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.

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.

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.

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.

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

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.

A billion-dollar bonanza

International Nuclear Information System (INIS)

Isaacs, J.

1993-01-01

In late May -- only weeks after Congress had rejected the president's economic stimulus package because it would add to the federal deficit -- the House of Representatives generously allocated an extra $1.2 billion to the Pentagon. This article discusses some of the rationalizations House members gave for the gift and describes the attempts of a bipartisan group to defeat this request for funds propounded by Pennsylvania Democrat John Murtha. This gist of the arguments for and against the $1.2 billion and the results of votes on the bill are presented

Summary and Comparison of the 2016 Billion-Ton Report with the 2011 U.S. Billion-Ton Update

Energy Technology Data Exchange (ETDEWEB)

None

2016-06-01

In terms of the magnitude of the resource potential, the results of the 2016 Billion-Ton Report (BT16) are consistent with the original 2005 Billion-Ton Study (BTS) and the 2011 report, U.S. Billion-Ton Update: Biomass Supply for a Bioenergy and Bioproducts Industry (BT2. An effort was made to reevaluate the potential forestland, agricultural, and waste resources at the roadside, then extend the analysis by adding transportation costs to a biorefinery under specified logistics assumptions to major resource fractions.

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

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.

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

Sneak Peek to the 2016 Billion-Ton Report

Energy Technology Data Exchange (ETDEWEB)

None

2016-06-01

The 2005 Billion-Ton Study became a landmark resource for bioenergy stakeholders, detailing for the first time the potential to produce at least one billion dry tons of biomass annually in a sustainable manner from U.S. agriculture and forest resources. The 2011 U.S. Billion-Ton Update expanded and updated the analysis, and in 2016, the U.S. Department of Energy’s Bioenergy Technologies Office plans to release the 2016 Billion-Ton Report: Advancing Domestic Resources for a Thriving Bioeconomy.

Countdown to Six Billion Teaching Kit.

Science.gov (United States)

Zero Population Growth, Inc., Washington, DC.

This teaching kit features six activities focused on helping students understand the significance of the world population reaching six billion for our society and our environment. Featured activities include: (1) History of the World: Part Six Billion; (2) A Woman's Place; (3) Baby-O-Matic; (4) Earth: The Apple of Our Eye; (5) Needs vs. Wants; and…

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

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

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.

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.

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)

[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.

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.

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.

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

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

Biologically inspired technologies using artificial muscles

Science.gov (United States)

Bar-Cohen, Yoseph

2005-01-01

After billions of years of evolution, nature developed inventions that work, which are appropriate for the intended tasks and that last. The evolution of nature led to the introduction of highly effective and power efficient biological mechanisms that are scalable from micron to many meters in size. Imitating these mechanisms offers enormous potentials for the improvement of our life and the tools we use. Humans have always made efforts to imitate nature and we are increasingly reaching levels of advancement where it becomes significantly easier to imitate, copy, and adapt biological methods, processes and systems. Some of the biomimetic technologies that have emerged include artificial muscles, artificial intelligence, and artificial vision to which significant advances in materials science, mechanics, electronics, and computer science have contributed greatly. One of the newest fields of biomimetics is the electroactive polymers (EAP) that are also known as artificial muscles. To take advantage of these materials, efforts are made worldwide to establish a strong infrastructure addressing the need for comprehensive analytical modeling of their operation mechanism and develop effective processing and characterization techniques. The field is still in its emerging state and robust materials are not readily available however in recent years significant progress has been made and commercial products have already started to appear. This paper covers the state-of-the-art and challenges to making artificial muscles and their potential biomimetic applications.

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.

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.

Areva excellent business volume: backlog as of december 31, 2008: + 21.1% to 48.2 billion euros. 2008 revenue: + 10.4% to 13.2 billion euros

International Nuclear Information System (INIS)

2009-01-01

AREVA's backlog stood at 48.2 billion euros as of December 31, 2008, for 21.1% growth year-on-year, including 21.8% growth in Nuclear and 16.5% growth in Transmission and Distribution. The Nuclear backlog came to 42.5 billion euros at December 31, 2008. The Transmission and Distribution backlog came to 5.7 billion euros at year-end. The group recognized revenue of 13.2 billion euros in 2008, for year-on-year growth of 10.4% (+9.8% like-for-like). Revenue outside France was up 10.5% to 9.5 billion euros, representing 72% of total revenue. Revenue was up 6.5% in the Nuclear businesses (up 6.3% LFL), with strong performance in the Reactors and Services division (+10.9% LFL) and the Front End division (+7.2% LFL). The Transmission and Distribution division recorded growth of 17% (+15.8% LFL). Revenue for the fourth quarter of 2008 rose to 4.1 billion euros, up 5.2% (+1.6% LFL) from that of the fourth quarter of 2007. Revenue for the Front End division rose to 3.363 billion euros in 2008, up 7.1% over 2007 (+7.2% LFL). Foreign exchange (currency translations) had a negative impact of 53 million euros. Revenue for the Reactors and Services division rose to 3.037 billion euros, up 11.8% over 2007 (+10.9% LFL). Foreign exchange (currency translations) had a negative impact of 47 million euros. Revenue for the Back End division came to 1.692 billion euros, a drop of 2.7% (-2.5% LFL). Foreign exchange (currency translations) had a negative impact of 3.5 million euros. Revenue for the Transmission and Distribution division rose to 5.065 billion euros in 2008, up 17.0% (+15.8% LFL)

Winglets Save Billions of Dollars in Fuel Costs

Science.gov (United States)

2010-01-01

The upturned ends now featured on many airplane wings are saving airlines billions of dollars in fuel costs. Called winglets, the drag-reducing technology was advanced through the research of Langley Research Center engineer Richard Whitcomb and through flight tests conducted at Dryden Flight Research Center. Seattle-based Aviation Partners Boeing -- a partnership between Aviation Partners Inc., of Seattle, and The Boeing Company, of Chicago -- manufactures Blended Winglets, a unique design featured on Boeing aircraft around the world. These winglets have saved more than 2 billion gallons of jet fuel to date, representing a cost savings of more than $4 billion and a reduction of almost 21.5 million tons in carbon dioxide emissions.

Proton collider breaks the six-billion-dollar barrier

CERN Multimedia

Vaughan, C

1990-01-01

The SSC will cost at least 1 billion more than its estimated final price of 5.9 billion dollars. Critics in congress believe the final bill could be double that figure. The director of the SSC blames most of the increase in cost on technical problems with developing the superconducting magnets for the SSC (1/2 page).

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).

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.

Global artificial photosynthesis project: a scientific and legal introduction.

Science.gov (United States)

Faunce, Thomas

2011-12-01

With the global human population set to exceed 10 billion by 2050, its collective energy consumption to rise from 400 to over 500 EJ/yr and with the natural environment under increasing pressure from these sources as well as from anthropogenic climate change, political solutions such as the creation of an efficient carbon price and trading scheme may arrive too late. In this context, the scientific community is exploring technological remedies. Central to these options is artificial photosynthesis--the creation, particularly through nanotechnology, of devices capable to doing what plants have done for millions of years - transforming sunlight, water and carbon dioxide into food and fuel. This article argues that a Global Artificial Photosynthesis (GAP) project can raise the public profile and encourage the pace, complexity and funding of scientific collaborations in artificial photosynthesis research. The legal structure of a GAP project will be critical to prevent issues such as state sovereignty over energy and food resources and corporate intellectual monopoly privileges unduly inhibiting the important contribution of artificial photosynthesis to global public health and environmental sustainability. The article presents an introduction to the scientific and legal concepts behind a GAP project.

Connecting the last billion

OpenAIRE

Ben David, Yahel

2015-01-01

The last billion people to join the online world, are likely to face at least one of two obstacles:Part I: Rural Internet AccessRural, sparsely populated, areas make conventional infrastructure investments unfeasible: Bigcorporations attempt to address this challenge via the launch of Low-Earth-Orbiting (LEO) satelliteconstellations, fleets of high-altitude balloons, and giant solar-powered drones; although thesegrandiose initiatives hold potential, they are costly and risky. At the same time...

Effect of Transcranial Magnetic Stimulation on Neuronal Networks

Science.gov (United States)

Unsal, Ahmet; Hadimani, Ravi; Jiles, David

2013-03-01

The human brain contains around 100 billion nerve cells controlling our day to day activities. Consequently, brain disorders often result in impairments such as paralysis, loss of coordination and seizure. It has been said that 1 in 5 Americans suffer some diagnosable mental disorder. There is an urgent need to understand the disorders, prevent them and if possible, develop permanent cure for them. As a result, a significant amount of research activities is being directed towards brain research. Transcranial Magnetic Stimulation (TMS) is a promising tool for diagnosing and treating brain disorders. It is a non-invasive treatment method that produces a current flow in the brain which excites the neurons. Even though TMS has been verified to have advantageous effects on various brain related disorders, there have not been enough studies on the impact of TMS on cells. In this study, we are investigating the electrophysiological effects of TMS on one dimensional neuronal culture grown in a circular pathway. Electrical currents are produced on the neuronal networks depending on the directionality of the applied field. This aids in understanding how neuronal networks react under TMS treatment.

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

12 billion DM for Germany

International Nuclear Information System (INIS)

Anon.

1975-01-01

The German atomic industry has achieved the break-through to the world market: Brazil orders eight nuclear electricity generating plants from Siemens-AEG daughter Kraftwerk-Union. US concerns attacked the twelve billion DM deal, the biggest export order in the history of German industry. Without avail - the contract is to be signed in Bonn this week. (orig./LH) [de

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.

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.

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

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.

Potential effects of the next 100 billion hamburgers sold by McDonald's.

Science.gov (United States)

Spencer, Elsa H; Frank, Erica; McIntosh, Nichole F

2005-05-01

McDonald's has sold >100 billion beef-based hamburgers worldwide with a potentially considerable health impact. This paper explores whether there would be any advantages if the next 100 billion burgers were instead plant-based burgers. Nutrient composition of the beef hamburger patty and the McVeggie burger patty were obtained from the McDonald's website; sales data were obtained from the McDonald's customer service. Consuming 100 billion McDonald's beef burgers versus the same company's McVeggie burgers would provide, approximately, on average, an additional 550 million pounds of saturated fat and 1.2 billion total pounds of fat, as well as 1 billion fewer pounds of fiber, 660 million fewer pounds of protein, and no difference in calories. These data suggest that the McDonald's new McVeggie burger represents a less harmful fast-food choice than the beef burger.

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.

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.

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)

Seven Billion People: Fostering Productive Struggle

Science.gov (United States)

Murawska, Jaclyn M.

2018-01-01

How can a cognitively demanding real-world task such as the Seven Billion People problem promote productive struggle "and" help shape students' mathematical dispositions? Driving home from school one evening, Jaclyn Murawska heard a commentator on the radio announce three statements: (1) experts had determined that the world population…

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.

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

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.

Regional Feedstock Partnership Summary Report: Enabling the Billion-Ton Vision

Energy Technology Data Exchange (ETDEWEB)

Owens, Vance N. [South Dakota State Univ., Brookings, SD (United States). North Central Sun Grant Center; Karlen, Douglas L. [Dept. of Agriculture Agricultural Research Service, Ames, IA (United States). National Lab. for Agriculture and the Environment; Lacey, Jeffrey A. [Idaho National Lab. (INL), Idaho Falls, ID (United States). Process Science and Technology Division

2016-07-12

The U.S. Department of Energy (DOE) and the Sun Grant Initiative established the Regional Feedstock Partnership (referred to as the Partnership) to address information gaps associated with enabling the vision of a sustainable, reliable, billion-ton U.S. bioenergy industry by the year 2030 (i.e., the Billion-Ton Vision). Over the past 7 years (2008–2014), the Partnership has been successful at advancing the biomass feedstock production industry in the United States, with notable accomplishments. The Billion-Ton Study identifies the technical potential to expand domestic biomass production to offset up to 30% of U.S. petroleum consumption, while continuing to meet demands for food, feed, fiber, and export. This study verifies for the biofuels and chemical industries that a real and substantial resource base could justify the significant investment needed to develop robust conversion technologies and commercial-scale facilities. DOE and the Sun Grant Initiative established the Partnership to demonstrate and validate the underlying assumptions underpinning the Billion-Ton Vision to supply a sustainable and reliable source of lignocellulosic feedstock to a large-scale bioenergy industry. This report discusses the accomplishments of the Partnership, with references to accompanying scientific publications. These accomplishments include advances in sustainable feedstock production, feedstock yield, yield stability and stand persistence, energy crop commercialization readiness, information transfer, assessment of the economic impacts of achieving the Billion-Ton Vision, and the impact of feedstock species and environment conditions on feedstock quality characteristics.

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.

Nuclear budget for FY1991 up 3.6% to 409.7 billion yen

International Nuclear Information System (INIS)

Anon.

