possibilities asking “what if?”. “What if”, in this case, is related to the eventual rebellion of artificial intelligences, theme explored in the movies Tron (1982 and Tron Legacy (2010 trat portray the conflict between programs and users. The present paper examines these films, observing particularly the possibility programs empowering. Finally, is briefly mentioned the concept of cyborg as a possibility of response to human concerns.

  14. An ultra-low-voltage electronic implementation of inertial neuron model with nonmonotonous Liao's activation function.

    Science.gov (United States)

    Kant, Nasir Ali; Dar, Mohamad Rafiq; Khanday, Farooq Ahmad

    2015-01-01

    The output of every neuron in neural network is specified by the employed activation function (AF) and therefore forms the heart of neural networks. As far as the design of artificial neural networks (ANNs) is concerned, hardware approach is preferred over software one because it promises the full utilization of the application potential of ANNs. Therefore, besides some arithmetic blocks, designing AF in hardware is the most important for designing ANN. While attempting to design the AF in hardware, the designs should be compatible with the modern Very Large Scale Integration (VLSI) design techniques. In this regard, the implemented designs should: only be in Metal Oxide Semiconductor (MOS) technology in order to be compatible with the digital designs, provide electronic tunability feature, and be able to operate at ultra-low voltage. Companding is one of the promising circuit design techniques for achieving these goals. In this paper, 0.5 V design of Liao's AF using sinh-domain technique is introduced. Furthermore, the function is tested by implementing inertial neuron model. The performance of the AF and inertial neuron model have been evaluated through simulation results, using the PSPICE software with the MOS transistor models provided by the 0.18-μm Taiwan Semiconductor Manufacturer Complementary Metal Oxide Semiconductor (TSM CMOS) process.

  15. Neuronal medium that supports basic synaptic functions and activity of human neurons in vitro.

    Science.gov (United States)

    Bardy, Cedric; van den Hurk, Mark; Eames, Tameji; Marchand, Cynthia; Hernandez, Ruben V; Kellogg, Mariko; Gorris, Mark; Galet, Ben; Palomares, Vanessa; Brown, Joshua; Bang, Anne G; Mertens, Jerome; Böhnke, Lena; Boyer, Leah; Simon, Suzanne; Gage, Fred H

    2015-05-19

    Human cell reprogramming technologies offer access to live human neurons from patients and provide a new alternative for modeling neurological disorders in vitro. Neural electrical activity is the essence of nervous system function in vivo. Therefore, we examined neuronal activity in media widely used to culture neurons. We found that classic basal media, as well as serum, impair action potential generation and synaptic communication. To overcome this problem, we designed a new neuronal medium (BrainPhys basal + serum-free supplements) in which we adjusted the concentrations of inorganic salts, neuroactive amino acids, and energetic substrates. We then tested that this medium adequately supports neuronal activity and survival of human neurons in culture. Long-term exposure to this physiological medium also improved the proportion of neurons that were synaptically active. The medium was designed to culture human neurons but also proved adequate for rodent neurons. The improvement in BrainPhys basal medium to support neurophysiological activity is an important step toward reducing the gap between brain physiological conditions in vivo and neuronal models in vitro.

  16. NEURON and Python.

    Science.gov (United States)

    Hines, Michael L; Davison, Andrew P; Muller, Eilif

    2009-01-01

    The NEURON simulation program now allows Python to be used, alone or in combination with NEURON's traditional Hoc interpreter. Adding Python to NEURON has the immediate benefit of making available a very extensive suite of analysis tools written for engineering and science. It also catalyzes NEURON software development by offering users a modern programming tool that is recognized for its flexibility and power to create and maintain complex programs. At the same time, nothing is lost because all existing models written in Hoc, including graphical user interface tools, continue to work without change and are also available within the Python context. An example of the benefits of Python availability is the use of the xml module in implementing NEURON's Import3D and CellBuild tools to read MorphML and NeuroML model specifications.

  17. BigNeuron: Large-scale 3D Neuron Reconstruction from Optical Microscopy Images

    OpenAIRE

    Peng, Hanchuan; Hawrylycz, Michael; Roskams, Jane; Hill, Sean; Spruston, Nelson; Meijering, Erik; Ascoli, Giorgio A.

    2015-01-01

    textabstractUnderstanding the structure of single neurons is critical for understanding how they function within neural circuits. BigNeuron is a new community effort that combines modern bioimaging informatics, recent leaps in labeling and microscopy, and the widely recognized need for openness and standardization to provide a community resource for automated reconstruction of dendritic and axonal morphology of single neurons. Understanding the structure of single neurons is critical for unde...

  18. Optogenetic identification of hypothalamic orexin neuron projections to paraventricular spinally projecting neurons.

    Science.gov (United States)

    Dergacheva, Olga; Yamanaka, Akihiro; Schwartz, Alan R; Polotsky, Vsevolod Y; Mendelowitz, David

    2017-04-01

    Orexin neurons, and activation of orexin receptors, are generally thought to be sympathoexcitatory; however, the functional connectivity between orexin neurons and a likely sympathetic target, the hypothalamic spinally projecting neurons (SPNs) in the paraventricular nucleus of the hypothalamus (PVN) has not been established. To test the hypothesis that orexin neurons project directly to SPNs in the PVN, channelrhodopsin-2 (ChR2) was selectively expressed in orexin neurons to enable photoactivation of ChR2-expressing fibers while examining evoked postsynaptic currents in SPNs in rat hypothalamic slices. Selective photoactivation of orexin fibers elicited short-latency postsynaptic currents in all SPNs tested ( n = 34). These light-triggered responses were heterogeneous, with a majority being excitatory glutamatergic responses (59%) and a minority of inhibitory GABAergic (35%) and mixed glutamatergic and GABAergic currents (6%). Both glutamatergic and GABAergic responses were present in the presence of tetrodotoxin and 4-aminopyridine, suggesting a monosynaptic connection between orexin neurons and SPNs. In addition to generating postsynaptic responses, photostimulation facilitated action potential firing in SPNs (current clamp configuration). Glutamatergic, but not GABAergic, postsynaptic currents were diminished by application of the orexin receptor antagonist almorexant, indicating orexin release facilitates glutamatergic neurotransmission in this pathway. This work identifies a neuronal circuit by which orexin neurons likely exert sympathoexcitatory control of cardiovascular function. NEW & NOTEWORTHY This is the first study to establish, using innovative optogenetic approaches in a transgenic rat model, that there are robust heterogeneous projections from orexin neurons to paraventricular spinally projecting neurons, including excitatory glutamatergic and inhibitory GABAergic neurotransmission. Endogenous orexin release modulates glutamatergic, but not

  19. Accurate prediction of the dew points of acidic combustion gases by using an artificial neural network model

    International Nuclear Information System (INIS)

    ZareNezhad, Bahman; Aminian, Ali

    2011-01-01

    This paper presents a new approach based on using an artificial neural network (ANN) model for predicting the acid dew points of the combustion gases in process and power plants. The most important acidic combustion gases namely, SO 3 , SO 2 , NO 2 , HCl and HBr are considered in this investigation. Proposed Network is trained using the Levenberg-Marquardt back propagation algorithm and the hyperbolic tangent sigmoid activation function is applied to calculate the output values of the neurons of the hidden layer. According to the network's training, validation and testing results, a three layer neural network with nine neurons in the hidden layer is selected as the best architecture for accurate prediction of the acidic combustion gases dew points over wide ranges of acid and moisture concentrations. The proposed neural network model can have significant application in predicting the condensation temperatures of different acid gases to mitigate the corrosion problems in stacks, pollution control devices and energy recovery systems.

  20. A Spray-On Carbon Nanotube Artificial Neuron Strain Sensor for Composite Structural Health Monitoring

    Directory of Open Access Journals (Sweden)

    Gyeongrak Choi

    2016-07-01

    Full Text Available We present a nanocomposite strain sensor (NCSS to develop a novel structural health monitoring (SHM sensor that can be easily installed in a composite structure. An NCSS made of a multi-walled carbon nanotubes (MWCNT/epoxy composite was installed on a target structure with facile processing. We attempted to evaluate the NCSS sensing characteristics and benchmark compared to those of a conventional foil strain gauge. The response of the NCSS was fairly good and the result was nearly identical to the strain gauge. A neuron, which is a biomimetic long continuous NCSS, was also developed, and its vibration response was investigated for structural damage detection of a composite cantilever. The vibration response for damage detection was measured by tracking the first natural frequency, which demonstrated good result that matched the finite element (FE analysis.

  1. Essential roles of mitochondrial depolarization in neuron loss through microglial activation and attraction toward neurons.

    Science.gov (United States)

    Nam, Min-Kyung; Shin, Hyun-Ah; Han, Ji-Hye; Park, Dae-Wook; Rhim, Hyangshuk

    2013-04-10

    As life spans increased, neurodegenerative disorders that affect aging populations have also increased. Progressive neuronal loss in specific brain regions is the most common cause of neurodegenerative disease; however, key determinants mediating neuron loss are not fully understood. Using a model of mitochondrial membrane potential (ΔΨm) loss, we found only 25% cell loss in SH-SY5Y (SH) neuronal mono-cultures, but interestingly, 85% neuronal loss occurred when neurons were co-cultured with BV2 microglia. SH neurons overexpressing uncoupling protein 2 exhibited an increase in neuron-microglia interactions, which represent an early step in microglial phagocytosis of neurons. This result indicates that ΔΨm loss in SH neurons is an important contributor to recruitment of BV2 microglia. Notably, we show that ΔΨm loss in BV2 microglia plays a crucial role in microglial activation and phagocytosis of damaged SH neurons. Thus, our study demonstrates that ΔΨm loss in both neurons and microglia is a critical determinant of neuron loss. These findings also offer new insights into neuroimmunological and bioenergetical aspects of neurodegenerative disease. Copyright © 2013 Elsevier B.V. All rights reserved.

  2. Endorphinic neurons are contacting the tuberoinfundibular dopaminergic neurons in the rat brain

    International Nuclear Information System (INIS)

    Morel, G.; Pelletier, G.

    1986-01-01

    The anatomical relationships between endorphinic neurons and dopaminergic neurons were evaluated in the rat hypothalamus using a combination of immunocytochemistry and autoradiography. In the arcuate nucleus, endorphinic endings were seen making contacts with dopaminergic cell bodies and dendrites. No synapsis could be observed at the sites of contacts. These results strongly suggest that the endorphinic neurons are directly acting on dopaminergic neurons to modify the release of dopamine into the pituitary portal system

  3. The role of stochasticity in an information-optimal neural population code

    International Nuclear Information System (INIS)

    Stocks, N G; Nikitin, A P; McDonnell, M D; Morse, R P

    2009-01-01

    In this paper we consider the optimisation of Shannon mutual information (MI) in the context of two model neural systems. The first is a stochastic pooling network (population) of McCulloch-Pitts (MP) type neurons (logical threshold units) subject to stochastic forcing; the second is (in a rate coding paradigm) a population of neurons that each displays Poisson statistics (the so called 'Poisson neuron'). The mutual information is optimised as a function of a parameter that characterises the 'noise level'-in the MP array this parameter is the standard deviation of the noise; in the population of Poisson neurons it is the window length used to determine the spike count. In both systems we find that the emergent neural architecture and, hence, code that maximises the MI is strongly influenced by the noise level. Low noise levels leads to a heterogeneous distribution of neural parameters (diversity), whereas, medium to high noise levels result in the clustering of neural parameters into distinct groups that can be interpreted as subpopulations. In both cases the number of subpopulations increases with a decrease in noise level. Our results suggest that subpopulations are a generic feature of an information optimal neural population.

  4. Layer 5 Callosal Parvalbumin-Expressing Neurons: A Distinct Functional Group of GABAergic Neurons.

    Science.gov (United States)

    Zurita, Hector; Feyen, Paul L C; Apicella, Alfonso Junior

    2018-01-01

    Previous studies have shown that parvalbumin-expressing neurons (CC-Parv neurons) connect the two hemispheres of motor and sensory areas via the corpus callosum, and are a functional part of the cortical circuit. Here we test the hypothesis that layer 5 CC-Parv neurons possess anatomical and molecular mechanisms which dampen excitability and modulate the gating of interhemispheric inhibition. In order to investigate this hypothesis we use viral tracing to determine the anatomical and electrophysiological properties of layer 5 CC-Parv and parvalbumin-expressing (Parv) neurons of the mouse auditory cortex (AC). Here we show that layer 5 CC-Parv neurons had larger dendritic fields characterized by longer dendrites that branched farther from the soma, whereas layer 5 Parv neurons had smaller dendritic fields characterized by shorter dendrites that branched nearer to the soma. The layer 5 CC-Parv neurons are characterized by delayed action potential (AP) responses to threshold currents, lower firing rates, and lower instantaneous frequencies compared to the layer 5 Parv neurons. Kv1.1 containing K + channels are the main source of the AP repolarization of the layer 5 CC-Parv and have a major role in determining both the spike delayed response, firing rate and instantaneous frequency of these neurons.

  5. Long-term artificial sweetener acesulfame potassium treatment alters neurometabolic functions in C57BL/6J mice.

    Directory of Open Access Journals (Sweden)

    Wei-na Cong

    Full Text Available With the prevalence of obesity, artificial, non-nutritive sweeteners have been widely used as dietary supplements that provide sweet taste without excessive caloric load. In order to better understand the overall actions of artificial sweeteners, especially when they are chronically used, we investigated the peripheral and central nervous system effects of protracted exposure to a widely used artificial sweetener, acesulfame K (ACK. We found that extended ACK exposure (40 weeks in normal C57BL/6J mice demonstrated a moderate and limited influence on metabolic homeostasis, including altering fasting insulin and leptin levels, pancreatic islet size and lipid levels, without affecting insulin sensitivity and bodyweight. Interestingly, impaired cognitive memory functions (evaluated by Morris Water Maze and Novel Objective Preference tests were found in ACK-treated C57BL/6J mice, while no differences in motor function and anxiety levels were detected. The generation of an ACK-induced neurological phenotype was associated with metabolic dysregulation (glycolysis inhibition and functional ATP depletion and neurosynaptic abnormalities (dysregulation of TrkB-mediated BDNF and Akt/Erk-mediated cell growth/survival pathway in hippocampal neurons. Our data suggest that chronic use of ACK could affect cognitive functions, potentially via altering neuro-metabolic functions in male C57BL/6J mice.

  6. Neuronal synchrony: peculiarity and generality.

    Science.gov (United States)

    Nowotny, Thomas; Huerta, Ramon; Rabinovich, Mikhail I

    2008-09-01

    Synchronization in neuronal systems is a new and intriguing application of dynamical systems theory. Why are neuronal systems different as a subject for synchronization? (1) Neurons in themselves are multidimensional nonlinear systems that are able to exhibit a wide variety of different activity patterns. Their "dynamical repertoire" includes regular or chaotic spiking, regular or chaotic bursting, multistability, and complex transient regimes. (2) Usually, neuronal oscillations are the result of the cooperative activity of many synaptically connected neurons (a neuronal circuit). Thus, it is necessary to consider synchronization between different neuronal circuits as well. (3) The synapses that implement the coupling between neurons are also dynamical elements and their intrinsic dynamics influences the process of synchronization or entrainment significantly. In this review we will focus on four new problems: (i) the synchronization in minimal neuronal networks with plastic synapses (synchronization with activity dependent coupling), (ii) synchronization of bursts that are generated by a group of nonsymmetrically coupled inhibitory neurons (heteroclinic synchronization), (iii) the coordination of activities of two coupled neuronal networks (partial synchronization of small composite structures), and (iv) coarse grained synchronization in larger systems (synchronization on a mesoscopic scale). (c) 2008 American Institute of Physics.

  7. Discrimination of communication vocalizations by single neurons and groups of neurons in the auditory midbrain.

    Science.gov (United States)

    Schneider, David M; Woolley, Sarah M N

    2010-06-01

    Many social animals including songbirds use communication vocalizations for individual recognition. The perception of vocalizations depends on the encoding of complex sounds by neurons in the ascending auditory system, each of which is tuned to a particular subset of acoustic features. Here, we examined how well the responses of single auditory neurons could be used to discriminate among bird songs and we compared discriminability to spectrotemporal tuning. We then used biologically realistic models of pooled neural responses to test whether the responses of groups of neurons discriminated among songs better than the responses of single neurons and whether discrimination by groups of neurons was related to spectrotemporal tuning and trial-to-trial response variability. The responses of single auditory midbrain neurons could be used to discriminate among vocalizations with a wide range of abilities, ranging from chance to 100%. The ability to discriminate among songs using single neuron responses was not correlated with spectrotemporal tuning. Pooling the responses of pairs of neurons generally led to better discrimination than the average of the two inputs and the most discriminating input. Pooling the responses of three to five single neurons continued to improve neural discrimination. The increase in discriminability was largest for groups of neurons with similar spectrotemporal tuning. Further, we found that groups of neurons with correlated spike trains achieved the largest gains in discriminability. We simulated neurons with varying levels of temporal precision and measured the discriminability of responses from single simulated neurons and groups of simulated neurons. Simulated neurons with biologically observed levels of temporal precision benefited more from pooling correlated inputs than did neurons with highly precise or imprecise spike trains. These findings suggest that pooling correlated neural responses with the levels of precision observed in the

  8. Virulence phenotypes of low-passage clinical isolates of Nontypeable Haemophilus influenzae assessed using the chinchilla laniger model of otitis media

    Directory of Open Access Journals (Sweden)

    Hogg Justin

    2007-06-01

    Full Text Available Abstract Background The nontypeable Haemophilus influenzae (NTHi are associated with a spectrum of respiratory mucosal infections including: acute otitis media (AOM; chronic otitis media with effusion (COME; otorrhea; locally invasive diseases such as mastoiditis; as well as a range of systemic disease states, suggesting a wide range of virulence phenotypes. Genomic studies have demonstrated that each clinical strain contains a unique genic distribution from a population-based supragenome, the distributed genome hypothesis. These diverse clinical and genotypic findings suggest that each NTHi strain possesses a unique set of virulence factors that contributes to the course of the disease. Results The local and systemic virulence patterns of ten genomically characterized low-passage clinical NTHi strains (PittAA – PittJJ obtained from children with COME or otorrhea were stratified using the chinchilla model of otitis media (OM. Each isolate was used to bilaterally inoculate six animals and thereafter clinical assessments were carried out daily for 8 days by blinded observers. There was no statistical difference in the time it took for any of the 10 NTHi strains to induce otologic (local disease with respect to any or all of the other strains, however the differences in time to maximal local disease and the severity of local disease were both significant between the strains. Parameters of systemic disease indicated that the strains were not all equivalent: time to development of the systemic disease, maximal systemic scores and mortality were all statistically different among the strains. PittGG induced 100% mortality while PittBB, PittCC, and PittEE produced no mortality. Overall Pitt GG, PittII, and Pitt FF produced the most rapid and most severe local and systemic disease. A post hoc determination of the clinical origins of the 10 NTHi strains revealed that these three strains were of otorrheic origin, whereas the other 7 were from patients

  9. Sweet taste receptor serves to activate glucose- and leptin-responsive neurons in the hypothalamic arcuate nucleus and participates in glucose responsiveness.

    Directory of Open Access Journals (Sweden)

    Daisuke Kohno

    2016-11-01

    Full Text Available The hypothalamic feeding center plays an important role in energy homeostasis. In the feeding center, whole-body energy signals including hormones and nutrients are sensed, processed, and integrated. As a result, food intake and energy expenditure are regulated. Two types of glucose-sensing neurons exist in the hypothalamic arcuate nucleus (ARC: glucose-excited neurons and glucose-inhibited neurons. While some molecules are known to be related to glucose sensing in the hypothalamus, the mechanism underlying glucose sensing in the hypothalamus are not fully understood. The sweet taste receptor is a heterodimer of taste type 1 receptor 2 (T1R2 and taste type 1 receptor 3 (T1R3 and senses sweet tastes. T1R2 and T1R3 receptors are distributed in multiple organs including the tongue, pancreas, adipose tissue, and hypothalamus. However, the role of sweet taste receptors in the ARC remains to be clarified. To examine the role of sweet taste receptors in the ARC, cytosolic Ca2+ concentration ([Ca2+]i in isolated single ARC neurons were measured using Fura-2 fluorescent imaging. An artificial sweetener, sucralose at 10-5 M-10-2 M dose dependently increased [Ca2+]i in 12-16% of ARC neurons. The sucralose-induced [Ca2+]i increase was suppressed by a sweet taste receptor inhibitor, gurmarin. The sucralose-induced [Ca2+]i increase was inhibited under an extracellular Ca2+-free condition and in the presence of an L-type Ca2+ channel blocker, nitrendipine. Sucralose-responding neurons were activated by high-concentration of glucose. This response to glucose was markedly suppressed by gurmarin. More than half of sucralose-responding neurons were activated by leptin but not ghrelin. Percentage of proopiomelanocortin (POMC neurons among sucralose-responding neurons and sweet taste receptor expressing neurons were low, suggesting that majority of sucralose-responding neurons are non-POMC neurons. These data suggest that sweet taste receptor-mediated cellular

  10. Sweet Taste Receptor Serves to Activate Glucose- and Leptin-Responsive Neurons in the Hypothalamic Arcuate Nucleus and Participates in Glucose Responsiveness.

    Science.gov (United States)

    Kohno, Daisuke; Koike, Miho; Ninomiya, Yuzo; Kojima, Itaru; Kitamura, Tadahiro; Yada, Toshihiko

    2016-01-01

    The hypothalamic feeding center plays an important role in energy homeostasis. In the feeding center, whole-body energy signals including hormones and nutrients are sensed, processed, and integrated. As a result, food intake and energy expenditure are regulated. Two types of glucose-sensing neurons exist in the hypothalamic arcuate nucleus (ARC): glucose-excited neurons and glucose-inhibited neurons. While some molecules are known to be related to glucose sensing in the hypothalamus, the mechanisms underlying glucose sensing in the hypothalamus are not fully understood. The sweet taste receptor is a heterodimer of taste type 1 receptor 2 (T1R2) and taste type 1 receptor 3 (T1R3) and senses sweet tastes. T1R2 and T1R3 are distributed in multiple organs including the tongue, pancreas, adipose tissue, and hypothalamus. However, the role of sweet taste receptors in the ARC remains to be clarified. To examine the role of sweet taste receptors in the ARC, cytosolic Ca 2+ concentration ([Ca 2+ ] i ) in isolated single ARC neurons were measured using Fura-2 fluorescent imaging. An artificial sweetener, sucralose at 10 -5 -10 -2 M dose dependently increased [Ca 2+ ] i in 12-16% of ARC neurons. The sucralose-induced [Ca 2+ ] i increase was suppressed by a sweet taste receptor inhibitor, gurmarin. The sucralose-induced [Ca 2+ ] i increase was inhibited under an extracellular Ca 2+ -free condition and in the presence of an L-type Ca 2+ channel blocker, nitrendipine. Sucralose-responding neurons were activated by high-concentration of glucose. This response to glucose was markedly suppressed by gurmarin. More than half of sucralose-responding neurons were activated by leptin but not ghrelin. Percentages of proopiomelanocortin (POMC) neurons among sucralose-responding neurons and sweet taste receptor expressing neurons were low, suggesting that majority of sucralose-responding neurons are non-POMC neurons. These data suggest that sweet taste receptor-mediated cellular activation

  11. Dispersion of the intrinsic neuronal periods affects the relationship of the entrainment range to the coupling strength in the suprachiasmatic nucleus

    Science.gov (United States)

    Gu, Changgui; Yang, Huijie; Wang, Man

    2017-11-01

    Living beings on the Earth are subjected to and entrained (synchronized) to the natural 24-h light-dark cycle. Interestingly, they can also be entrained to an external artificial cycle of non-24-h periods. The range of these periods is called the entrainment range and it differs among species. In mammals, the entrainment range is regulated by a main clock located in the suprachiasmatic nucleus (SCN) which is composed of 10 000 neurons in the brain. Previous works have found that the entrainment range depends on the cellular coupling strength in the SCN. In particular, the entrainment range decreases with the increase of the cellular coupling strength, provided that all the neuronal oscillators are identical. However, the SCN neurons differ in the intrinsic periods that follow a normal distribution in a range from 22 to 28 h. In the present study, taking the dispersion of the intrinsic neuronal periods into account, we examined the relationship between the entrainment range and the coupling strength. Results from numerical simulations and theoretical analyses both show that the relationship is altered to be paraboliclike if the intrinsic neuronal periods are nonidentical, and the maximal entrainment range is obtained with a suitable coupling strength. Our results shed light on the role of the cellular coupling in the entrainment ability of the SCN network.

  12. Spinal cord: motor neuron diseases.

    Science.gov (United States)

    Rezania, Kourosh; Roos, Raymond P

    2013-02-01

    Spinal cord motor neuron diseases affect lower motor neurons in the ventral horn. This article focuses on the most common spinal cord motor neuron disease, amyotrophic lateral sclerosis, which also affects upper motor neurons. Also discussed are other motor neuron diseases that only affect the lower motor neurons. Despite the identification of several genes associated with familial amyotrophic lateral sclerosis, the pathogenesis of this complex disease remains elusive. Copyright © 2013 Elsevier Inc. All rights reserved.

  13. [Total artificial heart].

    Science.gov (United States)

    Antretter, H; Dumfarth, J; Höfer, D

    2015-09-01

    To date the CardioWest™ total artificial heart is the only clinically available implantable biventricular mechanical replacement for irreversible cardiac failure. This article presents the indications, contraindications, implantation procedere and postoperative treatment. In addition to a overview of the applications of the total artificial heart this article gives a brief presentation of the two patients treated in our department with the CardioWest™. The clinical course, postoperative rehabilitation, device-related complications and control mechanisms are presented. The total artificial heart is a reliable implant for treating critically ill patients with irreversible cardiogenic shock. A bridge to transplantation is feasible with excellent results.

  14. Artificial Intelligence in Astronomy

    Science.gov (United States)

    Devinney, E. J.; Prša, A.; Guinan, E. F.; Degeorge, M.

    2010-12-01

    From the perspective (and bias) as Eclipsing Binary researchers, we give a brief overview of the development of Artificial Intelligence (AI) applications, describe major application areas of AI in astronomy, and illustrate the power of an AI approach in an application developed under the EBAI (Eclipsing Binaries via Artificial Intelligence) project, which employs Artificial Neural Network technology for estimating light curve solution parameters of eclipsing binary systems.

  15. Modelling and Predicting the Breaking Strength and Mass Irregularity of Cotton Rotor-Spun Yarns Containing Cotton Fiber Recovered from Ginning Process by Using Artificial Neural Network Algorithm

    Directory of Open Access Journals (Sweden)

    Mohsen Shanbeh

    2011-01-01

    Full Text Available One of the main methods to reduce the production costs is waste recycling which is the most important challenge for the future. Cotton wastes collected from ginning process have desirable properties which could be used during spinning process. The purpose of this study was to develop predictive models of breaking strength and mass irregularity (CV% of cotton waste rotor-spun yarns containing cotton waste collected from ginning process by using the artificial neural network trained with backpropagation algorithm. Artificial neural network models have been developed based on rotor diameter, rotor speed, navel type, opener roller speed, ginning waste proportion and yarn linear density as input parameters. The parameters of artificial neural network model, namely, learning, and momentum rate, number of hidden layers and number of hidden processing elements (neurons were optimized to get the best predictive models. The findings showed that the breaking strength and mass irregularity of rotor spun yarns could be predicted satisfactorily by artificial neural network. The maximum error in predicting the breaking strength and mass irregularity of testing data was 8.34% and 6.65%, respectively.

  16. [Preparation of nano-nacre artificial bone].

    Science.gov (United States)

    Chen, Jian-ting; Tang, Yong-zhi; Zhang, Jian-gang; Wang, Jian-jun; Xiao, Ying

    2008-12-01

    To assess the improvements in the properties of nano-nacre artificial bone prepared on the basis of nacre/polylactide acid composite artificial bone and its potential for clinical use. The compound of nano-scale nacre powder and poly-D, L-lactide acid (PDLLA) was used to prepare the cylindrical hollow artificial bone, whose properties including raw material powder scale, pore size, porosity and biomechanical characteristics were compared with another artificial bone made of micron-scale nacre powder and PDLLA. Scanning electron microscope showed that the average particle size of the nano-nacre powder was 50.4-/+12.4 nm, and the average pore size of the artificial bone prepared using nano-nacre powder was 215.7-/+77.5 microm, as compared with the particle size of the micron-scale nacre powder of 5.0-/+3.0 microm and the pore size of the resultant artificial bone of 205.1-/+72.0 microm. The porosities of nano-nacre artificial bone and the micron-nacre artificial bone were (65.4-/+2.9)% and (53.4-/+2.2)%, respectively, and the two artificial bones had comparable compressive strength and Young's modulus, but the flexural strength of the nano-nacre artificial bone was lower than that of the micro-nacre artificial bone. The nano-nacre artificial bone allows better biodegradability and possesses appropriate pore size, porosity and biomechanical properties for use as a promising material in bone tissue engineering.

  17. Parkin Mutations Reduce the Complexity of Neuronal Processes in iPSC-derived Human Neurons

    Science.gov (United States)

    Ren, Yong; Jiang, Houbo; Hu, Zhixing; Fan, Kevin; Wang, Jun; Janoschka, Stephen; Wang, Xiaomin; Ge, Shaoyu; Feng, Jian

    2015-01-01

    Parkinson’s disease (PD) is characterized by the degeneration of nigral dopaminergic (DA) neurons and non-DA neurons in many parts of the brain. Mutations of parkin, an E3 ubiquitin ligase that strongly binds to microtubules, are the most frequent cause of recessively inherited Parkinson’s disease. The lack of robust PD phenotype in parkin knockout mice suggests a unique vulnerability of human neurons to parkin mutations. Here, we show that the complexity of neuronal processes as measured by total neurite length, number of terminals, number of branch points and Sholl analysis, was greatly reduced in induced pluripotent stem cell (iPSC)-derived TH+ or TH− neurons from PD patients with parkin mutations. Consistent with these, microtubule stability was significantly decreased by parkin mutations in iPSC-derived neurons. Overexpression of parkin, but not its PD-linked mutant nor GFP, restored the complexity of neuronal processes and the stability of microtubules. Consistent with these, the microtubule-depolymerizing agent colchicine mimicked the effect of parkin mutations by decreasing neurite length and complexity in control neurons while the microtubule-stabilizing drug taxol mimicked the effect of parkin overexpression by enhancing the morphology of parkin-deficient neurons. The results suggest that parkin maintains the morphological complexity of human neurons by stabilizing microtubules. PMID:25332110

  18. Metabolic reprogramming during neuronal differentiation from aerobic glycolysis to neuronal oxidative phosphorylation.

    Science.gov (United States)

    Zheng, Xinde; Boyer, Leah; Jin, Mingji; Mertens, Jerome; Kim, Yongsung; Ma, Li; Ma, Li; Hamm, Michael; Gage, Fred H; Hunter, Tony

    2016-06-10

    How metabolism is reprogrammed during neuronal differentiation is unknown. We found that the loss of hexokinase (HK2) and lactate dehydrogenase (LDHA) expression, together with a switch in pyruvate kinase gene splicing from PKM2 to PKM1, marks the transition from aerobic glycolysis in neural progenitor cells (NPC) to neuronal oxidative phosphorylation. The protein levels of c-MYC and N-MYC, transcriptional activators of the HK2 and LDHA genes, decrease dramatically. Constitutive expression of HK2 and LDHA during differentiation leads to neuronal cell death, indicating that the shut-off aerobic glycolysis is essential for neuronal survival. The metabolic regulators PGC-1α and ERRγ increase significantly upon neuronal differentiation to sustain the transcription of metabolic and mitochondrial genes, whose levels are unchanged compared to NPCs, revealing distinct transcriptional regulation of metabolic genes in the proliferation and post-mitotic differentiation states. Mitochondrial mass increases proportionally with neuronal mass growth, indicating an unknown mechanism linking mitochondrial biogenesis to cell size.

  19. Development and application of an optogenetic platform for controlling and imaging a large number of individual neurons

    Science.gov (United States)

    Mohammed, Ali Ibrahim Ali

    The understanding and treatment of brain disorders as well as the development of intelligent machines is hampered by the lack of knowledge of how the brain fundamentally functions. Over the past century, we have learned much about how individual neurons and neural networks behave, however new tools are critically needed to interrogate how neural networks give rise to complex brain processes and disease conditions. Recent innovations in molecular techniques, such as optogenetics, have enabled neuroscientists unprecedented precision to excite, inhibit and record defined neurons. The impressive sensitivity of currently available optogenetic sensors and actuators has now enabled the possibility of analyzing a large number of individual neurons in the brains of behaving animals. To promote the use of these optogenetic tools, this thesis integrates cutting edge optogenetic molecular sensors which is ultrasensitive for imaging neuronal activity with custom wide field optical microscope to analyze a large number of individual neurons in living brains. Wide-field microscopy provides a large field of view and better spatial resolution approaching the Abbe diffraction limit of fluorescent microscope. To demonstrate the advantages of this optical platform, we imaged a deep brain structure, the Hippocampus, and tracked hundreds of neurons over time while mouse was performing a memory task to investigate how those individual neurons related to behavior. In addition, we tested our optical platform in investigating transient neural network changes upon mechanical perturbation related to blast injuries. In this experiment, all blasted mice show a consistent change in neural network. A small portion of neurons showed a sustained calcium increase for an extended period of time, whereas the majority lost their activities. Finally, using optogenetic silencer to control selective motor cortex neurons, we examined their contributions to the network pathology of basal ganglia related to

  20. Protocol for culturing low density pure rat hippocampal neurons supported by mature mixed neuron cultures.

    Science.gov (United States)

    Yang, Qian; Ke, Yini; Luo, Jianhong; Tang, Yang

    2017-02-01

    primary hippocampal neuron cultures allow for subcellular morphological dissection, easy access to drug treatment and electrophysiology analysis of individual neurons, and is therefore an ideal model for the study of neuron physiology. While neuron and glia mixed cultures are relatively easy to prepare, pure neurons are particular hard to culture at low densities which are suitable for morphology studies. This may be due to a lack of neurotrophic factors such as brain derived neurotrophic factor (BDNF), neurotrophin-3 (NT3) and Glial cell line-derived neurotrophic factor (GDNF). In this study we used a two step protocol in which neuron-glia mixed cultures were initially prepared for maturation to support the growth of young neurons plated at very low densities. Our protocol showed that neurotrophic support resulted in physiologically functional hippocampal neurons with larger cell body, increased neurite length and decreased branching and complexity compared to cultures prepared using a conventional method. Our protocol provides a novel way to culture highly uniformed hippocampal neurons for acquiring high quality, neuron based data. Copyright © 2016 Elsevier B.V. All rights reserved.

  1. Imitating the Brain with Neurocomputer A "New" Way Towards Artificial General Intelligence

    Institute of Scientific and Technical Information of China (English)

    Tie-Jun Huang

    2017-01-01

    To achieve the artificial general intelligence (AGI),imitate the intelligence? or imitate the brain? This is the question! Most artificial intelligence (AI) approaches set the understanding of the intelligence principle as their premise.This may be correct to implement specific intelligence such as computing,symbolic logic,or what the AlphaGo could do.However,this is not correct for AGI,because to understand the principle of the brain intelligence is one of the most difficult challenges for our human beings.It is not wise to set such a question as the premise of the AGI mission.To achieve AGI,a practical approach is to build the so-called neurocomputer,which could be trained to produce autonomous intelligence and AGI.A neurocomputer imitates the biological neural network with neuromorphic devices which emulate the bio-neurons,synapses and other essential neural components.The neurocomputer could perceive the environment via sensors and interact with other entities via a physical body.The philosophy under the "new" approach,so-called as imitationalism in this paper,is the engineering methodology which has been practiced for thousands of years,and for many cases,such as the invention of the first airplane,succeeded.This paper compares the neurocomputer with the conventional computer.The major progress about neurocomputer is also reviewed.

  2. Artificial Intelligence Study (AIS).

    Science.gov (United States)

    1987-02-01

    ARTIFICIAL INTELLIGNECE HARDWARE ....... 2-50 AI Architecture ................................... 2-49 AI Hardware ....................................... 2...ftf1 829 ARTIFICIAL INTELLIGENCE STUDY (RIS)(U) MAY CONCEPTS 1/3 A~NLYSIS AGENCY BETHESA RD R B NOJESKI FED 6? CM-RP-97-1 NCASIFIED /01/6 M |K 1.0...p/ - - ., e -- CAA- RP- 87-1 SAOFŔ)11 I ARTIFICIAL INTELLIGENCE STUDY (AIS) tNo DTICFEBRUARY 1987 LECT 00 I PREPARED BY RESEARCH AND ANALYSIS

  3. Optimization Study of Hydrogen Gas Adsorption on Zig-zag Single-walled Carbon Nanotubes: The Artificial Neural Network Analysis

    Science.gov (United States)

    Nasruddin; Lestari, M.; Supriyadi; Sholahudin

    2018-03-01

    The use of hydrogen gas in fuel cell technology has a huge opportunity to be applied in upcoming vehicle technology. One of the most important problems in fuel cell technology is the hydrogen storage. The adsorption of hydrogen in carbon-based materials attracts a lot of attention because of its reliability. This study investigated the adsorption of hydrogen gas in Single-walled Carbon Nano Tubes (SWCNT) with chilarity of (0, 12), (0, 15), and (0, 18) to find the optimum chilarity. Artificial Neural Networks (ANN) can be used to predict the hydrogen storage capacity at different pressure and temperature conditions appropriately, using simulated series of data. The Artificial Neural Network is modeled as a predictor of the hydrogen adsorption capacity which provides solutions to some deficiencies in molecular dynamics (MD) simulations. In a previous study, ANN configurations have been developed for 77k, 233k, and 298k temperatures in hydrogen gas storage. To prepare this prediction, ANN is modeled to find out the configurations that exist in the set of training and validation of specified data selection, the distance between data, and the number of neurons that produce the smallest error. This configuration is needed to make an accurate artificial neural network. The configuration of neural network was then applied to this research. The neural network analysis results show that the best configuration of artificial neural network in hydrogen storage is at 233K temperature i.e. on SWCNT with chilarity of (0.12).

