WorldWideScience

Sample records for active neural circuits

  1. An Activity for Demonstrating the Concept of a Neural Circuit

    Science.gov (United States)

    Kreiner, David S.

    2012-01-01

    College students in two sections of a general psychology course participated in a demonstration of a simple neural circuit. The activity was based on a neural circuit that Jeffress proposed for localizing sounds. Students in one section responded to a questionnaire prior to participating in the activity, while students in the other section…

  2. Tools for Resolving Functional Activity and Connectivity within Intact Neural Circuits

    OpenAIRE

    Jennings, Joshua H.; Stuber, Garret D.

    2014-01-01

    Mammalian neural circuits are sophisticated biological systems that choreograph behavioral processes vital for survival. While the inherent complexity of discrete neural circuits has proven difficult to decipher, many parallel methodological developments promise to help delineate the function and connectivity of molecularly defined neural circuits. Here, we review recent technological advances designed to precisely monitor and manipulate neural circuit activity. We propose a holistic, multifa...

  3. Activity-dependent modulation of neural circuit synaptic connectivity

    OpenAIRE

    Tessier, Charles R.; Kendal Broadie

    2009-01-01

    In many nervous systems, the establishment of neural circuits is known to proceed via a two-stage process; 1) early, activity-independent wiring to produce a rough map characterized by excessive synaptic connections, and 2) subsequent, use-dependent pruning to eliminate inappropriate connections and reinforce maintained synapses. In invertebrates, however, evidence of the activity-dependent phase of synaptic refinement has been elusive, and the dogma has long been that invertebrate circ...

  4. Activity-dependent modulation of neural circuit synaptic connectivity

    Directory of Open Access Journals (Sweden)

    Charles R Tessier

    2009-07-01

    Full Text Available In many nervous systems, the establishment of neural circuits is known to proceed via a two-stage process; 1 early, activity-independent wiring to produce a rough map characterized by excessive synaptic connections, and 2 subsequent, use-dependent pruning to eliminate inappropriate connections and reinforce maintained synapses. In invertebrates, however, evidence of the activity-dependent phase of synaptic refinement has been elusive, and the dogma has long been that invertebrate circuits are “hard-wired” in a purely activity-independent manner. This conclusion has been challenged recently through the use of new transgenic tools employed in the powerful Drosophila system, which have allowed unprecedented temporal control and single neuron imaging resolution. These recent studies reveal that activity-dependent mechanisms are indeed required to refine circuit maps in Drosophila during precise, restricted windows of late-phase development. Such mechanisms of circuit refinement may be key to understanding a number of human neurological diseases, including developmental disorders such as Fragile X syndrome (FXS and autism, which are hypothesized to result from defects in synaptic connectivity and activity-dependent circuit function. This review focuses on our current understanding of activity-dependent synaptic connectivity in Drosophila, primarily through analyzing the role of the fragile X mental retardation protein (FMRP in the Drosophila FXS disease model. The particular emphasis of this review is on the expanding array of new genetically-encoded tools that are allowing cellular events and molecular players to be dissected with ever greater precision and detail.

  5. Impaired activity-dependent neural circuit assembly and refinement in autism spectrum disorder genetic models

    OpenAIRE

    Caleb Andrew Doll; Kendal eBroadie

    2014-01-01

    Early-use activity during circuit-specific critical periods refines brain circuitry by the coupled processes of eliminating inappropriate synapses and strengthening maintained synapses. We theorize these activity-dependent developmental processes are specifically impaired in autism spectrum disorders (ASDs). ASD genetic models in both mouse and Drosophila have pioneered our insights into normal activity-dependent neural circuit assembly and consolidation, and how these developmental mechanism...

  6. Demultiplexer circuit for neural stimulation

    Science.gov (United States)

    Wessendorf, Kurt O; Okandan, Murat; Pearson, Sean

    2012-10-09

    A demultiplexer circuit is disclosed which can be used with a conventional neural stimulator to extend the number of electrodes which can be activated. The demultiplexer circuit, which is formed on a semiconductor substrate containing a power supply that provides all the dc electrical power for operation of the circuit, includes digital latches that receive and store addressing information from the neural stimulator one bit at a time. This addressing information is used to program one or more 1:2.sup.N demultiplexers in the demultiplexer circuit which then route neural stimulation signals from the neural stimulator to an electrode array which is connected to the outputs of the 1:2.sup.N demultiplexer. The demultiplexer circuit allows the number of individual electrodes in the electrode array to be increased by a factor of 2.sup.N with N generally being in a range of 2-4.