1991-01-01

A total of yen409.7 billion was approved for the Governmental nuclear energy draft budget for fiscal 1991 on December 28, as the Cabinet gave its approval. The total, the highest ever, was divided into yen182.6 billion for the general account and yen227.1 billion for the special account for power resources development, representing a 3.6% increase over the ongoing fiscal year's level of yen395.5 billion. The draft budget will be examined for approval of the Diet session by the end of March. The nuclear energy budget devoted to research and development projects governed by the Science and Technology Agency amounts yen306.4 billion, up 3.5% exceeding yen300 billion for the first time. The nuclear budget for the Ministry of International Trade and Industry is yen98.1 billion, up 3.5%. For the other ministries, including the Ministry of Foreign Affairs, yen5.1 billion was allotted to nuclear energy-related projects. The Government had decided to raise the unit cost of the power plant siting promotion subsidies in the special account for power resources development by 25% --- from yen600/kw to yen750/kw --- in order to support the siting of plants. Consequently, the power resources siting account of the special accounts for both STA and MITI showed high levels of growth rates: 6.3% and 7.5%, respectively. (N.K.)

Water quality of the Little Arkansas River and Equus Beds Aquifer before and concurrent with large-scale artificial recharge, south-central Kansas, 1995-2012

Science.gov (United States)

Tappa, Daniel J.; Lanning-Rush, Jennifer L.; Klager, Brian J.; Hansen, Cristi V.; Ziegler, Andrew C.

2015-01-01

The city of Wichita artificially recharged about 1 billion gallons of water into the Equus Beds aquifer during 2007–2012 as part of Phase I recharge of the Artificial Storage and Recovery project. This report, prepared in cooperation by the U.S. Geological Survey and the city of Wichita, Kansas, summarizes Little Arkansas River (source-water for artificial recharge) andEquus Beds aquifer water quality before (1995–2006) and during (2007–2012) Artificial Storage and Recovery Phase I recharge. Additionally, aquifer water-quality distribution maps are presented and water-quality changes associated with Phase I recharge timing are described.

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.

Community access networks: how to connect the next billion to the ...

International Development Research Centre (IDRC) Digital Library (Canada)

Community access networks: how to connect the next billion to the Internet. Despite recent progress with mobile technology diffusion, more than four billion people worldwide are unconnected and have limited access to global communication infrastructure. The cost of implementing connectivity infrastructure in underserved ...

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.

Four billion people facing severe water scarcity.

Science.gov (United States)

Mekonnen, Mesfin M; Hoekstra, Arjen Y

2016-02-01

Freshwater scarcity is increasingly perceived as a global systemic risk. Previous global water scarcity assessments, measuring water scarcity annually, have underestimated experienced water scarcity by failing to capture the seasonal fluctuations in water consumption and availability. We assess blue water scarcity globally at a high spatial resolution on a monthly basis. We find that two-thirds of the global population (4.0 billion people) live under conditions of severe water scarcity at least 1 month of the year. Nearly half of those people live in India and China. Half a billion people in the world face severe water scarcity all year round. Putting caps to water consumption by river basin, increasing water-use efficiencies, and better sharing of the limited freshwater resources will be key in reducing the threat posed by water scarcity on biodiversity and human welfare.

How do you interpret a billion primary care records?

Directory of Open Access Journals (Sweden)

Martin Heaven

2017-04-01

To establish this we explored just over 1 billion unique Read coded records generated in the time period 1999 to 2015 by GP practices participating in the provision of anonymised records to SAIL, aligning, filtering and summarising the data in a series of observational exercises to generate hypotheses related to the capture and recording of the data. Results A fascinating journey through 1 billion GP practice generated pieces of information, embarked upon to aid interpretation of our Supporting People results, and providing insights into the patterns of recording within GP data.

Interactive (statistical) visualisation and exploration of a billion objects with vaex

NARCIS (Netherlands)

Breddels, M. A.

2016-01-01

With new catalogues arriving such as the Gaia DR1, containing more than a billion objects, new methods of handling and visualizing these data volumes are needed. We show that by calculating statistics on a regular (N-dimensional) grid, visualizations of a billion objects can be done within a second

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.

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.

2016 Billion-Ton Report: Advancing Domestic Resources for a Thriving Bioeconomy

Energy Technology Data Exchange (ETDEWEB)

None

2016-07-06

This product builds on previous efforts, namely the 2005 Billion-Ton Study (BTS) and the 2011 U.S. Billion-Ton Update (BT2).With each report, greater perspective is gained on the potential of biomass resources to contribute to a national energy strategy. Similarly, each successive report introduces new questions regarding commercialization challenges. BTS quantified the broad biophysical potential of biomass nationally, and BT2 elucidated the potential economic availability of these resources. These reports clearly established the potential availability of up to one billion tons of biomass resources nationally. However, many questions remain, including but not limited to crop yields, climate change impacts, logistical operations, and systems integration across production, harvest, and conversion. The present report aims to address many of these questions through empirically modeled energy crop yields, scenario analysis of resources delivered to biorefineries, and the addition of new feedstocks. Volume 2 of the 2016 Billion-Ton Report is expected to be released by the end of 2016. It seeks to evaluate environmental sustainability indicators of select scenarios from volume 1 and potential climate change impacts on future supplies.

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.

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.

Cosmic rays and the biosphere over 4 billion years

DEFF Research Database (Denmark)

Svensmark, Henrik

2006-01-01

Variations in the flux of cosmic rays (CR) at Earth during the last 4.6 billion years are constructed from information about the star formation rate in the Milky Way and the evolution of the solar activity. The constructed CR signal is compared with variations in the Earths biological productivit...... as recorded in the isotope delta C-13, which spans more than 3 billion years. CR and fluctuations in biological productivity show a remarkable correlation and indicate that the evolution of climate and the biosphere on the Earth is closely linked to the evolution of the Milky Way....

Sugar Antennae for Guidance Signals: Syndecans and Glypicans Integrate Directional Cues for Navigating Neurons

Directory of Open Access Journals (Sweden)

Christa Rhiner

2006-01-01

Full Text Available Attractive and repulsive signals guide migrating nerve cells in all directions when the nervous system starts to form. The neurons extend thin processes, axons, that connect over wide distances with other brain cells to form a complicated neuronal network. One of the most fascinating questions in neuroscience is how the correct wiring of billions of nerve cells in our brain is controlled. Several protein families are known to serve as guidance cues for navigating neurons and axons. Nevertheless, the combinatorial potential of these proteins seems to be insufficient to sculpt the entire neuronal network and the appropriate formation of connections. Recently, heparan sulfate proteoglycans (HSPGs, which are present on the cell surface of neurons and in the extracellular matrix through which neurons and axons migrate, have been found to play a role in regulating cell migration and axon guidance. Intriguingly, the large number of distinct modifications that can be put onto the sugar side chains of these PGs would in principle allow for an enormous diversity of HSPGs, which could help in regulating the vast number of guidance choices taken by individual neurons. In this review, we will focus on the role of the cell surface HSPGs syndecan and glypican and specific HS modifications in promoting neuronal migration, axon guidance, and synapse formation.

$17 billion needed by year 2000.

Science.gov (United States)

Finger, W R

1995-09-01

The United Nations Population Fund (UNFPA) estimates that US$17 billion will be needed to fund reproductive health care in developing countries by the year 2000. About US$10 billion of would go for family planning: currently, the amount spent on family planning is about US$5 billion. Donors are focusing on fewer countries because of limited resources. The United States Agency for International Development (USAID) is planning to phase out support for family planning in Jamaica and Brazil because the programs there have advanced sufficiently. Resources will be shifted to countries with more pressing needs. Dr. Richard Osborn, senior technical officer for UNFPA, states that UNFPA works with national program managers in allocating resources at the macro level (commodities, training). Currently, two-thirds of family planning funds spent worldwide come from developing country governments (mainly China, India, Indonesia, Mexico, South Africa, Turkey, and Bangladesh). Sustaining programs, much less adding new services, will be difficult. User fees and public-private partnerships are being considered; worldwide, consumers provide, currently, about 14% of family planning funds (The portion is higher in most Latin American countries.). In a few countries, insurance, social security, and other public-private arrangements contribute. Social marketing programs are being considered that would remove constraints on prescriptions and prices and improve the quality of services so that clients would be more willing to pay for contraceptives. Although governments are attempting to fit family planning into their health care budgets, estimates at the national level are difficult to make. Standards are needed to make expenditure estimates quickly and at low cost, according to Dr. Barbara Janowitz of FHI, which is developing guidelines. Studies in Bangladesh, Ecuador, Ghana, Mexico, and the Philippines are being conducted, with the assistance of The Evaluation Project at the Population

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.

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.

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.

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

Pretreatment with apoaequorin protects hippocampal CA1 neurons from oxygen-glucose deprivation.

Science.gov (United States)

Detert, Julia A; Adams, Erin L; Lescher, Jacob D; Lyons, Jeri-Anne; Moyer, James R

2013-01-01

Ischemic stroke affects ∼795,000 people each year in the U.S., which results in an estimated annual cost of $73.7 billion. Calcium is pivotal in a variety of neuronal signaling cascades, however, during ischemia, excess calcium influx can trigger excitotoxic cell death. Calcium binding proteins help neurons regulate/buffer intracellular calcium levels during ischemia. Aequorin is a calcium binding protein isolated from the jellyfish Aequorea victoria, and has been used for years as a calcium indicator, but little is known about its neuroprotective properties. The present study used an in vitro rat brain slice preparation to test the hypothesis that an intra-hippocampal infusion of apoaequorin (the calcium binding component of aequorin) protects neurons from ischemic cell death. Bilaterally cannulated rats received an apoaequorin infusion in one hemisphere and vehicle control in the other. Hippocampal slices were then prepared and subjected to 5 minutes of oxygen-glucose deprivation (OGD), and cell death was assayed by trypan blue exclusion. Apoaequorin dose-dependently protected neurons from OGD--doses of 1% and 4% (but not 0.4%) significantly decreased the number of trypan blue-labeled neurons. This effect was also time dependent, lasting up to 48 hours. This time dependent effect was paralleled by changes in cytokine and chemokine expression, indicating that apoaequorin may protect neurons via a neuroimmunomodulatory mechanism. These data support the hypothesis that pretreatment with apoaequorin protects neurons against ischemic cell death, and may be an effective neurotherapeutic.

Dynamic artificial neural networks with affective systems.

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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.

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.

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.

BUILDING A BILLION SPATIO-TEMPORAL OBJECT SEARCH AND VISUALIZATION PLATFORM

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D. Kakkar

2017-10-01

Full Text Available With funding from the Sloan Foundation and Harvard Dataverse, the Harvard Center for Geographic Analysis (CGA has developed a prototype spatio-temporal visualization platform called the Billion Object Platform or BOP. The goal of the project is to lower barriers for scholars who wish to access large, streaming, spatio-temporal datasets. The BOP is now loaded with the latest billion geo-tweets, and is fed a real-time stream of about 1 million tweets per day. The geo-tweets are enriched with sentiment and census/admin boundary codes when they enter the system. The system is open source and is currently hosted on Massachusetts Open Cloud (MOC, an OpenStack environment with all components deployed in Docker orchestrated by Kontena. This paper will provide an overview of the BOP architecture, which is built on an open source stack consisting of Apache Lucene, Solr, Kafka, Zookeeper, Swagger, scikit-learn, OpenLayers, and AngularJS. The paper will further discuss the approach used for harvesting, enriching, streaming, storing, indexing, visualizing and querying a billion streaming geo-tweets.

Building a Billion Spatio-Temporal Object Search and Visualization Platform

Science.gov (United States)

Kakkar, D.; Lewis, B.

2017-10-01

With funding from the Sloan Foundation and Harvard Dataverse, the Harvard Center for Geographic Analysis (CGA) has developed a prototype spatio-temporal visualization platform called the Billion Object Platform or BOP. The goal of the project is to lower barriers for scholars who wish to access large, streaming, spatio-temporal datasets. The BOP is now loaded with the latest billion geo-tweets, and is fed a real-time stream of about 1 million tweets per day. The geo-tweets are enriched with sentiment and census/admin boundary codes when they enter the system. The system is open source and is currently hosted on Massachusetts Open Cloud (MOC), an OpenStack environment with all components deployed in Docker orchestrated by Kontena. This paper will provide an overview of the BOP architecture, which is built on an open source stack consisting of Apache Lucene, Solr, Kafka, Zookeeper, Swagger, scikit-learn, OpenLayers, and AngularJS. The paper will further discuss the approach used for harvesting, enriching, streaming, storing, indexing, visualizing and querying a billion streaming geo-tweets.

$35 billion habit: will nuclear cost overruns bankrupt the utilities

International Nuclear Information System (INIS)

Morgan, R.E.