  4. Artificial brains. A million spiking-neuron integrated circuit with a scalable communication network and interface.

    Science.gov (United States)

    Merolla, Paul A; Arthur, John V; Alvarez-Icaza, Rodrigo; Cassidy, Andrew S; Sawada, Jun; Akopyan, Filipp; Jackson, Bryan L; Imam, Nabil; Guo, Chen; Nakamura, Yutaka; Brezzo, Bernard; Vo, Ivan; Esser, Steven K; Appuswamy, Rathinakumar; Taba, Brian; Amir, Arnon; Flickner, Myron D; Risk, William P; Manohar, Rajit; Modha, Dharmendra S

    2014-08-08

    Inspired by the brain's structure, we have developed an efficient, scalable, and flexible non-von Neumann architecture that leverages contemporary silicon technology. To demonstrate, we built a 5.4-billion-transistor chip with 4096 neurosynaptic cores interconnected via an intrachip network that integrates 1 million programmable spiking neurons and 256 million configurable synapses. Chips can be tiled in two dimensions via an interchip communication interface, seamlessly scaling the architecture to a cortexlike sheet of arbitrary size. The architecture is well suited to many applications that use complex neural networks in real time, for example, multiobject detection and classification. With 400-pixel-by-240-pixel video input at 30 frames per second, the chip consumes 63 milliwatts. Copyright © 2014, American Association for the Advancement of Science.

  5. Medical imaging was boosted by the discovery of artificial radioactivity; L'imagerie medicale revelee par la radioactivite

    Energy Technology Data Exchange (ETDEWEB)

    Demarthon, F; Dupuy-Maury, F; Donnars, O

    2002-08-01

    This article draws the history of medical imaging since the discovery of artificial radioactivity in 1934. The author reviews the PET (positron emission tomography) and MRI (magnetic resonance imaging) technologies and presents the recent progress in neuro-sciences that have been made possible by using these 2 technologies. Brain imaging has allowed to show: - the impact of emotions on logical mental processes and on mental performances, - the management of memory in the brain of talented quick reckoners, - the degeneration of neurons, and - the link between autism and the presence of structural and functional anomalies in the brain. (A.C.)

  6. The PM1 neurons, movement sensitive centrifugal visual brain neurons in the locust: anatomy, physiology, and modulation by identified octopaminergic neurons.

    Science.gov (United States)

    Stern, Michael

    2009-02-01

    The locust's optic lobe contains a system of wide-field, multimodal, centrifugal neurons. Two of these cells, the protocerebrum-medulla-neurons PM4a and b, are octopaminergic. This paper describes a second pair of large centrifugal neurons (the protocerebrum-medulla-neurons PM1a and PM1b) from the brain of Locusta migratoria based on intracellular cobalt fills, electrophysiology, and immunocytochemistry. They originate and arborise in the central brain and send processes into the medulla of the optic lobe. Double intracellular recording from the same cell suggests input in the central brain and output in the optic lobe. The neurons show immunoreactivity to gamma-amino-butyric acid and its synthesising enzyme, glutamate decarboxylase. The PM1 cells are movement sensitive and show habituation to repeated visual stimulation. Bath application of octopamine causes the response to dishabituate. A very similar effect is produced by electrical stimulation of one of an octopaminergic PM4 neuron. This effect can be blocked by application of the octopamine antagonists, mianserin and phentolamine. This readily accessible system of four wide-field neurons provides a system suitable for the investigation of octopaminergic effects on the visual system at the cellular level.

  7. Quo Vadis, Artificial Intelligence?

    OpenAIRE

    Berrar, Daniel; Sato, Naoyuki; Schuster, Alfons

    2010-01-01

    Since its conception in the mid 1950s, artificial intelligence with its great ambition to understand and emulate intelligence in natural and artificial environments alike is now a truly multidisciplinary field that reaches out and is inspired by a great diversity of other fields. Rapid advances in research and technology in various fields have created environments into which artificial intelligence could embed itself naturally and comfortably. Neuroscience with its desire to understand nervou...

  8. Inteligencia artificial en vehiculo

    OpenAIRE

    Amador Díaz, Pedro

    2012-01-01

    Desarrollo de un robot seguidor de líneas, en el que se implementan diversas soluciones de las áreas de sistemas embebidos e inteligencia artificial. Desenvolupament d'un robot seguidor de línies, en el qual s'implementen diverses solucions de les àrees de sistemes encastats i intel·ligència artificial. Follower robot development of lines, in which various solutions are implemented in the areas of artificial intelligence embedded systems.

  9. Determination of the rate constant for neuronal and extra-neuronal monoamine oxidase

    International Nuclear Information System (INIS)

    Cassis, L.; Ludwig, J.; Trendelenburg, U.

    1986-01-01

    In the rat vas deferens, neuronal deamination of 3 H-(-) noradrenaline ( 3 H-NA) to 3 H-dihydroxyphenethylglycol ( 3 HDOPEG) cannot be inhibited by pretreatment with a monoamine oxidase (MAO) inhibitor. However, in the extraneuronal compartment of the rat heart, inhibition of MAO abolishes the formation of 3 HDOPEG. To clarify this discrepancy, the authors determined the rate constant for MAO (/sup k/mao/) neuronally (rat vas deferens) and extraneuronally (rat heart). For neuronal /sup k/mao, vasa deferentia were incubated with 3 HNA for 300 minutes, and the cumulative formation of 3 HDOPEG measured. The delay in time before 3 HDOPEG achieves steady state (/sup tau/system), is inversely proportional to /sup k/mao. Because /sup tau/system is very short for neuronal MAO, an appreciable delay was only achieved after partial inhibition of MAO with various parglyline concentrations. To relate to the uninhibited enzyme, the percentage inhibition by pargyline was then determined in homogenate preparations. For extraneuronal MAO, a similar procedure was performed in perfused rat hearts. Results show a significantly greater /sup k/mao of neuronal origin, (/sup k/mao = .57min - 1) which when related to the fractional size of the neuronal compartment suggests a very high activity of neuronal MAO

  10. An artificial neural network model for periodic trajectory generation

    Science.gov (United States)

    Shankar, S.; Gander, R. E.; Wood, H. C.

    A neural network model based on biological systems was developed for potential robotic application. The model consists of three interconnected layers of artificial neurons or units: an input layer subdivided into state and plan units, an output layer, and a hidden layer between the two outer layers which serves to implement nonlinear mappings between the input and output activation vectors. Weighted connections are created between the three layers, and learning is effected by modifying these weights. Feedback connections between the output and the input state serve to make the network operate as a finite state machine. The activation vector of the plan units of the input layer emulates the supraspinal commands in biological central pattern generators in that different plan activation vectors correspond to different sequences or trajectories being recalled, even with different frequencies. Three trajectories were chosen for implementation, and learning was accomplished in 10,000 trials. The fault tolerant behavior, adaptiveness, and phase maintenance of the implemented network are discussed.

  11. Application of an artificial neural network to ready-mixed concretes mix design

    Directory of Open Access Journals (Sweden)

    Setién, J.

    2003-06-01

    Full Text Available This paper presents the practical application of cm artificial neural network (ANN to the problem of concrete mix in a factory. After a brief introduction to the complex problem of concrete mixes design and a quick review of the fundamental basis of neurocomputation, an optimal neural network model has been developed to cope with such a problem. For training the net, several control mixes have been fabricated recording in all cases both the characteristic 28 days compressive strength and the workability measured in terms of the slump of the Abrams' cone. After the training process of the net, the power of its predictive ability is checked by comparison of the results obtained with those corresponding to four reference mixes; in this way, it is shown that the considered approach can be used in multicriterial search for optimal concrete mixes.

    En este trabajo se presenta la aplicación práctica de una red neuronal artificial (ANN al problema de la dosificación de hormigones en planta. Tras una breve introducción a la compleja problemática de la dosificación de hormigones y un repaso a los fundamentos de la neurocomputación, se diseña un modelo de red neuronal óptimo para abordar el problema. Para entrenar dicha red, se realizan varias amasadas de prueba, registrándose para cada una de ellas la trabajabilidad, mediante la medida del asiento del cono de Abrams, y ¡a resistencia característica a los 28 días. Una vez entrenada la red, se pone a prueba su carácter predictivo comparando los resultados que proporciona con los de cuatro amasadas de referencia, demostrándose que esta aproximación puede ser utilizada como método multicriterial para la obtención de mezclas óptimas de hormigón.

  12. Criminal Aspects of Artificial Abortion

    OpenAIRE

    Hartmanová, Leona

    2016-01-01

    Criminal Aspects of Artificial Abortion This diploma thesis deals with the issue of artificial abortion, especially its criminal aspects. Legal aspects are not the most important aspects of artificial abortion. Social, ethical or ideological aspects are of the same importance but this diploma thesis cannot analyse all of them. The main issue with artificial abortion is whether it is possible to force a pregnant woman to carry a child and give birth to a child when she cannot or does not want ...

  13. The Relevance of AgRP Neuron-Derived GABA Inputs to POMC Neurons Differs for Spontaneous and Evoked Release.

    Science.gov (United States)

    Rau, Andrew R; Hentges, Shane T

    2017-08-02

    Hypothalamic agouti-related peptide (AgRP) neurons potently stimulate food intake, whereas proopiomelanocortin (POMC) neurons inhibit feeding. Whether AgRP neurons exert their orexigenic actions, at least in part, by inhibiting anorexigenic POMC neurons remains unclear. Here, the connectivity between GABA-releasing AgRP neurons and POMC neurons was examined in brain slices from male and female mice. GABA-mediated spontaneous IPSCs (sIPSCs) in POMC neurons were unaffected by disturbing GABA release from AgRP neurons either by cell type-specific deletion of the vesicular GABA transporter or by expression of botulinum toxin in AgRP neurons to prevent vesicle-associated membrane protein 2-dependent vesicle fusion. Additionally, there was no difference in the ability of μ-opioid receptor (MOR) agonists to inhibit sIPSCs in POMC neurons when MORs were deleted from AgRP neurons, and activation of the inhibitory designer receptor hM4Di on AgRP neurons did not affect sIPSCs recorded from POMC neurons. These approaches collectively indicate that AgRP neurons do not significantly contribute to the strong spontaneous GABA input to POMC neurons. Despite these observations, optogenetic stimulation of AgRP neurons reliably produced evoked IPSCs in POMC neurons, leading to the inhibition of POMC neuron firing. Thus, AgRP neurons can potently affect POMC neuron function without contributing a significant source of spontaneous GABA input to POMC neurons. Together, these results indicate that the relevance of GABAergic inputs from AgRP to POMC neurons is state dependent and highlight the need to consider different types of transmitter release in circuit mapping and physiologic regulation. SIGNIFICANCE STATEMENT Agouti-related peptide (AgRP) neurons play an important role in driving food intake, while proopiomelanocortin (POMC) neurons inhibit feeding. Despite the importance of these two well characterized neuron types in maintaining metabolic homeostasis, communication between these

  14. Development and steroid regulation of RFamide immunoreactivity in antennal-lobe neurons of the sphinx moth Manduca sexta.

    Science.gov (United States)

    Schachtner, Joachim; Trosowski, Björn; D'Hanis, Wolfgang; Stubner, Stephan; Homberg, Uwe

    2004-06-01

    During metamorphosis, the insect nervous system undergoes considerable remodeling: new neurons are integrated while larval neurons are remodeled or eliminated. To understand further the mechanisms involved in transforming larval to adult tissue we have mapped the metamorphic changes in a particularly well established brain area, the antennal lobe of the sphinx moth Manduca sexta, using an antiserum recognizing RFamide-related neuropeptides. Five types of RFamide-immunoreactive (ir) neurons could be distinguished in the antennal lobe, based on morphology and developmental appearance. Four cell types (types II-V, each consisting of one or two cells) showed RFamide immunostaining in the larva that persisted into metamorphosis. By contrast, the most prominent group (type I), a mixed population of local and projection neurons consisting of about 60 neurons in the adult antennal lobe, acquired immunostaining in a two-step process during metamorphosis. In a first step, from 5 to 7 days after pupal ecdysis, the number of labeled neurons reached about 25. In a second step, starting about 4 days later, the number of RFamide-ir neurons increased within 6 days to about 60. This two-step process parallels the rise and fall of the developmental hormone 20-hydroxyecdysone (20E) in the hemolymph. Artificially shifting the 20E peak to an earlier developmental time point resulted in the precocious appearance of RFamide immunostaining and led to premature formation of glomeruli. Prolonging high 20E concentrations to stages when the hormone titer starts to decline had no effect on the second increase of immunostained cell numbers. These results support the idea that the rise in 20E, which occurs after pupal ecdysis, plays a role in the first phase of RFamide expression and in glomeruli formation in the developing antennal lobes. The role of 20E in the second phase of RFamide expression is less clear, but increased cell numbers showing RFamide-ir do not appear to be a consequence of

  15. Metabolic reprogramming during neuronal differentiation.

    Science.gov (United States)

    Agostini, M; Romeo, F; Inoue, S; Niklison-Chirou, M V; Elia, A J; Dinsdale, D; Morone, N; Knight, R A; Mak, T W; Melino, G

    2016-09-01

    Newly generated neurons pass through a series of well-defined developmental stages, which allow them to integrate into existing neuronal circuits. After exit from the cell cycle, postmitotic neurons undergo neuronal migration, axonal elongation, axon pruning, dendrite morphogenesis and synaptic maturation and plasticity. Lack of a global metabolic analysis during early cortical neuronal development led us to explore the role of cellular metabolism and mitochondrial biology during ex vivo differentiation of primary cortical neurons. Unexpectedly, we observed a huge increase in mitochondrial biogenesis. Changes in mitochondrial mass, morphology and function were correlated with the upregulation of the master regulators of mitochondrial biogenesis, TFAM and PGC-1α. Concomitant with mitochondrial biogenesis, we observed an increase in glucose metabolism during neuronal differentiation, which was linked to an increase in glucose uptake and enhanced GLUT3 mRNA expression and platelet isoform of phosphofructokinase 1 (PFKp) protein expression. In addition, glutamate-glutamine metabolism was also increased during the differentiation of cortical neurons. We identified PI3K-Akt-mTOR signalling as a critical regulator role of energy metabolism in neurons. Selective pharmacological inhibition of these metabolic pathways indicate existence of metabolic checkpoint that need to be satisfied in order to allow neuronal differentiation.

  16. A novel perspective on neuron study: damaging and promoting effects in different neurons induced by mechanical stress.

    Science.gov (United States)

    Wang, Yazhou; Wang, Wei; Li, Zong; Hao, Shilei; Wang, Bochu

    2016-10-01

    A growing volume of experimental evidence demonstrates that mechanical stress plays a significant role in growth, proliferation, apoptosis, gene expression, electrophysiological properties and many other aspects of neurons. In this review, first, the mechanical microenvironment and properties of neurons under in vivo conditions are introduced and analyzed. Second, research works in recent decades on the effects of different mechanical forces, especially compression and tension, on various neurons, including dorsal root ganglion neurons, retinal ganglion cells, cerebral cortex neurons, hippocampus neurons, neural stem cells, and other neurons, are summarized. Previous research results demonstrate that mechanical stress can not only injure neurons by damaging their morphology, impacting their electrophysiological characteristics and gene expression, but also promote neuron self-repair. Finally, some future perspectives in neuron research are discussed.

  17. Survival motor neuron protein in motor neurons determines synaptic integrity in spinal muscular atrophy.

    Science.gov (United States)

    Martinez, Tara L; Kong, Lingling; Wang, Xueyong; Osborne, Melissa A; Crowder, Melissa E; Van Meerbeke, James P; Xu, Xixi; Davis, Crystal; Wooley, Joe; Goldhamer, David J; Lutz, Cathleen M; Rich, Mark M; Sumner, Charlotte J

    2012-06-20

    The inherited motor neuron disease spinal muscular atrophy (SMA) is caused by deficient expression of survival motor neuron (SMN) protein and results in severe muscle weakness. In SMA mice, synaptic dysfunction of both neuromuscular junctions (NMJs) and central sensorimotor synapses precedes motor neuron cell death. To address whether this synaptic dysfunction is due to SMN deficiency in motor neurons, muscle, or both, we generated three lines of conditional SMA mice with tissue-specific increases in SMN expression. All three lines of mice showed increased survival, weights, and improved motor behavior. While increased SMN expression in motor neurons prevented synaptic dysfunction at the NMJ and restored motor neuron somal synapses, increased SMN expression in muscle did not affect synaptic function although it did improve myofiber size. Together these data indicate that both peripheral and central synaptic integrity are dependent on motor neurons in SMA, but SMN may have variable roles in the maintenance of these different synapses. At the NMJ, it functions at the presynaptic terminal in a cell-autonomous fashion, but may be necessary for retrograde trophic signaling to presynaptic inputs onto motor neurons. Importantly, SMN also appears to function in muscle growth and/or maintenance independent of motor neurons. Our data suggest that SMN plays distinct roles in muscle, NMJs, and motor neuron somal synapses and that restored function of SMN at all three sites will be necessary for full recovery of muscle power.

  18. Stochastic IMT (Insulator-Metal-Transition Neurons: An Interplay of Thermal and Threshold Noise at Bifurcation

    Directory of Open Access Journals (Sweden)

    Abhinav Parihar

    2018-04-01

    Full Text Available Artificial neural networks can harness stochasticity in multiple ways to enable a vast class of computationally powerful models. Boltzmann machines and other stochastic neural networks have been shown to outperform their deterministic counterparts by allowing dynamical systems to escape local energy minima. Electronic implementation of such stochastic networks is currently limited to addition of algorithmic noise to digital machines which is inherently inefficient; albeit recent efforts to harness physical noise in devices for stochasticity have shown promise. To succeed in fabricating electronic neuromorphic networks we need experimental evidence of devices with measurable and controllable stochasticity which is complemented with the development of reliable statistical models of such observed stochasticity. Current research literature has sparse evidence of the former and a complete lack of the latter. This motivates the current article where we demonstrate a stochastic neuron using an insulator-metal-transition (IMT device, based on electrically induced phase-transition, in series with a tunable resistance. We show that an IMT neuron has dynamics similar to a piecewise linear FitzHugh-Nagumo (FHN neuron and incorporates all characteristics of a spiking neuron in the device phenomena. We experimentally demonstrate spontaneous stochastic spiking along with electrically controllable firing probabilities using Vanadium Dioxide (VO2 based IMT neurons which show a sigmoid-like transfer function. The stochastic spiking is explained by two noise sources - thermal noise and threshold fluctuations, which act as precursors of bifurcation. As such, the IMT neuron is modeled as an Ornstein-Uhlenbeck (OU process with a fluctuating boundary resulting in transfer curves that closely match experiments. The moments of interspike intervals are calculated analytically by extending the first-passage-time (FPT models for Ornstein-Uhlenbeck (OU process to include a

  19. Leptin signaling in GABA neurons, but not glutamate neurons, is required for reproductive function.

    Science.gov (United States)

    Zuure, Wieteke A; Roberts, Amy L; Quennell, Janette H; Anderson, Greg M

    2013-11-06

    The adipocyte-derived hormone leptin acts in the brain to modulate the central driver of fertility: the gonadotropin releasing hormone (GnRH) neuronal system. This effect is indirect, as GnRH neurons do not express leptin receptors (LEPRs). Here we test whether GABAergic or glutamatergic neurons provide the intermediate pathway between the site of leptin action and the GnRH neurons. Leptin receptors were deleted from GABA and glutamate neurons using Cre-Lox transgenics, and the downstream effects on puberty onset and reproduction were examined. Both mouse lines displayed the expected increase in body weight and region-specific loss of leptin signaling in the hypothalamus. The GABA neuron-specific LEPR knock-out females and males showed significantly delayed puberty onset. Adult fertility observations revealed that these knock-out animals have decreased fecundity. In contrast, glutamate neuron-specific LEPR knock-out mice displayed normal fertility. Assessment of the estrogenic hypothalamic-pituitary-gonadal axis regulation in females showed that leptin action on GABA neurons is not necessary for estradiol-mediated suppression of tonic luteinizing hormone secretion (an indirect measure of GnRH neuron activity) but is required for regulation of a full preovulatory-like luteinizing hormone surge. In conclusion, leptin signaling in GABAergic (but not glutamatergic neurons) plays a critical role in the timing of puberty onset and is involved in fertility regulation throughout adulthood in both sexes. These results form an important step in explaining the role of central leptin signaling in the reproductive system. Limiting the leptin-to-GnRH mediators to GABAergic cells will enable future research to focus on a few specific types of neurons.

  20. Progranulin is expressed within motor neurons and promotes neuronal cell survival

    Directory of Open Access Journals (Sweden)

    Kay Denis G

    2009-10-01

    Full Text Available Abstract Background Progranulin is a secreted high molecular weight growth factor bearing seven and one half copies of the cysteine-rich granulin-epithelin motif. While inappropriate over-expression of the progranulin gene has been associated with many cancers, haploinsufficiency leads to atrophy of the frontotemporal lobes and development of a form of dementia (frontotemporal lobar degeneration with ubiquitin positive inclusions, FTLD-U associated with the formation of ubiquitinated inclusions. Recent reports indicate that progranulin has neurotrophic effects, which, if confirmed would make progranulin the only neuroprotective growth factor that has been associated genetically with a neurological disease in humans. Preliminary studies indicated high progranulin gene expression in spinal cord motor neurons. However, it is uncertain what the role of Progranulin is in normal or diseased motor neuron function. We have investigated progranulin gene expression and subcellular localization in cultured mouse embryonic motor neurons and examined the effect of progranulin over-expression and knockdown in the NSC-34 immortalized motor neuron cell line upon proliferation and survival. Results In situ hybridisation and immunohistochemical techniques revealed that the progranulin gene is highly expressed by motor neurons within the mouse spinal cord and in primary cultures of dissociated mouse embryonic spinal cord-dorsal root ganglia. Confocal microscopy coupled to immunocytochemistry together with the use of a progranulin-green fluorescent protein fusion construct revealed progranulin to be located within compartments of the secretory pathway including the Golgi apparatus. Stable transfection of the human progranulin gene into the NSC-34 motor neuron cell line stimulates the appearance of dendritic structures and provides sufficient trophic stimulus to survive serum deprivation for long periods (up to two months. This is mediated at least in part through

  1. Un pequeño cerebro artificial basado en ácidos nucleicos

    Directory of Open Access Journals (Sweden)

    Jorge Eduardo Ortiz

    2000-04-01

    Full Text Available En este artículo se presenta un modelo teórico de una Red Neuronal Artificial simple implementada en ADN. El trabajo muestra que, aunque lejos en el tiempo, existe la posibilidad de diseñar sistemas de cómputo que simulen algunas características del cerebro humano. Ese hecho contribuirá a solucionar problemas prácticos, en distintas áreas de la actividad humana, que hasta el momento no tienen solución con la actual tecnología electrónica de computadores. Este artículo da un primer paso en este sentido con el modelamiento de un perceptrón de dos entradas y salida discreta con funciones de activación escalón; para dicho fin se hace uso de sistemas de stickers.

  2. Glass promotes the differentiation of neuronal and non-neuronal cell types in the Drosophila eye

    Science.gov (United States)

    Morrison, Carolyn A.; Chen, Hao; Cook, Tiffany; Brown, Stuart

    2018-01-01

    Transcriptional regulators can specify different cell types from a pool of equivalent progenitors by activating distinct developmental programs. The Glass transcription factor is expressed in all progenitors in the developing Drosophila eye, and is maintained in both neuronal and non-neuronal cell types. Glass is required for neuronal progenitors to differentiate as photoreceptors, but its role in non-neuronal cone and pigment cells is unknown. To determine whether Glass activity is limited to neuronal lineages, we compared the effects of misexpressing it in neuroblasts of the larval brain and in epithelial cells of the wing disc. Glass activated overlapping but distinct sets of genes in these neuronal and non-neuronal contexts, including markers of photoreceptors, cone cells and pigment cells. Coexpression of other transcription factors such as Pax2, Eyes absent, Lozenge and Escargot enabled Glass to induce additional genes characteristic of the non-neuronal cell types. Cell type-specific glass mutations generated in cone or pigment cells using somatic CRISPR revealed autonomous developmental defects, and expressing Glass specifically in these cells partially rescued glass mutant phenotypes. These results indicate that Glass is a determinant of organ identity that acts in both neuronal and non-neuronal cells to promote their differentiation into functional components of the eye. PMID:29324767

  3. HCS-Neurons: identifying phenotypic changes in multi-neuron images upon drug treatments of high-content screening.

    Science.gov (United States)

    Charoenkwan, Phasit; Hwang, Eric; Cutler, Robert W; Lee, Hua-Chin; Ko, Li-Wei; Huang, Hui-Ling; Ho, Shinn-Ying

    2013-01-01

    High-content screening (HCS) has become a powerful tool for drug discovery. However, the discovery of drugs targeting neurons is still hampered by the inability to accurately identify and quantify the phenotypic changes of multiple neurons in a single image (named multi-neuron image) of a high-content screen. Therefore, it is desirable to develop an automated image analysis method for analyzing multi-neuron images. We propose an automated analysis method with novel descriptors of neuromorphology features for analyzing HCS-based multi-neuron images, called HCS-neurons. To observe multiple phenotypic changes of neurons, we propose two kinds of descriptors which are neuron feature descriptor (NFD) of 13 neuromorphology features, e.g., neurite length, and generic feature descriptors (GFDs), e.g., Haralick texture. HCS-neurons can 1) automatically extract all quantitative phenotype features in both NFD and GFDs, 2) identify statistically significant phenotypic changes upon drug treatments using ANOVA and regression analysis, and 3) generate an accurate classifier to group neurons treated by different drug concentrations using support vector machine and an intelligent feature selection method. To evaluate HCS-neurons, we treated P19 neurons with nocodazole (a microtubule depolymerizing drug which has been shown to impair neurite development) at six concentrations ranging from 0 to 1000 ng/mL. The experimental results show that all the 13 features of NFD have statistically significant difference with respect to changes in various levels of nocodazole drug concentrations (NDC) and the phenotypic changes of neurites were consistent to the known effect of nocodazole in promoting neurite retraction. Three identified features, total neurite length, average neurite length, and average neurite area were able to achieve an independent test accuracy of 90.28% for the six-dosage classification problem. This NFD module and neuron image datasets are provided as a freely downloadable

  4. Results on a Binding Neuron Model and Their Implications for Modified Hourglass Model for Neuronal Network

    Directory of Open Access Journals (Sweden)

    Viswanathan Arunachalam

    2013-01-01

    Full Text Available The classical models of single neuron like Hodgkin-Huxley point neuron or leaky integrate and fire neuron assume the influence of postsynaptic potentials to last till the neuron fires. Vidybida (2008 in a refreshing departure has proposed models for binding neurons in which the trace of an input is remembered only for a finite fixed period of time after which it is forgotten. The binding neurons conform to the behaviour of real neurons and are applicable in constructing fast recurrent networks for computer modeling. This paper develops explicitly several useful results for a binding neuron like the firing time distribution and other statistical characteristics. We also discuss the applicability of the developed results in constructing a modified hourglass network model in which there are interconnected neurons with excitatory as well as inhibitory inputs. Limited simulation results of the hourglass network are presented.

  5. A New Population of Parvocellular Oxytocin Neurons Controlling Magnocellular Neuron Activity and Inflammatory Pain Processing.

    Science.gov (United States)

    Eliava, Marina; Melchior, Meggane; Knobloch-Bollmann, H Sophie; Wahis, Jérôme; da Silva Gouveia, Miriam; Tang, Yan; Ciobanu, Alexandru Cristian; Triana Del Rio, Rodrigo; Roth, Lena C; Althammer, Ferdinand; Chavant, Virginie; Goumon, Yannick; Gruber, Tim; Petit-Demoulière, Nathalie; Busnelli, Marta; Chini, Bice; Tan, Linette L; Mitre, Mariela; Froemke, Robert C; Chao, Moses V; Giese, Günter; Sprengel, Rolf; Kuner, Rohini; Poisbeau, Pierrick; Seeburg, Peter H; Stoop, Ron; Charlet, Alexandre; Grinevich, Valery

    2016-03-16

    Oxytocin (OT) is a neuropeptide elaborated by the hypothalamic paraventricular (PVN) and supraoptic (SON) nuclei. Magnocellular OT neurons of these nuclei innervate numerous forebrain regions and release OT into the blood from the posterior pituitary. The PVN also harbors parvocellular OT cells that project to the brainstem and spinal cord, but their function has not been directly assessed. Here, we identified a subset of approximately 30 parvocellular OT neurons, with collateral projections onto magnocellular OT neurons and neurons of deep layers of the spinal cord. Evoked OT release from these OT neurons suppresses nociception and promotes analgesia in an animal model of inflammatory pain. Our findings identify a new population of OT neurons that modulates nociception in a two tier process: (1) directly by release of OT from axons onto sensory spinal cord neurons and inhibiting their activity and (2) indirectly by stimulating OT release from SON neurons into the periphery. Copyright © 2016 Elsevier Inc. All rights reserved.

  6. Artificial Hydration and Nutrition

    Science.gov (United States)

    ... Crisis Situations Pets and Animals myhealthfinder Food and Nutrition Healthy Food Choices Weight Loss and Diet Plans ... Your Health Resources Healthcare Management Artificial Hydration and Nutrition Artificial Hydration and Nutrition Share Print Patients who ...

  7. Artificial Evolution for the Detection of Group Identities in Complex Artificial Societies

    DEFF Research Database (Denmark)

    Grappiolo, Corrado; Togelius, Julian; Yannakakis, Georgios N.

    2013-01-01

    This paper aims at detecting the presence of group structures in complex artificial societies by solely observing and analysing the interactions occurring among the artificial agents. Our approach combines: (1) an unsupervised method for clustering interactions into two possible classes, namely in...

  8. Artificial intelligence

    International Nuclear Information System (INIS)

    Perret-Galix, D.

    1992-01-01

    A vivid example of the growing need for frontier physics experiments to make use of frontier technology is in the field of artificial intelligence and related themes. This was reflected in the second international workshop on 'Software Engineering, Artificial Intelligence and Expert Systems in High Energy and Nuclear Physics' which took place from 13-18 January at France Telecom's Agelonde site at La Londe des Maures, Provence. It was the second in a series, the first having been held at Lyon in 1990

  9. Neurochemistry of neurons in the ventrolateral medulla activated by hypotension: Are the same neurons activated by glucoprivation?

    Science.gov (United States)

    Parker, Lindsay M; Le, Sheng; Wearne, Travis A; Hardwick, Kate; Kumar, Natasha N; Robinson, Katherine J; McMullan, Simon; Goodchild, Ann K

    2017-06-15

    Previous studies have demonstrated that a range of stimuli activate neurons, including catecholaminergic neurons, in the ventrolateral medulla. Not all catecholaminergic neurons are activated and other neurochemical content is largely unknown hence whether stimulus specific populations exist is unclear. Here we determine the neurochemistry (using in situ hybridization) of catecholaminergic and noncatecholaminergic neurons which express c-Fos immunoreactivity throughout the rostrocaudal extent of the ventrolateral medulla, in Sprague Dawley rats treated with hydralazine or saline. Distinct neuronal populations containing PPCART, PPPACAP, and PPNPY mRNAs, which were largely catecholaminergic, were activated by hydralazine but not saline. Both catecholaminergic and noncatecholaminergic neurons containing preprotachykinin and prepro-enkephalin (PPE) mRNAs were also activated, with the noncatecholaminergic population located in the rostral C1 region. Few GlyT2 neurons were activated. A subset of these data was then used to compare the neuronal populations activated by 2-deoxyglucose evoked glucoprivation (Brain Structure and Function (2015) 220:117). Hydralazine activated more neurons than 2-deoxyglucose but similar numbers of catecholaminergic neurons. Commonly activated populations expressing PPNPY and PPE mRNAs were defined. These likely include PPNPY expressing catecholaminergic neurons projecting to vasopressinergic and corticotrophin releasing factor neurons in the paraventricular nucleus, which when activated result in elevated plasma vasopressin and corticosterone. Stimulus specific neurons included noncatecholaminergic neurons and a few PPE positive catecholaminergic neuron but neurochemical codes were largely unidentified. Reasons for the lack of identification of stimulus specific neurons, readily detectable using electrophysiology in anaesthetized preparations and for which neural circuits can be defined, are discussed. © 2017 Wiley Periodicals, Inc.

  10. Neuronal growth on L- and D-cysteine self-assembled monolayers reveals neuronal chiral sensitivity.

    Science.gov (United States)

    Baranes, Koby; Moshe, Hagay; Alon, Noa; Schwartz, Shmulik; Shefi, Orit

    2014-05-21

    Studying the interaction between neuronal cells and chiral molecules is fundamental for the design of novel biomaterials and drugs. Chirality influences all biological processes that involve intermolecular interaction. One common method used to study cellular interactions with different enantiomeric targets is the use of chiral surfaces. Based on previous studies that demonstrated the importance of cysteine in the nervous system, we studied the effect of L- and D-cysteine on single neuronal growth. L-Cysteine, which normally functions as a neuromodulator or a neuroprotective antioxidant, causes damage at elevated levels, which may occur post trauma. In this study, we grew adult neurons in culture enriched with L- and D-cysteine as free compounds or as self-assembled monolayers of chiral surfaces and examined the effect on the neuronal morphology and adhesion. Notably, we have found that exposure to the L-cysteine enantiomer inhibited, and even prevented, neuronal attachment more severely than exposure to the D-cysteine enantiomer. Atop the L-cysteine surfaces, neuronal growth was reduced and degenerated. Since the cysteine molecules were attached to the surface via the thiol groups, the neuronal membrane was exposed to the molecular chiral site. Thus, our results have demonstrated high neuronal chiral sensitivity, revealing chiral surfaces as indirect regulators of neuronal cells and providing a reference for studying chiral drugs.

  11. A Squeezed Artificial Neural Network for the Symbolic Network Reliability Functions of Binary-State Networks.

    Science.gov (United States)

    Yeh, Wei-Chang

    Network reliability is an important index to the provision of useful information for decision support in the modern world. There is always a need to calculate symbolic network reliability functions (SNRFs) due to dynamic and rapid changes in network parameters. In this brief, the proposed squeezed artificial neural network (SqANN) approach uses the Monte Carlo simulation to estimate the corresponding reliability of a given designed matrix from the Box-Behnken design, and then the Taguchi method is implemented to find the appropriate number of neurons and activation functions of the hidden layer and the output layer in ANN to evaluate SNRFs. According to the experimental results of the benchmark networks, the comparison appears to support the superiority of the proposed SqANN method over the traditional ANN-based approach with at least 16.6% improvement in the median absolute deviation in the cost of extra 2 s on average for all experiments.Network reliability is an important index to the provision of useful information for decision support in the modern world. There is always a need to calculate symbolic network reliability functions (SNRFs) due to dynamic and rapid changes in network parameters. In this brief, the proposed squeezed artificial neural network (SqANN) approach uses the Monte Carlo simulation to estimate the corresponding reliability of a given designed matrix from the Box-Behnken design, and then the Taguchi method is implemented to find the appropriate number of neurons and activation functions of the hidden layer and the output layer in ANN to evaluate SNRFs. According to the experimental results of the benchmark networks, the comparison appears to support the superiority of the proposed SqANN method over the traditional ANN-based approach with at least 16.6% improvement in the median absolute deviation in the cost of extra 2 s on average for all experiments.

  12. Disruption of astrocyte-neuron cholesterol cross talk affects neuronal function in Huntington's disease.

    Science.gov (United States)

    Valenza, M; Marullo, M; Di Paolo, E; Cesana, E; Zuccato, C; Biella, G; Cattaneo, E

    2015-04-01

    In the adult brain, neurons require local cholesterol production, which is supplied by astrocytes through apoE-containing lipoproteins. In Huntington's disease (HD), such cholesterol biosynthesis in the brain is severely reduced. Here we show that this defect, occurring in astrocytes, is detrimental for HD neurons. Astrocytes bearing the huntingtin protein containing increasing CAG repeats secreted less apoE-lipoprotein-bound cholesterol in the medium. Conditioned media from HD astrocytes and lipoprotein-depleted conditioned media from wild-type (wt) astrocytes were equally detrimental in a neurite outgrowth assay and did not support synaptic activity in HD neurons, compared with conditions of cholesterol supplementation or conditioned media from wt astrocytes. Molecular perturbation of cholesterol biosynthesis and efflux in astrocytes caused similarly altered astrocyte-neuron cross talk, whereas enhancement of glial SREBP2 and ABCA1 function reversed the aspects of neuronal dysfunction in HD. These findings indicate that astrocyte-mediated cholesterol homeostasis could be a potential therapeutic target to ameliorate neuronal dysfunction in HD.