1980-01-01

The Nuclear Regulatory Commission (NRC) has proposed some 150 modifications in the design and operation of nuclear power plants as a result of the accident at Three Mile Island. The Atomic Industrial Forum estimates the total cost of the NRC's proposed rule changes at $35.5 billion ($3.5 billion in capital costs for the entire industry, and $32 billion in outage and construction-delay costs to the utilities) for existing facilities and for those with construction well underway. The changes range from improved training for reactor workers to a major overhaul of the reactor-containment design. The nuclear industry is asking the NRC to modify the proposals citing excessive costs (like the $100 million changes needed for a plant that cost $17 million to build) and safety (some of the complex regulations may interfere with safety). Financing the changes has become a major problem for the utilities. If the regulators allow all the costs to be passed along to the consumer, the author feels electricity will be too expensive for the consumer

Backlog at December 31, 2007: euro 39,8 billion, up by 55% from year-end 2006. 2007 sales revenue: euro 11.9 billion, up by 9.8% (+10.4% like-for-like)

International Nuclear Information System (INIS)

2008-01-01

The AREVA group's backlog reached a record level of euro 39.834 billion as of December 31, 2007, up by 55% from that of year-end 2006. In Nuclear, the backlog was euro 34.927 billion at year-end 2007 (+58%), due in particular to the signature of a contract in a record amount with the Chinese utility CGNPC. The series of agreements concluded provide among other things for the construction of two new-generation EPR nuclear islands and the supply of all of the materials and services needed for their operation through 2027. CGNPC also bought 35% of the production of UraMin, the mining company acquired by AREVA in August 2007. Industrial cooperation in the Back End of the cycle was launched with the signature of an agreement between China and France. In addition, the group signed several long-term contracts in significant amounts, particularly with KHNP of South Korea, EDF and Japanese utilities. The Transmission and Distribution division won several major contracts in Libya and Qatar at the end of the year approaching a total of euro 750 million. For the entire year, new orders grew by 34% to euro 5.816 billion. The backlog, meanwhile, grew by 40% to euro 4.906 billion at year-end. The group cleared sales revenue of euro 11.923 billion in 2007, up by 9.8% (+10.4% like-for-like) in relation to 2006 sales of euro 10.863 billion. Sales revenue for the 4. quarter of 2007 rose to euro 3.858 billion, for growth of 16.7% (+18.8% like-for-like) over one year. Sales revenue for the year was marked by: - Growth of 7.6% (+10.6% like-for-like) in Front End sales revenue, which rose to euro 3.140 billion. The division's Enrichment operations posted strong growth. - Sales were up by 17.5% (+15.2% like-for-like) to euro 2.717 billion in the Reactors and Services division. Sales revenue was driven in particular by the growth of Services operations, after weak demand in 2006, by progress on OL3 construction, and by the start of Flamanville 3, the second EPR. For the Back End division

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.

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.

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

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.

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)

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

A two-billion-year history for the lunar dynamo.

Science.gov (United States)

Tikoo, Sonia M; Weiss, Benjamin P; Shuster, David L; Suavet, Clément; Wang, Huapei; Grove, Timothy L

2017-08-01

Magnetic studies of lunar rocks indicate that the Moon generated a core dynamo with surface field intensities of ~20 to 110 μT between at least 4.25 and 3.56 billion years ago (Ga). The field subsequently declined to lunar dynamo by at least 1 billion years. Such a protracted history requires an extraordinarily long-lived power source like core crystallization or precession. No single dynamo mechanism proposed thus far can explain the strong fields inferred for the period before 3.56 Ga while also allowing the dynamo to persist in such a weakened state beyond ~2.5 Ga. Therefore, our results suggest that the dynamo was powered by at least two distinct mechanisms operating during early and late lunar history.

[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.

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.

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.

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

Artificial insemination in pigs today.

Science.gov (United States)

Knox, R V

2016-01-01

Use of artificial insemination (AI) for breeding pigs has been instrumental for facilitating global improvements in fertility, genetics, labor, and herd health. The establishment of AI centers for management of boars and production of semen has allowed for selection of boars for fertility and sperm production using in vitro and in vivo measures. Today, boars can be managed for production of 20 to 40 traditional AI doses containing 2.5 to 3.0 billion motile sperm in 75 to 100 mL of extender or 40 to 60 doses with 1.5 to 2.0 billion sperm in similar or reduced volumes for use in cervical or intrauterine AI. Regardless of the sperm dose, in liquid form, extenders are designed to sustain sperm fertility for 3 to 7 days. On farm, AI is the predominant form for commercial sow breeding and relies on manual detection of estrus with sows receiving two cervical or two intrauterine inseminations of the traditional or low sperm doses on each day detected in standing estrus. New approaches for increasing rates of genetic improvement through use of AI are aimed at methods to continue to lower the number of sperm in an AI dose and reducing the number of inseminations through use of a single, fixed-time AI after ovulation induction. Both approaches allow greater selection pressure for economically important swine traits in the sires and help extend the genetic advantages through AI on to more production farms. Copyright © 2016 Elsevier Inc. All rights reserved.

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

Fiscal 1988 draft budget for nuclear energy up 1.9% to yen 369 billion

International Nuclear Information System (INIS)

Anon.

1988-01-01

AT the cabinet meeting held on December 28, the government approved the fiscal 1988 draft budget, with a general account of yen 56.6 trillion. The nuclear energy related budget is yen 181.124 billion from the general account and yen 186.098 billion from the special account for power sources development, totalling yen 367.222 billion, up 1.9% on the previous year. The largest appropriation goes to the Science and Technology Agency (STA) totaling yen 271 billion. The STA is promoting safety studies and R and D for extensive nuclear energy utilization but the budget shows a 0.7% decrease from the previous year, reflecting completion of the construction of JT-60, which is one of the Agency's major projects. MITI, with its budget of yen 91 billion will carry on policies related to the promotion of commercial nuclear power program as well as support for the industrialization program of the nuclear fuel cycle. Nuclear related budget of Ministry of Foreign Affairs is yen 2.8 billion, consisting mainly of IAEA subscriptions and contributions and OECD/NEA subscriptions. Besides these three government agencies, a large sum of yen 1.2 billion is allocated to the Okinawa Development Agency for the prevention and elimination of melon-flies in Kume Island and islands around Okinawa main island. The draft government budget will be submitted to the ordinary session of the Diet when it resumes towards the end of January. After deliberation in the Budget Committees of the House of Representatives and the House of Councilors, the draft budget will be put to the vote in the plenary session. Assuming that all proceeds smoothly, the budget is expected to be approved by the end of March without any major revision. (author)

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.

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.

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.

Sucralose Promotes Food Intake through NPY and a Neuronal Fasting Response.

Science.gov (United States)

Wang, Qiao-Ping; Lin, Yong Qi; Zhang, Lei; Wilson, Yana A; Oyston, Lisa J; Cotterell, James; Qi, Yue; Khuong, Thang M; Bakhshi, Noman; Planchenault, Yoann; Browman, Duncan T; Lau, Man Tat; Cole, Tiffany A; Wong, Adam C N; Simpson, Stephen J; Cole, Adam R; Penninger, Josef M; Herzog, Herbert; Neely, G Gregory

2016-07-12

Non-nutritive sweeteners like sucralose are consumed by billions of people. While animal and human studies have demonstrated a link between synthetic sweetener consumption and metabolic dysregulation, the mechanisms responsible remain unknown. Here we use a diet supplemented with sucralose to investigate the long-term effects of sweet/energy imbalance. In flies, chronic sweet/energy imbalance promoted hyperactivity, insomnia, glucose intolerance, enhanced sweet taste perception, and a sustained increase in food and calories consumed, effects that are reversed upon sucralose removal. Mechanistically, this response was mapped to the ancient insulin, catecholamine, and NPF/NPY systems and the energy sensor AMPK, which together comprise a novel neuronal starvation response pathway. Interestingly, chronic sweet/energy imbalance promoted increased food intake in mammals as well, and this also occurs through an NPY-dependent mechanism. Together, our data show that chronic consumption of a sweet/energy imbalanced diet triggers a conserved neuronal fasting response and increases the motivation to eat. Copyright © 2016 Elsevier Inc. All rights reserved.

Brains are not just neurons. Comment on “Toward a computational framework for cognitive biology: Unifying approaches from cognitive neuroscience and comparative cognition” by Fitch

Science.gov (United States)

Huber, Ludwig

2014-09-01

This comment addresses the first component of Fitch's framework: the computational power of single neurons [3]. Although I agree that traditional models of neural computation have vastly underestimated the computational power of single neurons, I am hesitant to follow him completely. The exclusive focus on neurons is likely to underestimate the importance of other cells in the brain. In the last years, two such cell types have received appropriate attention by neuroscientists: interneurons and glia. Interneurons are small, tightly packed cells involved in the control of information processing in learning and memory. Rather than transmitting externally (like motor or sensory neurons), these neurons process information within internal circuits of the brain (therefore also called 'relay neurons'). Some specialized interneuron subtypes temporally regulate the flow of information in a given cortical circuit during relevant behavioral events [4]. In the human brain approx. 100 billion interneurons control information processing and are implicated in disorders such as epilepsy and Parkinson's.

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.

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.

Working Paper 5: Beyond Collier's Bottom Billion | IDRC ...

International Development Research Centre (IDRC) Digital Library (Canada)

2010-12-16

Dec 16, 2010 ... The heart of the narrative presented in the book is that a group of almost 60 countries, with a population of about a billion people, are caught in four main traps. Their prospects for escaping the traps are poor, and they need a set of actions from the international community to achieve the rapid rates of growth ...

Congress OKs $2 Billion Boost for the NIH.

Science.gov (United States)

2017-07-01

President Donald Trump last week signed a $1.1 trillion spending bill for fiscal year 2017, including a welcome $2 billion boost for the NIH that will support former Vice President Joe Biden's Cancer Moonshot initiative, among other priorities. However, researchers who rely heavily on NIH grant funding remain concerned about proposed cuts for 2018. ©2017 American Association for Cancer Research.

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...

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.

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)

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.

1.6 billion euros for nuclear research through the 'Horizon 2020' program

International Nuclear Information System (INIS)

Anon.

2014-01-01

The European Union Council has approved the budget for the future European program for research and innovation called 'Horizon 2020'. A global funding of 77 billion euros has been allocated to 'Horizon 2020' for the 2014 to 2020 years. The share for nuclear sciences will reach 1.6 billion euros and will break down as follows: 316 million euros for fundamental research on fission, 728 million euros for fundamental research on fusion (ITER not included) and 560 million euros for the research projects of the European Joint Research Center (JRC). (A.C.)

Honeybees Produce Millimolar Concentrations of Non-Neuronal Acetylcholine for Breeding: Possible Adverse Effects of Neonicotinoids.

Directory of Open Access Journals (Sweden)

Ignaz Wessler

Full Text Available The worldwide use of neonicotinoid pesticides has caused concern on account of their involvement in the decline of bee populations, which are key pollinators in most ecosystems. Here we describe a role of non-neuronal acetylcholine (ACh for breeding of Apis mellifera carnica and a so far unknown effect of neonicotinoids on non-target insects. Royal jelly or larval food are produced by the hypopharyngeal gland of nursing bees and contain unusually high ACh concentrations (4-8 mM. ACh is extremely well conserved in royal jelly or brood food because of the acidic pH of 4.0. This condition protects ACh from degradation thus ensuring delivery of intact ACh to larvae. Raising the pH to ≥5.5 and applying cholinesterase reduced the content of ACh substantially (by 75-90% in larval food. When this manipulated brood was tested in artificial larval breeding experiments, the survival rate was higher with food supplemented by 100% with ACh (6 mM than with food not supplemented with ACh. ACh release from the hypopharyngeal gland and its content in brood food declined by 80%, when honeybee colonies were exposed for 4 weeks to high concentrations of the neonicotinoids clothianidin (100 parts per billion [ppb] or thiacloprid (8,800 ppb. Under these conditions the secretory cells of the gland were markedly damaged and brood development was severely compromised. Even field-relevant low concentrations of thiacloprid (200 ppb or clothianidin (1 and 10 ppb reduced ACh level in the brood food and showed initial adverse effects on brood development. Our findings indicate a hitherto unknown target of neonicotinoids to induce adverse effects on non-neuronal ACh which should be considered when re-assessing the environmental risks of these compounds. To our knowledge this is a new biological mechanism, and we suggest that, in addition to their well documented neurotoxic effects, neonicotinoids may contribute to honeybee colony losses consecutive to a reduction of the ACh

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.

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

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...

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.

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%.

Empowering billions with food safety and food security

International Nuclear Information System (INIS)

Pillai, Suresh D.

2009-01-01

Full text: There are virtually millions of people -who die needlessly every year due to contaminated water and food. There are virtually many millions more who are starving due to an inadequate supply of food. Billions of pounds of food are unnecessarily wasted due to insect and other damage. Deaths and illness due to contaminated food or inadequate food are at catastrophic levels in many regions of the world. A majority of the food and water borne illnesses and deaths are preventable. It can be prevented by improved food production methods, improved food processing technologies, improved food distribution systems and improved personal hygiene. Food irradiation technology is over 100 years old. Yet, this technology is poorly understood by governments and corporate decision makers all around the world. Many consumers also are unfortunately misinformed of this technology. There is an urgent need for nations and people around the world to empower themselves with the knowledge and the expertise to harness this powerful technology. Widespread and sensible adoption of this technology can empower billions around the world with clean and abundant food supplies. It is unconscionable in the 21st century for governments to allow people to die or go hungry when the technology to prevent them is readily available

Nuclear business worth billions begins

International Nuclear Information System (INIS)

Beer, G.; Marcan, P.; Slovak, K.

2005-01-01

specific data regarding the direct costs of decommissioning. Preliminary estimates state 50 billions Slovak crowns (1.28 billions EUR), but the actual costs will mainly depend on the volume of nuclear waste to be disposed of. (authors)

Price of next big thing in physics: $6.7 billion

CERN Multimedia

Overbye, Dennis

2007-01-01

The price of exploring inner space went up Thursday. The machine discusses in a news conference in Beijing, will be 20 miles long and would cost about $6.7 billion and 13'000 person-years of labor to be built. (1,5 page)

Interaction, at Ambient Temperature and 80 °C, between Minerals and Artificial Seawaters Resembling the Present Ocean Composition and that of 4.0 Billion Years Ago.

Science.gov (United States)

Carneiro, Cristine E A; Stabile, Antonio C; Gomes, Frederico P; da Costa, Antonio C S; Zaia, Cássia T B V; Zaia, Dimas A M

2017-09-01

Probably one of the most important roles played by minerals in the origin of life on Earth was to pre-concentrate biomolecules from the prebiotic seas. There are other ways to pre concentrate biomolecules such as wetting/drying cycles and freezing/sublimation. However, adsorption is most important. If the pre-concentration did not occur-because of degradation of the minerals-other roles played by them such as protection against degradation, formation of polymers, or even as primitive cell walls would be seriously compromised. We studied the interaction of two artificial seawaters with kaolinite, bentonite, montmorillonite, goethite, ferrihydrite and quartz. One seawater has a major cation and anion composition similar to that of the oceans of the Earth 4.0 billion years ago (ASW 4.0 Ga). In the other, the major cations and anions are an average of the compositions of the seawaters of today (ASWT). When ASWT, which is rich in Na + and Cl - , interacted with bentonite and montmorrilonite structural collapse occurred on the 001 plane. However, ASW 4.0 Ga, which is rich in Mg 2+ and SO 4 2- , did not induce this behavior. When ASW 4.0 Ga was reacted with the minerals for 24 h at room temperature and 80 °C, the release of Si and Al to the fluid was below 1 % of the amount in the minerals-meaning that dissolution of the minerals did not occur. In general, minerals adsorbed Mg 2+ and K + from the ASW 4.0 Ga and these cations could be used for the formation of polymers. Also, when the minerals were mixed with ASW 4.0 Ga at 80 °C and ASWT at room temperature or 80 °C it caused the precipitation of CaSO 4 ∙2H 2 O and halite, respectively. Finally, further experiments (adsorption, formation of polymers, protection of molecules against degradation, primitive cell wall formation) performed under the conditions described in this paper will probably be more representative of what happened on the prebiotic Earth.

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.

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

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....

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)

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)

Document de travail 5: Beyond Collier's Bottom Billion | CRDI ...

International Development Research Centre (IDRC) Digital Library (Canada)

16 déc. 2010 ... L'ouvrage de Paul Collier, The Bottom Billion, suscite un grand intérêt dans le domaine du développement. Il repose sur la thèse selon laquelle un groupe de près de 60 pays, dont la population totale avoisine un milliard de personnes, sont pris dans quatre pièges principaux.

A parts-per-billion measurement of the antiproton magnetic moment

CERN Document Server

Smorra, C; Borchert, M J; Harrington, J A; Higuchi, T; Nagahama, H; Tanaka, T; Mooser, A; Schneider, G; Blaum, K; Matsuda, Y; Ospelkaus, C; Quint, W; Walz, J; Yamazaki, Y; Ulmer, S

2017-01-01

Precise comparisons of the fundamental properties of matter–antimatter conjugates provide sensitive tests of charge–parity–time (CPT) invariance1, which is an important symmetry that rests on basic assumptions of the standard model of particle physics. Experiments on mesons2, leptons3, 4 and baryons5, 6 have compared different properties of matter–antimatter conjugates with fractional uncertainties at the parts-per-billion level or better. One specific quantity, however, has so far only been known to a fractional uncertainty at the parts-per-million level7, 8: the magnetic moment of the antiproton, . The extraordinary difficulty in measuring with high precision is caused by its intrinsic smallness; for example, it is 660 times smaller than the magnetic moment of the positron3. Here we report a high-precision measurement of in units of the nuclear magneton μN with a fractional precision of 1.5 parts per billion (68% confidence level). We use a two-particle spectroscopy method in an advanced cryogenic ...

International collaboration in SSC (or any $4 billion scientific project)

International Nuclear Information System (INIS)

Lederman, L.M.

1988-01-01

In this paper, the author discusses the superconducting supercollider. This is a project that costs U.S. $4.4 billion. The author spends a short time giving the motivation (which is a scientific motivation) and also giving the idea of how it is possible, with U.S. deficits

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.

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

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

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.

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.

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.

Oncology pharma costs to exceed $150 billion by 2020.

Science.gov (United States)

2016-10-01

Worldwide costs of oncology drugs will rise above $150 billion by 2020, according to a report by the IMS Institute for Healthcare Informatics. Many factors are in play, according to IMS, including the new wave of expensive immunotherapies. Pembrolizumab (Keytruda), priced at $150,000 per year per patient, and nivolumab (Opdivo), priced at $165,000, may be harbingers of the market for cancer immunotherapies.

Cost of solving mysteries of universe: $6.7 billion

CERN Multimedia

Overbye, Dennis

2007-01-01

"An international consortium of physicists on Thursday released the first detailed design of what they believe will be the next big thing in physics. The machine, 20 miles long, will slam together electrons and their opposites, positrons, to produce fireballs of energy re-creating conditions when the universe was only a trillionth of a second old. It would cost about $6.7 billion." (1 page)

Areva - First quarter 2009 revenue climbs 8.5% to 3.003 billion euros

International Nuclear Information System (INIS)

2009-04-01

First quarter 2009 revenue was up 8.5% compared with the same period last year, to 3.003 billion euros. At constant exchange rates and consolidation scope, growth came to 3.9%. Currency translation had a positive impact of 57 million euros over the quarter. Changes in the consolidation scope had an impact of 66 million euros, primarily due to the consolidation of acquisitions made in 2008 in Transmission and Distribution and in Renewable Energies. The growth engines for first quarter revenue were the Reactors and Services division and the Transmission and Distribution division, with growth of 9.2% and 16.1% respectively. Outside France, revenue rose to 2.032 billion euros, compared with 1.857 billion euros in the first quarter of 2008, and represents 68% of total revenue. Orders were steady in the first quarter, particularly in the Front End, which posted several significant contracts with US and Asian utilities, and in Transmission and Distribution, with orders up sharply in Asia and South America. As of March 31, 2009, the group's backlog reached 49.5 billion euros, for 28.3% growth year-on-year, including 31.3% growth in Nuclear and 10.2% in Transmission and Distribution. For the year as a whole, the group confirms its outlook for backlog and revenue growth as well as rising operating income It should be noted that revenue may vary significantly from one quarter to the next in nuclear operations. Accordingly, quarterly data cannot be viewed as a reliable indicator of annual trends

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

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

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

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.

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.

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.

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

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.

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.)

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.

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.

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.

Ubiquitous Supercritical Wing Design Cuts Billions in Fuel Costs

Science.gov (United States)

2015-01-01

A Langley Research Center engineer’s work in the 1960s and ’70s to develop a wing with better performance near the speed of sound resulted in a significant increase in subsonic efficiency. The design was shared with industry. Today, Renton, Washington-based Boeing Commercial Airplanes, as well as most other plane manufacturers, apply it to all their aircraft, saving the airline industry billions of dollars in fuel every year.

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.

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.

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.

Community access networks: how to connect the next billion to the ...

International Development Research Centre (IDRC) Digital Library (Canada)

Community access networks: how to connect the next billion to the Internet ... services is a prerequisite to sustainable socio-economic development. ... It will provide case studies and formulate recommendations with respect to ... An IDRC delegation will join international delegates and city representatives at the ICLEI World ...

Interactive (statistical) visualisation and exploration of a billion objects with vaex

Science.gov (United States)

Breddels, M. A.

2017-06-01

With new catalogues arriving such as the Gaia DR1, containing more than a billion objects, new methods of handling and visualizing these data volumes are needed. We show that by calculating statistics on a regular (N-dimensional) grid, visualizations of a billion objects can be done within a second on a modern desktop computer. This is achieved using memory mapping of hdf5 files together with a simple binning algorithm, which are part of a Python library called vaex. This enables efficient exploration or large datasets interactively, making science exploration of large catalogues feasible. Vaex is a Python library and an application, which allows for interactive exploration and visualization. The motivation for developing vaex is the catalogue of the Gaia satellite, however, vaex can also be used on SPH or N-body simulations, any other (future) catalogues such as SDSS, Pan-STARRS, LSST, etc. or other tabular data. The homepage for vaex is http://vaex.astro.rug.nl.

One billion cubic meters of gas produced in Kikinda area

Energy Technology Data Exchange (ETDEWEB)

Vicicevic, M; Duric, N

1969-10-01

The Kikinda gas reservoir has just passed a milestone in producing one billion cubic meters of natural gas. The reservoir was discovered in 1962, and its present production amounts to 26 million cu m. One of the biggest problems was formation of hydrates, which has successfully been solved by using methanol. Four tables show production statistics by years and productive formations.

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...

Spatial variability in oceanic redox structure 1.8 billion years ago

DEFF Research Database (Denmark)

Poulton, Simon W.; Fralick, Philip W.; Canfield, Donald Eugene

2010-01-01

to reconstruct oceanic redox conditions from the 1.88- to 1.83-billion-year-old Animikie group from the Superior region, North America. We find that surface waters were oxygenated, whereas at mid-depths, anoxic and sulphidic (euxinic) conditions extended over 100 km from the palaeoshoreline. The spatial extent...

[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

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.

2016 Billion-ton report: Advancing domestic resources for a thriving bioeconomy, Volume 1: Economic availability of feedstock

Science.gov (United States)

M.H. Langholtz; B.J. Stokes; L.M. Eaton

2016-01-01

This product builds on previous efforts, namely the 2005 Billion-Ton Study (BTS) and the 2011 U.S. Billion-Ton Update (BT2).With each report, greater perspective is gained on the potential of biomass resources to contribute to a national energy strategy. Similarly, each successive report introduces new questions regarding commercialization challenges. BTS quantified...

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

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.

Gaia: Science with 1 billion objects in three dimensions

Science.gov (United States)

Prusti, Timo

2018-02-01

Gaia is an operational satellite in the ESA science programme. It is gathering data for more than a billion objects. Gaia measures positions and motions of stars in our Milky Way Galaxy, but captures many asteroids and extragalactic sources as well. The first data release has already been made and exploitation by the world-wide scientific community is underway. Further data releases will be made with further increasing accuracy. Gaia is well underway to provide its promised set of fundamental astronomical data.

Universities Report $1.8-Billion in Earnings on Inventions in 2011

Science.gov (United States)

Blumenstyk, Goldie

2012-01-01

Universities and their inventors earned more than $1.8-billion from commercializing their academic research in the 2011 fiscal year, collecting royalties from new breeds of wheat, from a new drug for the treatment of HIV, and from longstanding arrangements over enduring products like Gatorade. Northwestern University earned the most of any…

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.

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

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.

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…

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.

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.

Readability of the web: a study on 1 billion web pages

NARCIS (Netherlands)

de Heus, Marije; Hiemstra, Djoerd

We have performed a readability study on more than 1 billion web pages. The Automated Readability Index was used to determine the average grade level required to easily comprehend a website. Some of the results are that a 16-year-old can easily understand 50% of the web and an 18-year old can easily

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

Three counting methods agree on cell and neuron number in chimpanzee primary visual cortex

Directory of Open Access Journals (Sweden)

Daniel James Miller

2014-05-01

Full Text Available Determining the cellular composition of specific brain regions is crucial to our understanding of the function of neurobiological systems. It is therefore useful to identify the extent to which different methods agree when estimating the same properties of brain circuitry. In this study, we estimated the number of neuronal and non-neuronal cells in the primary visual cortex (area 17 or V1 of both hemispheres from a single chimpanzee. Specifically, we processed samples distributed across V1 of the right hemisphere after cortex was flattened into a sheet using two variations of the isotropic fractionator cell and neuron counting method. We processed the left hemisphere as serial brain slices for stereological investigation. The goal of this study was to evaluate the agreement between these methods in the most direct manner possible by comparing estimates of cell density across one brain region of interest in a single individual. In our hands, these methods produced similar estimates of the total cellular population (approximately 1 billion as well as the number of neurons (approximately 675 million in chimpanzee V1, providing evidence that both techniques estimate the same parameters of interest. In addition, our results indicate the strengths of each distinct tissue preparation procedure, highlighting the importance of attention to anatomical detail. In summary, we found that the isotropic fractionator and the stereological optical fractionator produced concordant estimates of the cellular composition of V1, and that this result supports the conclusion that chimpanzees conform to the primate pattern of exceptionally high packing density in V1. Ultimately, our data suggest that investigators can optimize their experimental approach by using any of these counting methods to obtain reliable cell and neuron counts.