  13. Hourly cooling load prediction of a vehicle in the southern region of Turkey by Artificial Neural Network

    International Nuclear Information System (INIS)

    Solmaz, Ozgur; Ozgoren, Muammer; Aksoy, Muharrem Hilmi

    2014-01-01

    Highlights: • An ANN model was developed to predict hourly cooling load of a vehicle. • Hourly meteorological data of 5 different provinces was used. • The agreement of the cooling load values between the calculations and predictions was fairly promising. • The ANN model could be successfully used to design automotive air conditioning systems. - Abstract: In this study, Artificial Neural Networks (ANNs) method for prediction hourly cooling load of a vehicle was implemented. The cooling load of the vehicle was calculated along the cooling season (1 May–30 September) for Antalya, Konya, Mersin, Mugla and Sanliurfa provinces in Turkey. For ANN model, seven neurons determinated as input signals of latitude, longitude, altitude, day of the year, hour of the day, hourly mean ambient air temperature and hourly solar radiation were used for the input layer of the network. One neuron producing an output signal of the hourly cooling load was utilized in the output layer. All data were divided into two categories for training and testing of the ANN. The 80% of the data was reserved to training and the remaining was used for testing of the model. Neuron numbers in the hidden layer from 7 to 40 were tested step by step to find the best matching ANN structure. The obtained results for different numbers of neurons were compared in terms of root mean squared error (RMSE), coefficient of determination (R 2 ) and mean absolute error (MAE). The best matching results for the training and testing were obtained as 8 neurons for the minimum testing RMSE value for the prediction of cooling load by the ANN model on the 23rd day of each month along the cooling season. For the model with 8 neurons RMSE, R 2 and MAE (Training/Testing) were found to be 0.0128/0.0259, 0.9959/0.9818 and 78.81/174.71 W/m 2 , respectively. It is shown that the cooling load of a vehicle can be successfully predicted by means of the ANNs from geographical characteristics and meteorological data

  14. Neuronal Migration Disorders

    Science.gov (United States)

    ... Understanding Sleep The Life and Death of a Neuron Genes At Work In The Brain Order Publications ... birth defects caused by the abnormal migration of neurons in the developing brain and nervous system. In ...

  15. High-frequency stimulation-induced peptide release synchronizes arcuate kisspeptin neurons and excites GnRH neurons

    Science.gov (United States)

    Qiu, Jian; Nestor, Casey C; Zhang, Chunguang; Padilla, Stephanie L; Palmiter, Richard D

    2016-01-01

    Kisspeptin (Kiss1) and neurokinin B (NKB) neurocircuits are essential for pubertal development and fertility. Kisspeptin neurons in the hypothalamic arcuate nucleus (Kiss1ARH) co-express Kiss1, NKB, dynorphin and glutamate and are postulated to provide an episodic, excitatory drive to gonadotropin-releasing hormone 1 (GnRH) neurons, the synaptic mechanisms of which are unknown. We characterized the cellular basis for synchronized Kiss1ARH neuronal activity using optogenetics, whole-cell electrophysiology, molecular pharmacology and single cell RT-PCR in mice. High-frequency photostimulation of Kiss1ARH neurons evoked local release of excitatory (NKB) and inhibitory (dynorphin) neuropeptides, which were found to synchronize the Kiss1ARH neuronal firing. The light-evoked synchronous activity caused robust excitation of GnRH neurons by a synaptic mechanism that also involved glutamatergic input to preoptic Kiss1 neurons from Kiss1ARH neurons. We propose that Kiss1ARH neurons play a dual role of driving episodic secretion of GnRH through the differential release of peptide and amino acid neurotransmitters to coordinate reproductive function. DOI: http://dx.doi.org/10.7554/eLife.16246.001 PMID:27549338

  16. Medical imaging was boosted by the discovery of artificial radioactivity; L'imagerie medicale revelee par la radioactivite

    Energy Technology Data Exchange (ETDEWEB)

    Demarthon, F.; Dupuy-Maury, F.; Donnars, O

    2002-08-01

    This article draws the history of medical imaging since the discovery of artificial radioactivity in 1934. The author reviews the PET (positron emission tomography) and MRI (magnetic resonance imaging) technologies and presents the recent progress in neuro-sciences that have been made possible by using these 2 technologies. Brain imaging has allowed to show: - the impact of emotions on logical mental processes and on mental performances, - the management of memory in the brain of talented quick reckoners, - the degeneration of neurons, and - the link between autism and the presence of structural and functional anomalies in the brain. (A.C.)

  17. The use of artificial neural networks for mathematical modeling of the effect of composition and production conditions on the properties of PVC floor coverings

    Directory of Open Access Journals (Sweden)

    Radovanović Rajko M.

    2017-01-01

    Full Text Available The application of PVC floor coverings is strongly connected with their end-use properties, which depend on the composition and processing conditions. It is very difficult to estimate the proper influence of the production parameters on the characteristics of PVC floor coverings due to their complex composition and various preparation procedures. The effect of different processing variables (such as time of bowling, temperature of bowling and composition of PVC plastisol on the mechanical properties of PVC floor coverings was investigated. The influence of different input parameters on the mechanical properties was successfully determined using an artificial neural network with an optimized number of hidden neurons. The Garson and Yoon models were applied to calculate and describe the variable contributions in the artificial neural networks. [Projekat Ministarstva nauke Republike Srbije, br. III 45022

  18. Statistics of Visual Responses to Image Object Stimuli from Primate AIT Neurons to DNN Neurons.

    Science.gov (United States)

    Dong, Qiulei; Wang, Hong; Hu, Zhanyi

    2018-02-01

    Under the goal-driven paradigm, Yamins et al. ( 2014 ; Yamins & DiCarlo, 2016 ) have shown that by optimizing only the final eight-way categorization performance of a four-layer hierarchical network, not only can its top output layer quantitatively predict IT neuron responses but its penultimate layer can also automatically predict V4 neuron responses. Currently, deep neural networks (DNNs) in the field of computer vision have reached image object categorization performance comparable to that of human beings on ImageNet, a data set that contains 1.3 million training images of 1000 categories. We explore whether the DNN neurons (units in DNNs) possess image object representational statistics similar to monkey IT neurons, particularly when the network becomes deeper and the number of image categories becomes larger, using VGG19, a typical and widely used deep network of 19 layers in the computer vision field. Following Lehky, Kiani, Esteky, and Tanaka ( 2011 , 2014 ), where the response statistics of 674 IT neurons to 806 image stimuli are analyzed using three measures (kurtosis, Pareto tail index, and intrinsic dimensionality), we investigate the three issues in this letter using the same three measures: (1) the similarities and differences of the neural response statistics between VGG19 and primate IT cortex, (2) the variation trends of the response statistics of VGG19 neurons at different layers from low to high, and (3) the variation trends of the response statistics of VGG19 neurons when the numbers of stimuli and neurons increase. We find that the response statistics on both single-neuron selectivity and population sparseness of VGG19 neurons are fundamentally different from those of IT neurons in most cases; by increasing the number of neurons in different layers and the number of stimuli, the response statistics of neurons at different layers from low to high do not substantially change; and the estimated intrinsic dimensionality values at the low

  19. Recurrently connected and localized neuronal communities initiate coordinated spontaneous activity in neuronal networks

    Science.gov (United States)

    Amin, Hayder; Maccione, Alessandro; Nieus, Thierry

    2017-01-01

    Developing neuronal systems intrinsically generate coordinated spontaneous activity that propagates by involving a large number of synchronously firing neurons. In vivo, waves of spikes transiently characterize the activity of developing brain circuits and are fundamental for activity-dependent circuit formation. In vitro, coordinated spontaneous spiking activity, or network bursts (NBs), interleaved within periods of asynchronous spikes emerge during the development of 2D and 3D neuronal cultures. Several studies have investigated this type of activity and its dynamics, but how a neuronal system generates these coordinated events remains unclear. Here, we investigate at a cellular level the generation of network bursts in spontaneously active neuronal cultures by exploiting high-resolution multielectrode array recordings and computational network modelling. Our analysis reveals that NBs are generated in specialized regions of the network (functional neuronal communities) that feature neuronal links with high cross-correlation peak values, sub-millisecond lags and that share very similar structural connectivity motifs providing recurrent interactions. We show that the particular properties of these local structures enable locally amplifying spontaneous asynchronous spikes and that this mechanism can lead to the initiation of NBs. Through the analysis of simulated and experimental data, we also show that AMPA currents drive the coordinated activity, while NMDA and GABA currents are only involved in shaping the dynamics of NBs. Overall, our results suggest that the presence of functional neuronal communities with recurrent local connections allows a neuronal system to generate spontaneous coordinated spiking activity events. As suggested by the rules used for implementing our computational model, such functional communities might naturally emerge during network development by following simple constraints on distance-based connectivity. PMID:28749937

  20. Recurrently connected and localized neuronal communities initiate coordinated spontaneous activity in neuronal networks.

    Directory of Open Access Journals (Sweden)

    Davide Lonardoni

    2017-07-01

    Full Text Available Developing neuronal systems intrinsically generate coordinated spontaneous activity that propagates by involving a large number of synchronously firing neurons. In vivo, waves of spikes transiently characterize the activity of developing brain circuits and are fundamental for activity-dependent circuit formation. In vitro, coordinated spontaneous spiking activity, or network bursts (NBs, interleaved within periods of asynchronous spikes emerge during the development of 2D and 3D neuronal cultures. Several studies have investigated this type of activity and its dynamics, but how a neuronal system generates these coordinated events remains unclear. Here, we investigate at a cellular level the generation of network bursts in spontaneously active neuronal cultures by exploiting high-resolution multielectrode array recordings and computational network modelling. Our analysis reveals that NBs are generated in specialized regions of the network (functional neuronal communities that feature neuronal links with high cross-correlation peak values, sub-millisecond lags and that share very similar structural connectivity motifs providing recurrent interactions. We show that the particular properties of these local structures enable locally amplifying spontaneous asynchronous spikes and that this mechanism can lead to the initiation of NBs. Through the analysis of simulated and experimental data, we also show that AMPA currents drive the coordinated activity, while NMDA and GABA currents are only involved in shaping the dynamics of NBs. Overall, our results suggest that the presence of functional neuronal communities with recurrent local connections allows a neuronal system to generate spontaneous coordinated spiking activity events. As suggested by the rules used for implementing our computational model, such functional communities might naturally emerge during network development by following simple constraints on distance-based connectivity.

  1. Artificial neural network forecast application for fine particulate matter concentration using meteorological data

    Directory of Open Access Journals (Sweden)

    M. Memarianfard

    2017-09-01

    Full Text Available Most parts of the urban areas are faced with the problem of floating fine particulate matter. Therefore, it is crucial to estimate the amounts of fine particulate matter concentrations through the urban atmosphere. In this research, an artificial neural network technique was utilized to model the PM2.5 dispersion in Tehran City. Factors which are influencing the predicted value consist of weather-related and air pollution-related data, i.e. wind speed, humidity, temperature, SO2, CO, NO2, and PM2.5 as target values. These factors have been considered in 19 measuring stations (zones over urban area across Tehran City during four years, from March 2011 to March 2015. The results indicate that the network with hidden layer including six neurons at training epoch 113, has the best performance with the lowest error value (MSE=0.049438 on considering PM2.5 concentrations across metropolitan areas in Tehran. Furthermore, the “R” value for regression analysis of training, validation, test, and all data are 0.65898, 0.6419, 0.54027, and 0.62331, respectively. This study also represents the artificial neural networks have satisfactory implemented for resolving complex patterns in the field of air pollution.

  2. Mesmerising mirror neurons.

    Science.gov (United States)

    Heyes, Cecilia

    2010-06-01

    Mirror neurons have been hailed as the key to understanding social cognition. I argue that three currents of thought-relating to evolution, atomism and telepathy-have magnified the perceived importance of mirror neurons. When they are understood to be a product of associative learning, rather than an adaptation for social cognition, mirror neurons are no longer mesmerising, but they continue to raise important questions about both the psychology of science and the neural bases of social cognition. Copyright 2010 Elsevier Inc. All rights reserved.

  3. to view fulltext PDF

    Indian Academy of Sciences (India)

    (albeit informal) studenL Our educational model was ahead of ... deeply instilled in Pitt, and remained an essential part of Pitt's science throughout .... Stanford Medical School, and we had lunch outside on the patio, absorbed in discussion of.

  4. Communities Putting Prevention to Work: The Healthy Corner Store Initiative

    Centers for Disease Control (CDC) Podcasts

    This podcast is an interview with Dr. Stephanie Jilcott Pitts, Associate Professor in the Department of Public Health at East Carolina University. Dr. Pitts answers questions about her study involving a healthy corner store initiative in North Carolina.

  5. USING ARTIFICIAL NEURAL NETWORKS (ANNs FOR SEDIMENT LOAD FORECASTING OF TALKHEROOD RIVER MOUTH

    Directory of Open Access Journals (Sweden)

    Vahid Nourani

    2009-01-01

    Full Text Available Without a doubt the carried sediment load by a river is the most important factor in creating and formation of the related Delta in the river mouth. Therefore, accurate forecasting of the river sediment load can play a significant role for study on the river Delta. However considering the complexity and non-linearity of the phenomenon, the classic experimental or physical-based approaches usually could not handle the problem so well. In this paper, Artificial Neural Network (ANN as a non-linear black box interpolator tool is used for modeling suspended sediment load which discharges to the Talkherood river mouth, located in northern west Iran. For this purpose, observed time series of water discharge at current and previous time steps are used as the model input neurons and the model output neuron will be the forecasted sediment load at the current time step. In this way, various schemes of the ANN approach are examined in order to achieve the best network as well as the best architecture of the model. The obtained results are also compared with the results of two other classic methods (i.e., linear regression and rating curve methods in order to approve the efficiency and ability of the proposed method.

  6. Three-Dimensional Human iPSC-Derived Artificial Skeletal Muscles Model Muscular Dystrophies and Enable Multilineage Tissue Engineering.

    Science.gov (United States)

    Maffioletti, Sara Martina; Sarcar, Shilpita; Henderson, Alexander B H; Mannhardt, Ingra; Pinton, Luca; Moyle, Louise Anne; Steele-Stallard, Heather; Cappellari, Ornella; Wells, Kim E; Ferrari, Giulia; Mitchell, Jamie S; Tyzack, Giulia E; Kotiadis, Vassilios N; Khedr, Moustafa; Ragazzi, Martina; Wang, Weixin; Duchen, Michael R; Patani, Rickie; Zammit, Peter S; Wells, Dominic J; Eschenhagen, Thomas; Tedesco, Francesco Saverio

    2018-04-17

    Generating human skeletal muscle models is instrumental for investigating muscle pathology and therapy. Here, we report the generation of three-dimensional (3D) artificial skeletal muscle tissue from human pluripotent stem cells, including induced pluripotent stem cells (iPSCs) from patients with Duchenne, limb-girdle, and congenital muscular dystrophies. 3D skeletal myogenic differentiation of pluripotent cells was induced within hydrogels under tension to provide myofiber alignment. Artificial muscles recapitulated characteristics of human skeletal muscle tissue and could be implanted into immunodeficient mice. Pathological cellular hallmarks of incurable forms of severe muscular dystrophy could be modeled with high fidelity using this 3D platform. Finally, we show generation of fully human iPSC-derived, complex, multilineage muscle models containing key isogenic cellular constituents of skeletal muscle, including vascular endothelial cells, pericytes, and motor neurons. These results lay the foundation for a human skeletal muscle organoid-like platform for disease modeling, regenerative medicine, and therapy development. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

  7. The Japanese artificial organs scene: current status.

    Science.gov (United States)

    Mitamura, Yoshinori; Murabayashi, Shun

    2005-08-01

    Artificial organs and regenerative medicine are the subjects of very active research and development (R&D) in Japan and various artificial organs are widely used in patients. Results of the R&D are presented at the annual conference of the Japanese Society for Artificial Organs (JSAO). Progress in the fields of artificial organs and regenerative medicine are reviewed annually in the Japanese Journal of Artificial Organs. The official English-language journal of JSAO, Journal of Artificial Organs, also publishes many original articles by Japanese researchers. Although the annual conference and the publications of JSAO provide the world with update information on artificial organs and regenerative medicine in Japan, the information is not always understood appropriately in the rest of the world, mainly due to language problems. This article therefore introduces the current status of artificial organs and regenerative medicine in Japan. Artificial hearts and metabolic support systems are reviewed here and other interesting areas such as regenerative medicine can be found elsewhere.

  8. EXPERIMENTOS CON UNA PROTECCION BASADA EN REDES DE NEURONAS ARTIFICIALES;EXPERIMENTS WITH A PROTECTION BASED ON ARTIFICIALS NEURONS NETWORKS

    Directory of Open Access Journals (Sweden)

    Orlys Ernesto - Torres Breffe y otros

    2011-11-01

    Full Text Available En este trabajo se muestran los resultados de los experimentos físicos realizados con una protección basadatotalmente en Redes de Neuronas Artificiales para un transformador eléctrico a escala de laboratorio. Se demuestraque unas Redes de Neuronas Artificiales entrenadas, con datos provenientes de regímenes simuladosmatemáticamente, opera correctamente con señales de regímenes provenientes de casos reales, al menos a escalade laboratorio y con niveles reducidos de intensidad. Se describe la instalación experimental, tanto desde el puntode vista de hardware como software utilizando la tecnología de National Instrument. Se entrenan diferentes tipos deRedes Neuronales y todas aprendieron a proteger correctamente. Estos experimentos establecen las pautas para eldesarrollo de Relés Electrónicos Inteligentes que no se ajustarían, al menos con datos y valores de difícilcomprensión como los actuales relés, sino que se entrenarían una vez mediante la simulación matemática y laexperiencia práctica los haría cada vez mejores.In this work is shown the results of the physical experiments done with a protection totally based in Artificial NeuralNetworks for an electric transformer of the laboratory scale. Its demonstrated that some Artificial Neural Networkstrained with data coming from a mathematically simulated regimens, it operates correctly with signals coming fromreal cases, at least to laboratory scale and with reduced levels of intensity. The experimental installation is described,so much from the hardware and software point of view, using the technology of National Instrument. Different typesof Artificial Neural Nets are trained and all learned how to protect correctly. These experiments establish the rules forthe development of Intelligent Electronic Relays that would not be adjusted, at least with complex data and valueslike the current relays, they will be trained once from the mathematically simulation and the practical

  9. Motor Neuron Diseases

    Science.gov (United States)

    ... and other neurodegenerative diseases to better understand the function of neurons and other support cells and identify candidate therapeutic ... and other neurodegenerative diseases to better understand the function of neurons and other support cells and identify candidate therapeutic ...

  10. Leptin Action on GABAergic Neurons Prevents Obesity and Reduces Inhibitory Tone to POMC Neurons

    OpenAIRE

    Vong, Linh; Ye, Chianping; Yang, Zongfang; Choi, Brian; Chua, Streamson; Lowell, Bradford B.

    2011-01-01

    Leptin acts in the brain to prevent obesity. The underlying neurocircuitry responsible for this is poorly understood, in part due to incomplete knowledge regarding first order, leptin-responsive neurons. To address this, we and others have been removing leptin receptors from candidate first order neurons. While functionally relevant neurons have been identified, the observed effects have been small suggesting that most first order neurons remain unidentified. Here we take an alternative appro...

  11. Prediction of the binary density of the ILs+ water using back-propagated feed forward artificial neural network

    Directory of Open Access Journals (Sweden)

    Shojaee Safar Ali

    2014-01-01

    Full Text Available In this study, feasibility of a back-propagated artificial neural network to correlate the binary density of ionic liquids (ILs mixtures containing water as the common solvent has been investigated. To verify the optimized parameters of the neural network, total of 1668 data were collected and divided into two different subsets. The first subsets consisted of more than two-third (1251 data points of data bank was used to find the optimum parameters including weights and biases, number of neurons (7 neurons, transfer functions in hidden and output layer which were tansig and purelin, respectively. In addition, the correlative capability of network was examined using testing subset (417 data points not considered during the training stage. The overall obtained results revealed that the proposed network is accurate enough to correlate the binary density of the ionic liquids mixtures with average absolute relative deviation (AARD % and average relative deviation (ARD % of 1.56% and -0.04 %, respectively. Finally, the correlative capability of the proposed ANN model was compared with one of the available correlations proposed by Rodríguez and Brennecke.

  12. Learning of time series through neuron-to-neuron instruction

    Energy Technology Data Exchange (ETDEWEB)

    Miyazaki, Y [Department of Physics, Kyoto University, Kyoto 606-8502, (Japan); Kinzel, W [Institut fuer Theoretische Physik, Universitaet Wurzburg, 97074 Wurzburg (Germany); Shinomoto, S [Department of Physics, Kyoto University, Kyoto (Japan)

    2003-02-07

    A model neuron with delayline feedback connections can learn a time series generated by another model neuron. It has been known that some student neurons that have completed such learning under the instruction of a teacher's quasi-periodic sequence mimic the teacher's time series over a long interval, even after instruction has ceased. We found that in addition to such faithful students, there are unfaithful students whose time series eventually diverge exponentially from that of the teacher. In order to understand the circumstances that allow for such a variety of students, the orbit dimension was estimated numerically. The quasi-periodic orbits in question were found to be confined in spaces with dimensions significantly smaller than that of the full phase space.

  13. Learning of time series through neuron-to-neuron instruction

    International Nuclear Information System (INIS)

    Miyazaki, Y; Kinzel, W; Shinomoto, S

    2003-01-01

    A model neuron with delayline feedback connections can learn a time series generated by another model neuron. It has been known that some student neurons that have completed such learning under the instruction of a teacher's quasi-periodic sequence mimic the teacher's time series over a long interval, even after instruction has ceased. We found that in addition to such faithful students, there are unfaithful students whose time series eventually diverge exponentially from that of the teacher. In order to understand the circumstances that allow for such a variety of students, the orbit dimension was estimated numerically. The quasi-periodic orbits in question were found to be confined in spaces with dimensions significantly smaller than that of the full phase space

  14. New S-box calculation approach for Rijndael-AES based on an artificial neural network

    Directory of Open Access Journals (Sweden)

    Jaime David Rios Arrañaga

    2017-11-01

    Full Text Available The S-box is a basic important component in symmetric key encryption, used in block ciphers to confuse or hide the relationship between the plaintext and the ciphertext. In this paper a way to develop the transformation of an input of the S-box specified in AES encryption system through an artificial neural network and the multiplicative inverse in Galois Field is presented. With this implementation more security is achieved since the values of the S-box remain hidden and the inverse table serves as a distractor since it would appear to be the complete S-box. This is implemented on MATLAB and HSPICE using a network of perceptron neurons with a hidden layer and null error.

  15. Neurons of self-defence: neuronal innervation of the exocrine defence glands in stick insects.

    Science.gov (United States)

    Stolz, Konrad; von Bredow, Christoph-Rüdiger; von Bredow, Yvette M; Lakes-Harlan, Reinhard; Trenczek, Tina E; Strauß, Johannes

    2015-01-01

    Stick insects (Phasmatodea) use repellent chemical substances (allomones) for defence which are released from so-called defence glands in the prothorax. These glands differ in size between species, and are under neuronal control from the CNS. The detailed neural innervation and possible differences between species are not studied so far. Using axonal tracing, the neuronal innervation is investigated comparing four species. The aim is to document the complexity of defence gland innervation in peripheral nerves and central motoneurons in stick insects. In the species studied here, the defence gland is innervated by the intersegmental nerve complex (ISN) which is formed by three nerves from the prothoracic (T1) and suboesophageal ganglion (SOG), as well as a distinct suboesophageal nerve (Nervus anterior of the suboesophageal ganglion). In Carausius morosus and Sipyloidea sipylus, axonal tracing confirmed an innervation of the defence glands by this N. anterior SOG as well as N. anterior T1 and N. posterior SOG from the intersegmental nerve complex. In Peruphasma schultei, which has rather large defence glands, only the innervation by the N. anterior SOG was documented by axonal tracing. In the central nervous system of all species, 3-4 neuron types are identified by axonal tracing which send axons in the N. anterior SOG likely innervating the defence gland as well as adjacent muscles. These neurons are mainly suboesophageal neurons with one intersegmental neuron located in the prothoracic ganglion. The neuron types are conserved in the species studied, but the combination of neuron types is not identical. In addition, the central nervous system in S. sipylus contains one suboesophageal and one prothoracic neuron type with axons in the intersegmental nerve complex contacting the defence gland. Axonal tracing shows a very complex innervation pattern of the defence glands of Phasmatodea which contains different neurons in different nerves from two adjacent body segments

  16. The Relevance of AgRP Neuron-Derived GABA Inputs to POMC Neurons Differs for Spontaneous and Evoked Release

    OpenAIRE

    Rau, Andrew R.; Hentges, Shane T.

    2017-01-01

    Hypothalamic agouti-related peptide (AgRP) neurons potently stimulate food intake, whereas proopiomelanocortin (POMC) neurons inhibit feeding. Whether AgRP neurons exert their orexigenic actions, at least in part, by inhibiting anorexigenic POMC neurons remains unclear. Here, the connectivity between GABA-releasing AgRP neurons and POMC neurons was examined in brain slices from male and female mice. GABA-mediated spontaneous IPSCs (sIPSCs) in POMC neurons were unaffected by disturbing GABA re...

  17. An FPGA-based silicon neuronal network with selectable excitability silicon neurons

    Directory of Open Access Journals (Sweden)

    Jing eLi

    2012-12-01

    Full Text Available This paper presents a digital silicon neuronal network which simulates the nerve system in creatures and has the ability to execute intelligent tasks, such as associative memory. Two essential elements, the mathematical-structure-based digital spiking silicon neuron (DSSN and the transmitter release based silicon synapse, allow the network to show rich dynamic behaviors and are computationally efficient for hardware implementation. We adopt mixed pipeline and parallel structure and shift operations to design a sufficient large and complex network without excessive hardware resource cost. The network with $256$ full-connected neurons is built on a Digilent Atlys board equipped with a Xilinx Spartan-6 LX45 FPGA. Besides, a memory control block and USB control block are designed to accomplish the task of data communication between the network and the host PC. This paper also describes the mechanism of associative memory performed in the silicon neuronal network. The network is capable of retrieving stored patterns if the inputs contain enough information of them. The retrieving probability increases with the similarity between the input and the stored pattern increasing. Synchronization of neurons is observed when the successful stored pattern retrieval occurs.

  18. Soft computing in artificial intelligence

    CERN Document Server

    Matson, Eric

    2014-01-01

    This book explores the concept of artificial intelligence based on knowledge-based algorithms. Given the current hardware and software technologies and artificial intelligence theories, we can think of how efficient to provide a solution, how best to implement a model and how successful to achieve it. This edition provides readers with the most recent progress and novel solutions in artificial intelligence. This book aims at presenting the research results and solutions of applications in relevance with artificial intelligence technologies. We propose to researchers and practitioners some methods to advance the intelligent systems and apply artificial intelligence to specific or general purpose. This book consists of 13 contributions that feature fuzzy (r, s)-minimal pre- and β-open sets, handling big coocurrence matrices, Xie-Beni-type fuzzy cluster validation, fuzzy c-regression models, combination of genetic algorithm and ant colony optimization, building expert system, fuzzy logic and neural network, ind...

  19. Sensory neurons do not induce motor neuron loss in a human stem cell model of spinal muscular atrophy.

    Science.gov (United States)

    Schwab, Andrew J; Ebert, Allison D

    2014-01-01

    Spinal muscular atrophy (SMA) is an autosomal recessive disorder leading to paralysis and early death due to reduced SMN protein. It is unclear why there is such a profound motor neuron loss, but recent evidence from fly and mouse studies indicate that cells comprising the whole sensory-motor circuit may contribute to motor neuron dysfunction and loss. Here, we used induced pluripotent stem cells derived from SMA patients to test whether sensory neurons directly contribute to motor neuron loss. We generated sensory neurons from SMA induced pluripotent stem cells and found no difference in neuron generation or survival, although there was a reduced calcium response to depolarizing stimuli. Using co-culture of SMA induced pluripotent stem cell derived sensory neurons with control induced pluripotent stem cell derived motor neurons, we found no significant reduction in motor neuron number or glutamate transporter boutons on motor neuron cell bodies or neurites. We conclude that SMA sensory neurons do not overtly contribute to motor neuron loss in this human stem cell system.

  20. Neuron matters: electric activation of neuronal tissue is dependent on the interaction between the neuron and the electric field.

    Science.gov (United States)

    Ye, Hui; Steiger, Amanda

    2015-08-12

    In laboratory research and clinical practice, externally-applied electric fields have been widely used to control neuronal activity. It is generally accepted that neuronal excitability is controlled by electric current that depolarizes or hyperpolarizes the excitable cell membrane. What determines the amount of polarization? Research on the mechanisms of electric stimulation focus on the optimal control of the field properties (frequency, amplitude, and direction of the electric currents) to improve stimulation outcomes. Emerging evidence from modeling and experimental studies support the existence of interactions between the targeted neurons and the externally-applied electric fields. With cell-field interaction, we suggest a two-way process. When a neuron is positioned inside an electric field, the electric field will induce a change in the resting membrane potential by superimposing an electrically-induced transmembrane potential (ITP). At the same time, the electric field can be perturbed and re-distributed by the cell. This cell-field interaction may play a significant role in the overall effects of stimulation. The redistributed field can cause secondary effects to neighboring cells by altering their geometrical pattern and amount of membrane polarization. Neurons excited by the externally-applied electric field can also affect neighboring cells by ephaptic interaction. Both aspects of the cell-field interaction depend on the biophysical properties of the neuronal tissue, including geometric (i.e., size, shape, orientation to the field) and electric (i.e., conductivity and dielectricity) attributes of the cells. The biophysical basis of the cell-field interaction can be explained by the electromagnetism theory. Further experimental and simulation studies on electric stimulation of neuronal tissue should consider the prospect of a cell-field interaction, and a better understanding of tissue inhomogeneity and anisotropy is needed to fully appreciate the neural

  1. AgRP neurons regulate development of dopamine neuronal plasticity and nonfood-associated behaviors

    Science.gov (United States)

    Dietrich, Marcelo O; Bober, Jeremy; Ferreira, Jozélia G; Tellez, Luis A; Mineur, Yann S; Souza, Diogo O; Gao, Xiao-Bing; Picciotto, Marina R; Araújo, Ivan; Liu, Zhong-Wu; Horvath, Tamas L

    2012-01-01

    It is not known whether behaviors unrelated to feeding are affected by hypothalamic regulators of hunger. We found that impairment of Agouti-related protein (AgRP) circuitry by either Sirt1 knockdown in AgRP-expressing neurons or early postnatal ablation of these neurons increased exploratory behavior and enhanced responses to cocaine. In AgRP circuit–impaired mice, ventral tegmental dopamine neurons exhibited enhanced spike timing–dependent long-term potentiation, altered amplitude of miniature postsynaptic currents and elevated dopamine in basal forebrain. Thus, AgRP neurons determine the set point of the reward circuitry and associated behaviors. PMID:22729177

  2. Artificial neural systems using memristive synapses and nano-crystalline silicon thin-film transistors

    Science.gov (United States)

    Cantley, Kurtis D.

    Future computer systems will not rely solely on digital processing of inputs from well-defined data sets. They will also be required to perform various computational tasks using large sets of ill-defined information from the complex environment around them. The most efficient processor of this type of information known today is the human brain. Using a large number of primitive elements (˜1010 neurons in the neocortex) with high parallel connectivity (each neuron has ˜104 synapses), brains have the remarkable ability to recognize and classify patterns, predict outcomes, and learn from and adapt to incredibly diverse sets of problems. A reasonable goal in the push to increase processing power of electronic systems would thus be to implement artificial neural networks in hardware that are compatible with today's digital processors. This work focuses on the feasibility of utilizing non-crystalline silicon devices in neuromorphic electronics. Hydrogenated amorphous silicon (a-Si:H) nanowire transistors with Schottky barrier source/drain junctions, as well as a-Si:H/Ag resistive switches are fabricated and characterized. In the transistors, it is found that the on-current scales linearly with the effective width W eff of the channel nanowire array down to at least 20 nm. The solid-state electrolyte resistive switches (memristors) are shown to exhibit the proper current-voltage hysteresis. SPICE models of similar devices are subsequently developed to investigate their performance in neural circuits. The resulting SPICE simulations demonstrate spiking properties and synaptic learning rules that are incredibly similar to those in biology. Specifically, the neuron circuits can be designed to mimic the firing characteristics of real neurons, and Hebbian learning rules are investigated. Finally, some applications are presented, including associative learning analogous to the classical conditioning experiments originally performed by Pavlov, and frequency and pattern

  3. Artificial Intelligence

    CERN Document Server

    Warwick, Kevin

    2011-01-01

    if AI is outside your field, or you know something of the subject and would like to know more then Artificial Intelligence: The Basics is a brilliant primer.' - Nick Smith, Engineering and Technology Magazine November 2011 Artificial Intelligence: The Basics is a concise and cutting-edge introduction to the fast moving world of AI. The author Kevin Warwick, a pioneer in the field, examines issues of what it means to be man or machine and looks at advances in robotics which have blurred the boundaries. Topics covered include: how intelligence can be defined whether machines can 'think' sensory

  4. Multilayer perceptron classification of unknown volatile chemicals from the firing rates of insect olfactory sensory neurons and its application to biosensor design.

    Science.gov (United States)

    Bachtiar, Luqman R; Unsworth, Charles P; Newcomb, Richard D; Crampin, Edmund J

    2013-01-01

    In this letter, we use the firing rates from an array of olfactory sensory neurons (OSNs) of the fruit fly, Drosophila melanogaster, to train an artificial neural network (ANN) to distinguish different chemical classes of volatile odorants. Bootstrapping is implemented for the optimized networks, providing an accurate estimate of a network's predicted values. Initially a simple linear predictor was used to assess the complexity of the data and was found to provide low prediction performance. A nonlinear ANN in the form of a single multilayer perceptron (MLP) was also used, providing a significant increase in prediction performance. The effect of the number of hidden layers and hidden neurons of the MLP was investigated and found to be effective in enhancing network performance with both a single and a double hidden layer investigated separately. A hybrid array of MLPs was investigated and compared against the single MLP architecture. The hybrid MLPs were found to classify all vectors of the validation set, presenting the highest degree of prediction accuracy. Adjustment of the number of hidden neurons was investigated, providing further performance gain. In addition, noise injection was investigated, proving successful for certain network designs. It was found that the best-performing MLP was that of the double-hidden-layer hybrid MLP network without the use of noise injection. Furthermore, the level of performance was examined when different numbers of OSNs used were varied from the maximum of 24 to only 5 OSNs. Finally, the ideal OSNs were identified that optimized network performance. The results obtained from this study provide strong evidence of the usefulness of ANNs in the field of olfaction for the future realization of a signal processing back end for an artificial olfactory biosensor.

  5. Charlas sobre inteligencia artificial

    OpenAIRE

    Álvarez Sánchez, José Ramón; Ferrández Vicente, José Manuel; Paz López, Félix de la

    2014-01-01

    Serie: Informática en Radio 3 La Inteligencia Artificial es una de las ciencias que causa mayor impacto en la sociedad, mucho más si tenemos en cuenta que cambiará el futuro de la humanidad. En España existen actualmente un nutrido grupo de equipos de investigación relacionados con las tecnologías de computación natural-artificial que aúnan sus esfuerzos a través de la RTNAC la Red Temática en Tecnologías de Computación Natural-Artificial . La UNED participa en todas sus actividades desde ...

  6. Artificial Intelligence Project

    Science.gov (United States)

    1990-01-01

    Symposium on Aritificial Intelligence and Software Engineering Working Notes, March 1989. Blumenthal, Brad, "An Architecture for Automating...Artificial Intelligence Project Final Technical Report ARO Contract: DAAG29-84-K-OGO Artificial Intelligence LaboratO"ry The University of Texas at...Austin N>.. ~ ~ JA 1/I 1991 n~~~ Austin, Texas 78712 ________k A,.tificial Intelligence Project i Final Technical Report ARO Contract: DAAG29-84-K-0060

  7. Rearing the southern green stink bug using an artificial dry diet and an artificial plant Criação do percevejo-verde usando dieta artificial seca e planta artificial

    Directory of Open Access Journals (Sweden)

    ANTÔNIO RICARDO PANIZZI

    2000-09-01

    Full Text Available Laboratory and greenhouse studies were conducted with an artificial dry diet to rear nymphs, and with an artificial plant as substrate for egg laying by the southern green stink bug, Nezara viridula (L.. The artificial diet was composed of: soybean protein (15 g; potato starch (7.5 g; dextrose (7.5 g; sucrose (2.5 g; cellulose (12.5 g; vitamin mixture (niacinamide 1 g, calcium pantothenate 1 g, thiamine 0.25 g, riboflavin 0.5 g, pyridoxine 0.25 g, folic acid 0.25 g, biotin 0.02 mL, vitamin B12 1 g - added to 1,000 mL of distilled water (5.0 mL; soybean oil (20 mL; wheat germ (17.9 g; and water (30 mL. Nymphs showed normal feeding behavior when fed on the artificial diet. Nymphal development time was longer than or similar to that of nymphs fed on soybean pods. Total nymphal mortality was low (ca. 30%, both for nymphs reared on the artificial diet, and for nymphs fed on soybean pods. At adult emergence, fresh body weights were significantly (PForam conduzidos estudos em laboratório e em casa de vegetação, com uma dieta artificial seca para a criação de ninfas e com um modelo de planta artificial como substrato para a colocação de ovos por adultos do percevejo-verde, Nezara viridula (L.. Os componentes da dieta artificial foram: proteína de soja (15 g; fécula de batata (7,5 g; dextrose (7,5 g; sacarose (2,5 g; celulose (12,5 g; mistura vitamínica (niacinamida 1 g, pantotenato de cálcio 1 g, tiamina 0,25 g, riboflavina 0,5 g, piridoxina 0,25 g, ácido fólico 0,25 g, biotina 0,02 mL, vitamina B12 1 g, adicionada em 1.000 mL de água destilada (5,0 mL; óleo de soja (20 mL; germe de trigo (17,9 g; e água (30 mL. As ninfas alimentaram-se normalmente da dieta, embora o tempo de desenvolvimento tenha sido em um caso, maior, e em outro, semelhante, ao das ninfas que se alimentaram de vagens da soja. A mortalidade total das ninfas foi baixa (ca. 30%, tanto na dieta como na vagem de soja. Na emergência, os adultos apresentaram peso fresco

  8. Beyond AI: Artificial Dreams Conference

    CERN Document Server

    Zackova, Eva; Kelemen, Jozef; Beyond Artificial Intelligence : The Disappearing Human-Machine Divide

    2015-01-01

    This book is an edited collection of chapters based on the papers presented at the conference “Beyond AI: Artificial Dreams” held in Pilsen in November 2012. The aim of the conference was to question deep-rooted ideas of artificial intelligence and cast critical reflection on methods standing at its foundations.  Artificial Dreams epitomize our controversial quest for non-biological intelligence, and therefore the contributors of this book tried to fully exploit such a controversy in their respective chapters, which resulted in an interdisciplinary dialogue between experts from engineering, natural sciences and humanities.   While pursuing the Artificial Dreams, it has become clear that it is still more and more difficult to draw a clear divide between human and machine. And therefore this book tries to portrait such an image of what lies beyond artificial intelligence: we can see the disappearing human-machine divide, a very important phenomenon of nowadays technological society, the phenomenon which i...

  9. Orexin neurons receive glycinergic innervations.

    Directory of Open Access Journals (Sweden)

    Mari Hondo

    Full Text Available Glycine, a nonessential amino-acid that acts as an inhibitory neurotransmitter in the central nervous system, is currently used as a dietary supplement to improve the quality of sleep, but its mechanism of action is poorly understood. We confirmed the effects of glycine on sleep/wakefulness behavior in mice when administered peripherally. Glycine administration increased non-rapid eye movement (NREM sleep time and decreased the amount and mean episode duration of wakefulness when administered in the dark period. Since peripheral administration of glycine induced fragmentation of sleep/wakefulness states, which is a characteristic of orexin deficiency, we examined the effects of glycine on orexin neurons. The number of Fos-positive orexin neurons markedly decreased after intraperitoneal administration of glycine to mice. To examine whether glycine acts directly on orexin neurons, we examined the effects of glycine on orexin neurons by patch-clamp electrophysiology. Glycine directly induced hyperpolarization and cessation of firing of orexin neurons. These responses were inhibited by a specific glycine receptor antagonist, strychnine. Triple-labeling immunofluorescent analysis showed close apposition of glycine transporter 2 (GlyT2-immunoreactive glycinergic fibers onto orexin-immunoreactive neurons. Immunoelectron microscopic analysis revealed that GlyT2-immunoreactive terminals made symmetrical synaptic contacts with somata and dendrites of orexin neurons. Double-labeling immunoelectron microscopy demonstrated that glycine receptor alpha subunits were localized in the postsynaptic membrane of symmetrical inhibitory synapses on orexin neurons. Considering the importance of glycinergic regulation during REM sleep, our observations suggest that glycine injection might affect the activity of orexin neurons, and that glycinergic inhibition of orexin neurons might play a role in physiological sleep regulation.

  10. Artificial Photosynthesis: Beyond Mimicking Nature

    International Nuclear Information System (INIS)

    Dau, Holger; Fujita, Etsuko; Sun, Licheng

    2017-01-01

    In this Editorial, Guest Editors Holger Dau, Etsuko Fujita, and Licheng Sun introduce the Special Issue of ChemSusChem on “Artificial Photosynthesis for Sustainable Fuels”. Here, they discuss the need for non-fossil based fuels, introduce both biological and artificial photosynthesis, and outline various important concepts in artificial photosynthesis, including molecular and solid-state catalysts for water oxidation and hydrogen evolution, catalytic CO 2 reduction, and photoelectrochemical systems.

  11. Parallel artificial liquid membrane extraction

    DEFF Research Database (Denmark)

    Gjelstad, Astrid; Rasmussen, Knut Einar; Parmer, Marthe Petrine

    2013-01-01

    This paper reports development of a new approach towards analytical liquid-liquid-liquid membrane extraction termed parallel artificial liquid membrane extraction. A donor plate and acceptor plate create a sandwich, in which each sample (human plasma) and acceptor solution is separated by an arti......This paper reports development of a new approach towards analytical liquid-liquid-liquid membrane extraction termed parallel artificial liquid membrane extraction. A donor plate and acceptor plate create a sandwich, in which each sample (human plasma) and acceptor solution is separated...... by an artificial liquid membrane. Parallel artificial liquid membrane extraction is a modification of hollow-fiber liquid-phase microextraction, where the hollow fibers are replaced by flat membranes in a 96-well plate format....

  12. AgRP Neurons Can Increase Food Intake during Conditions of Appetite Suppression and Inhibit Anorexigenic Parabrachial Neurons.

    Science.gov (United States)

    Essner, Rachel A; Smith, Alison G; Jamnik, Adam A; Ryba, Anna R; Trutner, Zoe D; Carter, Matthew E

    2017-09-06

    To maintain energy homeostasis, orexigenic (appetite-inducing) and anorexigenic (appetite suppressing) brain systems functionally interact to regulate food intake. Within the hypothalamus, neurons that express agouti-related protein (AgRP) sense orexigenic factors and orchestrate an increase in food-seeking behavior. In contrast, calcitonin gene-related peptide (CGRP)-expressing neurons in the parabrachial nucleus (PBN) suppress feeding. PBN CGRP neurons become active in response to anorexigenic hormones released following a meal, including amylin, secreted by the pancreas, and cholecystokinin (CCK), secreted by the small intestine. Additionally, exogenous compounds, such as lithium chloride (LiCl), a salt that creates gastric discomfort, and lipopolysaccharide (LPS), a bacterial cell wall component that induces inflammation, exert appetite-suppressing effects and activate PBN CGRP neurons. The effects of increasing the homeostatic drive to eat on feeding behavior during appetite suppressing conditions are unknown. Here, we show in mice that food deprivation or optogenetic activation of AgRP neurons induces feeding to overcome the appetite suppressing effects of amylin, CCK, and LiCl, but not LPS. AgRP neuron photostimulation can also increase feeding during chemogenetic-mediated stimulation of PBN CGRP neurons. AgRP neuron stimulation reduces Fos expression in PBN CGRP neurons across all conditions. Finally, stimulation of projections from AgRP neurons to the PBN increases feeding following administration of amylin, CCK, and LiCl, but not LPS. These results demonstrate that AgRP neurons are sufficient to increase feeding during noninflammatory-based appetite suppression and to decrease activity in anorexigenic PBN CGRP neurons, thereby increasing food intake during homeostatic need. SIGNIFICANCE STATEMENT The motivation to eat depends on the relative balance of activity in distinct brain regions that induce or suppress appetite. An abnormal amount of activity in

  13. Effects of weak electric fields on the activity of neurons and neuronal networks

    International Nuclear Information System (INIS)

    Jeffreys, J.G.R.; Deans, J.; Bikson, M.; Fox, J.

    2003-01-01

    Electric fields applied to brain tissue will affect cellular properties. They will hyperpolarise the ends of cells closest to the positive part of the field, and depolarise ends closest to the negative. In the case of neurons this affects excitability. How these changes in transmembrane potential are distributed depends on the length constant of the neuron, and on its geometry; if the neuron is electrically compact, the change in transmembrane potential becomes an almost linear function of distance in the direction of the field. Neurons from the mammalian hippocampus, maintained in tissue slices in vitro, are significantly affected by fields of around 1-5 Vm -1 . (author)

  14. The role of stochasticity in an information-optimal neural population code

    Energy Technology Data Exchange (ETDEWEB)

    Stocks, N G; Nikitin, A P [School of Engineering, University of Warwick, Coventry CV4 7AL (United Kingdom); McDonnell, M D [Institute for Telecommunications Research, University of South Australia, SA 5095 (Australia); Morse, R P, E-mail: n.g.stocks@warwick.ac.u [School of Life and Health Sciences, Aston University, Birmingham B4 7ET (United Kingdom)

    2009-12-01

    In this paper we consider the optimisation of Shannon mutual information (MI) in the context of two model neural systems. The first is a stochastic pooling network (population) of McCulloch-Pitts (MP) type neurons (logical threshold units) subject to stochastic forcing; the second is (in a rate coding paradigm) a population of neurons that each displays Poisson statistics (the so called 'Poisson neuron'). The mutual information is optimised as a function of a parameter that characterises the 'noise level'-in the MP array this parameter is the standard deviation of the noise; in the population of Poisson neurons it is the window length used to determine the spike count. In both systems we find that the emergent neural architecture and, hence, code that maximises the MI is strongly influenced by the noise level. Low noise levels leads to a heterogeneous distribution of neural parameters (diversity), whereas, medium to high noise levels result in the clustering of neural parameters into distinct groups that can be interpreted as subpopulations. In both cases the number of subpopulations increases with a decrease in noise level. Our results suggest that subpopulations are a generic feature of an information optimal neural population.

  15. The mirror-neuron system.

    Science.gov (United States)

    Rizzolatti, Giacomo; Craighero, Laila

    2004-01-01

    A category of stimuli of great importance for primates, humans in particular, is that formed by actions done by other individuals. If we want to survive, we must understand the actions of others. Furthermore, without action understanding, social organization is impossible. In the case of humans, there is another faculty that depends on the observation of others' actions: imitation learning. Unlike most species, we are able to learn by imitation, and this faculty is at the basis of human culture. In this review we present data on a neurophysiological mechanism--the mirror-neuron mechanism--that appears to play a fundamental role in both action understanding and imitation. We describe first the functional properties of mirror neurons in monkeys. We review next the characteristics of the mirror-neuron system in humans. We stress, in particular, those properties specific to the human mirror-neuron system that might explain the human capacity to learn by imitation. We conclude by discussing the relationship between the mirror-neuron system and language.

  16. Artificial organs versus regenerative medicine: is it true?

    Science.gov (United States)

    Nosé, Yukihiko; Okubo, Hisashi

    2003-09-01

    Individuals engaged in the fields of artificial kidney and artificial heart have often mistakenly stated that "the era of artificial organs is over; regenerative medicine is the future." Contrarily, we do not believe artificial organs and regenerative medicine are different medical technologies. As a matter of fact, artificial organs developed during the last 50 years have been used as a bridge to regeneration. The only difference between regenerative medicine and artificial organs is that artificial organs for the bridge to regeneration promote tissue regeneration in situ, instead of outside the body (for example, vascular prostheses, neuroprostheses, bladder substitutes, skin prostheses, bone prostheses, cartilage prostheses, ligament prostheses, etc.). All of these artificial organs are successful because tissue regeneration over a man-made prosthesis is established inside the patient's body (artificial organs to support regeneration). Another usage of the group of artificial organs for the bridge to regeneration is to sustain the functions of the patient's diseased organs during the regeneration process of the body's healthy tissues and/or organs. This particular group includes artificial kidney, hepatic assist, respiratory assist, and circulatory assist. Proof of regeneration of these healthy tissues and/or organs is demonstrated in the short-term recovery of end-stage organ failure patients (artificial organs for bridge to regeneration). A third group of artificial organs for the bridge to regeneration accelerates the regenerating process of the patient's healthy tissues and organs. This group includes neurostimulators, artificial blood (red cells) blood oxygenators, and plasmapheresis devices, including hemodiafiltrators. So-called "therapeutic artificial organs" fall into this category (artificial organs to accelerate regeneration). Thus, almost all of today's artificial organs are useful in the bridge to regeneration of healthy natural tissues and organs

  17. Spike timing precision of neuronal circuits.

    Science.gov (United States)

    Kilinc, Deniz; Demir, Alper

    2018-04-17

    Spike timing is believed to be a key factor in sensory information encoding and computations performed by the neurons and neuronal circuits. However, the considerable noise and variability, arising from the inherently stochastic mechanisms that exist in the neurons and the synapses, degrade spike timing precision. Computational modeling can help decipher the mechanisms utilized by the neuronal circuits in order to regulate timing precision. In this paper, we utilize semi-analytical techniques, which were adapted from previously developed methods for electronic circuits, for the stochastic characterization of neuronal circuits. These techniques, which are orders of magnitude faster than traditional Monte Carlo type simulations, can be used to directly compute the spike timing jitter variance, power spectral densities, correlation functions, and other stochastic characterizations of neuronal circuit operation. We consider three distinct neuronal circuit motifs: Feedback inhibition, synaptic integration, and synaptic coupling. First, we show that both the spike timing precision and the energy efficiency of a spiking neuron are improved with feedback inhibition. We unveil the underlying mechanism through which this is achieved. Then, we demonstrate that a neuron can improve on the timing precision of its synaptic inputs, coming from multiple sources, via synaptic integration: The phase of the output spikes of the integrator neuron has the same variance as that of the sample average of the phases of its inputs. Finally, we reveal that weak synaptic coupling among neurons, in a fully connected network, enables them to behave like a single neuron with a larger membrane area, resulting in an improvement in the timing precision through cooperation.

  18. Development of Artificial Neural Network Model for Diesel Fuel Properties Prediction using Vibrational Spectroscopy.

    Science.gov (United States)

    Bolanča, Tomislav; Marinović, Slavica; Ukić, Sime; Jukić, Ante; Rukavina, Vinko

    2012-06-01

    This paper describes development of artificial neural network models which can be used to correlate and predict diesel fuel properties from several FTIR-ATR absorbances and Raman intensities as input variables. Multilayer feed forward and radial basis function neural networks have been used to rapid and simultaneous prediction of cetane number, cetane index, density, viscosity, distillation temperatures at 10% (T10), 50% (T50) and 90% (T90) recovery, contents of total aromatics and polycyclic aromatic hydrocarbons of commercial diesel fuels. In this study two-phase training procedures for multilayer feed forward networks were applied. While first phase training algorithm was constantly the back propagation one, two second phase training algorithms were varied and compared, namely: conjugate gradient and quasi Newton. In case of radial basis function network, radial layer was trained using K-means radial assignment algorithm and three different radial spread algorithms: explicit, isotropic and K-nearest neighbour. The number of hidden layer neurons and experimental data points used for the training set have been optimized for both neural networks in order to insure good predictive ability by reducing unnecessary experimental work. This work shows that developed artificial neural network models can determine main properties of diesel fuels simultaneously based on a single and fast IR or Raman measurement.

  19. Neuro-Compatible Metabolic Glycan Labeling of Primary Hippocampal Neurons in Noncontact, Sandwich-Type Neuron-Astrocyte Coculture.

    Science.gov (United States)

    Choi, Ji Yu; Park, Matthew; Cho, Hyeoncheol; Kim, Mi-Hee; Kang, Kyungtae; Choi, Insung S

    2017-12-20

    Glycans are intimately involved in several facets of neuronal development and neuropathology. However, the metabolic labeling of surface glycans in primary neurons is a difficult task because of the neurotoxicity of unnatural monosaccharides that are used as a metabolic precursor, hindering the progress of metabolic engineering in neuron-related fields. Therefore, in this paper, we report a neurosupportive, neuron-astrocyte coculture system that neutralizes the neurotoxic effects of unnatural monosaccharides, allowing for the long-term observation and characterization of glycans in primary neurons in vitro. Polysialic acids in neurons are selectively imaged, via the metabolic labeling of sialoglycans with peracetylated N-azidoacetyl-d-mannosamine (Ac 4 ManNAz), for up to 21 DIV. Two-color labeling shows that neuronal activities, such as neurite outgrowth and recycling of membrane components, are highly dynamic and change over time during development. In addition, the insertion sites of membrane components are suggested to not be random, but be predominantly localized in developing neurites. This work provides a new research platform and also suggests advanced 3D systems for metabolic-labeling studies of glycans in primary neurons.

  20. A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses.

    Science.gov (United States)

    Qiao, Ning; Mostafa, Hesham; Corradi, Federico; Osswald, Marc; Stefanini, Fabio; Sumislawska, Dora; Indiveri, Giacomo

    2015-01-01

    Implementing compact, low-power artificial neural processing systems with real-time on-line learning abilities is still an open challenge. In this paper we present a full-custom mixed-signal VLSI device with neuromorphic learning circuits that emulate the biophysics of real spiking neurons and dynamic synapses for exploring the properties of computational neuroscience models and for building brain-inspired computing systems. The proposed architecture allows the on-chip configuration of a wide range of network connectivities, including recurrent and deep networks, with short-term and long-term plasticity. The device comprises 128 K analog synapse and 256 neuron circuits with biologically plausible dynamics and bi-stable spike-based plasticity mechanisms that endow it with on-line learning abilities. In addition to the analog circuits, the device comprises also asynchronous digital logic circuits for setting different synapse and neuron properties as well as different network configurations. This prototype device, fabricated using a 180 nm 1P6M CMOS process, occupies an area of 51.4 mm(2), and consumes approximately 4 mW for typical experiments, for example involving attractor networks. Here we describe the details of the overall architecture and of the individual circuits and present experimental results that showcase its potential. By supporting a wide range of cortical-like computational modules comprising plasticity mechanisms, this device will enable the realization of intelligent autonomous systems with on-line learning capabilities.

  1. Artificial Intelligence in Space Platforms.

    Science.gov (United States)

    1984-12-01

    computer algorithms, there still appears to be a need for Artificial Inteligence techniques in the navigation area. The reason is that navigaion, in...RD-RI32 679 ARTIFICIAL INTELLIGENCE IN SPACE PLRTFORNSMU AIR FORCE 1/𔃼 INST OF TECH WRIGHT-PRTTERSON AFB OH SCHOOL OF ENGINEERING M A WRIGHT DEC 94...i4 Preface The purpose of this study was to analyze the feasibility of implementing Artificial Intelligence techniques to increase autonomy for

  2. Trimaran Resistance Artificial Neural Network

    Science.gov (United States)

    2011-01-01

    11th International Conference on Fast Sea Transportation FAST 2011, Honolulu, Hawaii, USA, September 2011 Trimaran Resistance Artificial Neural Network Richard...Trimaran Resistance Artificial Neural Network 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e... Artificial Neural Network and is restricted to the center and side-hull configurations tested. The value in the parametric model is that it is able to

  3. Artificial Intelligence and Moral intelligence

    OpenAIRE

    Laura Pana

    2008-01-01

    We discuss the thesis that the implementation of a moral code in the behaviour of artificial intelligent systems needs a specific form of human and artificial intelligence, not just an abstract intelligence. We present intelligence as a system with an internal structure and the structural levels of the moral system, as well as certain characteristics of artificial intelligent agents which can/must be treated as 1- individual entities (with a complex, specialized, autonomous or selfdetermined,...

  4. Artificial Intelligence and Economic Theories

    OpenAIRE

    Marwala, Tshilidzi; Hurwitz, Evan

    2017-01-01

    The advent of artificial intelligence has changed many disciplines such as engineering, social science and economics. Artificial intelligence is a computational technique which is inspired by natural intelligence such as the swarming of birds, the working of the brain and the pathfinding of the ants. These techniques have impact on economic theories. This book studies the impact of artificial intelligence on economic theories, a subject that has not been extensively studied. The theories that...

  5. Internet advertising of artificial tanning in Australia.

    Science.gov (United States)

    Team, Victoria; Markovic, Milica

    2006-08-01

    Artificial tanning, defined as deliberate exposure to ultraviolet rays produced by artificial tanning devices, is a new and emerging public health issue in Australia and globally. Epidemiological research suggests that artificial tanning may contribute to the incidence of melanoma, nonmelanoma skin cancer as well as other health problems. Given that Australia has a high incidence of skin cancer, we have undertaken a study to explore how artificial tanning has been promoted to its users. The aim was to analyze the completeness and accuracy of information about artificial tanning. A content analysis of web sites of tanning salons and distributors of tanning equipment in Australia was conducted. A total of 22 web sites were analyzed. None of the solarium operators or distributors of equipment provided full information about the risks of artificial tanning. Fifty-nine percent of web advertisements had no information and 41% provided only partial information regarding the risks of artificial tanning. Pictures with the image of bronze-tanned bodies, predominantly women, were used by all web advertisers. In light of the success of sun-safety campaigns in Australia, the findings of future epidemiological research on the prevalence of artificial tanning and sociological and anthropological research on why people utilize artificial tanning should be a basis for developing effective targeted health promotion on the elimination of artificial tanning in the country.

  6. Differences in predators of artificial and real songbirds nests: Evidence of bias in artificial nest studies

    Science.gov (United States)

    Frank R. Thompson; Dirk E. Burhans

    2004-01-01

    In the past two decades, many researchers have used artificial nest to measure relative rates of nest predation. Recent comparisons show that real and artificial nests may not be depredated at the same rate, but no one has examined the mechanisms underlying these patterns. We determined differences in predator-specific predation rates of real and artificial nests. we...

  7. Encoding and retrieval of artificial visuoauditory memory traces in the auditory cortex requires the entorhinal cortex.

    Science.gov (United States)

    Chen, Xi; Guo, Yiping; Feng, Jingyu; Liao, Zhengli; Li, Xinjian; Wang, Haitao; Li, Xiao; He, Jufang

    2013-06-12

    Damage to the medial temporal lobe impairs the encoding of new memories and the retrieval of memories acquired immediately before the damage in human. In this study, we demonstrated that artificial visuoauditory memory traces can be established in the rat auditory cortex and that their encoding and retrieval depend on the entorhinal cortex of the medial temporal lobe in the rat. We trained rats to associate a visual stimulus with electrical stimulation of the auditory cortex using a classical conditioning protocol. After conditioning, we examined the associative memory traces electrophysiologically (i.e., visual stimulus-evoked responses of auditory cortical neurons) and behaviorally (i.e., visual stimulus-induced freezing and visual stimulus-guided reward retrieval). The establishment of a visuoauditory memory trace in the auditory cortex, which was detectable by electrophysiological recordings, was achieved over 20-30 conditioning trials and was blocked by unilateral, temporary inactivation of the entorhinal cortex. Retrieval of a previously established visuoauditory memory was also affected by unilateral entorhinal cortex inactivation. These findings suggest that the entorhinal cortex is necessary for the encoding and involved in the retrieval of artificial visuoauditory memory in the auditory cortex, at least during the early stages of memory consolidation.

  8. Reminiscences from Pittendrigh's Last PhD Student

    Indian Academy of Sciences (India)

    When Ron Konopka came to Pitt for advice on searching for clock mutants in .... As a mentor of my graduate research, Pitt was very "hands-off" and not very interested ... projects) were OUR projects - not his projects - and he was good at giving ...

  9. From Neurons to Brain: Adaptive Self-Wiring of Neurons

    OpenAIRE

    Segev, Ronen; Ben-Jacob, Eshel

    1998-01-01

    During embryonic morpho-genesis, a collection of individual neurons turns into a functioning network with unique capabilities. Only recently has this most staggering example of emergent process in the natural world, began to be studied. Here we propose a navigational strategy for neurites growth cones, based on sophisticated chemical signaling. We further propose that the embryonic environment (the neurons and the glia cells) acts as an excitable media in which concentric and spiral chemical ...

  10. Motor Neurons

    DEFF Research Database (Denmark)

    Hounsgaard, Jorn

    2017-01-01

    Motor neurons translate synaptic input from widely distributed premotor networks into patterns of action potentials that orchestrate motor unit force and motor behavior. Intercalated between the CNS and muscles, motor neurons add to and adjust the final motor command. The identity and functional...... in in vitro preparations is far from complete. Nevertheless, a foundation has been provided for pursuing functional significance of intrinsic response properties in motoneurons in vivo during motor behavior at levels from molecules to systems....

  11. Crosstalks between kisspeptin neurons and somatostatin neurons are not photoperiod dependent in the ewe hypothalamus.

    Science.gov (United States)

    Dufourny, Laurence; Lomet, Didier

    2017-12-01

    Seasonal reproduction is under the control of gonadal steroid feedback, itself synchronized by day-length or photoperiod. As steroid action on GnRH neurons is mostly indirect and therefore exerted through interneurons, we looked for neuroanatomical interactions between kisspeptin (KP) neurons and somatostatin (SOM) neurons, two populations targeted by sex steroids, in three diencephalic areas involved in the central control of ovulation and/or sexual behavior: the arcuate nucleus (ARC), the preoptic area (POA) and the ventrolateral part of the ventromedial hypothalamus (VMHvl). KP is the most potent secretagogue of GnRH secretion while SOM has been shown to centrally inhibit LH pulsatile release. Notably, hypothalamic contents of these two neuropeptides vary with photoperiod in specific seasonal species. Our hypothesis is that SOM inhibits KP neuron activity and therefore indirectly modulate GnRH release and that this effect may be seasonally regulated. We used sections from ovariectomized estradiol-replaced ewes killed after photoperiodic treatment mimicking breeding or anestrus season. We performed triple immunofluorescent labeling to simultaneously detect KP, SOM and synapsin, a marker for synaptic vesicles. Sections from the POA and from the mediobasal hypothalamus were examined using a confocal microscope. Randomly selected KP or SOM neurons were observed in the POA and ARC. SOM neurons were also observed in the VMHvl. In both the ARC and POA, nearly all KP neurons presented numerous SOM contacts. SOM neurons presented KP terminals more frequently in the ARC than in the POA and VMHvl. Quantitative analysis failed to demonstrate major seasonal variations of KP and SOM interactions. Our data suggest a possible inhibitory action of SOM on all KP neurons in both photoperiodic statuses. On the other hand, the physiological significance of KP modulation of SOM neuron activity and vice versa remain to be determined. Copyright © 2017 Elsevier Inc. All rights reserved.

  12. Fluid-driven origami-inspired artificial muscles

    Science.gov (United States)

    Li, Shuguang; Vogt, Daniel M.; Rus, Daniela; Wood, Robert J.

    2017-12-01

    Artificial muscles hold promise for safe and powerful actuation for myriad common machines and robots. However, the design, fabrication, and implementation of artificial muscles are often limited by their material costs, operating principle, scalability, and single-degree-of-freedom contractile actuation motions. Here we propose an architecture for fluid-driven origami-inspired artificial muscles. This concept requires only a compressible skeleton, a flexible skin, and a fluid medium. A mechanical model is developed to explain the interaction of the three components. A fabrication method is introduced to rapidly manufacture low-cost artificial muscles using various materials and at multiple scales. The artificial muscles can be programed to achieve multiaxial motions including contraction, bending, and torsion. These motions can be aggregated into systems with multiple degrees of freedom, which are able to produce controllable motions at different rates. Our artificial muscles can be driven by fluids at negative pressures (relative to ambient). This feature makes actuation safer than most other fluidic artificial muscles that operate with positive pressures. Experiments reveal that these muscles can contract over 90% of their initial lengths, generate stresses of ˜600 kPa, and produce peak power densities over 2 kW/kg—all equal to, or in excess of, natural muscle. This architecture for artificial muscles opens the door to rapid design and low-cost fabrication of actuation systems for numerous applications at multiple scales, ranging from miniature medical devices to wearable robotic exoskeletons to large deployable structures for space exploration.

  13. Artificial organ engineering

    CERN Document Server

    Annesini, Maria Cristina; Piemonte, Vincenzo; Turchetti, Luca

    2017-01-01

    Artificial organs may be considered as small-scale process plants, in which heat, mass and momentum transfer operations and, possibly, chemical transformations are carried out. This book proposes a novel analysis of artificial organs based on the typical bottom-up approach used in process engineering. Starting from a description of the fundamental physico-chemical phenomena involved in the process, the whole system is rebuilt as an interconnected ensemble of elemental unit operations. Each artificial organ is presented with a short introduction provided by expert clinicians. Devices commonly used in clinical practice are reviewed and their performance is assessed and compared by using a mathematical model based approach. Whilst mathematical modelling is a fundamental tool for quantitative descriptions of clinical devices, models are kept simple to remain focused on the essential features of each process. Postgraduate students and researchers in the field of chemical and biomedical engineering will find that t...

  14. Endocannabinoids mediate neuron-astrocyte communication.

    Science.gov (United States)

    Navarrete, Marta; Araque, Alfonso

    2008-03-27

    Cannabinoid receptors play key roles in brain function, and cannabinoid effects in brain physiology and drug-related behavior are thought to be mediated by receptors present in neurons. Neuron-astrocyte communication relies on the expression by astrocytes of neurotransmitter receptors. Yet, the expression of cannabinoid receptors by astrocytes in situ and their involvement in the neuron-astrocyte communication remain largely unknown. We show that hippocampal astrocytes express CB1 receptors that upon activation lead to phospholipase C-dependent Ca2+ mobilization from internal stores. These receptors are activated by endocannabinoids released by neurons, increasing astrocyte Ca2+ levels, which stimulate glutamate release that activates NMDA receptors in pyramidal neurons. These results demonstrate the existence of endocannabinoid-mediated neuron-astrocyte communication, revealing that astrocytes are targets of cannabinoids and might therefore participate in the physiology of cannabinoid-related addiction. They also reveal the existence of an endocannabinoid-glutamate signaling pathway where astrocytes serve as a bridge for nonsynaptic interneuronal communication.

  15. Introduction to Artificial Neural Networks

    DEFF Research Database (Denmark)

    Larsen, Jan

    1999-01-01

    The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks.......The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks....

  16. CRISPR Epigenome Editing of AKAP150 in DRG Neurons Abolishes Degenerative IVD-Induced Neuronal Activation.

    Science.gov (United States)

    Stover, Joshua D; Farhang, Niloofar; Berrett, Kristofer C; Gertz, Jason; Lawrence, Brandon; Bowles, Robby D

    2017-09-06

    Back pain is a major contributor to disability and has significant socioeconomic impacts worldwide. The degenerative intervertebral disc (IVD) has been hypothesized to contribute to back pain, but a better understanding of the interactions between the degenerative IVD and nociceptive neurons innervating the disc and treatment strategies that directly target these interactions is needed to improve our understanding and treatment of back pain. We investigated degenerative IVD-induced changes to dorsal root ganglion (DRG) neuron activity and utilized CRISPR epigenome editing as a neuromodulation strategy. By exposing DRG neurons to degenerative IVD-conditioned media under both normal and pathological IVD pH levels, we demonstrate that degenerative IVDs trigger interleukin (IL)-6-induced increases in neuron activity to thermal stimuli, which is directly mediated by AKAP and enhanced by acidic pH. Utilizing this novel information on AKAP-mediated increases in nociceptive neuron activity, we developed lentiviral CRISPR epigenome editing vectors that modulate endogenous expression of AKAP150 by targeted promoter histone methylation. When delivered to DRG neurons, these epigenome-modifying vectors abolished degenerative IVD-induced DRG-elevated neuron activity while preserving non-pathologic neuron activity. This work elucidates the potential for CRISPR epigenome editing as a targeted gene-based pain neuromodulation strategy. Copyright © 2017 The American Society of Gene and Cell Therapy. Published by Elsevier Inc. All rights reserved.

  17. Axonal regeneration and neuronal function are preserved in motor neurons lacking ß-actin in vivo.

    Directory of Open Access Journals (Sweden)

    Thomas R Cheever

    2011-03-01

    Full Text Available The proper localization of ß-actin mRNA and protein is essential for growth cone guidance and axon elongation in cultured neurons. In addition, decreased levels of ß-actin mRNA and protein have been identified in the growth cones of motor neurons cultured from a mouse model of Spinal Muscular Atrophy (SMA, suggesting that ß-actin loss-of-function at growth cones or pre-synaptic nerve terminals could contribute to the pathogenesis of this disease. However, the role of ß-actin in motor neurons in vivo and its potential relevance to disease has yet to be examined. We therefore generated motor neuron specific ß-actin knock-out mice (Actb-MNsKO to investigate the function of ß-actin in motor neurons in vivo. Surprisingly, ß-actin was not required for motor neuron viability or neuromuscular junction maintenance. Skeletal muscle from Actb-MNsKO mice showed no histological indication of denervation and did not significantly differ from controls in several measurements of physiologic function. Finally, motor axon regeneration was unimpaired in Actb-MNsKO mice, suggesting that ß-actin is not required for motor neuron function or regeneration in vivo.

  18. Acetaminophen inhibits neuronal inflammation and protects neurons from oxidative stress

    Directory of Open Access Journals (Sweden)

    Grammas Paula

    2009-03-01

    Full Text Available Abstract Background Recent studies have demonstrated a link between the inflammatory response, increased cytokine formation, and neurodegeneration in the brain. The beneficial effects of anti-inflammatory drugs in neurodegenerative diseases, such as Alzheimer's disease (AD, have been documented. Increasing evidence suggests that acetaminophen has unappreciated anti-oxidant and anti-inflammatory properties. The objectives of this study are to determine the effects of acetaminophen on cultured brain neuronal survival and inflammatory factor expression when exposed to oxidative stress. Methods Cerebral cortical cultured neurons are pretreated with acetaminophen and then exposed to the superoxide-generating compound menadione (5 μM. Cell survival is assessed by MTT assay and inflammatory protein (tumor necrosis factor alpha, interleukin-1, macrophage inflammatory protein alpha, and RANTES release quantitated by ELISA. Expression of pro- and anti-apoptotic proteins is assessed by western blots. Results Acetaminophen has pro-survival effects on neurons in culture. Menadione, a superoxide releasing oxidant stressor, causes a significant (p Conclusion These data show that acetaminophen has anti-oxidant and anti-inflammatory effects on neurons and suggest a heretofore unappreciated therapeutic potential for this drug in neurodegenerative diseases such as AD that are characterized by oxidant and inflammatory stress.

  19. How to teach artificial organs.

    Science.gov (United States)

    Zapanta, Conrad M; Borovetz, Harvey S; Lysaght, Michael J; Manning, Keefe B

    2011-01-01

    Artificial organs education is often an overlooked field for many bioengineering and biomedical engineering students. The purpose of this article is to describe three different approaches to teaching artificial organs. This article can serve as a reference for those who wish to offer a similar course at their own institutions or incorporate these ideas into existing courses. Artificial organ classes typically fulfill several ABET (Accreditation Board for Engineering and Technology) criteria, including those specific to bioengineering and biomedical engineering programs.

  20. Artificial Neural Network Analysis System

    Science.gov (United States)

    2001-02-27

    Contract No. DASG60-00-M-0201 Purchase request no.: Foot in the Door-01 Title Name: Artificial Neural Network Analysis System Company: Atlantic... Artificial Neural Network Analysis System 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Powell, Bruce C 5d. PROJECT NUMBER 5e. TASK NUMBER...34) 27-02-2001 Report Type N/A Dates Covered (from... to) ("DD MON YYYY") 28-10-2000 27-02-2001 Title and Subtitle Artificial Neural Network Analysis

  1. Artificial Psychology: The Psychology of AI

    Directory of Open Access Journals (Sweden)

    James A. Crowder

    2013-12-01

    Full Text Available Having artificially intelligent machines that think, learn, reason, experience, and can function autonomously, without supervision, is one of the most intriguing goals in all of Computer Science. As the types of problems we would like machines to solve get more complex, it is becoming a necessary goal as well. One of the many problems associated with this goal is that what learning and reasoning are have so many possible meanings that the solution can easily get lost in the sea of opinions and options. The goal of this paper is to establish some foundational principles, theory, and concepts that we feel are the backbone of real, autonomous Artificial Intelligence. With this fully autonomous, learning, reasoning, artificially intelligent system (an artificial brain, comes the need to possess constructs in its hardware and software that mimic processes and subsystems that exist within the human brain, including intuitive and emotional memory concepts. Presented here is a discussion of the psychological constructs of artificial intelligence and how they might play out in an artificial mind.

  2. Artificial Intelligence in Civil Engineering

    Directory of Open Access Journals (Sweden)

    Pengzhen Lu

    2012-01-01

    Full Text Available Artificial intelligence is a branch of computer science, involved in the research, design, and application of intelligent computer. Traditional methods for modeling and optimizing complex structure systems require huge amounts of computing resources, and artificial-intelligence-based solutions can often provide valuable alternatives for efficiently solving problems in the civil engineering. This paper summarizes recently developed methods and theories in the developing direction for applications of artificial intelligence in civil engineering, including evolutionary computation, neural networks, fuzzy systems, expert system, reasoning, classification, and learning, as well as others like chaos theory, cuckoo search, firefly algorithm, knowledge-based engineering, and simulated annealing. The main research trends are also pointed out in the end. The paper provides an overview of the advances of artificial intelligence applied in civil engineering.

  3. Neuronal avalanches and learning

    Energy Technology Data Exchange (ETDEWEB)

    Arcangelis, Lucilla de, E-mail: dearcangelis@na.infn.it [Department of Information Engineering and CNISM, Second University of Naples, 81031 Aversa (Italy)

    2011-05-01

    Networks of living neurons represent one of the most fascinating systems of biology. If the physical and chemical mechanisms at the basis of the functioning of a single neuron are quite well understood, the collective behaviour of a system of many neurons is an extremely intriguing subject. Crucial ingredient of this complex behaviour is the plasticity property of the network, namely the capacity to adapt and evolve depending on the level of activity. This plastic ability is believed, nowadays, to be at the basis of learning and memory in real brains. Spontaneous neuronal activity has recently shown features in common to other complex systems. Experimental data have, in fact, shown that electrical information propagates in a cortex slice via an avalanche mode. These avalanches are characterized by a power law distribution for the size and duration, features found in other problems in the context of the physics of complex systems and successful models have been developed to describe their behaviour. In this contribution we discuss a statistical mechanical model for the complex activity in a neuronal network. The model implements the main physiological properties of living neurons and is able to reproduce recent experimental results. Then, we discuss the learning abilities of this neuronal network. Learning occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. The system is able to learn all the tested rules, in particular the exclusive OR (XOR) and a random rule with three inputs. The learning dynamics exhibits universal features as function of the strength of plastic adaptation. Any rule could be learned provided that the plastic adaptation is sufficiently slow.