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...

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.

The Boring Billion, a slingshot for Complex Life on Earth.

Science.gov (United States)

Mukherjee, Indrani; Large, Ross R; Corkrey, Ross; Danyushevsky, Leonid V

2018-03-13

The period 1800 to 800 Ma ("Boring Billion") is believed to mark a delay in the evolution of complex life, primarily due to low levels of oxygen in the atmosphere. Earlier studies highlight the remarkably flat C, Cr isotopes and low trace element trends during the so-called stasis, caused by prolonged nutrient, climatic, atmospheric and tectonic stability. In contrast, we suggest a first-order variability of bio-essential trace element availability in the oceans by combining systematic sampling of the Proterozoic rock record with sensitive geochemical analyses of marine pyrite by LA-ICP-MS technique. We also recall that several critical biological evolutionary events, such as the appearance of eukaryotes, origin of multicellularity & sexual reproduction, and the first major diversification of eukaryotes (crown group) occurred during this period. Therefore, it appears possible that the period of low nutrient trace elements (1800-1400 Ma) caused evolutionary pressures which became an essential trigger for promoting biological innovations in the eukaryotic domain. Later periods of stress-free conditions, with relatively high nutrient trace element concentration, facilitated diversification. We propose that the "Boring Billion" was a period of sequential stepwise evolution and diversification of complex eukaryotes, triggering evolutionary pathways that made possible the later rise of micro-metazoans and their macroscopic counterparts.

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.

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.

Galaxy growth in a massive halo in the first billion years of cosmic history

Science.gov (United States)

Marrone, D. P.; Spilker, J. S.; Hayward, C. C.; Vieira, J. D.; Aravena, M.; Ashby, M. L. N.; Bayliss, M. B.; Béthermin, M.; Brodwin, M.; Bothwell, M. S.; Carlstrom, J. E.; Chapman, S. C.; Chen, Chian-Chou; Crawford, T. M.; Cunningham, D. J. M.; De Breuck, C.; Fassnacht, C. D.; Gonzalez, A. H.; Greve, T. R.; Hezaveh, Y. D.; Lacaille, K.; Litke, K. C.; Lower, S.; Ma, J.; Malkan, M.; Miller, T. B.; Morningstar, W. R.; Murphy, E. J.; Narayanan, D.; Phadke, K. A.; Rotermund, K. M.; Sreevani, J.; Stalder, B.; Stark, A. A.; Strandet, M. L.; Tang, M.; Weiß, A.

2018-01-01

According to the current understanding of cosmic structure formation, the precursors of the most massive structures in the Universe began to form shortly after the Big Bang, in regions corresponding to the largest fluctuations in the cosmic density field. Observing these structures during their period of active growth and assembly—the first few hundred million years of the Universe—is challenging because it requires surveys that are sensitive enough to detect the distant galaxies that act as signposts for these structures and wide enough to capture the rarest objects. As a result, very few such objects have been detected so far. Here we report observations of a far-infrared-luminous object at redshift 6.900 (less than 800 million years after the Big Bang) that was discovered in a wide-field survey. High-resolution imaging shows it to be a pair of extremely massive star-forming galaxies. The larger is forming stars at a rate of 2,900 solar masses per year, contains 270 billion solar masses of gas and 2.5 billion solar masses of dust, and is more massive than any other known object at a redshift of more than 6. Its rapid star formation is probably triggered by its companion galaxy at a projected separation of 8 kiloparsecs. This merging companion hosts 35 billion solar masses of stars and has a star-formation rate of 540 solar masses per year, but has an order of magnitude less gas and dust than its neighbour and physical conditions akin to those observed in lower-metallicity galaxies in the nearby Universe. These objects suggest the presence of a dark-matter halo with a mass of more than 100 billion solar masses, making it among the rarest dark-matter haloes that should exist in the Universe at this epoch.

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.

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.

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)

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.

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.)

AREVA - First quarter 2011 revenue: 2.7% growth like for like to 1.979 billion euros

International Nuclear Information System (INIS)

2011-01-01

The group reported consolidated revenue of 1.979 billion euros in the 1. quarter of 2011, for 2.2% growth compared with the 1. quarter of 2010 (+ 2.7% like for like). The increase was driven by the Mining / Front End Business Group (+ 20.8% LFL). Revenue from outside France rose 12.0% to 1.22 billion euros and represented 62% of total revenue. The impacts of foreign exchange and changes in consolidation scope were negligible during the period. The March 11 events in Japan had no significant impact on the group's performance in the 1. quarter of 2011. The group's backlog of 43.5 billion euros at March 31, 2011 was stable in relation to March 31, 2010. The growth in the backlog of the Mining / Front End and Renewable Energies Business Groups offset the partial depletion of the backlog in the Reactors and Services and Back End Business Groups as contracts were completed

Two ten-billion-solar-mass black holes at the centres of giant elliptical galaxies.

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McConnell, Nicholas J; Ma, Chung-Pei; Gebhardt, Karl; Wright, Shelley A; Murphy, Jeremy D; Lauer, Tod R; Graham, James R; Richstone, Douglas O

2011-12-08

Observational work conducted over the past few decades indicates that all massive galaxies have supermassive black holes at their centres. Although the luminosities and brightness fluctuations of quasars in the early Universe suggest that some were powered by black holes with masses greater than 10 billion solar masses, the remnants of these objects have not been found in the nearby Universe. The giant elliptical galaxy Messier 87 hosts the hitherto most massive known black hole, which has a mass of 6.3 billion solar masses. Here we report that NGC 3842, the brightest galaxy in a cluster at a distance from Earth of 98 megaparsecs, has a central black hole with a mass of 9.7 billion solar masses, and that a black hole of comparable or greater mass is present in NGC 4889, the brightest galaxy in the Coma cluster (at a distance of 103 megaparsecs). These two black holes are significantly more massive than predicted by linearly extrapolating the widely used correlations between black-hole mass and the stellar velocity dispersion or bulge luminosity of the host galaxy. Although these correlations remain useful for predicting black-hole masses in less massive elliptical galaxies, our measurements suggest that different evolutionary processes influence the growth of the largest galaxies and their black holes.

Origins fourteen billion years of cosmic evolution

CERN Document Server

Tyson, Neil deGrasse

2004-01-01

Origins explores cosmic science's stunning new insights into the formation and evolution of our universe--of the cosmos, of galaxies and galaxy clusters, of stars within galaxies, of planets that orbit those stars, and of different forms of life that take us back to the first three seconds and forward through three billion years of life on Earth to today's search for life on other planets. Drawing on the current cross-pollination of geology, biology and astrophysics, Origins explains the thrilling daily breakthroughs in our knowledge of the universe from dark energy to life on Mars to the mysteries of space and time. Distilling complex science in clear and lively prose, co-authors Neil deGrasse Tyson and Donald Goldsmith conduct a galvanising tour of the cosmos revealing what the universe has been up to while turning part of itself into us.

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.

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.

[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.

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

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.

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...

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

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.

The Cellular Composition and Glia-Neuron Ratio in the Spinal Cord of a Human and a Nonhuman Primate: Comparison With Other Species and Brain Regions.

Science.gov (United States)

Bahney, Jami; von Bartheld, Christopher S

2018-04-01

The cellular composition of brains shows largely conserved, gradual evolutionary trends between species. In the primate spinal cord, however, the glia-neuron ratio was reported to be greatly increased over that in the rodent spinal cord. Here, we re-examined the cellular composition of the spinal cord of one human and one nonhuman primate species by employing two different counting methods, the isotropic fractionator and stereology. We also determined whether segmental differences in cellular composition, possibly reflecting increased fine motor control of the upper extremities, may explain a sharply increased glia-neuron ratio in primates. In the cynomolgus monkey spinal cord, the isotropic fractionator and stereology yielded 206-275 million cells, of which 13.3-25.1% were neurons (28-69 million). Stereological estimates yielded 21.1% endothelial cells and 65.5% glial cells (glia-neuron ratio of 4.9-5.6). In human spinal cords, the isotropic fractionator and stereology generated estimates of 1.5-1.7 billion cells and 197-222 million neurons (13.4% neurons, 12.2% endothelial cells, 74.8% glial cells), and a glia-neuron ratio of 5.6-7.1, with estimates of neuron numbers in the human spinal cord based on morphological criteria. The non-neuronal to neuron ratios in human and cynomolgus monkey spinal cords were 6.5 and 3.2, respectively, suggesting that previous reports overestimated this ratio. We did not find significant segmental differences in the cellular composition between cervical, thoracic and lumbar levels. When compared with brain regions, the spinal cord showed gradual increases of the glia-neuron ratio with increasing brain mass, similar to the cerebral cortex and the brainstem. Anat Rec, 301:697-710, 2018. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

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.

Anhui Tongling Invests 1 Billion Yuan to Set up “Copper Industry Fund”

Institute of Scientific and Technical Information of China (English)

2012-01-01

<正>On September 12, the signing ceremony for "Anhui Copper Industry Fund" set up by Anhui Tongling Development & Investment Group Co., Ltd. and Shanghai V. Stone Investment Management Co., Ltd. was held in Tongling. The fund is 1 billion yuan.

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

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.

Electron capture detection of sulphur gases in carbon dioxide at the parts-per-billion level

International Nuclear Information System (INIS)

Pick, M.E.

1979-01-01

A gas chromatograph with an electron capture detector has been used to determine sulphur gases in CO 2 at the parts-per-billion level, with particular application to the analysis of coolant from CO 2 cooled nuclear reactors. For COS, CS 2 , CH 3 SH, H 2 S and (CH 3 ) 2 S 2 the detector has a sensitivity comparable with the more commonly used flame photometric detector, but it is much less sensitive towards (CH 3 ) 2 S and thiophene. In addition, the paper describes a simple method for trapping sulphur gases which might enable detection of sub parts-per-billion levels of sulphur compounds. (Auth.)

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...

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.

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

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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.

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

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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.

Rapid emergence of subaerial landmasses and onset of a modern hydrologic cycle 2.5 billion years ago.

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Bindeman, I N; Zakharov, D O; Palandri, J; Greber, N D; Dauphas, N; Retallack, G J; Hofmann, A; Lackey, J S; Bekker, A

2018-05-01

The history of the growth of continental crust is uncertain, and several different models that involve a gradual, decelerating, or stepwise process have been proposed 1-4 . Even more uncertain is the timing and the secular trend of the emergence of most landmasses above the sea (subaerial landmasses), with estimates ranging from about one billion to three billion years ago 5-7 . The area of emerged crust influences global climate feedbacks and the supply of nutrients to the oceans 8 , and therefore connects Earth's crustal evolution to surface environmental conditions 9-11 . Here we use the triple-oxygen-isotope composition of shales from all continents, spanning 3.7 billion years, to provide constraints on the emergence of continents over time. Our measurements show a stepwise total decrease of 0.08 per mille in the average triple-oxygen-isotope value of shales across the Archaean-Proterozoic boundary. We suggest that our data are best explained by a shift in the nature of water-rock interactions, from near-coastal in the Archaean era to predominantly continental in the Proterozoic, accompanied by a decrease in average surface temperatures. We propose that this shift may have coincided with the onset of a modern hydrological cycle owing to the rapid emergence of continental crust with near-modern average elevation and aerial extent roughly 2.5 billion years ago.

U of M seeking $1.1 billion in projects for Soudan Mine lab.

CERN Multimedia

2003-01-01

The University of Minnesota is hoping that groundbreaking research underway at its labs at the Soudan Underground Mine near Tower will help secure up to $1.1 billion in the next 5 to 20 years to expand its work into particle physics (1 page).