  4. Neuronal avalanches and learning

    International Nuclear Information System (INIS)

    Arcangelis, Lucilla de

    2011-01-01

    Networks of living neurons represent one of the most fascinating systems of biology. If the physical and chemical mechanisms at the basis of the functioning of a single neuron are quite well understood, the collective behaviour of a system of many neurons is an extremely intriguing subject. Crucial ingredient of this complex behaviour is the plasticity property of the network, namely the capacity to adapt and evolve depending on the level of activity. This plastic ability is believed, nowadays, to be at the basis of learning and memory in real brains. Spontaneous neuronal activity has recently shown features in common to other complex systems. Experimental data have, in fact, shown that electrical information propagates in a cortex slice via an avalanche mode. These avalanches are characterized by a power law distribution for the size and duration, features found in other problems in the context of the physics of complex systems and successful models have been developed to describe their behaviour. In this contribution we discuss a statistical mechanical model for the complex activity in a neuronal network. The model implements the main physiological properties of living neurons and is able to reproduce recent experimental results. Then, we discuss the learning abilities of this neuronal network. Learning occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. The system is able to learn all the tested rules, in particular the exclusive OR (XOR) and a random rule with three inputs. The learning dynamics exhibits universal features as function of the strength of plastic adaptation. Any rule could be learned provided that the plastic adaptation is sufficiently slow.

  5. Pathogenesis of motor neuron disease

    Institute of Scientific and Technical Information of China (English)

    Xuefei Wang

    2006-01-01

    OBJECTIVE: To summarize and analyze the factors and theories related to the attack of motor neuron disease, and comprehensively investigate the pathogenesis of motor neuron disease.DATA SOURCES: A search of Pubmed database was undertaken to identify articles about motor neuron disease published in English from January 1994 to June 2006 by using the keywords of "neurodegenerative diseases". Other literatures were collected by retrieving specific journals and articles.STUDY SELECTION: The data were checked primarily, articles related to the pathogenesis of motor neuron disease were involved, and those obviously irrelated to the articles were excluded.DATA EXTRACTION: Totally 54 articles were collected, 30 of them were involved, and the other 24 were excluded.DATA SYNTHESIS: The pathogenesis of motor neuron disease has multiple factors, and the present related theories included free radical oxidation, excitotoxicity, genetic and immune factors, lack of neurotrophic factor,injury of neurofilament, etc. The studies mainly come from transgenic animal models, cell culture in vitro and patients with familial motor neuron disease, but there are still many restrictions and disadvantages.CONCLUSION: It is necessary to try to find whether there is internal association among different mechanisms,comprehensively investigate the pathogenesis of motor neuron diseases, in order to provide reliable evidence for the clinical treatment.

  6. Fault tolerance of artificial neural networks with applications in critical systems

    Science.gov (United States)

    Protzel, Peter W.; Palumbo, Daniel L.; Arras, Michael K.

    1992-01-01

    This paper investigates the fault tolerance characteristics of time continuous recurrent artificial neural networks (ANN) that can be used to solve optimization problems. The principle of operations and performance of these networks are first illustrated by using well-known model problems like the traveling salesman problem and the assignment problem. The ANNs are then subjected to 13 simultaneous 'stuck at 1' or 'stuck at 0' faults for network sizes of up to 900 'neurons'. The effects of these faults is demonstrated and the cause for the observed fault tolerance is discussed. An application is presented in which a network performs a critical task for a real-time distributed processing system by generating new task allocations during the reconfiguration of the system. The performance degradation of the ANN under the presence of faults is investigated by large-scale simulations, and the potential benefits of delegating a critical task to a fault tolerant network are discussed.

  7. Fluid-driven origami-inspired artificial muscles.

    Science.gov (United States)

    Li, Shuguang; Vogt, Daniel M; Rus, Daniela; Wood, Robert J

    2017-12-12

    Artificial muscles hold promise for safe and powerful actuation for myriad common machines and robots. However, the design, fabrication, and implementation of artificial muscles are often limited by their material costs, operating principle, scalability, and single-degree-of-freedom contractile actuation motions. Here we propose an architecture for fluid-driven origami-inspired artificial muscles. This concept requires only a compressible skeleton, a flexible skin, and a fluid medium. A mechanical model is developed to explain the interaction of the three components. A fabrication method is introduced to rapidly manufacture low-cost artificial muscles using various materials and at multiple scales. The artificial muscles can be programed to achieve multiaxial motions including contraction, bending, and torsion. These motions can be aggregated into systems with multiple degrees of freedom, which are able to produce controllable motions at different rates. Our artificial muscles can be driven by fluids at negative pressures (relative to ambient). This feature makes actuation safer than most other fluidic artificial muscles that operate with positive pressures. Experiments reveal that these muscles can contract over 90% of their initial lengths, generate stresses of ∼600 kPa, and produce peak power densities over 2 kW/kg-all equal to, or in excess of, natural muscle. This architecture for artificial muscles opens the door to rapid design and low-cost fabrication of actuation systems for numerous applications at multiple scales, ranging from miniature medical devices to wearable robotic exoskeletons to large deployable structures for space exploration. Copyright © 2017 the Author(s). Published by PNAS.

  8. BigNeuron: Large-Scale 3D Neuron Reconstruction from Optical Microscopy Images

    NARCIS (Netherlands)

    H. Peng (Hanchuan); M. Hawrylycz (Michael); J. Roskams (Jane); S. Hill (Sean); N. Spruston (Nelson); E. Meijering (Erik); G.A. Ascoli (Giorgio A.)

    2015-01-01

    textabstractUnderstanding the structure of single neurons is critical for understanding how they function within neural circuits. BigNeuron is a new community effort that combines modern bioimaging informatics, recent leaps in labeling and microscopy, and the widely recognized need for openness and

  9. Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Petr Maca

    2016-01-01

    Full Text Available The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI and the standardized precipitation evaporation index (SPEI and were derived for the period of 1948–2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons.

  10. Understanding metal homeostasis in primary cultured neurons. Studies using single neuron subcellular and quantitative metallomics.

    Science.gov (United States)

    Colvin, Robert A; Lai, Barry; Holmes, William R; Lee, Daewoo

    2015-07-01

    The purpose of this study was to demonstrate how single cell quantitative and subcellular metallomics inform us about both the spatial distribution and cellular mechanisms of metal buffering and homeostasis in primary cultured neurons from embryonic rat brain, which are often used as models of human disease involving metal dyshomeostasis. The present studies utilized synchrotron radiation X-ray fluorescence (SRXRF) and focused primarily on zinc and iron, two abundant metals in neurons that have been implicated in the pathogenesis of neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease. Total single cell contents for calcium, iron, zinc, copper, manganese, and nickel were determined. Resting steady state zinc showed a diffuse distribution in both soma and processes, best defined by the mass profile of the neuron with an enrichment in the nucleus compared with the cytoplasm. Zinc buffering and homeostasis was studied using two modes of cellular zinc loading - transporter and ionophore (pyrithione) mediated. Single neuron zinc contents were shown to statistically significantly increase by either loading method - ionophore: 160 million to 7 billion; transporter 160 million to 280 million atoms per neuronal soma. The newly acquired and buffered zinc still showed a diffuse distribution. Soma and processes have about equal abilities to take up zinc via transporter mediated pathways. Copper levels are distributed diffusely as well, but are relatively higher in the processes relative to zinc levels. Prior studies have observed iron puncta in certain cell types, but others have not. In the present study, iron puncta were characterized in several primary neuronal types. The results show that iron puncta could be found in all neuronal types studied and can account for up to 50% of the total steady state content of iron in neuronal soma. Although other metals can be present in iron puncta, they are predominantly iron containing and do not appear to be

  11. Neuromorphic Silicon Neuron Circuits

    Science.gov (United States)

    Indiveri, Giacomo; Linares-Barranco, Bernabé; Hamilton, Tara Julia; van Schaik, André; Etienne-Cummings, Ralph; Delbruck, Tobi; Liu, Shih-Chii; Dudek, Piotr; Häfliger, Philipp; Renaud, Sylvie; Schemmel, Johannes; Cauwenberghs, Gert; Arthur, John; Hynna, Kai; Folowosele, Fopefolu; Saighi, Sylvain; Serrano-Gotarredona, Teresa; Wijekoon, Jayawan; Wang, Yingxue; Boahen, Kwabena

    2011-01-01

    Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain–machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin–Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips. PMID:21747754

  12. Neuromorphic silicon neuron circuits

    Directory of Open Access Journals (Sweden)

    Giacomo eIndiveri

    2011-05-01

    Full Text Available Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain-machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance based Hodgkin-Huxley models to bi-dimensional generalized adaptive Integrate and Fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips.

  13. Polymeric membrane materials for artificial organs.

    Science.gov (United States)

    Kawakami, Hiroyoshi

    2008-01-01

    Many polymeric materials have already been used in the field of artificial organs. However, the materials used in artificial organs are not necessarily created with the best material selectivity and materials design; therefore, the development of synthesized polymeric membrane materials for artificial organs based on well-defined designs is required. The approaches to the development of biocompatible polymeric materials fall into three categories: (1) control of physicochemical characteristics on material surfaces, (2) modification of material surfaces using biomolecules, and (3) construction of biomimetic membrane surfaces. This review will describe current issues regarding polymeric membrane materials for use in artificial organs.

  14. Artificial reefs: “Attraction versus Production”

    Directory of Open Access Journals (Sweden)

    Eduardo Barros Fagundes Netto

    2011-04-01

    Full Text Available The production of fish is the most common reason for the construction and installation of an artificial reef. More recently, environmental concerns and conservation of biological resources have been instrumental to the formulation of new goals of the research. One of the issues to be resolved is the biological function of “attraction vs. production” as a result of the use of artificial reefs. The uncertainty as to the answer to the question whether the artificial reefs will or not benefit the development of fish stocks could be solved if the artificial reefs would be managed as marine protected areas.

  15. Progress and Challenge of Artificial Intelligence

    Institute of Scientific and Technical Information of China (English)

    Zhong-Zhi Shi; Nan-Ning Zheng

    2006-01-01

    Artificial Intelligence (AI) is generally considered to be a subfield of computer science, that is concerned to attempt simulation, extension and expansion of human intelligence. Artificial intelligence has enjoyed tremendous success over the last fifty years. In this paper we only focus on visual perception, granular computing, agent computing, semantic grid. Human-level intelligence is the long-term goal of artificial intelligence. We should do joint research on basic theory and technology of intelligence by brain science, cognitive science, artificial intelligence and others. A new cross discipline intelligence science is undergoing a rapid development. Future challenges are given in final section.

  16. Afferent neuronal control of type-I gonadotropin releasing hormone (GnRH neurons in the human

    Directory of Open Access Journals (Sweden)

    Erik eHrabovszky

    2013-09-01

    Full Text Available Understanding the regulation of the human menstrual cycle represents an important ultimate challenge of reproductive neuroendocrine research. However, direct translation of information from laboratory animal experiments to the human is often complicated by strikingly different and unique reproductive strategies and central regulatory mechanisms that can be present in even closely related animal species. In all mammals studied so far, type-I gonadotropin releasing hormone (GnRH synthesizing neurons form the final common output way from the hypothalamus in the neuroendocrine control of the adenohypophysis. Under various physiological and pathological conditions, hormonal and metabolic signals either regulate GnRH neurons directly or act on upstream neuronal circuitries to influence the pattern of pulsatile GnRH secretion into the hypophysial portal circulation. Neuronal afferents to GnRH cells convey important metabolic-, stress-, sex steroid-, lactational- and circadian signals to the reproductive axis, among other effects. This article gives an overview of the available neuroanatomical literature that described the afferent regulation of human GnRH neurons by peptidergic, monoaminergic and amino acidergic neuronal systems. Recent studies of human genetics provided evidence that central peptidergic signaling by kisspeptins and neurokinin B play particularly important roles in puberty onset and later, in the sex steroid-dependent feedback regulation of GnRH neurons. This review article places special emphasis on the topographic distribution, sexual dimorphism, aging-dependent neuroanatomical changes and plastic connectivity to GnRH neurons of the critically important human hypothalamic kisspeptin and neurokinin B systems.

  17. Cometin is a novel neurotrophic factor that promotes neurite outgrowth and neuroblast migration in vitro and supports survival of spiral ganglion neurons in vivo

    DEFF Research Database (Denmark)

    Jørgensen, Jesper Roland; Fransson, Anette; Fjord-Larsen, Lone

    2012-01-01

    properties in vitro, combined with the restricted inner ear expression during development, we further investigated Cometin in relation to deafness. In neomycin deafened guinea pigs, two weeks intracochlear infusion of recombinant Cometin supports spiral ganglion neuron survival and function. In contrast...... to the control group receiving artificial perilymph, Cometin treated animals retain normal electrically-evoked brainstem response which is maintained several weeks after treatment cessation. Neuroprotection is also evident from stereological analysis of the spiral ganglion. Altogether, these studies show...

  18. A Re-configurable On-line Learning Spiking Neuromorphic Processor comprising 256 neurons and 128K synapses

    Directory of Open Access Journals (Sweden)

    Ning eQiao

    2015-04-01

    Full Text Available Implementing compact, low-power artificial neural processing systems with real-time on-line learning abilities is still an open challenge. In this paper we present a full-custom mixed-signal VLSI device with neuromorphic learning circuits that emulate the biophysics of real spiking neurons and dynamic synapses for exploring the properties of computational neuroscience models and for building brain-inspired computing systems. The proposed architecture allows the on-chip configuration of a wide range of network connectivities, including recurrent and deep networks with short-term and long-term plasticity. The device comprises 128 K analog synapse and 256 neuron circuits with biologically plausible dynamics and bi-stable spike-based plasticity mechanisms that endow it with on-line learning abilities. In addition to the analog circuits, the device comprises also asynchronous digital logic circuits for setting different synapse and neuron properties as well as different network configurations. This prototype device, fabricated using a 180 nm 1P6M CMOS process, occupies an area of 51.4 mm 2 , and consumes approximately 4 mW for typical experiments, for example involving attractor networks. Here we describe the details of the overall architecture and of the individual circuits and present experimental results that showcase its potential. By supporting a wide range of cortical-like computational modules comprising plasticity mechanisms, this device will enable the realization of intelligent autonomous systems with on-line learning capabilities.

  19. A low-density culture method of cerebellar granule neurons with paracrine support applicable for the study of neuronal morphogenesis.

    Science.gov (United States)

    Kubota, Kenta; Seno, Takeshi; Konishi, Yoshiyuki

    2013-11-20

    Cerebellar granule neuronal cultures have been used to study the molecular mechanisms underlying neuronal functions, including neuronal morphogenesis. However, a limitation of this system is the difficulty to analyze isolated neurons because these are required to be maintained at a high density. Therefore, in the present study, we aimed to develop a simple and cost-effective method for culturing low-density cerebellar granule neurons. Cerebellar granule cells at two different densities (low- and high-density) were co-cultivated in order for the low-density culture to be supported by the paracrine signals from the high-density culture. This method enabled morphology analysis of isolated cerebellar granule neurons without astrocytic feeder cultures or supplements such as B27. Using this method, we investigated the function of a polarity factor. Studies using hippocampal neurons suggested that glycogen synthase kinase-3 (GSK-3) is an essential regulator of neuronal polarity, and inhibition of GSK-3 results in the formation of multiple axons. Pharmacological inhibitors for GSK-3 (6-bromoindirubin-3'-oxime and lithium chloride) did not cause the formation of multiple axons of cerebellar granule neurons but significantly reduced their length. Consistent results were obtained by introducing kinase-dead form of GSK-3 beta (K85A). These results indicated that GSK-3 is not directly involved in the control of neuronal polarity in cerebellar granule neurons. Overall, this study provides a simple method for culturing low-density cerebellar granule neurons and insights in to the neuronal-type dependent function of GSK-3 in neuronal morphogenesis. © 2013 Elsevier B.V. All rights reserved.

  20. Perifornical orexinergic neurons modulate REM sleep by influencing locus coeruleus neurons in rats.

    Science.gov (United States)

    Choudhary, R C; Khanday, M A; Mitra, A; Mallick, B N

    2014-10-24

    Activation of the orexin (OX)-ergic neurons in the perifornical (PeF) area has been reported to induce waking and reduce rapid eye movement sleep (REMS). The activities of OX-ergic neurons are maximum during active waking and they progressively reduce during non-REMS (NREMS) and REMS. Apparently, the locus coeruleus (LC) neurons also behave in a comparable manner as that of the OX-ergic neurons particularly in relation to waking and REMS. Further, as PeF OX-ergic neurons send dense projections to LC, we argued that the former could drive the LC neurons to modulate waking and REMS. Studies in freely moving normally behaving animals where simultaneously neuro-chemo-anatomo-physio-behavioral information could be deciphered would significantly strengthen our understanding on the regulation of REMS. Therefore, in this study in freely behaving chronically prepared rats we stimulated the PeF neurons without or with simultaneous blocking of specific subtypes of OX-ergic receptors in the LC while electrophysiological recording characterizing sleep-waking was continued. Single dose of glutamate stimulation as well as sustained mild electrical stimulation of PeF (both bilateral) significantly increased waking and reduced REMS as compared to baseline. Simultaneous application of OX-receptor1 (OX1R) antagonist bilaterally into the LC prevented PeF stimulation-induced REMS suppression. Also, the effect of electrical stimulation of the PeF was long lasting as compared to that of the glutamate stimulation. Further, sustained electrical stimulation significantly decreased both REMS duration as well as REMS frequency, while glutamate stimulation decreased REMS duration only. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

  1. Dynamic cardiomyoplasty using artificial muscle.

    Science.gov (United States)

    Suzuki, Yasuyuki; Daitoku, Kazuyuki; Minakawa, Masahito; Fukui, Kozo; Fukuda, Ikuo

    2008-01-01

    Dynamic cardiomyoplasty using latissimus dorsi muscle was previously used to compensate for congestive heart failure. Now, however, this method is not acceptable because the long-term result was not as expected owing to fatigue of the skeletal muscle. BioMetal fiber developed by Toki Corporation is one of the artificial muscles activated by electric current. The behavior of this fiber is similar to that of organic muscle. We made an artificial muscle like the latissimus dorsi using BioMetal fiber and tested whether we could use this new muscle as a cardiac supporting device. Testing one Biometal fiber showed the following performance: practical use maximal generative force was 30 g, exercise variation was 50%, and the standard driving current was 220 mA. We created a 4 x 12-cm tabular artificial muscle using 8 BioMetal fibers as a cardiac support device. We also made a simulation circuit composed of a 6 x 8-cm soft bag with unidirectional valves, reservoir, and connecting tube. The simulation circuit was filled with water and the soft bag was wrapped with the artificial muscle device. After powering the device electrically at 9 V with a current of 220 mA for each fiber, we measured the inside pressure and observed the movement of the artificial device. The artificial muscle contracted in 0.5 s for peak time and squeezed the soft bag. The peak pressure inside the soft bag was measured as 10 mmHg. Although further work will be needed to enhance the speed of deformability and movement simulating contraction, we conclude that artificial muscle may be potentially useful as a cardiac assistance device that can be developed for dynamic cardiomyoplasty.

  2. The mirror neuron system.

    Science.gov (United States)

    Cattaneo, Luigi; Rizzolatti, Giacomo

    2009-05-01

    Mirror neurons are a class of neurons, originally discovered in the premotor cortex of monkeys, that discharge both when individuals perform a given motor act and when they observe others perform that same motor act. Ample evidence demonstrates the existence of a cortical network with the properties of mirror neurons (mirror system) in humans. The human mirror system is involved in understanding others' actions and their intentions behind them, and it underlies mechanisms of observational learning. Herein, we will discuss the clinical implications of the mirror system.

  3. How to make spinal motor neurons.

    Science.gov (United States)

    Davis-Dusenbery, Brandi N; Williams, Luis A; Klim, Joseph R; Eggan, Kevin

    2014-02-01

    All muscle movements, including breathing, walking, and fine motor skills rely on the function of the spinal motor neuron to transmit signals from the brain to individual muscle groups. Loss of spinal motor neuron function underlies several neurological disorders for which treatment has been hampered by the inability to obtain sufficient quantities of primary motor neurons to perform mechanistic studies or drug screens. Progress towards overcoming this challenge has been achieved through the synthesis of developmental biology paradigms and advances in stem cell and reprogramming technology, which allow the production of motor neurons in vitro. In this Primer, we discuss how the logic of spinal motor neuron development has been applied to allow generation of motor neurons either from pluripotent stem cells by directed differentiation and transcriptional programming, or from somatic cells by direct lineage conversion. Finally, we discuss methods to evaluate the molecular and functional properties of motor neurons generated through each of these techniques.

  4. Firing dynamics of an autaptic neuron

    International Nuclear Information System (INIS)

    Wang Heng-Tong; Chen Yong

    2015-01-01

    Autapses are synapses that connect a neuron to itself in the nervous system. Previously, both experimental and theoretical studies have demonstrated that autaptic connections in the nervous system have a significant physiological function. Autapses in nature provide self-delayed feedback, thus introducing an additional timescale to neuronal activities and causing many dynamic behaviors in neurons. Recently, theoretical studies have revealed that an autapse provides a control option for adjusting the response of a neuron: e.g., an autaptic connection can cause the electrical activities of the Hindmarsh–Rose neuron to switch between quiescent, periodic, and chaotic firing patterns; an autapse can enhance or suppress the mode-locking status of a neuron injected with sinusoidal current; and the firing frequency and interspike interval distributions of the response spike train can also be modified by the autapse. In this paper, we review recent studies that showed how an autapse affects the response of a single neuron. (topical review)

  5. Mirror neurons: functions, mechanisms and models.

    Science.gov (United States)

    Oztop, Erhan; Kawato, Mitsuo; Arbib, Michael A

    2013-04-12

    Mirror neurons for manipulation fire both when the animal manipulates an object in a specific way and when it sees another animal (or the experimenter) perform an action that is more or less similar. Such neurons were originally found in macaque monkeys, in the ventral premotor cortex, area F5 and later also in the inferior parietal lobule. Recent neuroimaging data indicate that the adult human brain is endowed with a "mirror neuron system," putatively containing mirror neurons and other neurons, for matching the observation and execution of actions. Mirror neurons may serve action recognition in monkeys as well as humans, whereas their putative role in imitation and language may be realized in human but not in monkey. This article shows the important role of computational models in providing sufficient and causal explanations for the observed phenomena involving mirror systems and the learning processes which form them, and underlines the need for additional circuitry to lift up the monkey mirror neuron circuit to sustain the posited cognitive functions attributed to the human mirror neuron system. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  6. Where do mirror neurons come from?

    Science.gov (United States)

    Heyes, Cecilia

    2010-03-01

    Debates about the evolution of the 'mirror neuron system' imply that it is an adaptation for action understanding. Alternatively, mirror neurons may be a byproduct of associative learning. Here I argue that the adaptation and associative hypotheses both offer plausible accounts of the origin of mirror neurons, but the associative hypothesis has three advantages. First, it provides a straightforward, testable explanation for the differences between monkeys and humans that have led some researchers to question the existence of a mirror neuron system. Second, it is consistent with emerging evidence that mirror neurons contribute to a range of social cognitive functions, but do not play a dominant, specialised role in action understanding. Finally, the associative hypothesis is supported by recent data showing that, even in adulthood, the mirror neuron system can be transformed by sensorimotor learning. The associative account implies that mirror neurons come from sensorimotor experience, and that much of this experience is obtained through interaction with others. Therefore, if the associative account is correct, the mirror neuron system is a product, as well as a process, of social interaction. (c) 2009 Elsevier Ltd. All rights reserved.

  7. Modern drug design: the implication of using artificial neuronal networks and multiple molecular dynamic simulations

    Science.gov (United States)

    Yakovenko, Oleksandr; Jones, Steven J. M.

    2018-01-01

    We report the implementation of molecular modeling approaches developed as a part of the 2016 Grand Challenge 2, the blinded competition of computer aided drug design technologies held by the D3R Drug Design Data Resource (https://drugdesigndata.org/). The challenge was focused on the ligands of the farnesoid X receptor (FXR), a highly flexible nuclear receptor of the cholesterol derivative chenodeoxycholic acid. FXR is considered an important therapeutic target for metabolic, inflammatory, bowel and obesity related diseases (Expert Opin Drug Metab Toxicol 4:523-532, 2015), but in the context of this competition it is also interesting due to the significant ligand-induced conformational changes displayed by the protein. To deal with these conformational changes we employed multiple simulations of molecular dynamics (MD). Our MD-based protocols were top-ranked in estimating the free energy of binding of the ligands and FXR protein. Our approach was ranked second in the prediction of the binding poses where we also combined MD with molecular docking and artificial neural networks. Our approach showed mediocre results for high-throughput scoring of interactions.

  8. Artificial organs and transplantation.

    Science.gov (United States)

    Splendiani, G; Cipriani, S; Vega, A; Casciani, C U

    2003-05-01

    Nowadays artificial devices are not able to totally and undefinitely replace the loss of function of all vital organs and artificial organs can be used only to bridge the time to transplantation, which must be considered the first choice in the therapeutical approach for many chronic diseases. Since general population aging process is leading to an increase of organ demand, the gap between performed and requested transplantation is hard to fill. Xenotransplantation is nowadays only an experimental alternative solution and we have to do our best using available artificial organs to increase and improve the survival of patients waiting for transplantation. In this meeting we particularly dealt about organ function replacing therapy, especially regarding the kidney, heart, liver, pancreas and ear.

  9. Artificial senses for characterization of food quality

    Institute of Scientific and Technical Information of China (English)

    HUANG Yan-bo; LAN Yu-bin; R.E. Lacey

    2004-01-01

    Food quality is of primary concern in the food industry and to the consumer. Systems that mimic human senses have been developed and applied to the characterization of food quality. The five primary senses are: vision, hearing, smell, taste and touch.In the characterization of food quality, people assess the samples sensorially and differentiate "good" from "bad" on a continuum.However, the human sensory system is subjective, with mental and physical inconsistencies, and needs time to work. Artificial senses such as machine vision, the electronic ear, electronic nose, electronic tongue, artificial mouth and even artificial the head have been developed that mimic the human senses. These artificial senses are coordinated individually or collectively by a pattern recognition technique, typically artificial neural networks, which have been developed based on studies of the mechanism of the human brain. Such a structure has been used to formulate methods for rapid characterization of food quality. This research presents and discusses individual artificial sensing systems. With the concept of multi-sensor data fusion these sensor systems can work collectively in some way. Two such fused systems, artificial mouth and artificial head, are described and discussed. It indicates that each of the individual systems has their own artificially sensing ability to differentiate food samples. It further indicates that with a more complete mimic of human intelligence the fused systems are more powerful than the individual systems in differentiation of food samples.

  10. Mirror neurons: from origin to function.

    Science.gov (United States)

    Cook, Richard; Bird, Geoffrey; Catmur, Caroline; Press, Clare; Heyes, Cecilia

    2014-04-01

    This article argues that mirror neurons originate in sensorimotor associative learning and therefore a new approach is needed to investigate their functions. Mirror neurons were discovered about 20 years ago in the monkey brain, and there is now evidence that they are also present in the human brain. The intriguing feature of many mirror neurons is that they fire not only when the animal is performing an action, such as grasping an object using a power grip, but also when the animal passively observes a similar action performed by another agent. It is widely believed that mirror neurons are a genetic adaptation for action understanding; that they were designed by evolution to fulfill a specific socio-cognitive function. In contrast, we argue that mirror neurons are forged by domain-general processes of associative learning in the course of individual development, and, although they may have psychological functions, they do not necessarily have a specific evolutionary purpose or adaptive function. The evidence supporting this view shows that (1) mirror neurons do not consistently encode action "goals"; (2) the contingency- and context-sensitive nature of associative learning explains the full range of mirror neuron properties; (3) human infants receive enough sensorimotor experience to support associative learning of mirror neurons ("wealth of the stimulus"); and (4) mirror neurons can be changed in radical ways by sensorimotor training. The associative account implies that reliable information about the function of mirror neurons can be obtained only by research based on developmental history, system-level theory, and careful experimentation.

  11. High-Degree Neurons Feed Cortical Computations.

    Directory of Open Access Journals (Sweden)

    Nicholas M Timme

    2016-05-01

    Full Text Available Recent work has shown that functional connectivity among cortical neurons is highly varied, with a small percentage of neurons having many more connections than others. Also, recent theoretical developments now make it possible to quantify how neurons modify information from the connections they receive. Therefore, it is now possible to investigate how information modification, or computation, depends on the number of connections a neuron receives (in-degree or sends out (out-degree. To do this, we recorded the simultaneous spiking activity of hundreds of neurons in cortico-hippocampal slice cultures using a high-density 512-electrode array. This preparation and recording method combination produced large numbers of neurons recorded at temporal and spatial resolutions that are not currently available in any in vivo recording system. We utilized transfer entropy (a well-established method for detecting linear and nonlinear interactions in time series and the partial information decomposition (a powerful, recently developed tool for dissecting multivariate information processing into distinct parts to quantify computation between neurons where information flows converged. We found that computations did not occur equally in all neurons throughout the networks. Surprisingly, neurons that computed large amounts of information tended to receive connections from high out-degree neurons. However, the in-degree of a neuron was not related to the amount of information it computed. To gain insight into these findings, we developed a simple feedforward network model. We found that a degree-modified Hebbian wiring rule best reproduced the pattern of computation and degree correlation results seen in the real data. Interestingly, this rule also maximized signal propagation in the presence of network-wide correlations, suggesting a mechanism by which cortex could deal with common random background input. These are the first results to show that the extent to

  12. Artificial humidification for the mechanically ventilated patient.

    Science.gov (United States)

    Selvaraj, N

    Caring for patients who are mechanically ventilated poses many challenges for critical care nurses. It is important to humidify the patient's airways artificially to prevent complications such as ventilator-associated pneumonia. There is no gold standard to determine which type of humidification is best for patients who are artificially ventilated. This article provides an overview of commonly used artificial humidification for mechanically ventilated patients and discusses nurses' responsibilities in caring for patients receiving artificial humidification.

  13. Selective serotonergic excitation of callosal projection neurons

    Directory of Open Access Journals (Sweden)

    Daniel eAvesar

    2012-03-01

    Full Text Available Serotonin (5-HT acting as a neurotransmitter in the cerebral cortex is critical for cognitive function, yet how 5-HT regulates information processing in cortical circuits is not well understood. We tested the serotonergic responsiveness of layer 5 pyramidal neurons (L5PNs of the mouse medial prefrontal cortex (mPFC, and found 3 distinct response types: long-lasting 5-HT1A (1A receptor-dependent inhibitory responses (84% of L5PNs, 5-HT2A (2A receptor-dependent excitatory responses (9%, and biphasic responses in which 2A-dependent excitation followed brief inhibition (5%. Relative to 5-HT-inhibited neurons, those excited by 5-HT had physiological properties characteristic of callosal/commissural (COM neurons that project to the contralateral cortex. We tested whether serotonergic responses in cortical pyramidal neurons are correlated with their axonal projection pattern using retrograde fluorescent labeling of COM and corticopontine-projecting (CPn neurons. 5-HT generated excitatory or biphasic responses in all 5-HT-responsive layer 5 COM neurons. Conversely, CPn neurons were universally inhibited by 5-HT. Serotonergic excitation of COM neurons was blocked by the 2A antagonist MDL 11939, while serotonergic inhibition of CPn neurons was blocked by the 1A antagonist WAY 100635, confirming a role for these two receptor subtypes in regulating pyramidal neuron activity. Selective serotonergic excitation of COM neurons was not layer-specific, as COM neurons in layer 2/3 were also selectively excited by 5-HT relative to their non-labeled pyramidal neuron neighbors. Because neocortical 2A receptors are implicated in the etiology and pathophysiology of schizophrenia, we propose that COM neurons may represent a novel cellular target for intervention in psychiatric disease.

  14. Impact of Artificial Intelligence on Economic Theory

    OpenAIRE

    Tshilidzi Marwala

    2015-01-01

    Artificial intelligence has impacted many aspects of human life. This paper studies the impact of artificial intelligence on economic theory. In particular we study the impact of artificial intelligence on the theory of bounded rationality, efficient market hypothesis and prospect theory.

  15. An artificial ecosystem model used in the study of social, economic and technological dynamics: An artificial electrical energy market

    International Nuclear Information System (INIS)

    Arjona, D.

    1998-01-01

    This paper will present the artificial ecosystem as a tool, in the development of multi agent models for the simulation of economic and technological dynamics (as well as other possible applications). This tool is based on the mechanics of an artificial society and consists of autonomous artificial agents that interact with individuals that have different characteristics and behavior and other that have a similar conduct to their own. Initial conditions are assumed not to be controllable, however they can be influenced. The importance of the concept of the ecosystem is in understanding great units in the light of their own components which are relevant for the analysis and become interdependent among themselves and with other essential components that hold the total operation of the system. Ideas for the development of a simulation model based on autonomous intelligent agents are presented. These agents will have a brain that is based on artificial intelligence technologies. The Sand Kings Simulation Model, an artificial ecosystem model developed by the author, is described as well as the application of artificial intelligence to this artificial life model. An application to a real life problem is also offered as an artificial energy market that is currently being developed by the author is described

  16. Persistence of the cell-cycle checkpoint kinase Wee1 in SadA- and SadB-deficient neurons disrupts neuronal polarity.

    Science.gov (United States)

    Müller, Myriam; Lutter, Daniela; Püschel, Andreas W

    2010-01-15

    Wee1 is well characterized as a cell-cycle checkpoint kinase that regulates the entry into mitosis in dividing cells. Here we identify a novel function of Wee1 in postmitotic neurons during the establishment of distinct axonal and dendritic compartments, which is an essential step during neuronal development. Wee1 is expressed in unpolarized neurons but is downregulated after neurons have extended an axon. Suppression of Wee1 impairs the formation of minor neurites but does not interfere with axon formation. However, neuronal polarity is disrupted when neurons fail to downregulate Wee1. The kinases SadA and SadB (Sad kinases) phosphorylate Wee1 and are required to initiate its downregulation in polarized neurons. Wee1 expression persists in neurons that are deficient in SadA and SadB and disrupts neuronal polarity. Knockdown of Wee1 rescues the Sada(-/-);Sadb(-/-) mutant phenotype and restores normal polarity in these neurons. Our results demonstrate that the regulation of Wee1 by SadA and SadB kinases is essential for the differentiation of polarized neurons.

  17. Intratelencephalic corticostriatal neurons equally excite striatonigral and striatopallidal neurons and their discharge activity is selectively reduced in experimental parkinsonism

    OpenAIRE

    Ballion, B. (B.); Mallet, N. (Nicolas); Bezard, E. (E.); Lanciego, J.L. (José Luis); Gonon, F. (Francois)

    2008-01-01

    Striatonigral and striatopallidal neurons form distinct populations of striatal projection neurons. Their discharge activity is imbalanced after dopaminergic degeneration in Parkinson's disease. Striatal projection neurons receive massive cortical excitatory inputs from bilateral intratelencephalic (IT) neurons projecting to both the ipsilateral and contralateral striatum and from collateral axons of ipsilateral neurons that send their main axon through the pyramidal tract (PT). Previous anat...

  18. Synaptic Circuit Organization of Motor Corticothalamic Neurons

    Science.gov (United States)

    Yamawaki, Naoki

    2015-01-01

    Corticothalamic (CT) neurons in layer 6 constitute a large but enigmatic class of cortical projection neurons. How they are integrated into intracortical and thalamo-cortico-thalamic circuits is incompletely understood, especially outside of sensory cortex. Here, we investigated CT circuits in mouse forelimb motor cortex (M1) using multiple circuit-analysis methods. Stimulating and recording from CT, intratelencephalic (IT), and pyramidal tract (PT) projection neurons, we found strong CT↔ CT and CT↔ IT connections; however, CT→IT connections were limited to IT neurons in layer 6, not 5B. There was strikingly little CT↔ PT excitatory connectivity. Disynaptic inhibition systematically accompanied excitation in these pathways, scaling with the amplitude of excitation according to both presynaptic (class-specific) and postsynaptic (cell-by-cell) factors. In particular, CT neurons evoked proportionally more inhibition relative to excitation (I/E ratio) than IT neurons. Furthermore, the amplitude of inhibition was tuned to match the amount of excitation at the level of individual neurons; in the extreme, neurons receiving no excitation received no inhibition either. Extending these studies to dissect the connectivity between cortex and thalamus, we found that M1-CT neurons and thalamocortical neurons in the ventrolateral (VL) nucleus were remarkably unconnected in either direction. Instead, VL axons in the cortex excited both IT and PT neurons, and CT axons in the thalamus excited other thalamic neurons, including those in the posterior nucleus, which additionally received PT excitation. These findings, which contrast in several ways with previous observations in sensory areas, illuminate the basic circuit organization of CT neurons within M1 and between M1 and thalamus. PMID:25653383

  19. Bistability induces episodic spike communication by inhibitory neurons in neuronal networks.

    Science.gov (United States)

    Kazantsev, V B; Asatryan, S Yu

    2011-09-01

    Bistability is one of the important features of nonlinear dynamical systems. In neurodynamics, bistability has been found in basic Hodgkin-Huxley equations describing the cell membrane dynamics. When the neuron is clamped near its threshold, the stable rest potential may coexist with the stable limit cycle describing periodic spiking. However, this effect is often neglected in network computations where the neurons are typically reduced to threshold firing units (e.g., integrate-and-fire models). We found that the bistability may induce spike communication by inhibitory coupled neurons in the spiking network. The communication is realized in the form of episodic discharges with synchronous (correlated) spikes during the episodes. A spiking phase map is constructed to describe the synchronization and to estimate basic spike phase locking modes.

  20. miR-155 Deletion in Mice Overcomes Neuron-Intrinsic and Neuron-Extrinsic Barriers to Spinal Cord Repair.

    Science.gov (United States)

    Gaudet, Andrew D; Mandrekar-Colucci, Shweta; Hall, Jodie C E; Sweet, David R; Schmitt, Philipp J; Xu, Xinyang; Guan, Zhen; Mo, Xiaokui; Guerau-de-Arellano, Mireia; Popovich, Phillip G

    2016-08-10

    Axon regeneration after spinal cord injury (SCI) fails due to neuron-intrinsic mechanisms and extracellular barriers including inflammation. microRNA (miR)-155-5p is a small, noncoding RNA that negatively regulates mRNA translation. In macrophages, miR-155-5p is induced by inflammatory stimuli and elicits a response that could be toxic after SCI. miR-155 may also independently alter expression of genes that regulate axon growth in neurons. Here, we hypothesized that miR-155 deletion would simultaneously improve axon growth and reduce neuroinflammation after SCI by acting on both neurons and macrophages. New data show that miR-155 deletion attenuates inflammatory signaling in macrophages, reduces macrophage-mediated neuron toxicity, and increases macrophage-elicited axon growth by ∼40% relative to control conditions. In addition, miR-155 deletion increases spontaneous axon growth from neurons; adult miR-155 KO dorsal root ganglion (DRG) neurons extend 44% longer neurites than WT neurons. In vivo, miR-155 deletion augments conditioning lesion-induced intraneuronal expression of SPRR1A, a regeneration-associated gene; ∼50% more injured KO DRG neurons expressed SPRR1A versus WT neurons. After dorsal column SCI, miR-155 KO mouse spinal cord has reduced neuroinflammation and increased peripheral conditioning-lesion-enhanced axon regeneration beyond the epicenter. Finally, in a model of spinal contusion injury, miR-155 deletion improves locomotor function at postinjury times corresponding with the arrival and maximal appearance of activated intraspinal macrophages. In miR-155 KO mice, improved locomotor function is associated with smaller contusion lesions and decreased accumulation of inflammatory macrophages. Collectively, these data indicate that miR-155 is a novel therapeutic target capable of simultaneously overcoming neuron-intrinsic and neuron-extrinsic barriers to repair after SCI. Axon regeneration after spinal cord injury (SCI) fails due to neuron

  1. miR-155 Deletion in Mice Overcomes Neuron-Intrinsic and Neuron-Extrinsic Barriers to Spinal Cord Repair

    Science.gov (United States)

    Mandrekar-Colucci, Shweta; Hall, Jodie C.E.; Sweet, David R.; Schmitt, Philipp J.; Xu, Xinyang; Guan, Zhen; Mo, Xiaokui; Guerau-de-Arellano, Mireia

    2016-01-01

    Axon regeneration after spinal cord injury (SCI) fails due to neuron-intrinsic mechanisms and extracellular barriers including inflammation. microRNA (miR)-155–5p is a small, noncoding RNA that negatively regulates mRNA translation. In macrophages, miR-155-5p is induced by inflammatory stimuli and elicits a response that could be toxic after SCI. miR-155 may also independently alter expression of genes that regulate axon growth in neurons. Here, we hypothesized that miR-155 deletion would simultaneously improve axon growth and reduce neuroinflammation after SCI by acting on both neurons and macrophages. New data show that miR-155 deletion attenuates inflammatory signaling in macrophages, reduces macrophage-mediated neuron toxicity, and increases macrophage-elicited axon growth by ∼40% relative to control conditions. In addition, miR-155 deletion increases spontaneous axon growth from neurons; adult miR-155 KO dorsal root ganglion (DRG) neurons extend 44% longer neurites than WT neurons. In vivo, miR-155 deletion augments conditioning lesion-induced intraneuronal expression of SPRR1A, a regeneration-associated gene; ∼50% more injured KO DRG neurons expressed SPRR1A versus WT neurons. After dorsal column SCI, miR-155 KO mouse spinal cord has reduced neuroinflammation and increased peripheral conditioning-lesion-enhanced axon regeneration beyond the epicenter. Finally, in a model of spinal contusion injury, miR-155 deletion improves locomotor function at postinjury times corresponding with the arrival and maximal appearance of activated intraspinal macrophages. In miR-155 KO mice, improved locomotor function is associated with smaller contusion lesions and decreased accumulation of inflammatory macrophages. Collectively, these data indicate that miR-155 is a novel therapeutic target capable of simultaneously overcoming neuron-intrinsic and neuron-extrinsic barriers to repair after SCI. SIGNIFICANCE STATEMENT Axon regeneration after spinal cord injury (SCI) fails

  2. A study of single multiplicative neuron model with nonlinear filters for hourly wind speed prediction

    International Nuclear Information System (INIS)

    Wu, Xuedong; Zhu, Zhiyu; Su, Xunliang; Fan, Shaosheng; Du, Zhaoping; Chang, Yanchao; Zeng, Qingjun

    2015-01-01

    Wind speed prediction is one important methods to guarantee the wind energy integrated into the whole power system smoothly. However, wind power has a non–schedulable nature due to the strong stochastic nature and dynamic uncertainty nature of wind speed. Therefore, wind speed prediction is an indispensable requirement for power system operators. Two new approaches for hourly wind speed prediction are developed in this study by integrating the single multiplicative neuron model and the iterated nonlinear filters for updating the wind speed sequence accurately. In the presented methods, a nonlinear state–space model is first formed based on the single multiplicative neuron model and then the iterated nonlinear filters are employed to perform dynamic state estimation on wind speed sequence with stochastic uncertainty. The suggested approaches are demonstrated using three cases wind speed data and are compared with autoregressive moving average, artificial neural network, kernel ridge regression based residual active learning and single multiplicative neuron model methods. Three types of prediction errors, mean absolute error improvement ratio and running time are employed for different models’ performance comparison. Comparison results from Tables 1–3 indicate that the presented strategies have much better performance for hourly wind speed prediction than other technologies. - Highlights: • Developed two novel hybrid modeling methods for hourly wind speed prediction. • Uncertainty and fluctuations of wind speed can be better explained by novel methods. • Proposed strategies have online adaptive learning ability. • Proposed approaches have shown better performance compared with existed approaches. • Comparison and analysis of two proposed novel models for three cases are provided

  3. Direct Neuronal Reprogramming for Disease Modeling Studies Using Patient-Derived Neurons: What Have We Learned?

    Directory of Open Access Journals (Sweden)

    Janelle Drouin-Ouellet

    2017-09-01

    Full Text Available Direct neuronal reprogramming, by which a neuron is formed via direct conversion from a somatic cell without going through a pluripotent intermediate stage, allows for the possibility of generating patient-derived neurons. A unique feature of these so-called induced neurons (iNs is the potential to maintain aging and epigenetic signatures of the donor, which is critical given that many diseases of the CNS are age related. Here, we review the published literature on the work that has been undertaken using iNs to model human brain disorders. Furthermore, as disease-modeling studies using this direct neuronal reprogramming approach are becoming more widely adopted, it is important to assess the criteria that are used to characterize the iNs, especially in relation to the extent to which they are mature adult neurons. In particular: i what constitutes an iN cell, ii which stages of conversion offer the earliest/optimal time to assess features that are specific to neurons and/or a disorder and iii whether generating subtype-specific iNs is critical to the disease-related features that iNs express. Finally, we discuss the range of potential biomedical applications that can be explored using patient-specific models of neurological disorders with iNs, and the challenges that will need to be overcome in order to realize these applications.

  4. Electrophysiological properties of neurons derived from human stem cells and iNeurons in vitro.

    Science.gov (United States)

    Halliwell, Robert F

    2017-06-01

    Functional studies of neurons have traditionally used nervous system tissues from a variety of non-human vertebrate and invertebrate species, even when the focus of much of this research has been directed at understanding human brain function. Over the last decade, the identification and isolation of human stem cells from embryonic, tissue (or adult) and induced pluripotent stem cells (iPSCs) has revolutionized the availability of human neurons for experimental studies in vitro. In addition, the direct conversion of terminally differentiated fibroblasts into Induced neurons (iN) has generated great excitement because of the likely value of such human stem cell derived neurons (hSCNs) and iN cells in drug discovery, neuropharmacology, neurotoxicology and regenerative medicine. This review addresses the current state of our knowledge of functional receptors and ion channels expressed in neurons derived from human stem cells and iNeurons and identifies gaps and questions that might be investigated in future studies; it focusses almost exclusively on what is known about the electrophysiological properties of neurons derived from human stem cells and iN cells in vitro with an emphasis on voltage and ligand gated ion channels, since these mediate synaptic signalling in the nervous system and they are at the heart of neuropharmacology. Copyright © 2016 Elsevier Ltd. All rights reserved.

  5. What Is an Artificial Muscle? A Systemic Approach.

    OpenAIRE

    Bertrand Tondu

    2015-01-01

    Artificial muscles define a large category of actuators we propose to analyze in a systemic framework by considering any artificial muscle as an open-loop stable system for any output which represents an artificial muscle dimension resulting from its “contraction”, understood in a broad meaning. This approach makes it possible to distinguish the artificial muscle from other actuators and to specify an original model for a linear artificial muscle, according to the theory of linear systems. Su...

  6. Three-dimensional distribution of sensory stimulation-evoked neuronal activity of spinal dorsal horn neurons analyzed by in vivo calcium imaging.

    Science.gov (United States)

    Nishida, Kazuhiko; Matsumura, Shinji; Taniguchi, Wataru; Uta, Daisuke; Furue, Hidemasa; Ito, Seiji

    2014-01-01

    The spinal dorsal horn comprises heterogeneous populations of interneurons and projection neurons, which form neuronal circuits crucial for processing of primary sensory information. Although electrophysiological analyses have uncovered sensory stimulation-evoked neuronal activity of various spinal dorsal horn neurons, monitoring these activities from large ensembles of neurons is needed to obtain a comprehensive view of the spinal dorsal horn circuitry. In the present study, we established in vivo calcium imaging of multiple spinal dorsal horn neurons by using a two-photon microscope and extracted three-dimensional neuronal activity maps of these neurons in response to cutaneous sensory stimulation. For calcium imaging, a fluorescence resonance energy transfer (FRET)-based calcium indicator protein, Yellow Cameleon, which is insensitive to motion artifacts of living animals was introduced into spinal dorsal horn neurons by in utero electroporation. In vivo calcium imaging following pinch, brush, and heat stimulation suggests that laminar distribution of sensory stimulation-evoked neuronal activity in the spinal dorsal horn largely corresponds to that of primary afferent inputs. In addition, cutaneous pinch stimulation elicited activities of neurons in the spinal cord at least until 2 spinal segments away from the central projection field of primary sensory neurons responsible for the stimulated skin point. These results provide a clue to understand neuronal processing of sensory information in the spinal dorsal horn.

  7. Three-dimensional distribution of sensory stimulation-evoked neuronal activity of spinal dorsal horn neurons analyzed by in vivo calcium imaging.

    Directory of Open Access Journals (Sweden)

    Kazuhiko Nishida

    Full Text Available The spinal dorsal horn comprises heterogeneous populations of interneurons and projection neurons, which form neuronal circuits crucial for processing of primary sensory information. Although electrophysiological analyses have uncovered sensory stimulation-evoked neuronal activity of various spinal dorsal horn neurons, monitoring these activities from large ensembles of neurons is needed to obtain a comprehensive view of the spinal dorsal horn circuitry. In the present study, we established in vivo calcium imaging of multiple spinal dorsal horn neurons by using a two-photon microscope and extracted three-dimensional neuronal activity maps of these neurons in response to cutaneous sensory stimulation. For calcium imaging, a fluorescence resonance energy transfer (FRET-based calcium indicator protein, Yellow Cameleon, which is insensitive to motion artifacts of living animals was introduced into spinal dorsal horn neurons by in utero electroporation. In vivo calcium imaging following pinch, brush, and heat stimulation suggests that laminar distribution of sensory stimulation-evoked neuronal activity in the spinal dorsal horn largely corresponds to that of primary afferent inputs. In addition, cutaneous pinch stimulation elicited activities of neurons in the spinal cord at least until 2 spinal segments away from the central projection field of primary sensory neurons responsible for the stimulated skin point. These results provide a clue to understand neuronal processing of sensory information in the spinal dorsal horn.

  8. Molecular and functional differences in voltage-activated sodium currents between GABA projection neurons and dopamine neurons in the substantia nigra

    OpenAIRE

    Ding, Shengyuan; Wei, Wei; Zhou, Fu-Ming

    2011-01-01

    GABA projection neurons (GABA neurons) in the substantia nigra pars reticulata (SNr) and dopamine projection neurons (DA neurons) in substantia nigra pars compacta (SNc) have strikingly different firing properties. SNc DA neurons fire low-frequency, long-duration spikes, whereas SNr GABA neurons fire high-frequency, short-duration spikes. Since voltage-activated sodium (NaV) channels are critical to spike generation, the different firing properties raise the possibility that, compared with DA...

  9. The biophysics of neuronal growth

    International Nuclear Information System (INIS)

    Franze, Kristian; Guck, Jochen

    2010-01-01

    For a long time, neuroscience has focused on biochemical, molecular biological and electrophysiological aspects of neuronal physiology and pathology. However, there is a growing body of evidence indicating the importance of physical stimuli for neuronal growth and development. In this review we briefly summarize the historical background of neurobiophysics and give an overview over the current understanding of neuronal growth from a physics perspective. We show how biophysics has so far contributed to a better understanding of neuronal growth and discuss current inconsistencies. Finally, we speculate how biophysics may contribute to the successful treatment of lesions to the central nervous system, which have been considered incurable until very recently.

  10. Reinforcement Learning Based Artificial Immune Classifier

    Directory of Open Access Journals (Sweden)

    Mehmet Karakose

    2013-01-01

    Full Text Available One of the widely used methods for classification that is a decision-making process is artificial immune systems. Artificial immune systems based on natural immunity system can be successfully applied for classification, optimization, recognition, and learning in real-world problems. In this study, a reinforcement learning based artificial immune classifier is proposed as a new approach. This approach uses reinforcement learning to find better antibody with immune operators. The proposed new approach has many contributions according to other methods in the literature such as effectiveness, less memory cell, high accuracy, speed, and data adaptability. The performance of the proposed approach is demonstrated by simulation and experimental results using real data in Matlab and FPGA. Some benchmark data and remote image data are used for experimental results. The comparative results with supervised/unsupervised based artificial immune system, negative selection classifier, and resource limited artificial immune classifier are given to demonstrate the effectiveness of the proposed new method.

  11. Scaling of brain metabolism with a fixed energy budget per neuron: implications for neuronal activity, plasticity and evolution.

    Science.gov (United States)

    Herculano-Houzel, Suzana

    2011-03-01

    It is usually considered that larger brains have larger neurons, which consume more energy individually, and are therefore accompanied by a larger number of glial cells per neuron. These notions, however, have never been tested. Based on glucose and oxygen metabolic rates in awake animals and their recently determined numbers of neurons, here I show that, contrary to the expected, the estimated glucose use per neuron is remarkably constant, varying only by 40% across the six species of rodents and primates (including humans). The estimated average glucose use per neuron does not correlate with neuronal density in any structure. This suggests that the energy budget of the whole brain per neuron is fixed across species and brain sizes, such that total glucose use by the brain as a whole, by the cerebral cortex and also by the cerebellum alone are linear functions of the number of neurons in the structures across the species (although the average glucose consumption per neuron is at least 10× higher in the cerebral cortex than in the cerebellum). These results indicate that the apparently remarkable use in humans of 20% of the whole body energy budget by a brain that represents only 2% of body mass is explained simply by its large number of neurons. Because synaptic activity is considered the major determinant of metabolic cost, a conserved energy budget per neuron has several profound implications for synaptic homeostasis and the regulation of firing rates, synaptic plasticity, brain imaging, pathologies, and for brain scaling in evolution.

  12. Scaling of brain metabolism with a fixed energy budget per neuron: implications for neuronal activity, plasticity and evolution.

    Directory of Open Access Journals (Sweden)

    Suzana Herculano-Houzel

    Full Text Available It is usually considered that larger brains have larger neurons, which consume more energy individually, and are therefore accompanied by a larger number of glial cells per neuron. These notions, however, have never been tested. Based on glucose and oxygen metabolic rates in awake animals and their recently determined numbers of neurons, here I show that, contrary to the expected, the estimated glucose use per neuron is remarkably constant, varying only by 40% across the six species of rodents and primates (including humans. The estimated average glucose use per neuron does not correlate with neuronal density in any structure. This suggests that the energy budget of the whole brain per neuron is fixed across species and brain sizes, such that total glucose use by the brain as a whole, by the cerebral cortex and also by the cerebellum alone are linear functions of the number of neurons in the structures across the species (although the average glucose consumption per neuron is at least 10× higher in the cerebral cortex than in the cerebellum. These results indicate that the apparently remarkable use in humans of 20% of the whole body energy budget by a brain that represents only 2% of body mass is explained simply by its large number of neurons. Because synaptic activity is considered the major determinant of metabolic cost, a conserved energy budget per neuron has several profound implications for synaptic homeostasis and the regulation of firing rates, synaptic plasticity, brain imaging, pathologies, and for brain scaling in evolution.

  13. Scaling of Brain Metabolism with a Fixed Energy Budget per Neuron: Implications for Neuronal Activity, Plasticity and Evolution

    Science.gov (United States)

    Herculano-Houzel, Suzana

    2011-01-01

    It is usually considered that larger brains have larger neurons, which consume more energy individually, and are therefore accompanied by a larger number of glial cells per neuron. These notions, however, have never been tested. Based on glucose and oxygen metabolic rates in awake animals and their recently determined numbers of neurons, here I show that, contrary to the expected, the estimated glucose use per neuron is remarkably constant, varying only by 40% across the six species of rodents and primates (including humans). The estimated average glucose use per neuron does not correlate with neuronal density in any structure. This suggests that the energy budget of the whole brain per neuron is fixed across species and brain sizes, such that total glucose use by the brain as a whole, by the cerebral cortex and also by the cerebellum alone are linear functions of the number of neurons in the structures across the species (although the average glucose consumption per neuron is at least 10× higher in the cerebral cortex than in the cerebellum). These results indicate that the apparently remarkable use in humans of 20% of the whole body energy budget by a brain that represents only 2% of body mass is explained simply by its large number of neurons. Because synaptic activity is considered the major determinant of metabolic cost, a conserved energy budget per neuron has several profound implications for synaptic homeostasis and the regulation of firing rates, synaptic plasticity, brain imaging, pathologies, and for brain scaling in evolution. PMID:21390261

  14. Artificial intelligence in nanotechnology.

    Science.gov (United States)

    Sacha, G M; Varona, P

    2013-11-15

    During the last decade there has been increasing use of artificial intelligence tools in nanotechnology research. In this paper we review some of these efforts in the context of interpreting scanning probe microscopy, the study of biological nanosystems, the classification of material properties at the nanoscale, theoretical approaches and simulations in nanoscience, and generally in the design of nanodevices. Current trends and future perspectives in the development of nanocomputing hardware that can boost artificial-intelligence-based applications are also discussed. Convergence between artificial intelligence and nanotechnology can shape the path for many technological developments in the field of information sciences that will rely on new computer architectures and data representations, hybrid technologies that use biological entities and nanotechnological devices, bioengineering, neuroscience and a large variety of related disciplines.

  15. Artificial intelligence in nanotechnology

    Science.gov (United States)

    Sacha, G. M.; Varona, P.

    2013-11-01

    During the last decade there has been increasing use of artificial intelligence tools in nanotechnology research. In this paper we review some of these efforts in the context of interpreting scanning probe microscopy, the study of biological nanosystems, the classification of material properties at the nanoscale, theoretical approaches and simulations in nanoscience, and generally in the design of nanodevices. Current trends and future perspectives in the development of nanocomputing hardware that can boost artificial-intelligence-based applications are also discussed. Convergence between artificial intelligence and nanotechnology can shape the path for many technological developments in the field of information sciences that will rely on new computer architectures and data representations, hybrid technologies that use biological entities and nanotechnological devices, bioengineering, neuroscience and a large variety of related disciplines.

  16. Artificial intelligence in nanotechnology

    International Nuclear Information System (INIS)

    Sacha, G M; Varona, P

    2013-01-01

    During the last decade there has been increasing use of artificial intelligence tools in nanotechnology research. In this paper we review some of these efforts in the context of interpreting scanning probe microscopy, the study of biological nanosystems, the classification of material properties at the nanoscale, theoretical approaches and simulations in nanoscience, and generally in the design of nanodevices. Current trends and future perspectives in the development of nanocomputing hardware that can boost artificial-intelligence-based applications are also discussed. Convergence between artificial intelligence and nanotechnology can shape the path for many technological developments in the field of information sciences that will rely on new computer architectures and data representations, hybrid technologies that use biological entities and nanotechnological devices, bioengineering, neuroscience and a large variety of related disciplines. (topical review)

  17. GABAergic inhibition through synergistic astrocytic neuronal interaction transiently decreases vasopressin neuronal activity during hypoosmotic challenge.

    Science.gov (United States)

    Wang, Yu-Feng; Sun, Min-Yu; Hou, Qiuling; Hamilton, Kathryn A

    2013-04-01

    The neuropeptide vasopressin is crucial to mammalian osmotic regulation. Local hypoosmotic challenge transiently decreases and then increases vasopressin secretion. To investigate mechanisms underlying this transient response, we examined the effects of hypoosmotic challenge on the electrical activity of rat hypothalamic supraoptic nucleus (SON) vasopressin neurons using patch-clamp recordings. We found that 5 min exposure of hypothalamic slices to hypoosmotic solution transiently increased inhibitory postsynaptic current (IPSC) frequency and reduced the firing rate of vasopressin neurons. Recovery occurred by 10 min of exposure, even though the osmolality remained low. The γ-aminobutyric acid (GABA)A receptor blocker, gabazine, blocked the IPSCs and the hypoosmotic suppression of firing. The gliotoxin l-aminoadipic acid blocked the increase in IPSC frequency at 5 min and the recovery of firing at 10 min, indicating astrocytic involvement in hypoosmotic modulation of vasopressin neuronal activity. Moreover, β-alanine, an osmolyte of astrocytes and GABA transporter (GAT) inhibitor, blocked the increase in IPSC frequency at 5 min of hypoosmotic challenge. Confocal microscopy of immunostained SON sections revealed that astrocytes and magnocellular neurons both showed positive staining of vesicular GATs (VGAT). Hypoosmotic stimulation in vivo reduced the number of VGAT-expressing neurons, and increased co-localisation and molecular association of VGAT with glial fibrillary acidic protein that increased significantly by 10 min. By 30 min, neuronal VGAT labelling was partially restored, and astrocytic VGAT was relocated to the ventral portion while it decreased in the somatic zone of the SON. Thus, synergistic astrocytic and neuronal GABAergic inhibition could ensure that vasopressin neuron firing is only transiently suppressed under hypoosmotic conditions. © 2013 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

  18. Autapse-induced synchronization in a coupled neuronal network

    International Nuclear Information System (INIS)

    Ma, Jun; Song, Xinlin; Jin, Wuyin; Wang, Chuni

    2015-01-01

    Highlights: • The functional effect of autapse on neuronal activity is detected. • Autapse driving plays active role in regulating electrical activities as pacemaker. • It confirms biological experimental results for rhythm synchronization between heterogeneous cells. - Abstract: The effect of autapse on coupled neuronal network is detected. In our studies, three identical neurons are connected with ring type and autapse connected to one neuron of the network. The autapse connected to neuron can impose time-delayed feedback in close loop on the neuron thus the dynamics of membrane potentials can be changed. Firstly, the effect of autapse driving on single neuron is confirmed that negative feedback can calm down the neuronal activity while positive feedback can excite the neuronal activity. Secondly, the collective electrical behaviors of neurons are regulated by a pacemaker, which associated with the autapse forcing. By using appropriate gain and time delay in the autapse, the neurons can reach synchronization and the membrane potentials of all neurons can oscillate with the same rhythm under mutual coupling. It indicates that autapse forcing plays an important role in changing the collective electric activities of neuronal network, and appropriate electric modes can be selected due to the switch of feedback type(positive or negative) in autapse. And the autapse-induced synchronization in network is also consistent with some biological experiments about synchronization between nonidentical neurons.

  19. Multifaceted effects of oligodendroglial exosomes on neurons: impact on neuronal firing rate, signal transduction and gene regulation.

    Science.gov (United States)

    Fröhlich, Dominik; Kuo, Wen Ping; Frühbeis, Carsten; Sun, Jyh-Jang; Zehendner, Christoph M; Luhmann, Heiko J; Pinto, Sheena; Toedling, Joern; Trotter, Jacqueline; Krämer-Albers, Eva-Maria

    2014-09-26

    Exosomes are small membranous vesicles of endocytic origin that are released by almost every cell type. They exert versatile functions in intercellular communication important for many physiological and pathological processes. Recently, exosomes attracted interest with regard to their role in cell-cell communication in the nervous system. We have shown that exosomes released from oligodendrocytes upon stimulation with the neurotransmitter glutamate are internalized by neurons and enhance the neuronal stress tolerance. Here, we demonstrate that oligodendroglial exosomes also promote neuronal survival during oxygen-glucose deprivation, a model of cerebral ischaemia. We show the transfer from oligodendrocytes to neurons of superoxide dismutase and catalase, enzymes which are known to help cells to resist oxidative stress. Additionally, we identify various effects of oligodendroglial exosomes on neuronal physiology. Electrophysiological analysis using in vitro multi-electrode arrays revealed an increased firing rate of neurons exposed to oligodendroglial exosomes. Moreover, gene expression analysis and phosphorylation arrays uncovered differentially expressed genes and altered signal transduction pathways in neurons after exosome treatment. Our study thus provides new insight into the broad spectrum of action of oligodendroglial exosomes and their effects on neuronal physiology. The exchange of extracellular vesicles between neural cells may exhibit remarkable potential to impact brain performance. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

  20. Direct evidence for activity-dependent glucose phosphorylation in neurons with implications for the astrocyte-to-neuron lactate shuttle.

    Science.gov (United States)

    Patel, Anant B; Lai, James C K; Chowdhury, Golam M I; Hyder, Fahmeed; Rothman, Douglas L; Shulman, Robert G; Behar, Kevin L

    2014-04-08

    Previous (13)C magnetic resonance spectroscopy experiments have shown that over a wide range of neuronal activity, approximately one molecule of glucose is oxidized for every molecule of glutamate released by neurons and recycled through astrocytic glutamine. The measured kinetics were shown to agree with the stoichiometry of a hypothetical astrocyte-to-neuron lactate shuttle model, which predicted negligible functional neuronal uptake of glucose. To test this model, we measured the uptake and phosphorylation of glucose in nerve terminals isolated from rats infused with the glucose analog, 2-fluoro-2-deoxy-D-glucose (FDG) in vivo. The concentrations of phosphorylated FDG (FDG6P), normalized with respect to known neuronal metabolites, were compared in nerve terminals, homogenate, and cortex of anesthetized rats with and without bicuculline-induced seizures. The increase in FDG6P in nerve terminals agreed well with the increase in cortical neuronal glucose oxidation measured previously under the same conditions in vivo, indicating that direct uptake and oxidation of glucose in nerve terminals is substantial under resting and activated conditions. These results suggest that neuronal glucose-derived pyruvate is the major oxidative fuel for activated neurons, not lactate-derived from astrocytes, contradicting predictions of the original astrocyte-to-neuron lactate shuttle model under the range of study conditions.

  1. Artificial Intelligence--Applications in Education.

    Science.gov (United States)

    Poirot, James L.; Norris, Cathleen A.

    1987-01-01

    This first in a projected series of five articles discusses artificial intelligence and its impact on education. Highlights include the history of artificial intelligence and the impact of microcomputers; learning processes; human factors and interfaces; computer assisted instruction and intelligent tutoring systems; logic programing; and expert…

  2. Artificial intelligence in medicine.

    OpenAIRE

    Ramesh, A. N.; Kambhampati, C.; Monson, J. R. T.; Drew, P. J.

    2004-01-01

    INTRODUCTION: Artificial intelligence is a branch of computer science capable of analysing complex medical data. Their potential to exploit meaningful relationship with in a data set can be used in the diagnosis, treatment and predicting outcome in many clinical scenarios. METHODS: Medline and internet searches were carried out using the keywords 'artificial intelligence' and 'neural networks (computer)'. Further references were obtained by cross-referencing from key articles. An overview of ...

  3. Artificial intelligence in cardiology

    OpenAIRE

    Bonderman, Diana

    2017-01-01

    Summary Decision-making is complex in modern medicine and should ideally be based on available data, structured knowledge and proper interpretation in the context of an individual patient. Automated algorithms, also termed artificial intelligence that are able to extract meaningful patterns from data collections and build decisions upon identified patterns may be useful assistants in clinical decision-making processes. In this article, artificial intelligence-based studies in clinical cardiol...

  4. Intelligence: Real or artificial?

    OpenAIRE

    Schlinger, Henry D.

    1992-01-01

    Throughout the history of the artificial intelligence movement, researchers have strived to create computers that could simulate general human intelligence. This paper argues that workers in artificial intelligence have failed to achieve this goal because they adopted the wrong model of human behavior and intelligence, namely a cognitive essentialist model with origins in the traditional philosophies of natural intelligence. An analysis of the word “intelligence” suggests that it originally r...

  5. The artificial neural networks: An approach to artificial intelligence; Un approccio ``biologico`` all`intelligenza artificiale

    Energy Technology Data Exchange (ETDEWEB)

    Taraglio, Sergio; Zanela, Andrea [ENEA, Casaccia (Italy). Dipt. Innovazione

    1997-05-01

    The artificial neural networks try to simulate the functionalities of the nervous system through a complex network of simple computing elements. In this work is presented an introduction to the neural networks and some of their possible applications, especially in the field of Artificial Intelligence.

  6. A chimeric path to neuronal synchronization

    Science.gov (United States)

    Essaki Arumugam, Easwara Moorthy; Spano, Mark L.

    2015-01-01

    Synchronization of neuronal activity is associated with neurological disorders such as epilepsy. This process of neuronal synchronization is not fully understood. To further our understanding, we have experimentally studied the progression of this synchronization from normal neuronal firing to full synchronization. We implemented nine FitzHugh-Nagumo neurons (a simplified Hodgkin-Huxley model) via discrete electronics. For different coupling parameters (synaptic strengths), the neurons in the ring were either unsynchronized or completely synchronized when locally coupled in a ring. When a single long-range connection (nonlocal coupling) was introduced, an intermediate state known as a chimera appeared. The results indicate that (1) epilepsy is likely not only a dynamical disease but also a topological disease, strongly tied to the connectivity of the underlying network of neurons, and (2) the synchronization process in epilepsy may not be an "all or none" phenomenon, but can pass through an intermediate stage (chimera).

  7. A chimeric path to neuronal synchronization

    Energy Technology Data Exchange (ETDEWEB)

    Essaki Arumugam, Easwara Moorthy; Spano, Mark L. [School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona 85287-9709 (United States)

    2015-01-15

    Synchronization of neuronal activity is associated with neurological disorders such as epilepsy. This process of neuronal synchronization is not fully understood. To further our understanding, we have experimentally studied the progression of this synchronization from normal neuronal firing to full synchronization. We implemented nine FitzHugh-Nagumo neurons (a simplified Hodgkin-Huxley model) via discrete electronics. For different coupling parameters (synaptic strengths), the neurons in the ring were either unsynchronized or completely synchronized when locally coupled in a ring. When a single long-range connection (nonlocal coupling) was introduced, an intermediate state known as a chimera appeared. The results indicate that (1) epilepsy is likely not only a dynamical disease but also a topological disease, strongly tied to the connectivity of the underlying network of neurons, and (2) the synchronization process in epilepsy may not be an “all or none” phenomenon, but can pass through an intermediate stage (chimera)

  8. A chimeric path to neuronal synchronization

    International Nuclear Information System (INIS)

    Essaki Arumugam, Easwara Moorthy; Spano, Mark L.

    2015-01-01

    Synchronization of neuronal activity is associated with neurological disorders such as epilepsy. This process of neuronal synchronization is not fully understood. To further our understanding, we have experimentally studied the progression of this synchronization from normal neuronal firing to full synchronization. We implemented nine FitzHugh-Nagumo neurons (a simplified Hodgkin-Huxley model) via discrete electronics. For different coupling parameters (synaptic strengths), the neurons in the ring were either unsynchronized or completely synchronized when locally coupled in a ring. When a single long-range connection (nonlocal coupling) was introduced, an intermediate state known as a chimera appeared. The results indicate that (1) epilepsy is likely not only a dynamical disease but also a topological disease, strongly tied to the connectivity of the underlying network of neurons, and (2) the synchronization process in epilepsy may not be an “all or none” phenomenon, but can pass through an intermediate stage (chimera)

  9. [Mirror neurons].

    Science.gov (United States)

    Rubia Vila, Francisco José

    2011-01-01

    Mirror neurons were recently discovered in frontal brain areas of the monkey. They are activated when the animal makes a specific movement, but also when the animal observes the same movement in another animal. Some of them also respond to the emotional expression of other animals of the same species. These mirror neurons have also been found in humans. They respond to or "reflect" actions of other individuals in the brain and are thought to represent the basis for imitation and empathy and hence the neurobiological substrate for "theory of mind", the potential origin of language and the so-called moral instinct.

  10. Ethical Considerations in Artificial Intelligence Courses

    OpenAIRE

    Burton, Emanuelle; Goldsmith, Judy; Koenig, Sven; Kuipers, Benjamin; Mattei, Nicholas; Walsh, Toby

    2017-01-01

    The recent surge in interest in ethics in artificial intelligence may leave many educators wondering how to address moral, ethical, and philosophical issues in their AI courses. As instructors we want to develop curriculum that not only prepares students to be artificial intelligence practitioners, but also to understand the moral, ethical, and philosophical impacts that artificial intelligence will have on society. In this article we provide practical case studies and links to resources for ...

  11. Clonación artificial de controladores basados en técnicas de inteligencia artificial

    Directory of Open Access Journals (Sweden)

    Javier Antonio Ballesteros

    2004-11-01

    Full Text Available En el presente articulo se expone un avance de anteproyecto, sobre técnicas de inteligencia Artificial, su aplicación a procesos industriales y herramientas para realizar simulación. Se citan algunos trabajos en los cuales hay resultados, y que prestan un soporte lógico para continuar con el desarrollo del proyecto. Ademas, se analiza, la función que cumple el controlador el cual debe ser preciso, confiable y que a su vez reduzca costos. Por estas razones se tomó como base del proyecto un controlador que posea estas características para que se puedan aplicar técnicas de lnteligencia Artificial y poder realizar Ia colación artificial.

  12. Identification of neuronal network properties from the spectral analysis of calcium imaging signals in neuronal cultures.

    Science.gov (United States)

    Tibau, Elisenda; Valencia, Miguel; Soriano, Jordi

    2013-01-01

    Neuronal networks in vitro are prominent systems to study the development of connections in living neuronal networks and the interplay between connectivity, activity and function. These cultured networks show a rich spontaneous activity that evolves concurrently with the connectivity of the underlying network. In this work we monitor the development of neuronal cultures, and record their activity using calcium fluorescence imaging. We use spectral analysis to characterize global dynamical and structural traits of the neuronal cultures. We first observe that the power spectrum can be used as a signature of the state of the network, for instance when inhibition is active or silent, as well as a measure of the network's connectivity strength. Second, the power spectrum identifies prominent developmental changes in the network such as GABAA switch. And third, the analysis of the spatial distribution of the spectral density, in experiments with a controlled disintegration of the network through CNQX, an AMPA-glutamate receptor antagonist in excitatory neurons, reveals the existence of communities of strongly connected, highly active neurons that display synchronous oscillations. Our work illustrates the interest of spectral analysis for the study of in vitro networks, and its potential use as a network-state indicator, for instance to compare healthy and diseased neuronal networks.

  13. Artificial Reefs

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — An artificial reef is a human-made underwater structure, typically built to promote marine life in areas with a generally featureless bottom, control erosion, block...