Neural Network Molecule: a Solution of the Inverse Biometry Problem through Software Support of Quantum Superposition on Outputs of the Network of Artificial Neurons

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Vladimir I. Volchikhin

2017-12-01

Full Text Available Introduction: The aim of the study is to accelerate the solution of neural network biometrics inverse problem on an ordinary desktop computer. Materials and Methods: To speed up the calculations, the artificial neural network is introduced into the dynamic mode of “jittering” of the states of all 256 output bits. At the same time, too many output states of the neural network are logarithmically folded by transitioning to the Hamming distance space between the code of the image “Own” and the codes of the images “Alien”. From the database of images of “Alien” 2.5 % of the most similar images are selected. In the next generation, 97.5 % of the discarded images are restored with GOST R 52633.2-2010 procedures by crossing parent images and obtaining descendant images from them. Results: Over a period of about 10 minutes, 60 generations of directed search for the solution of the inverse problem can be realized that allows inversing matrices of neural network functionals of dimension 416 inputs to 256 outputs with restoration of up to 97 % information on unknown biometric parameters of the image “Own”. Discussion and Conclusions: Supporting for 10 minutes of computer time the 256 qubit quantum superposition allows on a conventional computer to bypass the actual infinity of analyzed states in 5050 (50 to 50 times more than the same computer could process realizing the usual calculations. The increase in the length of the supported quantum superposition by 40 qubits is equivalent to increasing the processor clock speed by about a billion times. It is for this reason that it is more profitable to increase the number of quantum superpositions supported by the software emulator in comparison with the creation of a more powerful processor.

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

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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.

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

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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.

Uranium in Canada: Billion-dollar industry

International Nuclear Information System (INIS)

Whillans, R.T.

1989-01-01

In 1988, Canada maintained its position as the world's leading producer and exporter of uranium; five primary uranium producers reported concentrate output containing 12,400 MT of uranium, or about one-third of Western production. Uranium shipments made by these producers in 1988 exceeded 13,200 MT, worth Canadian $1.1 billion. Because domestic requirements represent only 15% of current Canadian output, most of Canada's uranium production is available for export. Despite continued market uncertainty in 1988, Canada's uranium producers signed new sales contracts for some 14,000 MT, twice the 1987 level. About 90% of this new volume is with the US, now Canada's major uranium customer. The recent implementation of the Canada/US Free Trade agreement brings benefits to both countries; the uranium industries in each can now develop in an orderly, free market. Canada's uranium industry was restructured and consolidated in 1988 through merger and acquisition; three new uranium projects advanced significantly. Canada's new policy on nonresident ownership in the uranium mining sector, designed to encourage both Canadian and foreign investment, should greatly improve efforts to finance the development of recent Canadian uranium discoveries

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

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

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

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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.

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

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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.

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

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

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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.

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

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

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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.

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

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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

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

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.

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.

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

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.

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

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.

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

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%.

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.

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.

Guangdong Aluminum to Raise RMB 3 billion for New Production Base in Guizhou

Institute of Scientific and Technical Information of China (English)

2010-01-01

<正>On July 7, a loan signing ceremony was held between the Guangdong Aluminum Group, China Construction Bank, Hua Xia Bank and Guangzhou Bank Consortium. It is reported that these banks will provide Guangdong Aluminum Group with RMB 30 billion for an alu-minum oxide and supporting bauxite mining project in Guizhou.

Ultrarelativistic heavy ion collisions: the first billion seconds

Energy Technology Data Exchange (ETDEWEB)

Baym, Gordon

2016-12-15

I first review the early history of the ultrarelativistic heavy ion program, starting with the 1974 Bear Mountain Workshop, and the 1983 Aurora meeting of the U.S. Nuclear Science Committtee, just one billion seconds ago, which laid out the initial science goals of an ultrarelativistic collider. The primary goal, to discover the properties of nuclear matter at the highest energy densities, included finding new states of matter – the quark-gluon plasma primarily – and to use collisions to open a new window on related problems of matter in cosmology, neutron stars, supernovae, and elsewhere. To bring out how the study of heavy ions and hot, dense matter in QCD has been fulfilling these goals, I concentrate on a few topics, the phase diagram of matter in QCD, and connections of heavy ion physics to cold atoms, cosmology, and neutron stars.

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.

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

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)

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.

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.

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.

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

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.

LLNL's Big Science Capabilities Help Spur Over $796 Billion in U.S. Economic Activity Sequencing the Human Genome

Energy Technology Data Exchange (ETDEWEB)

Stewart, Jeffrey S. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2015-07-28

LLNL’s successful history of taking on big science projects spans beyond national security and has helped create billions of dollars per year in new economic activity. One example is LLNL’s role in helping sequence the human genome. Over $796 billion in new economic activity in over half a dozen fields has been documented since LLNL successfully completed this Grand Challenge.

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

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

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.

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....

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

Orbital forcing of climate 1.4 billion years ago

DEFF Research Database (Denmark)

Zhang, Shuichang; Wang, Xiaomei; Hammarlund, Emma U

2015-01-01

Fluctuating climate is a hallmark of Earth. As one transcends deep into Earth time, however, both the evidence for and the causes of climate change become difficult to establish. We report geochemical and sedimentological evidence for repeated, short-term climate fluctuations from the exceptionally...... well-preserved ∼1.4-billion-year-old Xiamaling Formation of the North China Craton. We observe two patterns of climate fluctuations: On long time scales, over what amounts to tens of millions of years, sediments of the Xiamaling Formation record changes in geochemistry consistent with long-term changes...... reflect what appear to be orbitally forced changes in wind patterns and ocean circulation as they influenced rates of organic carbon flux, trace metal accumulation, and the source of detrital particles to the sediment....

Molecular dynamics beyonds the limits: Massive scaling on 72 racks of a BlueGene/P and supercooled glass dynamics of a 1 billion particles system

KAUST Repository

Allsopp, Nicholas

2012-04-01

We report scaling results on the world\\'s largest supercomputer of our recently developed Billions-Body Molecular Dynamics (BBMD) package, which was especially designed for massively parallel simulations of the short-range atomic dynamics in structural glasses and amorphous materials. The code was able to scale up to 72 racks of an IBM BlueGene/P, with a measured 89% efficiency for a system with 100 billion particles. The code speed, with 0.13. s per iteration in the case of 1 billion particles, paves the way to the study of billion-body structural glasses with a resolution increase of two orders of magnitude with respect to the largest simulation ever reported. We demonstrate the effectiveness of our code by studying the liquid-glass transition of an exceptionally large system made by a binary mixture of 1 billion particles. © 2012.

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)

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.

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.

Energy tax price tag for CPI: $1.2 billion, jobs, and production

International Nuclear Information System (INIS)

Begley, R.

1993-01-01

If President Clinton's proposed energy tax had been fully in place last year, it would have cost the US chemical industry an additional $1.2 billion and 9,900 jobs, according to Chemical Manufacturers Association (CMA; Washington) estimates. It also would have driven output down 3% and prices up 5%, CMA says. Allen Lenz, CMA director/trade and economics, says the increase in production costs that would accompany the tax will not be shared by foreign competitors, cannot be neutralized with higher border taxes because of existing trade agreements, and provides another reason to move production offshore. Worse, the US chemical industry's generally impressive trade surplus declined by $2.5 billion last year, and a further drop is projected for this year. The margin of error gets thinner all the time as competition increases, Lenz says. We're not concerned only with the chemical industry, but the rest of US-based manufacturing because they taken half our output, he adds. One problem is the energy intensiveness of the chemical process industries-a CMA report says that 55% of the cost of producing ethylene glycol is energy related. And double taxation of such things as coproducts returned for credit to oil refineries could add up to $115 million/year, the report says

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.

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.

A parts-per-billion measurement of the antiproton magnetic moment.

Science.gov (United States)

Smorra, C; Sellner, S; Borchert, M J; Harrington, J A; Higuchi, T; Nagahama, H; Tanaka, T; Mooser, A; Schneider, G; Bohman, M; Blaum, K; Matsuda, Y; Ospelkaus, C; Quint, W; Walz, J; Yamazaki, Y; Ulmer, S

2017-10-18

Precise comparisons of the fundamental properties of matter-antimatter conjugates provide sensitive tests of charge-parity-time (CPT) invariance, which is an important symmetry that rests on basic assumptions of the standard model of particle physics. Experiments on mesons, leptons and baryons have compared different properties of matter-antimatter conjugates with fractional uncertainties at the parts-per-billion level or better. One specific quantity, however, has so far only been known to a fractional uncertainty at the parts-per-million level: the magnetic moment of the antiproton, . The extraordinary difficulty in measuring with high precision is caused by its intrinsic smallness; for example, it is 660 times smaller than the magnetic moment of the positron. Here we report a high-precision measurement of in units of the nuclear magneton μ N with a fractional precision of 1.5 parts per billion (68% confidence level). We use a two-particle spectroscopy method in an advanced cryogenic multi-Penning trap system. Our result  = -2.7928473441(42)μ N (where the number in parentheses represents the 68% confidence interval on the last digits of the value) improves the precision of the previous best measurement by a factor of approximately 350. The measured value is consistent with the proton magnetic moment, μ p  = 2.792847350(9)μ N , and is in agreement with CPT invariance. Consequently, this measurement constrains the magnitude of certain CPT-violating effects to below 1.8 × 10 -24 gigaelectronvolts, and a possible splitting of the proton-antiproton magnetic moments by CPT-odd dimension-five interactions to below 6 × 10 -12 Bohr magnetons.

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 ...

A 17-billion-solar-mass black hole in a group galaxy with a diffuse core.

Science.gov (United States)

Thomas, Jens; Ma, Chung-Pei; McConnell, Nicholas J; Greene, Jenny E; Blakeslee, John P; Janish, Ryan

2016-04-21

Quasars are associated with and powered by the accretion of material onto massive black holes; the detection of highly luminous quasars with redshifts greater than z = 6 suggests that black holes of up to ten billion solar masses already existed 13 billion years ago. Two possible present-day 'dormant' descendants of this population of 'active' black holes have been found in the galaxies NGC 3842 and NGC 4889 at the centres of the Leo and Coma galaxy clusters, which together form the central region of the Great Wall--the largest local structure of galaxies. The most luminous quasars, however, are not confined to such high-density regions of the early Universe; yet dormant black holes of this high mass have not yet been found outside of modern-day rich clusters. Here we report observations of the stellar velocity distribution in the galaxy NGC 1600--a relatively isolated elliptical galaxy near the centre of a galaxy group at a distance of 64 megaparsecs from Earth. We use orbit superposition models to determine that the black hole at the centre of NGC 1600 has a mass of 17 billion solar masses. The spatial distribution of stars near the centre of NGC 1600 is rather diffuse. We find that the region of depleted stellar density in the cores of massive elliptical galaxies extends over the same radius as the gravitational sphere of influence of the central black holes, and interpret this as the dynamical imprint of the black holes.

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.

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.

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.

Tendances Carbone no. 75 'The CDM: let's not discard a tool that raised over US$ 200 billion'

International Nuclear Information System (INIS)

Shishlov, Igor

2012-01-01

Among the publications of CDC Climat Research, 'Tendances Carbone' bulletin specifically studies the developments of the European market for CO 2 allowances. This issue addresses the following points: Everyone wonders which miraculous instrument will enable the Green Climate Fund to mobilize the pledged US$100 billion per year in climate finance by 2020. Developing countries are now asking for interim targets to quench their mounting skepticism that this level of commitment can be reached. In the meantime paradoxically, the Clean Development Mechanism (CDM) - a tool that managed to leverage over US$200 billion of mostly private investment for climate change mitigation - is left dying without much regret

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.

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.

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.

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.

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.

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.

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.

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.

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.

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...

Missing billions. How the Australian government's climate policy is penalising farmers

International Nuclear Information System (INIS)

Riguet, T.

2006-10-01

The Climate Institute analysis suggests ratifying the Kyoto Protocol and implementing a national emissions trading scheme today could provide Australian farmers with an income of $1.8 billion over the period 2008-2012, due to the emissions saved by limiting land clearing. Separately, a report to the National Farmers Federation by the Allen Consulting Group earlier this year concluded that a carbon emission trading system which recognised Kyoto Protocol rules could create an additional income stream of $0.7-0.9 billion over a five year period from revenue to farmers from forestry sinks. These two studies suggest that ratification of the Kyoto Protocol and the introduction of a national emissions trading scheme could provide farmers an income stream in the order of $2.5 billion. A central tenet of the Federal Government's greenhouse policy for over a decade has been to not ratify Kyoto, but to meet its Kyoto target - a national emissions increase of 8% from 1990 levels, in the period 2008-2012. Australia's National Greenhouse Gas Accounts show that farmers, by reducing land clearing rates since 1990, have offset substantial increases in greenhouse gas emissions from other sectors, mainly energy. Official Federal Government projections show that without land clearing reductions, Australia's greenhouse emissions would be 30% above 1990 levels by 2010. Australia's farmers have been responsible for virtually the entire share of the nation's greenhouse gas emissions reductions, but their efforts, worth around $2 billion, have not been recognised or financially rewarded by the Government. By reducing land clearing, farmers have already reduced greenhouse gas emissions by about 75 million tonnes since 1990. By 2010, the savings are projected to be about 83 million tonnes. This level of emissions reductions is equivalent to eliminating the total annual emissions of New Zealand or Ireland. Over that same period, emissions from energy and transport have and continue to sky

Mars’ First Billion Years: Key Findings, Key Unsolved Paradoxes, and Future Exploration

Science.gov (United States)

Ehlmann, Bethany

2017-10-01

In the evolution of terrestrial planets, the first billion years are the period most shrouded in mystery: How vigorous is early atmospheric loss? How do planetary climates respond to a brightening sun? When and how are plate tectonic recycling processes initiated? How do voluminous volcanism and heavy impact bombardment influence the composition of the atmosphere? Under what conditions might life arise? Looking outward to terrestrial planets around other stars, the record from Venus, Earth and Mars in this solar system is crucial for developing models of physical can chemical processes. Of these three worlds, Mars provides the longest record of planetary evolution from the first billion years, comprising >50% of exposed geologic units, which are only lightly overprinted by later processes.Orbital observations of the last decade have revealed abundant evidence for surface waters in the form of lakes, valley networks, and evidence of chemically open-system near-surface weathering. Groundwaters at temperatures ranging from just above freezing to hydrothermal have also left a rich record of process in the mineralogical record. A rsuite of environments - similar in diversity to Earth’s - has been discovered on Mars with water pH, temperature, redox, and chemistries varying in space and time.Here, I will focus on the consequences of the aqueous alteration of the Martian crust on the composition of the atmosphere based on recent work studying aspects of the volatile budget (Usui et al., 2015; Edwards & Ehlmann, 2015; Hu et al., 2015; Jakosky et al., 2017, Wordsworth et al., 2017, and Ehlmann, in prep.). The solid crust and mantle of Mars act as volatile reservoirs and volatile sources through volcanism, mineral precipitation, and release of gases. We examine the extent to which the budget is understood or ill-understood for hydrogen and carbon, and associated phases H2O, CO2, and CH4. Additionally, I identify some key stratigraphies where a combination of focused in

Galaxy evolution. Evidence for mature bulges and an inside-out quenching phase 3 billion years after the Big Bang.