  14. Engineering a Light-Attenuating Artificial Iris

    Science.gov (United States)

    Shareef, Farah J.; Sun, Shan; Kotecha, Mrignayani; Kassem, Iris; Azar, Dimitri; Cho, Michael

    2016-01-01

    Purpose Discomfort from light exposure leads to photophobia, glare, and poor vision in patients with congenital or trauma-induced iris damage. Commercial artificial iris lenses are static in nature to provide aesthetics without restoring the natural iris's dynamic response to light. A new photo-responsive artificial iris was therefore developed using a photochromic material with self-adaptive light transmission properties and encased in a transparent biocompatible polymer matrix. Methods The implantable artificial iris was designed and engineered using Photopia, a class of photo-responsive materials (termed naphthopyrans) embedded in polyethylene. Photopia was reshaped into annular disks that were spin-coated with polydimethylsiloxane (PDMS) to form our artificial iris lens of controlled thickness. Results Activated by UV and blue light in approximately 5 seconds with complete reversal in less than 1 minute, the artificial iris demonstrates graded attenuation of up to 40% of visible and 60% of UV light. There optical characteristics are suitable to reversibly regulate the incident light intensity. In vitro cell culture experiments showed up to 60% cell death within 10 days of exposure to Photopia, but no significant cell death observed when cultured with the artificial iris with protective encapsulation. Nuclear magnetic resonance spectroscopy confirmed these results as there was no apparent leakage of potentially toxic photochromic material from the ophthalmic device. Conclusions Our artificial iris lens mimics the functionality of the natural iris by attenuating light intensity entering the eye with its rapid reversible change in opacity and thus potentially providing an improved treatment option for patients with iris damage. PMID:27116547

  15. Engineering a Light-Attenuating Artificial Iris.

    Science.gov (United States)

    Shareef, Farah J; Sun, Shan; Kotecha, Mrignayani; Kassem, Iris; Azar, Dimitri; Cho, Michael

    2016-04-01

    Discomfort from light exposure leads to photophobia, glare, and poor vision in patients with congenital or trauma-induced iris damage. Commercial artificial iris lenses are static in nature to provide aesthetics without restoring the natural iris's dynamic response to light. A new photo-responsive artificial iris was therefore developed using a photochromic material with self-adaptive light transmission properties and encased in a transparent biocompatible polymer matrix. The implantable artificial iris was designed and engineered using Photopia, a class of photo-responsive materials (termed naphthopyrans) embedded in polyethylene. Photopia was reshaped into annular disks that were spin-coated with polydimethylsiloxane (PDMS) to form our artificial iris lens of controlled thickness. Activated by UV and blue light in approximately 5 seconds with complete reversal in less than 1 minute, the artificial iris demonstrates graded attenuation of up to 40% of visible and 60% of UV light. There optical characteristics are suitable to reversibly regulate the incident light intensity. In vitro cell culture experiments showed up to 60% cell death within 10 days of exposure to Photopia, but no significant cell death observed when cultured with the artificial iris with protective encapsulation. Nuclear magnetic resonance spectroscopy confirmed these results as there was no apparent leakage of potentially toxic photochromic material from the ophthalmic device. Our artificial iris lens mimics the functionality of the natural iris by attenuating light intensity entering the eye with its rapid reversible change in opacity and thus potentially providing an improved treatment option for patients with iris damage.

  16. Artificial organs 2011: a year in review.

    Science.gov (United States)

    Malchesky, Paul S

    2012-03-01

    In this Editor's Review, articles published in 2011 are organized by category and briefly summarized. As the official journal of The International Federation for Artificial Organs, The International Faculty for Artificial Organs, and the International Society for Rotary Blood Pumps, Artificial Organs continues in the original mission of its founders "to foster communications in the field of artificial organs on an international level."Artificial Organs continues to publish developments and clinical applications of artificial organ technologies in this broad and expanding field of organ replacement, recovery, and regeneration from all over the world. We take this time also to express our gratitude to our authors for offering their work to this journal. We offer our very special thanks to our reviewers who give so generously of time and expertise to review, critique, and especially provide meaningful suggestions to the author's work whether eventually accepted or rejected. Without these excellent and dedicated reviewers, the quality expected from such a journal would not be possible. We also express our special thanks to our Publisher, Wiley-Blackwell, for their expert attention and support in the production and marketing of Artificial Organs. In this Editor's Review, that historically has been widely well-received by our readership, we aim to provide a brief reflection of the currently available worldwide knowledge that is intended to advance and better human life while providing insight for continued application of technologies and methods of organ replacement, recovery, and regeneration. We look forward to recording further advances in the coming years. © 2012, Copyright the Author. Artificial Organs © 2012, International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

  17. Artificial Organs 2012: a year in review.

    Science.gov (United States)

    Malchesky, Paul S

    2013-03-01

    In this editor's review, articles published in 2012 are organized by category and briefly summarized. We aim to provide a brief reflection of the currently available worldwide knowledge that is intended to advance and better human life while providing insight for continued application of technologies and methods of organ replacement, recovery, and regeneration. As the official journal of the International Federation for Artificial Organs, the International Faculty for Artificial Organs, and the International Society for Rotary Blood Pumps, Artificial Organs continues in the original mission of its founders "to foster communications in the field of artificial organs on an international level." Artificial Organs continues to publish developments and clinical applications of artificial organ technologies in this broad and expanding field of organ replacement, recovery, and regeneration from all over the world. We take this time also to express our gratitude to our authors for offering their work to this journal. We offer our very special thanks to our reviewers who give so generously of time and expertise to review, critique, and especially provide such meaningful suggestions to the author's work whether eventually accepted or rejected, and especially to those whose native tongue is not English. Without these excellent and dedicated reviewers, the quality expected from such a journal could not be possible. We also express our special thanks to our publisher, Wiley Periodicals, for their expert attention and support in the production and marketing of Artificial Organs. We look forward to recording further advances in the coming years. © 2013, Copyright the Author. Artificial Organs © 2013, International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

  18. Long descending cervical propriospinal neurons differ from thoracic propriospinal neurons in response to low thoracic spinal injury

    Directory of Open Access Journals (Sweden)

    Stelzner Dennis J

    2010-11-01

    Full Text Available Abstract Background Propriospinal neurons, with axonal projections intrinsic to the spinal cord, have shown a greater regenerative response than supraspinal neurons after axotomy due to spinal cord injury (SCI. Our previous work focused on the response of axotomized short thoracic propriospinal (TPS neurons following a low thoracic SCI (T9 spinal transection or moderate spinal contusion injury in the rat. The present investigation analyzes the intrinsic response of cervical propriospinal neurons having long descending axons which project into the lumbosacral enlargement, long descending propriospinal tract (LDPT axons. These neurons also were axotomized by T9 spinal injury in the same animals used in our previous study. Results Utilizing laser microdissection (LMD, qRT-PCR, and immunohistochemistry, we studied LDPT neurons (located in the C5-C6 spinal segments between 3-days, and 1-month following a low thoracic (T9 spinal cord injury. We examined the response of 89 genes related to growth factors, cell surface receptors, apoptosis, axonal regeneration, and neuroprotection/cell survival. We found a strong and significant down-regulation of ~25% of the genes analyzed early after injury (3-days post-injury with a sustained down-regulation in most instances. In the few genes that were up-regulated (Actb, Atf3, Frs2, Hspb1, Nrap, Stat1 post-axotomy, the expression for all but one was down-regulated by 2-weeks post-injury. We also compared the uninjured TPS control neurons to the uninjured LDPT neurons used in this experiment for phenotypic differences between these two subpopulations of propriospinal neurons. We found significant differences in expression in 37 of the 84 genes examined between these two subpopulations of propriospinal neurons with LDPT neurons exhibiting a significantly higher base line expression for all but 3 of these genes compared to TPS neurons. Conclusions Taken collectively these data indicate a broad overall down

  19. Sucrose compared with artificial sweeteners

    DEFF Research Database (Denmark)

    Sørensen, Lone Brinkmann; Vasilaras, Tatjana H; Astrup, Arne

    2014-01-01

    There is a lack of appetite studies in free-living subjects supplying the habitual diet with either sucrose or artificially sweetened beverages and foods. Furthermore, the focus of artificial sweeteners has only been on the energy intake (EI) side of the energy-balance equation. The data are from...

  20. Neuronal replacement therapy: previous achievements and challenges ahead

    Science.gov (United States)

    Grade, Sofia; Götz, Magdalena

    2017-10-01

    Lifelong neurogenesis and incorporation of newborn neurons into mature neuronal circuits operates in specialized niches of the mammalian brain and serves as role model for neuronal replacement strategies. However, to which extent can the remaining brain parenchyma, which never incorporates new neurons during the adulthood, be as plastic and readily accommodate neurons in networks that suffered neuronal loss due to injury or neurological disease? Which microenvironment is permissive for neuronal replacement and synaptic integration and which cells perform best? Can lost function be restored and how adequate is the participation in the pre-existing circuitry? Could aberrant connections cause malfunction especially in networks dominated by excitatory neurons, such as the cerebral cortex? These questions show how important connectivity and circuitry aspects are for regenerative medicine, which is the focus of this review. We will discuss the impressive advances in neuronal replacement strategies and success from exogenous as well as endogenous cell sources. Both have seen key novel technologies, like the groundbreaking discovery of induced pluripotent stem cells and direct neuronal reprogramming, offering alternatives to the transplantation of fetal neurons, and both herald great expectations. For these to become reality, neuronal circuitry analysis is key now. As our understanding of neuronal circuits increases, neuronal replacement therapy should fulfill those prerequisites in network structure and function, in brain-wide input and output. Now is the time to incorporate neural circuitry research into regenerative medicine if we ever want to truly repair brain injury.

  1. Intensive Insulin Therapy: Tight Blood Sugar Control

    Science.gov (United States)

    ... specific situation. McCulloch DK. General principles of insulin therapy in diabetes mellitus. http://www.uptodate.com/home. Accessed Dec. ... Diabetes Association. http://www.diabetes.org/living-with-diabetes/treatment-and-care/blood-glucose-control/checking-your-blood- ...

  2. Temporal characteristics of gustatory responses in rat parabrachial neurons vary by stimulus and chemosensitive neuron type.

    Science.gov (United States)

    Geran, Laura; Travers, Susan

    2013-01-01

    It has been demonstrated that temporal features of spike trains can increase the amount of information available for gustatory processing. However, the nature of these temporal characteristics and their relationship to different taste qualities and neuron types are not well-defined. The present study analyzed the time course of taste responses from parabrachial (PBN) neurons elicited by multiple applications of "sweet" (sucrose), "salty" (NaCl), "sour" (citric acid), and "bitter" (quinine and cycloheximide) stimuli in an acute preparation. Time course varied significantly by taste stimulus and best-stimulus classification. Across neurons, the ensemble code for the three electrolytes was similar initially but quinine diverged from NaCl and acid during the second 500 ms of stimulation and all four qualities became distinct just after 1s. This temporal evolution was reflected in significantly broader tuning during the initial response. Metric space analyses of quality discrimination by individual neurons showed that increases in information (H) afforded by temporal factors was usually explained by differences in rate envelope, which had a greater impact during the initial 2s (22.5% increase in H) compared to the later response (9.5%). Moreover, timing had a differential impact according to cell type, with between-quality discrimination in neurons activated maximally by NaCl or citric acid most affected. Timing was also found to dramatically improve within-quality discrimination (80% increase in H) in neurons that responded optimally to bitter stimuli (B-best). Spikes from B-best neurons were also more likely to occur in bursts. These findings suggest that among PBN taste neurons, time-dependent increases in mutual information can arise from stimulus- and neuron-specific differences in response envelope during the initial dynamic period. A stable rate code predominates in later epochs.

  3. Estimation of Leakage Ratio Using Principal Component Analysis and Artificial Neural Network in Water Distribution Systems

    Directory of Open Access Journals (Sweden)

    Dongwoo Jang

    2018-03-01

    Full Text Available Leaks in a water distribution network (WDS constitute losses of water supply caused by pipeline failure, operational loss, and physical factors. This has raised the need for studies on the factors affecting the leakage ratio and estimation of leakage volume in a water supply system. In this study, principal component analysis (PCA and artificial neural network (ANN were used to estimate the volume of water leakage in a WDS. For the study, six main effective parameters were selected and standardized data obtained through the Z-score method. The PCA-ANN model was devised and the leakage ratio was estimated. An accuracy assessment was performed to compare the measured leakage ratio to that of the simulated model. The results showed that the PCA-ANN method was more accurate for estimating the leakage ratio than a single ANN simulation. In addition, the estimation results differed according to the number of neurons in the ANN model’s hidden layers. In this study, an ANN with multiple hidden layers was found to be the best method for estimating the leakage ratio with 12–12 neurons. This suggested approaches to improve the accuracy of leakage ratio estimation, as well as a scientific approach toward the sustainable management of water distribution systems.

  4. Mirror neurons and language in schizophrenia

    OpenAIRE

    Bendová, Marie

    2016-01-01

    Mirror neurons are a specific kind of visuomotor neurons that are involved in action execution and also in action perception. The mirror mechanism is linked to a variety of complex psychological functions such as social-cognitive functions and language. People with schizophrenia have often difficulties both in mirror neuron system and in language skills. In the first part of our research we studied the connectivity of mirror neuron areas (such as IFG, STG, PMC, SMC and so on) by fMRI in resti...

  5. The Correlation between Insertion Depth of Prodisc-C Artificial Disc and Postoperative Kyphotic Deformity: Clinical Importance of Insertion Depth of Artificial Disc.

    Science.gov (United States)

    Lee, Do-Youl; Kim, Se-Hoon; Suh, Jung-Keun; Cho, Tai-Hyoung; Chung, Yong-Gu

    2012-09-01

    This study was designed to investigate the correlation between insertion depth of artificial disc and postoperative kyphotic deformity after Prodisc-C total disc replacement surgery, and the range of artificial disc insertion depth which is effective in preventing postoperative whole cervical or segmental kyphotic deformity. A retrospective radiological analysis was performed in 50 patients who had undergone single level total disc replacement surgery. Records were reviewed to obtain demographic data. Preoperative and postoperative radiographs were assessed to determine C2-7 Cobb's angle and segmental angle and to investigate postoperative kyphotic deformity. A formula was introduced to calculate insertion depth of Prodisc-C artificial disc. Statistical analysis was performed to search the correlation between insertion depth of Prodisc-C artificial disc and postoperative kyphotic deformity, and to estimate insertion depth of Prodisc-C artificial disc to prevent postoperative kyphotic deformity. In this study no significant statistical correlation was observed between insertion depth of Prodisc-C artificial disc and postoperative kyphotic deformity regarding C2-7 Cobb's angle. Statistical correlation between insertion depth of Prodisc-C artificial disc and postoperative kyphotic deformity was observed regarding segmental angle (p<0.05). It failed to estimate proper insertion depth of Prodisc-C artificial disc effective in preventing postoperative kyphotic deformity. Postoperative segmental kyphotic deformity is associated with insertion depth of Prodisc-C artificial disc. Anterior located artificial disc leads to lordotic segmental angle and posterior located artificial disc leads to kyphotic segmental angle postoperatively. But C2-7 Cobb's angle is not affected by artificial disc location after the surgery.

  6. Bioengineering of Artificial Lymphoid Organs.

    Science.gov (United States)

    Nosenko, M A; Drutskaya, M S; Moisenovich, M M; Nedospasov, S A

    2016-01-01

    This review addresses the issue of bioengineering of artificial lymphoid organs.Progress in this field may help to better understand the nature of the structure-function relations that exist in immune organs. Artifical lymphoid organs may also be advantageous in the therapy or correction of immunodefficiencies, autoimmune diseases, and cancer. The structural organization, development, and function of lymphoid tissue are analyzed with a focus on the role of intercellular contacts and on the cytokine signaling pathways regulating these processes. We describe various polymeric materials, as scaffolds, for artificial tissue engineering. Finally, published studies in which artificial lymphoid organs were generated are reviewed and possible future directions in the field are discussed.

  7. Effect of correlating adjacent neurons for identifying communications: Feasibility experiment in a cultured neuronal network

    OpenAIRE

    Yoshi Nishitani; Chie Hosokawa; Yuko Mizuno-Matsumoto; Tomomitsu Miyoshi; Shinichi Tamura

    2017-01-01

    Neuronal networks have fluctuating characteristics, unlike the stable characteristics seen in computers. The underlying mechanisms that drive reliable communication among neuronal networks and their ability to perform intelligible tasks remain unknown. Recently, in an attempt to resolve this issue, we showed that stimulated neurons communicate via spikes that propagate temporally, in the form of spike trains. We named this phenomenon “spike wave propagation”. In these previous studies, using ...

  8. Applicability of empirical correlations for estimating global solar radiation

    International Nuclear Information System (INIS)

    Gopinathan, K.K.; Baholo, M.

    1987-01-01

    Three empirical models suggested by different investigators, for estimating monthly mean daily global radiation on a horizontal surface, are compared statistically to test their universal applicability. The models thus compared are those suggested by Rietveld, Glover and McCulloch and Gopinathan. The models are compared by calculating the root mean square error, mean bias error and mean relative percentage error values. The model suggested by Gopinathan yields the best results in terms of root mean square, mean bias and mean percentage errors. The model by Rietveld is the second best and the model by Glover and McCulloch comes at third place. However, the differences in the magnitude of errors among the three models are very small and all the three models can be considered to be accurate for global radiation estimation for any location in the world

  9. Artificial Immune Networks: Models and Applications

    Directory of Open Access Journals (Sweden)

    Xian Shen

    2008-06-01

    Full Text Available Artificial Immune Systems (AIS, which is inspired by the nature immune system, has been applied for solving complex computational problems in classification, pattern rec- ognition, and optimization. In this paper, the theory of the natural immune system is first briefly introduced. Next, we compare some well-known AIS and their applications. Several representative artificial immune networks models are also dis- cussed. Moreover, we demonstrate the applications of artificial immune networks in various engineering fields.

  10. Interactive Exploration for Continuously Expanding Neuron Databases.

    Science.gov (United States)

    Li, Zhongyu; Metaxas, Dimitris N; Lu, Aidong; Zhang, Shaoting

    2017-02-15

    This paper proposes a novel framework to help biologists explore and analyze neurons based on retrieval of data from neuron morphological databases. In recent years, the continuously expanding neuron databases provide a rich source of information to associate neuronal morphologies with their functional properties. We design a coarse-to-fine framework for efficient and effective data retrieval from large-scale neuron databases. In the coarse-level, for efficiency in large-scale, we employ a binary coding method to compress morphological features into binary codes of tens of bits. Short binary codes allow for real-time similarity searching in Hamming space. Because the neuron databases are continuously expanding, it is inefficient to re-train the binary coding model from scratch when adding new neurons. To solve this problem, we extend binary coding with online updating schemes, which only considers the newly added neurons and update the model on-the-fly, without accessing the whole neuron databases. In the fine-grained level, we introduce domain experts/users in the framework, which can give relevance feedback for the binary coding based retrieval results. This interactive strategy can improve the retrieval performance through re-ranking the above coarse results, where we design a new similarity measure and take the feedback into account. Our framework is validated on more than 17,000 neuron cells, showing promising retrieval accuracy and efficiency. Moreover, we demonstrate its use case in assisting biologists to identify and explore unknown neurons. Copyright © 2017 Elsevier Inc. All rights reserved.

  11. Neuron Morphology Influences Axon Initial Segment Plasticity.

    Science.gov (United States)

    Gulledge, Allan T; Bravo, Jaime J

    2016-01-01

    In most vertebrate neurons, action potentials are initiated in the axon initial segment (AIS), a specialized region of the axon containing a high density of voltage-gated sodium and potassium channels. It has recently been proposed that neurons use plasticity of AIS length and/or location to regulate their intrinsic excitability. Here we quantify the impact of neuron morphology on AIS plasticity using computational models of simplified and realistic somatodendritic morphologies. In small neurons (e.g., dentate granule neurons), excitability was highest when the AIS was of intermediate length and located adjacent to the soma. Conversely, neurons having larger dendritic trees (e.g., pyramidal neurons) were most excitable when the AIS was longer and/or located away from the soma. For any given somatodendritic morphology, increasing dendritic membrane capacitance and/or conductance favored a longer and more distally located AIS. Overall, changes to AIS length, with corresponding changes in total sodium conductance, were far more effective in regulating neuron excitability than were changes in AIS location, while dendritic capacitance had a larger impact on AIS performance than did dendritic conductance. The somatodendritic influence on AIS performance reflects modest soma-to-AIS voltage attenuation combined with neuron size-dependent changes in AIS input resistance, effective membrane time constant, and isolation from somatodendritic capacitance. We conclude that the impact of AIS plasticity on neuron excitability will depend largely on somatodendritic morphology, and that, in some neurons, a shorter or more distally located AIS may promote, rather than limit, action potential generation.

  12. The Past, Present, and Future of Artificial Life

    Directory of Open Access Journals (Sweden)

    Wendy eAguilar

    2014-10-01

    Full Text Available For millennia people have wondered what makes the living different from the non-living. Beginning in the mid-1980s, artificial life has studied living systems using a synthetic approach: build life in order to understand it better, be it by means of software, hardware, or wetware. This review provides a summary of the advances that led to the development of artificial life, its current research topics, and open problems and opportunities. We classify artificial life research into fourteen themes: origins of life, autonomy, self-organization, adaptation (including evolution, development, and learning, ecology, artificial societies, behavior, computational biology, artificial chemistries, information, living technology, art, and philosophy. Being interdisciplinary, artificial life seems to be losing its boundaries and merging with other fields.

  13. Design of an artificial neural network, with the topology oriented to the reconstruction of neutron spectra; Diseno de una red neuronal artificial, con la topologia orientada a la reconstruccion del espectro de neutrones

    Energy Technology Data Exchange (ETDEWEB)

    Arteaga A, T.; Ortiz R, J.M.; Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.; Mercado S, G.A. [Unidades Academicas de Estudios Nucleares, Ingenieria Electrica y Matematicas, Universidad de Zacatecas, A.P. 336, 98000 Zacatecas (Mexico)]. e-mail: tarcicio70@yahoo.co.uk

    2006-07-01

    People that live in high places respect to the sea level, in latitudes far from the equator or that they travel by plane, they are exposed to atmospheres of high radiation generated by the cosmic rays. Another atmosphere with radiation is the medical equipment, particle accelerators and nuclear reactors. The evaluation of the biological risk for neutron radiation requires an appropriate and sure dosimetry. A commonly used system is the Bonner Sphere Spectrometer (EEB) with the purpose of reconstructing the spectrum that is important because the equivalent dose for neutrons depends strongly on its energy. The count rates obtained in each sphere are treated, in most of the cases, for iterative methods, Monte Carlo or Maximum Entropy. Each one of them has difficulties that it motivates to the development of complementary procedures. Recently it has been used Artificial Neural Networks, ANN) and not yet conclusive results have been obtained. In this work it was designed an ANN to obtain the neutron energy spectrum neutrons starting from the counting rate of count of an EEB. The ANN was trained with 129 reference spectra obtained of the IAEA (1990, 2001), 24 were built as defined energy, including isotopic sources of neutrons of reference and operational, of accelerators, reactors, mathematical functions, and of defined energy with several peaks. The spectrum was transformed from lethargy units to energy and were reaccommodated in 31 energies using the Monte Carlo code 4C. The reaccommodated spectra and the response matrix UTA4 were used to calculate the prospective count rates in the EEB. These rates were used as entrance and its respective spectrum was used as output during the net training. The net design is Retropropagation type with 5 layers of 7, 140, 140, 140 and 31 neurons, transfer function logsig, tansig, logsig, logsig, logsig respectively. Training algorithm, traingdx. After the training, the net was proven with a group of training spectra and others that

  14. A DISTRIBUTED SMART HOME ARTIFICIAL INTELLIGENCE SYSTEM

    DEFF Research Database (Denmark)

    Lynggaard, Per

    2013-01-01

    A majority of the research performed today explore artificial intelligence in smart homes by using a centralized approach where a smart home server performs the necessary calculations. This approach has some disadvantages that can be overcome by shifting focus to a distributed approach where...... the artificial intelligence system is implemented as distributed as agents running parts of the artificial intelligence system. This paper presents a distributed smart home architecture that distributes artificial intelligence in smart homes and discusses the pros and cons of such a concept. The presented...... distributed model is a layered model. Each layer offers a different complexity level of the embedded distributed artificial intelligence. At the lowest layer smart objects exists, they are small cheap embedded microcontroller based smart devices that are powered by batteries. The next layer contains a more...

  15. Serotonin neurons in the dorsal raphe mediate the anticataplectic action of orexin neurons by reducing amygdala activity.

    Science.gov (United States)

    Hasegawa, Emi; Maejima, Takashi; Yoshida, Takayuki; Masseck, Olivia A; Herlitze, Stefan; Yoshioka, Mitsuhiro; Sakurai, Takeshi; Mieda, Michihiro

    2017-04-25

    Narcolepsy is a sleep disorder caused by the loss of orexin (hypocretin)-producing neurons and marked by excessive daytime sleepiness and a sudden weakening of muscle tone, or cataplexy, often triggered by strong emotions. In a mouse model for narcolepsy, we previously demonstrated that serotonin neurons of the dorsal raphe nucleus (DRN) mediate the suppression of cataplexy-like episodes (CLEs) by orexin neurons. Using an optogenetic tool, in this paper we show that the acute activation of DRN serotonin neuron terminals in the amygdala, but not in nuclei involved in regulating rapid eye-movement sleep and atonia, suppressed CLEs. Not only did stimulating serotonin nerve terminals reduce amygdala activity, but the chemogenetic inhibition of the amygdala using designer receptors exclusively activated by designer drugs also drastically decreased CLEs, whereas chemogenetic activation increased them. Moreover, the optogenetic inhibition of serotonin nerve terminals in the amygdala blocked the anticataplectic effects of orexin signaling in DRN serotonin neurons. Taken together, the results suggest that DRN serotonin neurons, as a downstream target of orexin neurons, inhibit cataplexy by reducing the activity of amygdala as a center for emotional processing.

  16. Mathematical Relationships between Neuron Morphology and Neurite Growth Dynamics in Drosophila melanogaster Larva Class IV Sensory Neurons

    Science.gov (United States)

    Ganguly, Sujoy; Liang, Xin; Grace, Michael; Lee, Daniel; Howard, Jonathon

    The morphology of neurons is diverse and reflects the diversity of neuronal functions, yet the principles that govern neuronal morphogenesis are unclear. In an effort to better understand neuronal morphogenesis we will be focusing on the development of the dendrites of class IV sensory neuron in Drosophila melanogaster. In particular we attempt to determine how the the total length, and the number of branches of dendrites are mathematically related to the dynamics of neurite growth and branching. By imaging class IV neurons during early embryogenesis we are able to measure the change in neurite length l (t) as a function of time v (t) = dl / dt . We found that the distribution of v (t) is well characterized by a hyperbolic secant distribution, and that the addition of new branches per unit time is well described by a Poisson process. Combining these measurements with the assumption that branching occurs with equal probability anywhere along the dendrite we were able to construct a mathematical model that provides reasonable agreement with the observed number of branches, and total length of the dendrites of the class IV sensory neuron.

  17. Beyond Critical Exponents in Neuronal Avalanches

    Science.gov (United States)

    Friedman, Nir; Butler, Tom; Deville, Robert; Beggs, John; Dahmen, Karin

    2011-03-01

    Neurons form a complex network in the brain, where they interact with one another by firing electrical signals. Neurons firing can trigger other neurons to fire, potentially causing avalanches of activity in the network. In many cases these avalanches have been found to be scale independent, similar to critical phenomena in diverse systems such as magnets and earthquakes. We discuss models for neuronal activity that allow for the extraction of testable, statistical predictions. We compare these models to experimental results, and go beyond critical exponents.

  18. Sleep-Active Neurons: Conserved Motors of Sleep

    Science.gov (United States)

    Bringmann, Henrik

    2018-01-01

    Sleep is crucial for survival and well-being. This behavioral and physiological state has been studied in all major genetically accessible model animals, including rodents, fish, flies, and worms. Genetic and optogenetic studies have identified several neurons that control sleep, making it now possible to compare circuit mechanisms across species. The “motor” of sleep across animal species is formed by neurons that depolarize at the onset of sleep to actively induce this state by directly inhibiting wakefulness. These sleep-inducing neurons are themselves controlled by inhibitory or activating upstream pathways, which act as the “drivers” of the sleep motor: arousal inhibits “sleep-active” neurons whereas various sleep-promoting “tiredness” pathways converge onto sleep-active neurons to depolarize them. This review provides the first overview of sleep-active neurons across the major model animals. The occurrence of sleep-active neurons and their regulation by upstream pathways in both vertebrate and invertebrate species suggests that these neurons are general and ancient components that evolved early in the history of nervous systems. PMID:29618588

  19. Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms.

    Science.gov (United States)

    Garro, Beatriz A; Vázquez, Roberto A

    2015-01-01

    Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems.

  20. Context-aware modeling of neuronal morphologies

    Directory of Open Access Journals (Sweden)

    Benjamin eTorben-Nielsen

    2014-09-01

    Full Text Available Neuronal morphologies are pivotal for brain functioning: physical overlap between dendrites and axons constrain the circuit topology, and the precise shape and composition of dendrites determine the integration of inputs to produce an output signal. At the same time, morphologies are highly diverse and variant. The variance, presumably, originates from neurons developing in a densely packed brain substrate where they interact (e.g., repulsion or attraction with other actors in this substrate. However, when studying neurons their context is never part of the analysis and they are treated as if they existed in isolation.Here we argue that to fully understand neuronal morphology and its variance it is important to consider neurons in relation to each other and to other actors in the surrounding brain substrate, i.e., their context. We propose a context-aware computational framework, NeuroMaC, in which large numbers of neurons can be grown simultaneously according to growth rules expressed in terms of interactions between the developing neuron and the surrounding brain substrate.As a proof of principle, we demonstrate that by using NeuroMaC we can generate accurate virtual morphologies of distinct classes both in isolation and as part of neuronal forests. Accuracy is validated against population statistics of experimentally reconstructed morphologies. We show that context-aware generation of neurons can explain characteristics of variation. Indeed, plausible variation is an inherent property of the morphologies generated by context-aware rules. We speculate about the applicability of this framework to investigate morphologies and circuits, to classify healthy and pathological morphologies, and to generate large quantities of morphologies for large-scale modeling.

  1. Artificial organs 2010: a year in review.

    Science.gov (United States)

    Malchesky, Paul S

    2011-03-01

    In this Editor's Review, articles published in 2010 are organized by category and briefly summarized. As the official journal of The International Federation for Artificial Organs, The International Faculty for Artificial Organs, and the International Society for Rotary Blood Pumps, Artificial Organs continues in the original mission of its founders "to foster communications in the field of artificial organs on an international level."Artificial Organs continues to publish developments and clinical applications of artificial organ technologies in this broad and expanding field of organ Replacement, Recovery, and Regeneration from all over the world. We take this time also to express our gratitude to our authors for offering their work to this journal. We offer our very special thanks to our reviewers who give so generously of time and expertise to review, critique, and especially provide such meaningful suggestions to the author's work whether eventually accepted or rejected and especially to those whose native tongue is not English. Without these excellent and dedicated reviewers the quality expected from such a journal could not be possible. We also express our special thanks to our Publisher, Wiley-Blackwell, for their expert attention and support in the production and marketing of Artificial Organs. In this Editor's Review, that historically has been widely received by our readership, we aim to provide a brief reflection of the currently available worldwide knowledge that is intended to advance and better human life while providing insight for continued application of technologies and methods of organ Replacement, Recovery, and Regeneration. We look forward to recording further advances in the coming years. © 2011, Copyright the Author. Artificial Organs © 2011, International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

  2. The Neuronal Ceroid-Lipofuscinoses

    Science.gov (United States)

    Bennett, Michael J.; Rakheja, Dinesh

    2013-01-01

    The neuronal ceroid-lipofuscinoses (NCL's, Batten disease) represent a group of severe neurodegenerative diseases, which mostly present in childhood. The phenotypes are similar and include visual loss, seizures, loss of motor and cognitive function, and early death. At autopsy, there is massive neuronal loss with characteristic storage in…

  3. Successive neuron loss in the thalamus and cortex in a mouse model of infantile neuronal ceroid lipofuscinosis.

    Science.gov (United States)

    Kielar, Catherine; Maddox, Lucy; Bible, Ellen; Pontikis, Charlie C; Macauley, Shannon L; Griffey, Megan A; Wong, Michael; Sands, Mark S; Cooper, Jonathan D

    2007-01-01

    Infantile neuronal ceroid lipofuscinosis (INCL) is caused by deficiency of the lysosomal enzyme, palmitoyl protein thioesterase 1 (PPT1). We have investigated the onset and progression of pathological changes in Ppt1 deficient mice (Ppt1-/-) and the development of their seizure phenotype. Surprisingly, cortical atrophy and neuron loss occurred only late in disease progression but were preceded by localized astrocytosis within individual thalamic nuclei and the progressive loss of thalamic neurons that relay different sensory modalities to the cortex. This thalamic neuron loss occurred first within the visual system and only subsequently in auditory and somatosensory relay nuclei or the inhibitory reticular thalamic nucleus. The loss of granule neurons and GABAergic interneurons followed in each corresponding cortical region, before the onset of seizure activity. These findings provide novel evidence for successive neuron loss within the thalamus and cortex in Ppt1-/- mice, revealing the thalamus as an important early focus of INCL pathogenesis.

  4. The Hypocretin/Orexin Neuronal Networks in Zebrafish.

    Science.gov (United States)

    Elbaz, Idan; Levitas-Djerbi, Talia; Appelbaum, Lior

    2017-01-01

    The hypothalamic Hypocretin/Orexin (Hcrt) neurons secrete two Hcrt neuropeptides. These neurons and peptides play a major role in the regulation of feeding, sleep wake cycle, reward-seeking, addiction, and stress. Loss of Hcrt neurons causes the sleep disorder narcolepsy. The zebrafish has become an attractive model to study the Hcrt neuronal network because it is a transparent vertebrate that enables simple genetic manipulation, imaging of the structure and function of neuronal circuits in live animals, and high-throughput monitoring of behavioral performance during both day and night. The zebrafish Hcrt network comprises ~16-60 neurons, which similar to mammals, are located in the hypothalamus and widely innervate the brain and spinal cord, and regulate various fundamental behaviors such as feeding, sleep, and wakefulness. Here we review how the zebrafish contributes to the study of the Hcrt neuronal system molecularly, anatomically, physiologically, and pathologically.

  5. Imitation, mirror neurons and autism

    OpenAIRE

    Williams, Justin H.G.; Whiten, Andrew; Suddendorf, Thomas; Perrett, David I.

    2001-01-01

    Various deficits in the cognitive functioning of people with autism have been documented in recent years but these provide only partial explanations for the condition. We focus instead on an imitative disturbance involving difficulties both in copying actions and in inhibiting more stereotyped mimicking, such as echolalia. A candidate for the neural basis of this disturbance may be found in a recently discovered class of neurons in frontal cortex, 'mirror neurons' (MNs). These neurons show ac...

  6. Palmitoylethanolamide Blunts Amyloid-β42-Induced Astrocyte Activation and Improves Neuronal Survival in Primary Mouse Cortical Astrocyte-Neuron Co-Cultures.

    Science.gov (United States)

    Beggiato, Sarah; Borelli, Andrea Celeste; Ferraro, Luca; Tanganelli, Sergio; Antonelli, Tiziana; Tomasini, Maria Cristina

    2018-01-01

    Based on the pivotal role of astrocytes in brain homeostasis and the strong metabolic cooperation existing between neurons and astrocytes, it has been suggested that astrocytic dysfunctions might cause and/or contribute to neuroinflammation and neurodegenerative processes. Therapeutic approaches aimed at both neuroprotection and neuroinflammation reduction may prove particularly effective in slowing the progression of these diseases. The endogenous lipid mediator palmitoylethanolamide (PEA) displayed neuroprotective and anti(neuro)inflammatory properties, and demonstrated interesting potential as a novel treatment for Alzheimer's disease. We firstly evaluated whether astrocytes could participate in regulating the Aβ42-induced neuronal damage, by using primary mouse astrocytes cell cultures and mixed astrocytes-neurons cultures. Furthermore, the possible protective effects of PEA against Aβ42-induced neuronal toxicity have also been investigated by evaluating neuronal viability, apoptosis, and morphometric parameters. The presence of astrocytes pre-exposed to Aβ42 (0.5μM; 24 h) induced a reduction of neuronal viability in primary mouse astrocytes-neurons co-cultures. Furthermore, under these experimental conditions, an increase in the number of neuronal apoptotic nuclei and a decrease in the number of MAP-2 positive neurons were observed. Finally, astrocytic Aβ42 pre-exposure induced an increase in the number of neurite aggregations/100μm as compared to control (i.e., untreated) astrocytes-neurons co-cultures. These effects were not observed in neurons cultured in the presence of astrocytes pre-exposed to PEA (0.1μM), applied 1 h before and maintained during Aβ42 treatment. Astrocytes contribute to Aβ42-induced neurotoxicity and PEA, by blunting Aβ42-induced astrocyte activation, improved neuronal survival in mouse astrocyte-neuron co-cultures.

  7. Connectivity and dynamics of neuronal networks as defined by the shape of individual neurons

    International Nuclear Information System (INIS)

    Ahnert, Sebastian E; A N Travencolo, Bruno; Costa, Luciano da Fontoura

    2009-01-01

    Biological neuronal networks constitute a special class of dynamical systems, as they are formed by individual geometrical components, namely the neurons. In the existing literature, relatively little attention has been given to the influence of neuron shape on the overall connectivity and dynamics of the emerging networks. The current work addresses this issue by considering simplified neuronal shapes consisting of circular regions (soma/axons) with spokes (dendrites). Networks are grown by placing these patterns randomly in the two-dimensional (2D) plane and establishing connections whenever a piece of dendrite falls inside an axon. Several topological and dynamical properties of the resulting graph are measured, including the degree distribution, clustering coefficients, symmetry of connections, size of the largest connected component, as well as three hierarchical measurements of the local topology. By varying the number of processes of the individual basic patterns, we can quantify relationships between the individual neuronal shape and the topological and dynamical features of the networks. Integrate-and-fire dynamics on these networks is also investigated with respect to transient activation from a source node, indicating that long-range connections play an important role in the propagation of avalanches.

  8. Neuronal Differentiation Modulated by Polymeric Membrane Properties.

    Science.gov (United States)

    Morelli, Sabrina; Piscioneri, Antonella; Drioli, Enrico; De Bartolo, Loredana

    2017-01-01

    In this study, different collagen-blend membranes were successfully constructed by blending collagen with chitosan (CHT) or poly(lactic-co-glycolic acid) (PLGA) to enhance their properties and thus create new biofunctional materials with great potential use for neuronal tissue engineering and regeneration. Collagen blending strongly affected membrane properties in the following ways: (i) it improved the surface hydrophilicity of both pure CHT and PLGA membranes, (ii) it reduced the stiffness of CHT membranes, but (iii) it did not modify the good mechanical properties of PLGA membranes. Then, we investigated the effect of the different collagen concentrations on the neuronal behavior of the membranes developed. Morphological observations, immunocytochemistry, and morphometric measures demonstrated that the membranes developed, especially CHT/Col30, PLGA, and PLGA/Col1, provided suitable microenvironments for neuronal growth owing to their enhanced properties. The most consistent neuronal differentiation was obtained in neurons cultured on PLGA-based membranes, where a well-developed neuronal network was achieved due to their improved mechanical properties. Our findings suggest that tensile strength and elongation at break are key material parameters that have potential influence on both axonal elongation and neuronal structure and organization, which are of fundamental importance for the maintenance of efficient neuronal growth. Hence, our study has provided new insights regarding the effects of membrane mechanical properties on neuronal behavior, and thus it may help to design and improve novel instructive biomaterials for neuronal tissue engineering. © 2017 S. Karger AG, Basel.

  9. Artificial Intelligence and Its Importance in Education.

    Science.gov (United States)

    Tilmann, Martha J.

    Artificial intelligence, or the study of ideas that enable computers to be intelligent, is discussed in terms of what it is, what it has done, what it can do, and how it may affect the teaching of tomorrow. An extensive overview of artificial intelligence examines its goals and applications and types of artificial intelligence including (1) expert…

  10. Minimally Naturalistic Artificial Intelligence

    OpenAIRE

    Hansen, Steven Stenberg

    2017-01-01

    The rapid advancement of machine learning techniques has re-energized research into general artificial intelligence. While the idea of domain-agnostic meta-learning is appealing, this emerging field must come to terms with its relationship to human cognition and the statistics and structure of the tasks humans perform. The position of this article is that only by aligning our agents' abilities and environments with those of humans do we stand a chance at developing general artificial intellig...

  11. Artificial Muscles: Mechanisms, Applications, and Challenges.

    Science.gov (United States)

    Mirvakili, Seyed M; Hunter, Ian W

    2018-02-01

    The area of artificial muscle is a highly interdisciplinary field of research that has evolved rapidly in the last 30 years. Recent advances in nanomaterial fabrication and characterization, specifically carbon nanotubes and nanowires, have had major contributions in the development of artificial muscles. However, what can artificial muscles really do for humans? This question is considered here by first examining nature's solutions to this design problem and then discussing the structure, actuation mechanism, applications, and limitations of recently developed artificial muscles, including highly oriented semicrystalline polymer fibers; nanocomposite actuators; twisted nanofiber yarns; thermally activated shape-memory alloys; ionic-polymer/metal composites; dielectric-elastomer actuators; conducting polymers; stimuli-responsive gels; piezoelectric, electrostrictive, magnetostrictive, and photostrictive actuators; photoexcited actuators; electrostatic actuators; and pneumatic actuators. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  12. Energy Model of Neuron Activation.

    Science.gov (United States)

    Romanyshyn, Yuriy; Smerdov, Andriy; Petrytska, Svitlana

    2017-02-01

    On the basis of the neurophysiological strength-duration (amplitude-duration) curve of neuron activation (which relates the threshold amplitude of a rectangular current pulse of neuron activation to the pulse duration), as well as with the use of activation energy constraint (the threshold curve corresponds to the energy threshold of neuron activation by a rectangular current pulse), an energy model of neuron activation by a single current pulse has been constructed. The constructed model of activation, which determines its spectral properties, is a bandpass filter. Under the condition of minimum-phase feature of the neuron activation model, on the basis of Hilbert transform, the possibilities of phase-frequency response calculation from its amplitude-frequency response have been considered. Approximation to the amplitude-frequency response by the response of the Butterworth filter of the first order, as well as obtaining the pulse response corresponding to this approximation, give us the possibility of analyzing the efficiency of activating current pulses of various shapes, including analysis in accordance with the energy constraint.

  13. Artificial neural network approach to modeling of alcoholic fermentation of thick juice from sugar beet processing

    Directory of Open Access Journals (Sweden)

    Jokić Aleksandar I.

    2012-01-01

    Full Text Available In this paper the bioethanol production in batch culture by free Saccharomyces cerevisiae cells from thick juice as intermediate product of sugar beet processing was examined. The obtained results suggest that it is possible to decrease fermentation time for the cultivation medium based on thick juice with starting sugar content of 5-15 g kg-1. For the fermentation of cultivation medium based on thick juice with starting sugar content of 20 and 25 g kg-1 significant increase in ethanol content was attained during the whole fermentation process, resulting in 12.51 and 10.95 dm3 m-3 ethanol contents after 48 h, respectively. Other goals of this work were to investigate the possibilities for experimental results prediction using artificial neural networks (ANNs and to find its optimal topology. A feed-forward back-propagation artificial neural network was used to test the hypothesis. As input variables fermentation time and starting sugar content were used. Neural networks had one output value, ethanol content, yeast cell number or sugar content. There was one hidden layer and the optimal number of neurons was found to be nine for all selected network outputs. In this study transfer function was tansig and the selected learning rule was Levenberg-Marquardt. Results suggest that artificial neural networks are good prediction tool for selected network outputs. It was found that experimental results are in very good agreement with computed ones. The coefficient of determination (the R-squared was found to be 0.9997, 0.9997 and 0.9999 for ethanol content, yeast cell number and sugar content, respectively.

  14. 7 CFR 51.2542 - U.S. Artificially Opened.

    Science.gov (United States)

    2010-01-01

    ... STANDARDS) United States Standards for Grades of Pistachio Nuts in the Shell § 51.2542 U.S. Artificially Opened. “U.S. Artificially Opened” consists of artificially opened pistachio nuts in the shell which meet...

  15. Bayesian artificial intelligence

    CERN Document Server

    Korb, Kevin B

    2003-01-01

    As the power of Bayesian techniques has become more fully realized, the field of artificial intelligence has embraced Bayesian methodology and integrated it to the point where an introduction to Bayesian techniques is now a core course in many computer science programs. Unlike other books on the subject, Bayesian Artificial Intelligence keeps mathematical detail to a minimum and covers a broad range of topics. The authors integrate all of Bayesian net technology and learning Bayesian net technology and apply them both to knowledge engineering. They emphasize understanding and intuition but also provide the algorithms and technical background needed for applications. Software, exercises, and solutions are available on the authors' website.

  16. Direct Reprogramming of Spiral Ganglion Non-neuronal Cells into Neurons: Toward Ameliorating Sensorineural Hearing Loss by Gene Therapy

    Directory of Open Access Journals (Sweden)

    Teppei Noda

    2018-02-01

    Full Text Available Primary auditory neurons (PANs play a critical role in hearing by transmitting sound information from the inner ear to the brain. Their progressive degeneration is associated with excessive noise, disease and aging. The loss of PANs leads to permanent hearing impairment since they are incapable of regenerating. Spiral ganglion non-neuronal cells (SGNNCs, comprised mainly of glia, are resident within the modiolus and continue to survive after PAN loss. These attributes make SGNNCs an excellent target for replacing damaged PANs through cellular reprogramming. We used the neurogenic pioneer transcription factor Ascl1 and the auditory neuron differentiation factor NeuroD1 to reprogram SGNNCs into induced neurons (iNs. The overexpression of both Ascl1 and NeuroD1 in vitro generated iNs at high efficiency. Transcriptome analyses revealed that iNs displayed a transcriptome profile resembling that of endogenous PANs, including expression of several key markers of neuronal identity: Tubb3, Map2, Prph, Snap25, and Prox1. Pathway analyses indicated that essential pathways in neuronal growth and maturation were activated in cells upon neuronal induction. Furthermore, iNs extended projections toward cochlear hair cells and cochlear nucleus neurons when cultured with each respective tissue. Taken together, our study demonstrates that PAN-like neurons can be generated from endogenous SGNNCs. This work suggests that gene therapy can be a viable strategy to treat sensorineural hearing loss caused by degeneration of PANs.

  17. How neurons migrate: a dynamic in-silico model of neuronal migration in the developing cortex

    LENUS (Irish Health Repository)

    Setty, Yaki

    2011-09-30

    Abstract Background Neuronal migration, the process by which neurons migrate from their place of origin to their final position in the brain, is a central process for normal brain development and function. Advances in experimental techniques have revealed much about many of the molecular components involved in this process. Notwithstanding these advances, how the molecular machinery works together to govern the migration process has yet to be fully understood. Here we present a computational model of neuronal migration, in which four key molecular entities, Lis1, DCX, Reelin and GABA, form a molecular program that mediates the migration process. Results The model simulated the dynamic migration process, consistent with in-vivo observations of morphological, cellular and population-level phenomena. Specifically, the model reproduced migration phases, cellular dynamics and population distributions that concur with experimental observations in normal neuronal development. We tested the model under reduced activity of Lis1 and DCX and found an aberrant development similar to observations in Lis1 and DCX silencing expression experiments. Analysis of the model gave rise to unforeseen insights that could guide future experimental study. Specifically: (1) the model revealed the possibility that under conditions of Lis1 reduced expression, neurons experience an oscillatory neuron-glial association prior to the multipolar stage; and (2) we hypothesized that observed morphology variations in rats and mice may be explained by a single difference in the way that Lis1 and DCX stimulate bipolar motility. From this we make the following predictions: (1) under reduced Lis1 and enhanced DCX expression, we predict a reduced bipolar migration in rats, and (2) under enhanced DCX expression in mice we predict a normal or a higher bipolar migration. Conclusions We present here a system-wide computational model of neuronal migration that integrates theory and data within a precise

  18. How neurons migrate: a dynamic in-silico model of neuronal migration in the developing cortex

    Directory of Open Access Journals (Sweden)

    Skoblov Nikita

    2011-09-01

    Full Text Available Abstract Background Neuronal migration, the process by which neurons migrate from their place of origin to their final position in the brain, is a central process for normal brain development and function. Advances in experimental techniques have revealed much about many of the molecular components involved in this process. Notwithstanding these advances, how the molecular machinery works together to govern the migration process has yet to be fully understood. Here we present a computational model of neuronal migration, in which four key molecular entities, Lis1, DCX, Reelin and GABA, form a molecular program that mediates the migration process. Results The model simulated the dynamic migration process, consistent with in-vivo observations of morphological, cellular and population-level phenomena. Specifically, the model reproduced migration phases, cellular dynamics and population distributions that concur with experimental observations in normal neuronal development. We tested the model under reduced activity of Lis1 and DCX and found an aberrant development similar to observations in Lis1 and DCX silencing expression experiments. Analysis of the model gave rise to unforeseen insights that could guide future experimental study. Specifically: (1 the model revealed the possibility that under conditions of Lis1 reduced expression, neurons experience an oscillatory neuron-glial association prior to the multipolar stage; and (2 we hypothesized that observed morphology variations in rats and mice may be explained by a single difference in the way that Lis1 and DCX stimulate bipolar motility. From this we make the following predictions: (1 under reduced Lis1 and enhanced DCX expression, we predict a reduced bipolar migration in rats, and (2 under enhanced DCX expression in mice we predict a normal or a higher bipolar migration. Conclusions We present here a system-wide computational model of neuronal migration that integrates theory and data within a

  19. Mini Review: Biomaterials for Enhancing Neuronal Repair

    Science.gov (United States)

    Cangellaris, Olivia V.; Gillette, Martha U.

    2018-04-01

    As they differentiate from neuroblasts, nascent neurons become highly polarized and elongate. Neurons extend and elaborate fine and fragile cellular extensions that form circuits enabling long-distance communication and signal integration within the body. While other organ systems are developing, projections of differentiating neurons find paths to distant targets. Subsequent post-developmental neuronal damage is catastrophic because the cues for reinnervation are no longer active. Advances in biomaterials are enabling fabrication of micro-environments that encourage neuronal regrowth and restoration of function by recreating these developmental cues. This mini-review considers new materials that employ topographical, chemical, electrical, and/or mechanical cues for use in neuronal repair. Manipulating and integrating these elements in different combinations will generate new technologies to enhance neural repair.

  20. Cholinergic Neurons in the Basal Forebrain Promote Wakefulness by Actions on Neighboring Non-Cholinergic Neurons: An Opto-Dialysis Study.

    Science.gov (United States)

    Zant, Janneke C; Kim, Tae; Prokai, Laszlo; Szarka, Szabolcs; McNally, James; McKenna, James T; Shukla, Charu; Yang, Chun; Kalinchuk, Anna V; McCarley, Robert W; Brown, Ritchie E; Basheer, Radhika

    2016-02-10

    Understanding the control of sleep-wake states by the basal forebrain (BF) poses a challenge due to the intermingled presence of cholinergic, GABAergic, and glutamatergic neurons. All three BF neuronal subtypes project to the cortex and are implicated in cortical arousal and sleep-wake control. Thus, nonspecific stimulation or inhibition studies do not reveal the roles of these different neuronal types. Recent studies using optogenetics have shown that "selective" stimulation of BF cholinergic neurons increases transitions between NREM sleep and wakefulness, implicating cholinergic projections to cortex in wake promotion. However, the interpretation of these optogenetic experiments is complicated by interactions that may occur within the BF. For instance, a recent in vitro study from our group found that cholinergic neurons strongly excite neighboring GABAergic neurons, including the subset of cortically projecting neurons, which contain the calcium-binding protein, parvalbumin (PV) (Yang et al., 2014). Thus, the wake-promoting effect of "selective" optogenetic stimulation of BF cholinergic neurons could be mediated by local excitation of GABA/PV or other non-cholinergic BF neurons. In this study, using a newly designed opto-dialysis probe to couple selective optical stimulation with simultaneous in vivo microdialysis, we demonstrated that optical stimulation of cholinergic neurons locally increased acetylcholine levels and increased wakefulness in mice. Surprisingly, the enhanced wakefulness caused by cholinergic stimulation was abolished by simultaneous reverse microdialysis of cholinergic receptor antagonists into BF. Thus, our data suggest that the wake-promoting effect of cholinergic stimulation requires local release of acetylcholine in the basal forebrain and activation of cortically projecting, non-cholinergic neurons, including the GABAergic/PV neurons. Optogenetics is a revolutionary tool to assess the roles of particular groups of neurons in behavioral

  1. The Languages of Neurons: An Analysis of Coding Mechanisms by Which Neurons Communicate, Learn and Store Information

    Directory of Open Access Journals (Sweden)

    Morris H. Baslow

    2009-11-01

    Full Text Available In this paper evidence is provided that individual neurons possess language, and that the basic unit for communication consists of two neurons and their entire field of interacting dendritic and synaptic connections. While information processing in the brain is highly complex, each neuron uses a simple mechanism for transmitting information. This is in the form of temporal electrophysiological action potentials or spikes (S operating on a millisecond timescale that, along with pauses (P between spikes constitute a two letter “alphabet” that generates meaningful frequency-encoded signals or neuronal S/P “words” in a primary language. However, when a word from an afferent neuron enters the dendritic-synaptic-dendritic field between two neurons, it is translated into a new frequency-encoded word with the same meaning, but in a different spike-pause language, that is delivered to and understood by the efferent neuron. It is suggested that this unidirectional inter-neuronal language-based word translation step is of utmost importance to brain function in that it allows for variations in meaning to occur. Thus, structural or biochemical changes in dendrites or synapses can produce novel words in the second language that have changed meanings, allowing for a specific signaling experience, either external or internal, to modify the meaning of an original word (learning, and store the learned information of that experience (memory in the form of an altered dendritic-synaptic-dendritic field.

  2. Nicotinic activation of laterodorsal tegmental neurons

    DEFF Research Database (Denmark)

    Ishibashi, Masaru; Leonard, Christopher S; Kohlmeier, Kristi A

    2009-01-01

    Identifying the neurological mechanisms underlying nicotine reinforcement is a healthcare imperative, if society is to effectively combat tobacco addiction. The majority of studies of the neurobiology of addiction have focused on dopamine (DA)-containing neurons of the ventral tegmental area (VTA......). However, recent data suggest that neurons of the laterodorsal tegmental (LDT) nucleus, which sends cholinergic, GABAergic, and glutamatergic-containing projections to DA-containing neurons of the VTA, are critical to gating normal functioning of this nucleus. The actions of nicotine on LDT neurons...... are unknown. We addressed this issue by examining the effects of nicotine on identified cholinergic and non-cholinergic LDT neurons using whole-cell patch clamp and Ca(2+)-imaging methods in brain slices from mice (P12-P45). Nicotine applied by puffer pipette or bath superfusion elicited membrane...

  3. Beyond Neuronal Activity Markers: Select Immediate Early Genes in Striatal Neuron Subtypes Functionally Mediate Psychostimulant Addiction

    Directory of Open Access Journals (Sweden)

    Ramesh Chandra

    2017-06-01

    Full Text Available Immediate early genes (IEGs were traditionally used as markers of neuronal activity in striatum in response to stimuli including drugs of abuse such as psychostimulants. Early studies using these neuronal activity markers led to important insights in striatal neuron subtype responsiveness to psychostimulants. Such studies have helped identify striatum as a critical brain center for motivational, reinforcement and habitual behaviors in psychostimulant addiction. While the use of IEGs as neuronal activity markers in response to psychostimulants and other stimuli persists today, the functional role and implications of these IEGs has often been neglected. Nonetheless, there is a subset of research that investigates the functional role of IEGs in molecular, cellular and behavioral alterations by psychostimulants through striatal medium spiny neuron (MSN subtypes, the two projection neuron subtypes in striatum. This review article will address and highlight the studies that provide a functional mechanism by which IEGs mediate psychostimulant molecular, cellular and behavioral plasticity through MSN subtypes. Insight into the functional role of IEGs in striatal MSN subtypes could provide improved understanding into addiction and neuropsychiatric diseases affecting striatum, such as affective disorders and compulsive disorders characterized by dysfunctional motivation and habitual behavior.

  4. Artificial intelligence in power system optimization

    CERN Document Server

    Ongsakul, Weerakorn

    2013-01-01

    With the considerable increase of AI applications, AI is being increasingly used to solve optimization problems in engineering. In the past two decades, the applications of artificial intelligence in power systems have attracted much research. This book covers the current level of applications of artificial intelligence to the optimization problems in power systems. This book serves as a textbook for graduate students in electric power system management and is also be useful for those who are interested in using artificial intelligence in power system optimization.

  5. Zebrafish transgenic constructs label specific neurons in Xenopus laevis spinal cord and identify frog V0v spinal neurons.

    Science.gov (United States)

    Juárez-Morales, José L; Martinez-De Luna, Reyna I; Zuber, Michael E; Roberts, Alan; Lewis, Katharine E

    2017-09-01

    A correctly functioning spinal cord is crucial for locomotion and communication between body and brain but there are fundamental gaps in our knowledge of how spinal neuronal circuitry is established and functions. To understand the genetic program that regulates specification and functions of this circuitry, we need to connect neuronal molecular phenotypes with physiological analyses. Studies using Xenopus laevis tadpoles have increased our understanding of spinal cord neuronal physiology and function, particularly in locomotor circuitry. However, the X. laevis tetraploid genome and long generation time make it difficult to investigate how neurons are specified. The opacity of X. laevis embryos also makes it hard to connect functional classes of neurons and the genes that they express. We demonstrate here that Tol2 transgenic constructs using zebrafish enhancers that drive expression in specific zebrafish spinal neurons label equivalent neurons in X. laevis and that the incorporation of a Gal4:UAS amplification cassette enables cells to be observed in live X. laevis tadpoles. This technique should enable the molecular phenotypes, morphologies and physiologies of distinct X. laevis spinal neurons to be examined together in vivo. We have used an islet1 enhancer to label Rohon-Beard sensory neurons and evx enhancers to identify V0v neurons, for the first time, in X. laevis spinal cord. Our work demonstrates the homology of spinal cord circuitry in zebrafish and X. laevis, suggesting that future work could combine their relative strengths to elucidate a more complete picture of how vertebrate spinal cord neurons are specified, and function to generate behavior. © 2017 Wiley Periodicals, Inc. Develop Neurobiol 77: 1007-1020, 2017. © 2017 Wiley Periodicals, Inc.

  6. Comparison of independent screens on differentially vulnerable motor neurons reveals alpha-synuclein as a common modifier in motor neuron diseases.

    Science.gov (United States)

    Kline, Rachel A; Kaifer, Kevin A; Osman, Erkan Y; Carella, Francesco; Tiberi, Ariana; Ross, Jolill; Pennetta, Giuseppa; Lorson, Christian L; Murray, Lyndsay M

    2017-03-01

    The term "motor neuron disease" encompasses a spectrum of disorders in which motor neurons are the primary pathological target. However, in both patients and animal models of these diseases, not all motor neurons are equally vulnerable, in that while some motor neurons are lost very early in disease, others remain comparatively intact, even at late stages. This creates a valuable system to investigate the factors that regulate motor neuron vulnerability. In this study, we aim to use this experimental paradigm to identify potential transcriptional modifiers. We have compared the transcriptome of motor neurons from healthy wild-type mice, which are differentially vulnerable in the childhood motor neuron disease Spinal Muscular Atrophy (SMA), and have identified 910 transcriptional changes. We have compared this data set with published microarray data sets on other differentially vulnerable motor neurons. These neurons were differentially vulnerable in the adult onset motor neuron disease Amyotrophic Lateral Sclerosis (ALS), but the screen was performed on the equivalent population of neurons from neurologically normal human, rat and mouse. This cross species comparison has generated a refined list of differentially expressed genes, including CELF5, Col5a2, PGEMN1, SNCA, Stmn1 and HOXa5, alongside a further enrichment for synaptic and axonal transcripts. As an in vivo validation, we demonstrate that the manipulation of a significant number of these transcripts can modify the neurodegenerative phenotype observed in a Drosophila line carrying an ALS causing mutation. Finally, we demonstrate that vector-mediated expression of alpha-synuclein (SNCA), a transcript decreased in selectively vulnerable motor neurons in all four screens, can extend life span, increase weight and decrease neuromuscular junction pathology in a mouse model of SMA. In summary, we have combined multiple data sets to identify transcripts, which are strong candidates for being phenotypic modifiers

  7. Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

    Science.gov (United States)

    Pastur-Romay, Lucas Antón; Cedrón, Francisco; Pazos, Alejandro; Porto-Pazos, Ana Belén

    2016-01-01

    Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure–Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron–Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods. PMID:27529225

  8. Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

    Directory of Open Access Journals (Sweden)

    Lucas Antón Pastur-Romay

    2016-08-01

    Full Text Available Over the past decade, Deep Artificial Neural Networks (DNNs have become the state-of-the-art algorithms in Machine Learning (ML, speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs. All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS, Quantitative Structure–Activity Relationship (QSAR research, protein structure prediction and genomics (and other omics data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron–Astrocyte Networks (DANAN could overcome the difficulties in architecture design, learning process and scalability of the current ML methods.

  9. Embodied artificial agents for understanding human social cognition.

    Science.gov (United States)

    Wykowska, Agnieszka; Chaminade, Thierry; Cheng, Gordon

    2016-05-05

    In this paper, we propose that experimental protocols involving artificial agents, in particular the embodied humanoid robots, provide insightful information regarding social cognitive mechanisms in the human brain. Using artificial agents allows for manipulation and control of various parameters of behaviour, appearance and expressiveness in one of the interaction partners (the artificial agent), and for examining effect of these parameters on the other interaction partner (the human). At the same time, using artificial agents means introducing the presence of artificial, yet human-like, systems into the human social sphere. This allows for testing in a controlled, but ecologically valid, manner human fundamental mechanisms of social cognition both at the behavioural and at the neural level. This paper will review existing literature that reports studies in which artificial embodied agents have been used to study social cognition and will address the question of whether various mechanisms of social cognition (ranging from lower- to higher-order cognitive processes) are evoked by artificial agents to the same extent as by natural agents, humans in particular. Increasing the understanding of how behavioural and neural mechanisms of social cognition respond to artificial anthropomorphic agents provides empirical answers to the conundrum 'What is a social agent?' © 2016 The Authors.

  10. Glutamate mediated astrocytic filtering of neuronal activity.

    Directory of Open Access Journals (Sweden)

    Gilad Wallach

    2014-12-01

    Full Text Available Neuron-astrocyte communication is an important regulatory mechanism in various brain functions but its complexity and role are yet to be fully understood. In particular, the temporal pattern of astrocyte response to neuronal firing has not been fully characterized. Here, we used neuron-astrocyte cultures on multi-electrode arrays coupled to Ca2+ imaging and explored the range of neuronal stimulation frequencies while keeping constant the amount of stimulation. Our results reveal that astrocytes specifically respond to the frequency of neuronal stimulation by intracellular Ca2+ transients, with a clear onset of astrocytic activation at neuron firing rates around 3-5 Hz. The cell-to-cell heterogeneity of the astrocyte Ca2+ response was however large and increasing with stimulation frequency. Astrocytic activation by neurons was abolished with antagonists of type I metabotropic glutamate receptor, validating the glutamate-dependence of this neuron-to-astrocyte pathway. Using a realistic biophysical model of glutamate-based intracellular calcium signaling in astrocytes, we suggest that the stepwise response is due to the supralinear dynamics of intracellular IP3 and that the heterogeneity of the responses may be due to the heterogeneity of the astrocyte-to-astrocyte couplings via gap junction channels. Therefore our results present astrocyte intracellular Ca2+ activity as a nonlinear integrator of glutamate-dependent neuronal activity.

  11. Glutamate Mediated Astrocytic Filtering of Neuronal Activity

    Science.gov (United States)

    Herzog, Nitzan; De Pittà, Maurizio; Jacob, Eshel Ben; Berry, Hugues; Hanein, Yael

    2014-01-01

    Neuron-astrocyte communication is an important regulatory mechanism in various brain functions but its complexity and role are yet to be fully understood. In particular, the temporal pattern of astrocyte response to neuronal firing has not been fully characterized. Here, we used neuron-astrocyte cultures on multi-electrode arrays coupled to Ca2+ imaging and explored the range of neuronal stimulation frequencies while keeping constant the amount of stimulation. Our results reveal that astrocytes specifically respond to the frequency of neuronal stimulation by intracellular Ca2+ transients, with a clear onset of astrocytic activation at neuron firing rates around 3-5 Hz. The cell-to-cell heterogeneity of the astrocyte Ca2+ response was however large and increasing with stimulation frequency. Astrocytic activation by neurons was abolished with antagonists of type I metabotropic glutamate receptor, validating the glutamate-dependence of this neuron-to-astrocyte pathway. Using a realistic biophysical model of glutamate-based intracellular calcium signaling in astrocytes, we suggest that the stepwise response is due to the supralinear dynamics of intracellular IP3 and that the heterogeneity of the responses may be due to the heterogeneity of the astrocyte-to-astrocyte couplings via gap junction channels. Therefore our results present astrocyte intracellular Ca2+ activity as a nonlinear integrator of glutamate-dependent neuronal activity. PMID:25521344

  12. Beta-band intermuscular coherence: a novel biomarker of upper motor neuron dysfunction in motor neuron disease

    Science.gov (United States)

    Fisher, Karen M.; Zaaimi, Boubker; Williams, Timothy L.; Baker, Stuart N.

    2012-01-01

    In motor neuron disease, the focus of therapy is to prevent or slow neuronal degeneration with neuroprotective pharmacological agents; early diagnosis and treatment are thus essential. Incorporation of needle electromyographic evidence of lower motor neuron degeneration into diagnostic criteria has undoubtedly advanced diagnosis, but even earlier diagnosis might be possible by including tests of subclinical upper motor neuron disease. We hypothesized that beta-band (15–30 Hz) intermuscular coherence could be used as an electrophysiological marker of upper motor neuron integrity in such patients. We measured intermuscular coherence in eight patients who conformed to established diagnostic criteria for primary lateral sclerosis and six patients with progressive muscular atrophy, together with 16 age-matched controls. In the primary lateral sclerosis variant of motor neuron disease, there is selective destruction of motor cortical layer V pyramidal neurons and degeneration of the corticospinal tract, without involvement of anterior horn cells. In progressive muscular atrophy, there is selective degeneration of anterior horn cells but a normal corticospinal tract. All patients with primary lateral sclerosis had abnormal motor-evoked potentials as assessed using transcranial magnetic stimulation, whereas these were similar to controls in progressive muscular atrophy. Upper and lower limb intermuscular coherence was measured during a precision grip and an ankle dorsiflexion task, respectively. Significant beta-band coherence was observed in all control subjects and all patients with progressive muscular atrophy tested, but not in the patients with primary lateral sclerosis. We conclude that intermuscular coherence in the 15–30 Hz range is dependent on an intact corticospinal tract but persists in the face of selective anterior horn cell destruction. Based on the distributions of coherence values measured from patients with primary lateral sclerosis and control

  13. Neuronal medium that supports basic synaptic functions and activity of human neurons in vitro

    NARCIS (Netherlands)

    Bardy, C.; Hurk, M. van den; Eames, T.; Marchand, C.; Hernandez, R.V.; Kellogg, M.; Gorris, M.A.J.; Galet, B.; Palomares, V.; Brown, J.; Bang, A.G.; Mertens, J.; Bohnke, L.; Boyer, L.; Simon, S.; Gage, F.H.

    2015-01-01

    Human cell reprogramming technologies offer access to live human neurons from patients and provide a new alternative for modeling neurological disorders in vitro. Neural electrical activity is the essence of nervous system function in vivo. Therefore, we examined neuronal activity in media widely

  14. Principles of artificial intelligence

    CERN Document Server

    Nilsson, Nils J

    1980-01-01

    A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, Principles of Artificial Intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of th

  15. Innervation by a GABAergic neuron depresses spontaneous release in glutamatergic neurons and unveils the clamping phenotype of synaptotagmin-1

    DEFF Research Database (Denmark)

    Wierda, Keimpe D B; Sørensen, Jakob Balslev

    2014-01-01

    The role of spontaneously occurring release events in glutamatergic and GABAergic neurons and their regulation is intensely debated. To study the interdependence of glutamatergic and GABAergic spontaneous release, we compared reciprocally connected "mixed" glutamatergic/GABAergic neuronal pairs...... from mice cultured on astrocyte islands with "homotypic" glutamatergic or GABAergic pairs and autaptic neurons. We measured mEPSC and mIPSC frequencies simultaneously from both neurons. Neuronal pairs formed both interneuronal synaptic and autaptic connections indiscriminately. We find that whereas m......EPSC and mIPSC frequencies did not deviate between autaptic and synaptic connections, the frequency of mEPSCs in mixed pairs was strongly depressed compared with either autaptic neurons or glutamatergic pairs. Simultaneous imaging of synapses, or comparison to evoked release amplitudes, showed...

  16. Biological Effects Of Artificial Illumination

    Science.gov (United States)

    Corth, Richard

    1980-10-01

    We are increasingly being warned of the possible effects of so called "polluted" light, that is light that differs in spectral content from that of sunlight. We should be concerned, we are told, because all animals and plants have evolved under this natural daylight and therefore any difference between that illuminant and the artificial illuminants that are on the market today, is suspect. The usual presentation of the differences between the sunlight and the artificial illuminants are as shown in Figure 1. Here we are shown the spectral power distribution of sunlight and Cool White fluorescent light. The spectral power distributions of each have been normalized to some convenient wavelength so that each can be seen and easily compared on the same figure. But this presentation is misleading for one does not experience artificial illuminants at the same intensity as one experiences sunlight. Sunlight intensities are ordinarily found to be in the 8000 to 10,000 footcandle range whereas artificial illuminants are rarely experienced at intensity levels greater than 100 footcandles. Therefore a representative difference between the two types of illumination conditions is more accurately represented as in Figure 2. Thus if evolutionary adaptations require that humans and other animals be exposed to sunlight to ensure wellbeing, it is clear that one must be exposed to sunlight intensities. It is not feasible to expect that artificially illuminated environments will be lit to the same intensity as sunlight

  17. Automatic Assessing of Tremor Severity Using Nonlinear Dynamics, Artificial Neural Networks and Neuro-Fuzzy Classifier

    Directory of Open Access Journals (Sweden)

    GEMAN, O.

    2014-02-01

    Full Text Available Neurological diseases like Alzheimer, epilepsy, Parkinson's disease, multiple sclerosis and other dementias influence the lives of patients, their families and society. Parkinson's disease (PD is a neurodegenerative disease that occurs due to loss of dopamine, a neurotransmitter and slow destruction of neurons. Brain area affected by progressive destruction of neurons is responsible for controlling movements, and patients with PD reveal rigid and uncontrollable gestures, postural instability, small handwriting and tremor. Commercial activity-promoting gaming systems such as the Nintendo Wii and Xbox Kinect can be used as tools for tremor, gait or other biomedical signals acquisitions. They also can aid for rehabilitation in clinical settings. This paper emphasizes the use of intelligent optical sensors or accelerometers in biomedical signal acquisition, and of the specific nonlinear dynamics parameters or fuzzy logic in Parkinson's disease tremor analysis. Nowadays, there is no screening test for early detection of PD. So, we investigated a method to predict PD, based on the image processing of the handwriting belonging to a candidate of PD. For classification and discrimination between healthy people and PD people we used Artificial Neural Networks (Radial Basis Function - RBF and Multilayer Perceptron - MLP and an Adaptive Neuro-Fuzzy Classifier (ANFC. In general, the results may be expressed as a prognostic (risk degree to contact PD.

  18. Population activity structure of excitatory and inhibitory neurons.

    Science.gov (United States)

    Bittner, Sean R; Williamson, Ryan C; Snyder, Adam C; Litwin-Kumar, Ashok; Doiron, Brent; Chase, Steven M; Smith, Matthew A; Yu, Byron M

    2017-01-01

    Many studies use population analysis approaches, such as dimensionality reduction, to characterize the activity of large groups of neurons. To date, these methods have treated each neuron equally, without taking into account whether neurons are excitatory or inhibitory. We studied population activity structure as a function of neuron type by applying factor analysis to spontaneous activity from spiking networks with balanced excitation and inhibition. Throughout the study, we characterized population activity structure by measuring its dimensionality and the percentage of overall activity variance that is shared among neurons. First, by sampling only excitatory or only inhibitory neurons, we found that the activity structures of these two populations in balanced networks are measurably different. We also found that the population activity structure is dependent on the ratio of excitatory to inhibitory neurons sampled. Finally we classified neurons from extracellular recordings in the primary visual cortex of anesthetized macaques as putative excitatory or inhibitory using waveform classification, and found similarities with the neuron type-specific population activity structure of a balanced network with excitatory clustering. These results imply that knowledge of neuron type is important, and allows for stronger statistical tests, when interpreting population activity structure.

  19. Population activity structure of excitatory and inhibitory neurons.

    Directory of Open Access Journals (Sweden)

    Sean R Bittner

    Full Text Available Many studies use population analysis approaches, such as dimensionality reduction, to characterize the activity of large groups of neurons. To date, these methods have treated each neuron equally, without taking into account whether neurons are excitatory or inhibitory. We studied population activity structure as a function of neuron type by applying factor analysis to spontaneous activity from spiking networks with balanced excitation and inhibition. Throughout the study, we characterized population activity structure by measuring its dimensionality and the percentage of overall activity variance that is shared among neurons. First, by sampling only excitatory or only inhibitory neurons, we found that the activity structures of these two populations in balanced networks are measurably different. We also found that the population activity structure is dependent on the ratio of excitatory to inhibitory neurons sampled. Finally we classified neurons from extracellular recordings in the primary visual cortex of anesthetized macaques as putative excitatory or inhibitory using waveform classification, and found similarities with the neuron type-specific population activity structure of a balanced network with excitatory clustering. These results imply that knowledge of neuron type is important, and allows for stronger statistical tests, when interpreting population activity structure.

  20. A phase plane analysis of neuron-astrocyte interactions.

    Science.gov (United States)

    Amiri, Mahmood; Montaseri, Ghazal; Bahrami, Fariba

    2013-08-01

    Intensive experimental studies have shown that astrocytes are active partners in modulation of synaptic transmission. In the present research, we study neuron-astrocyte signaling using a biologically inspired model of one neuron synapsing one astrocyte. In this model, the firing dynamics of the neuron is described by the Morris-Lecar model and the Ca(2+) dynamics of a single astrocyte explained by a functional model introduced by Postnov and colleagues. Using the coupled neuron-astrocyte model and based on the results of the phase plane analyses, it is demonstrated that the astrocyte is able to activate the silent neuron or change the neuron spiking frequency through bidirectional communication. This suggests that astrocyte feedback signaling is capable of modulating spike transmission frequency by changing neuron spiking frequency. This effect is described by a saddle-node on invariant circle bifurcation in the coupled neuron-astrocyte model. In this way, our results suggest that the neuron-astrocyte crosstalk has a fundamental role in producing diverse neuronal activities and therefore enhances the information processing capabilities of the brain. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.