Science.gov (United States)

Tacchella, S; Carollo, C M; Renzini, A; Förster Schreiber, N M; Lang, P; Wuyts, S; Cresci, G; Dekel, A; Genzel, R; Lilly, S J; Mancini, C; Newman, S; Onodera, M; Shapley, A; Tacconi, L; Woo, J; Zamorani, G

2015-04-17

Most present-day galaxies with stellar masses ≥10(11) solar masses show no ongoing star formation and are dense spheroids. Ten billion years ago, similarly massive galaxies were typically forming stars at rates of hundreds solar masses per year. It is debated how star formation ceased, on which time scales, and how this "quenching" relates to the emergence of dense spheroids. We measured stellar mass and star-formation rate surface density distributions in star-forming galaxies at redshift 2.2 with ~1-kiloparsec resolution. We find that, in the most massive galaxies, star formation is quenched from the inside out, on time scales less than 1 billion years in the inner regions, up to a few billion years in the outer disks. These galaxies sustain high star-formation activity at large radii, while hosting fully grown and already quenched bulges in their cores. Copyright © 2015, American Association for the Advancement of Science.

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

A field like today's? The strength of the geomagnetic field 1.1 billion years ago

Science.gov (United States)

Sprain, Courtney J.; Swanson-Hysell, Nicholas L.; Fairchild, Luke M.; Gaastra, Kevin

2018-06-01

Palaeomagnetic data from ancient rocks are one of the few types of observational data that can be brought to bear on the long-term evolution of Earth's core. A recent compilation of palaeointensity estimates from throughout Earth history has been interpreted to indicate that Earth's magnetic field strength increased in the Mesoproterozoic (between 1.5 and 1.0 billion years ago), with this increase taken to mark the onset of inner core nucleation. However, much of the data within the Precambrian palaeointensity database are from Thellier-style experiments with non-ideal behaviour that manifests in results such as double-slope Arai plots. Choices made when interpreting these data may significantly change conclusions about long-term trends in the intensity of Earth's geomagnetic field. In this study, we present new palaeointensity results from volcanics of the ˜1.1-billion-year-old North American Midcontinent Rift. While most of the results exhibit non-ideal double-slope or sagging behaviour in Arai plots, some flows have more ideal single-slope behaviour leading to palaeointensity estimates that may be some of the best constraints on the strength of Earth's field for this time. Taken together, new and previously published palaeointensity data from the Midcontinent Rift yield a median field strength estimate of 56.0 ZAm2—very similar to the median for the past 300 Myr. These field strength estimates are distinctly higher than those for the preceding billion years (Ga) after excluding ca. 1.3 Ga data that may be biased by non-ideal behaviour—consistent with an increase in field strength in the late Mesoproterozoic. However, given that ˜90 per cent of palaeointensity estimates from 1.1 to 0.5 Ga come from the Midcontinent Rift, it is difficult to evaluate whether these high values relative to those estimated for the preceding billion years are the result of a stepwise, sustained increase in dipole moment. Regardless, palaeointensity estimates from the Midcontinent

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.

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.

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.

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

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.

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.

Neuronal synchrony: peculiarity and generality.

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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.

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

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

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

Plate tectonic influences on Earth's baseline climate: a 2 billion-year record

Science.gov (United States)

McKenzie, R.; Evans, D. A.; Eglington, B. M.; Planavsky, N.

2017-12-01

Plate tectonic processes present strong influences on the long-term carbon cycle, and thus global climate. Here we utilize multiple aspects of the geologic record to assess the role plate tectonics has played in driving major icehouse­-greenhouse transitions for the past 2 billion years. Refined paleogeographic reconstructions allow us to quantitatively assess the area of continents in various latitudinal belts throughout this interval. From these data we are able to test the hypothesis that concentrating continental masses in low-latitudes will drive cooler climates due to increased silicate weathering. We further superimpose records of events that are believed to increase the `weatherability' of the crust, such as large igneous province emplacement, island-arc accretion, and continental collisional belts. Climatic records are then compared with global detrital zircon U-Pb age data as a proxy for continental magmatism. Our results show a consistent relationship between zircon-generating magmatism and icehouse-greenhouse transitions for > 2 billion years, whereas paleogeographic records show no clear consistent relationship between continental configurations and prominent climate transitions. Volcanic outgassing appears to exert a first-order control on major baseline climatic shifts; however, paleogeography likely plays an important role in the magnitude of this change. Notably, climatic extremes, such as the Cryogenian icehouse, occur during a combination of reduce volcanism and end-member concentrations of low-latitudinal continents.

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.

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.

[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.

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.

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.

[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.

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

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.

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

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.

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.

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

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).

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.)

Unlocking the EUR53 billion savings from smart meters in the EU. How increasing the adoption of dynamic tariffs could make or break the EU's smart grid investment

International Nuclear Information System (INIS)

Faruqui, Ahmad; Hledik, Ryan; Harris, Dan

2010-01-01

We estimate the cost of installing smart meters in the EU to be EUR51 billion, and that operational savings will be worth between EUR26 and 41 billion, leaving a gap of EUR10-25 billion between benefits and costs. Smart meters can fill this gap because they enable the provision of dynamic pricing, which reduces peak demand and lowers the need for building and running expensive peaking power plants. The present value of savings in peaking infrastructure could be as high as EUR67 billion for the EU if policy-makers can overcome barriers to consumers adopting dynamic tariffs, but only EUR14 billion otherwise. We outline a number of ways to increase the adoption of dynamic tariffs. (author)

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.

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...

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.

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

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.

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

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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.

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 ...

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

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

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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

Metabolic reprogramming during neuronal differentiation.

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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.

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

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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.

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

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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.

Dongfeng has fixed a sales goal of 80 billion yuan supported by Nissan

Institute of Scientific and Technical Information of China (English)

2003-01-01

<正> The Nissan and Dongfeng Group-based Dongfeng Automobile Co., Ltd, which has the largest investment in the history of the industry, opened officially for business on July 1. With a total investment of USD 2 billion and 70,000 employees, the company is the first joint venture in China which plans a full range of truck, light commercial and passenger vehicles. According to president Nakamura, the company has established a management

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

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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

Effective interventions for unintentional injuries: a systematic review and mortality impact assessment among the poorest billion

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Andres I Vecino-Ortiz, PhD

2018-05-01

Full Text Available Summary: Background: Between 1990 and 2015, the global injury mortality declined, but in countries where the poorest billion live, injuries are becoming an increasingly prevalent cause of death. The vulnerability of this population requires immediate attention from policy makers to implement effective interventions that lessen the burden of injuries in these countries. Our aim was two-fold; first, to review all the evidence on effective interventions for the five main types of unintentional injury; and second, to estimate the potential number of lives saved by effective injury interventions among the poorest billion. Methods: For our systematic review we used references in the Disability Control Priorities third edition, and searched PubMed and the Cochrane database for papers published until Sept 10, 2016, using a comprehensive search strategy to find interventions for the five major causes of unintentional injuries: road traffic crashes, falls, drowning, burns, and poisoning. Studies were included if they presented evidence with significant effects sizes for any outcome; no inclusions or exclusions made on the basis of where the study was carried out (ie, low-income, middle-income, or high-income country. Then we used data from the Global Burden of Disease 2015 study and a Monte Carlo simulation technique to estimate the potential annual attributable number of lives saved among the poorest billion by these evidence-based injury interventions. We estimated results for 84 countries where the poorest billion live. Findings: From the 513 papers identified, 47 were eligible for inclusion. We identified 11 interventions that had an effect on injury mortality. For road traffic deaths, the most successful interventions in preventing deaths are speed enforcement (>80 000 lives saved per year and drink-driving enforcement (>60 000 lives saved per year. Interventions potentially most effective in preventing deaths from drowning are formal swimming

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

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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.

Progranulin is expressed within motor neurons and promotes neuronal cell survival

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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

Un pequeño cerebro artificial basado en ácidos nucleicos

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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.

$17 billion needed for population programme to year 2000: Dr. Nafis Sadik launches State of World Population Report.

Science.gov (United States)

1995-01-01

Dr. Nafis Sadik, Executive Director of the United Nations Population Fund (UNFPA), in her address on July 11 to the Foreign Press Association in London on the occasion of the release of the "1995 State of the World Population Report," stated that governments needed to invest in people, and that the estimated amount needed to reduce population numbers in developing countries was $17 billion for the year 2000. Two-thirds of the cost would be supplied by the developing countries. She said that coordinating population policies globally through such documents as the Programme of Action from the Cairo Conference would aid in slowing population growth. World population, currently 5.7 billion, is projected to reach 7.1-7.83 billion in 2015 and 7.9-11.9 billion in 2050. She also noted that certain conditions faced by women bear upon unsustainable population growth. The cycle of poverty continues in developing countries because very young mothers, who face higher risks in pregnancy and childbirth than those who delay childbearing until after the age of 20, are less likely to continue their education, more likely to have lower-paying jobs, and have a higher rate of separation and divorce. The isolation of women from widespread political participation and the marginalization of women's concerns from mainstream topics has resulted in ineffective family planning programs, including prevention of illness or impairment related to pregnancy or childbirth. Women, in most societies, cannot fully participate in economic and public life, have limited access to positions of influence and power, have narrower occupational choices and lower earnings than men, and must struggle to reconcile activities outside the home with their traditional roles. Sustainable development can only be achieved when social development expands opportunities for individuals (men and women), and their families, empowering them in the attainment of their social, economic, political, and cultural aspirations.

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

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

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

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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.

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.

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 ...

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...

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

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

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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.

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

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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.

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.

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.

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

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 ...

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

Areva excellent business volume: backlog as of december 31, 2008: + 21.1% to 48.2 billion euros. 2008 revenue: + 10.4% to 13.2 billion euros; Areva excellent niveau d'activite: carnet de commandes au 31/12/2008: + 21,1% a 48,2 Mds d'euros. Chiffre d'affaires de l'exercice 2008: + 10,4% a 13,2 Mds d'euros

Energy Technology Data Exchange (ETDEWEB)

NONE

2009-07-01

AREVA's backlog stood at 48.2 billion euros as of December 31, 2008, for 21.1% growth year-on-year, including 21.8% growth in Nuclear and 16.5% growth in Transmission and Distribution. The Nuclear backlog came to 42.5 billion euros at December 31, 2008. The Transmission and Distribution backlog came to 5.7 billion euros at year-end. The group recognized revenue of 13.2 billion euros in 2008, for year-on-year growth of 10.4% (+9.8% like-for-like). Revenue outside France was up 10.5% to 9.5 billion euros, representing 72% of total revenue. Revenue was up 6.5% in the Nuclear businesses (up 6.3% LFL), with strong performance in the Reactors and Services division (+10.9% LFL) and the Front End division (+7.2% LFL). The Transmission and Distribution division recorded growth of 17% (+15.8% LFL). Revenue for the fourth quarter of 2008 rose to 4.1 billion euros, up 5.2% (+1.6% LFL) from that of the fourth quarter of 2007. Revenue for the Front End division rose to 3.363 billion euros in 2008, up 7.1% over 2007 (+7.2% LFL). Foreign exchange (currency translations) had a negative impact of 53 million euros. Revenue for the Reactors and Services division rose to 3.037 billion euros, up 11.8% over 2007 (+10.9% LFL). Foreign exchange (currency translations) had a negative impact of 47 million euros. Revenue for the Back End division came to 1.692 billion euros, a drop of 2.7% (-2.5% LFL). Foreign exchange (currency translations) had a negative impact of 3.5 million euros. Revenue for the Transmission and Distribution division rose to 5.065 billion euros in 2008, up 17.0% (+15.8% LFL)

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.)

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

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

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

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.

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.

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.

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.

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.

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.

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

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 ...

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...

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.

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.

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

Searching for Organics Preserved in 4.5 Billion Year Old Salt

Science.gov (United States)

Zolensky, Michael E.; Fries, M.; Steele, A.; Bodnar, R.

2012-01-01

Our understanding of early solar system fluids took a dramatic turn a decade ago with the discovery of fluid inclusion-bearing halite (NaCl) crystals in the matrix of two freshly fallen brecciated H chondrite falls, Monahans and Zag. Both meteorites are regolith breccias, and contain xenolithic halite (and minor admixed sylvite -- KCl, crystals in their regolith lithologies. The halites are purple to dark blue, due to the presence of color centers (electrons in anion vacancies) which slowly accumulated as 40K (in sylvite) decayed over billions of years. The halites were dated by K-Ar, Rb-Sr and I-Xe systematics to be 4.5 billion years old. The "blue" halites were a fantastic discovery for the following reasons: (1) Halite+sylvite can be dated (K is in sylvite and will substitute for Na in halite, Rb substitutes in halite for Na, and I substitutes for Cl). (2) The blue color is lost if the halite dissolves on Earth and reprecipitates (because the newly-formed halite has no color centers), so the color serves as a "freshness" or pristinity indicator. (3) Halite frequently contains aqueous fluid inclusions. (4) Halite contains no structural oxygen, carbon or hydrogen, making them ideal materials to measure these isotopic systems in any fluid inclusions. (5) It is possible to directly measure fluid inclusion formation temperatures, and thus directly measure the temperature of the mineralizing aqueous fluid. In addition to these two ordinary chondrites halite grains have been reliably reported in several ureilites, an additional ordinary chondrite (Jilin), and in the carbonaceous chondrite (Murchison), although these reports were unfortunately not taken seriously. We have lately found additional fluid inclusions in carbonates in several additional carbonaceous chondrites. Meteoritic aqueous fluid inclusions are apparently relatively widespread in meteorites, though very small and thus difficult to analyze.

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.

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

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...

Effective interventions for unintentional injuries: a systematic review and mortality impact assessment among the poorest billion.

Science.gov (United States)

Vecino-Ortiz, Andres I; Jafri, Aisha; Hyder, Adnan A

2018-05-01

Between 1990 and 2015, the global injury mortality declined, but in countries where the poorest billion live, injuries are becoming an increasingly prevalent cause of death. The vulnerability of this population requires immediate attention from policy makers to implement effective interventions that lessen the burden of injuries in these countries. Our aim was two-fold; first, to review all the evidence on effective interventions for the five main types of unintentional injury; and second, to estimate the potential number of lives saved by effective injury interventions among the poorest billion. For our systematic review we used references in the Disability Control Priorities third edition, and searched PubMed and the Cochrane database for papers published until Sept 10, 2016, using a comprehensive search strategy to find interventions for the five major causes of unintentional injuries: road traffic crashes, falls, drowning, burns, and poisoning. Studies were included if they presented evidence with significant effects sizes for any outcome; no inclusions or exclusions made on the basis of where the study was carried out (ie, low-income, middle-income, or high-income country). Then we used data from the Global Burden of Disease 2015 study and a Monte Carlo simulation technique to estimate the potential annual attributable number of lives saved among the poorest billion by these evidence-based injury interventions. We estimated results for 84 countries where the poorest billion live. From the 513 papers identified, 47 were eligible for inclusion. We identified 11 interventions that had an effect on injury mortality. For road traffic deaths, the most successful interventions in preventing deaths are speed enforcement (>80 000 lives saved per year) and drink-driving enforcement (>60 000 lives saved per year). Interventions potentially most effective in preventing deaths from drowning are formal swimming lessons for children younger than 14 years (>25 000 lives

The 2011-2015 physical and monetary balance for electricity: spending of over euro 50 billion in 2015

International Nuclear Information System (INIS)

Guggemos, Fabien; Meilhac, Christophe; Riedinger, Nicolas; Martial, Elodie; Mombel, David; Moreau, Sylvain; Bottin, Anne; Lavail, Jennyfer

2017-09-01

Electricity consumers (excluding the electricity sector itself) spent euro 52 billion in 2015 to consume 446 TWh. Taxes accounted for 27% of that expenditure (of which around one-half contributed to financing renewable sources of electricity and to geographical price adjustments), the cost of transmission 27%, and that of supply (including production and sales) 46%. Trade with other countries showed a positive balance of euro 2.3 billion. The residential sector was the main consuming sector, accounting for 35% of physical deliveries. Given the transmission and sales costs, higher on average for households than for businesses, the residential sector accounted for a greater proportion of the spending (48%). Conversely, industry accounted for 24% of physical consumption but only 15% of spending. The share of the services sector was around one-third, in both physical-unit and monetary terms

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.

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...

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.

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

Saving billions of dollars--and physicians' time--by streamlining billing practices.

Science.gov (United States)

Blanchfield, Bonnie B; Heffernan, James L; Osgood, Bradford; Sheehan, Rosemary R; Meyer, Gregg S

2010-06-01

The U.S. system of billing third parties for health care services is complex, expensive, and inefficient. Physicians end up using nearly 12 percent of their net patient service revenue to cover the costs of excessive administrative complexity. A single transparent set of payment rules for multiple payers, a single claim form, and standard rules of submission, among other innovations, would reduce the burden on the billing offices of physician organizations. On a national scale, our hypothetical modeling of these changes would translate into $7 billion of savings annually for physician and clinical services. Four hours of professional time per physician and five hours of practice support staff time could be saved each week.

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

Unlocking the Euro 53 billion savings from smart meters in the EU: How increasing the adoption of dynamic tariffs could make or break the EU's smart grid investment

Energy Technology Data Exchange (ETDEWEB)

Faruqui, Ahmad, E-mail: ahmad.faruqui@brattle.co [Brattle Group, 353 Sacramento Street, Suite 1140, San Francisco, CA 94111 (United States); Harris, Dan, E-mail: Dan.Harris@brattle.co [Via Pozzuoli 7, Scala B3, Rome 00182 (Italy); Hledik, Ryan, E-mail: Ryan.Hledik@brattle.co [Brattle Group, 353 Sacramento Street, Suite 1140, San Francisco, CA 94111 (United States)

2010-10-15

We estimate the cost of installing smart meters in the EU to be Euro 51 billion, and that operational savings will be worth between Euro 26 and 41 billion, leaving a gap of Euro 10-25 billion between benefits and costs. Smart meters can fill this gap because they enable the provision of dynamic pricing, which reduces peak demand and lowers the need for building and running expensive peaking power plants. The present value of savings in peaking infrastructure could be as high as Euro 67 billion for the EU if policy-makers can overcome barriers to consumers adopting dynamic tariffs, but only Euro 14 billion otherwise. We outline a number of ways to increase the adoption of dynamic tariffs.

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

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

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.

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 ...

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

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

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...

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.

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.

Wildlife Contact Rates at Artificial Feeding Sites in Texas

Science.gov (United States)

Campbell, Tyler A.; Long, David B.; Shriner, Susan A.

2013-06-01

Given the popularity of feeding white-tailed deer ( Odocoileus virginianus) in Texas and the increasing amount of corn that is distributed, more information is needed on the impacts of this activity on non-target wildlife. Our objectives were to report visitation, intra- and interspecific contact, and contact rates of wildlife at artificial feeding sites in Texas. Our study was conducted at three sites in Kleberg and Nueces counties, Texas. We trapped animals from February to April and August to September, 2009 and marked animals with passive integrated transponder (PIT) tags. At each site and season, we placed one feeder system containing a PIT tag reader within 600 m of trap locations. Readers detected PIT tags from a distance of 25 cm. We determined a contact event to occur when two different PIT tags were detected by feeder systems within 5 s. We recorded 62,719 passes by raccoons ( Procyon lotor), 103,512 passes by collared peccaries ( Pecari tajacu), 2,923 passes by feral swine ( Sus scrofa), 1,336 passes by fox squirrels ( Sciurus niger), and no passes by opossums ( Didelphis virginiana) at feeder systems. For site-season combinations in which contact events occurred, we found intraspecific contact rates (contacts per day) for raccoons, collared peccaries, and feral swine to be 0.81-124.77, 0.69-38.08, and 0.0-0.66, respectively. Throughout our study we distributed ~2,625 kg of whole kernel corn, which resulted in 6,351 contact events between marked wildlife (2.4 contacts per kg of corn). If 136 million kg of corn is distributed in Texas annually, we would expect >5.2 billion unnatural contact events between wildlife would result from this activity each year in Texas. Consequently, we do not believe that it is wise for natural resource managers to maintain artificial feeding sites for white-tailed deer or other wildlife due to pathogen transmission risks.

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....

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

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)

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.

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

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.

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.

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.

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.

An eccentric perspective on brain networks

NARCIS (Netherlands)

Reus, M.A. de

2015-01-01

The adult human brain comprises an estimated number of 80-100 billion neurons. These neurons do not operate independently, but are interconnected to each other through circa 100-500 trillion neuronal connections, together forming a network of incredible complexity. Although this vast system of

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

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

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,...

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...

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.

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...

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.

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 ...

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....

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.

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.

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.

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...

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.

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....

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.

Constraint on a Varying Proton-Electron Mass Ratio 1.5 Billion Years after the Big Bang

NARCIS (Netherlands)

Bagdonaite, J.; Ubachs, W.M.G.; Murphy, M.T.; Withmore, J.B.

2015-01-01

A molecular hydrogen absorber at a lookback time of 12.4 billion years, corresponding to 10% of the age of the Universe today, is analyzed to put a constraint on a varying proton-electron mass ratio, μ. A high resolution spectrum of the J1443+2724 quasar, which was observed with the Very Large

DeepX: Deep Learning Accelerator for Restricted Boltzmann Machine Artificial Neural Networks.

Science.gov (United States)

Kim, Lok-Won

2018-05-01

Although there have been many decades of research and commercial presence on high performance general purpose processors, there are still many applications that require fully customized hardware architectures for further computational acceleration. Recently, deep learning has been successfully used to learn in a wide variety of applications, but their heavy computation demand has considerably limited their practical applications. This paper proposes a fully pipelined acceleration architecture to alleviate high computational demand of an artificial neural network (ANN) which is restricted Boltzmann machine (RBM) ANNs. The implemented RBM ANN accelerator (integrating network size, using 128 input cases per batch, and running at a 303-MHz clock frequency) integrated in a state-of-the art field-programmable gate array (FPGA) (Xilinx Virtex 7 XC7V-2000T) provides a computational performance of 301-billion connection-updates-per-second and about 193 times higher performance than a software solution running on general purpose processors. Most importantly, the architecture enables over 4 times (12 times in batch learning) higher performance compared with a previous work when both are implemented in an FPGA device (XC2VP70).

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.

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.

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.

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

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.

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.

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.

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.

Fertility results of artificial inseminations performed with liquid boar semen stored in X-cell vs BTS extender.

Science.gov (United States)

Haugan, T; Gaustad, A H; Reksen, O; Gröhn, Y T; Hofmo, P O

2007-02-01

The objective of the present field study was to compare the fertility results for boar semen diluted in X-cell stored up to 4-5 days before artificial insemination (AI) with semen diluted in Beltsville thawing solution (BTS) used for AI following 2-3 days of storage (where the first day being the collection day). A total number of 2601 double inseminations in Norwegian herds were included in this two-trial study. All the boars used in the study were mature cross-bred Norwegian Landrace x Duroc (LD), which were routinely used for AI in Norway. The inseminated gilts and sows were Norwegian Landrace x Yorkshire (LY). The AI doses contained 2.5 billion spermatozoa, and consisted of a mixture of semen from three, occasionally four, boars (i.e. heterospermic semen). Fertility was measured in terms of the likelihood of farrowing and subsequent litter size. The fertility of the semen in both of the extenders was satisfactory and no significant differences were found either in semen stored 4-5 days in X-cell compared with 2-3 days in BTS or in semen stored 2-3 days in X-cell compared with 2-3 days in BTS. The storage capability findings for the long-term extender X-cell could significantly simplify the practical issues of semen production and the distribution of AI doses containing 2.5 billion spermatozoa. However, in pig production systems where all semen is used within 2-3 days, the short-term extender BTS is as good as the more expensive extender X-cell.

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.

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.

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.

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

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.

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

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.

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.

Artificial reefs: “Attraction versus Production”

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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.

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.

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.

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...

A Re-configurable On-line Learning Spiking Neuromorphic Processor comprising 256 neurons and 128K synapses

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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.

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.

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.

Dynamic cardiomyoplasty using artificial muscle.