  7. Distributed dynamical computation in neural circuits with propagating coherent activity patterns.

    Directory of Open Access Journals (Sweden)

    Pulin Gong

    2009-12-01

    Full Text Available Activity in neural circuits is spatiotemporally organized. Its spatial organization consists of multiple, localized coherent patterns, or patchy clusters. These patterns propagate across the circuits over time. This type of collective behavior has ubiquitously been observed, both in spontaneous activity and evoked responses; its function, however, has remained unclear. We construct a spatially extended, spiking neural circuit that generates emergent spatiotemporal activity patterns, thereby capturing some of the complexities of the patterns observed empirically. We elucidate what kind of fundamental function these patterns can serve by showing how they process information. As self-sustained objects, localized coherent patterns can signal information by propagating across the neural circuit. Computational operations occur when these emergent patterns interact, or collide with each other. The ongoing behaviors of these patterns naturally embody both distributed, parallel computation and cascaded logical operations. Such distributed computations enable the system to work in an inherently flexible and efficient way. Our work leads us to propose that propagating coherent activity patterns are the underlying primitives with which neural circuits carry out distributed dynamical computation.

  8. Auto-programmable impulse neural circuits

    Science.gov (United States)

    Watula, D.; Meador, J.

    1990-01-01

    Impulse neural networks use pulse trains to communicate neuron activation levels. Impulse neural circuits emulate natural neurons at a more detailed level than that typically employed by contemporary neural network implementation methods. An impulse neural circuit which realizes short term memory dynamics is presented. The operation of that circuit is then characterized in terms of pulse frequency modulated signals. Both fixed and programmable synapse circuits for realizing long term memory are also described. The implementation of a simple and useful unsupervised learning law is then presented. The implementation of a differential Hebbian learning rule for a specific mean-frequency signal interpretation is shown to have a straightforward implementation using digital combinational logic with a variation of a previously developed programmable synapse circuit. This circuit is expected to be exploited for simple and straightforward implementation of future auto-adaptive neural circuits.

  9. Impaired activity-dependent neural circuit assembly and refinement in autism spectrum disorder genetic models

    Directory of Open Access Journals (Sweden)

    Caleb Andrew Doll

    2014-02-01

    Full Text Available Early-use activity during circuit-specific critical periods refines brain circuitry by the coupled processes of eliminating inappropriate synapses and strengthening maintained synapses. We theorize these activity-dependent developmental processes are specifically impaired in autism spectrum disorders (ASDs. ASD genetic models in both mouse and Drosophila have pioneered our insights into normal activity-dependent neural circuit assembly and consolidation, and how these developmental mechanisms go awry in specific genetic conditions. The monogenic Fragile X syndrome (FXS, a common cause of heritable ASD and intellectual disability, has been particularly well linked to defects in activity-dependent critical period processes. The Fragile X Mental Retardation Protein (FMRP is positively activity-regulated in expression and function, in turn regulates excitability and activity in a negative feedback loop, and appears to be required for the activity-dependent remodeling of synaptic connectivity during early-use critical periods. The Drosophila FXS model has been shown to functionally conserve the roles of human FMRP in synaptogenesis, and has been centrally important in generating our current mechanistic understanding of the FXS disease state. Recent advances in Drosophila optogenetics, transgenic calcium reporters, highly-targeted transgenic drivers for individually-identified neurons, and a vastly improved connectome of the brain are now being combined to provide unparalleled opportunities to both manipulate and monitor activity-dependent processes during critical period brain development in defined neural circuits. The field is now poised to exploit this new Drosophila transgenic toolbox for the systematic dissection of activity-dependent mechanisms in normal versus ASD brain development, particularly utilizing the well-established Drosophila FXS disease model.

  10. Fluorescence-based monitoring of in vivo neural activity using a circuit-tracing pseudorabies virus.

    Directory of Open Access Journals (Sweden)

    Andrea E Granstedt

    Full Text Available The study of coordinated activity in neuronal circuits has been challenging without a method to simultaneously report activity and connectivity. Here we present the first use of pseudorabies virus (PRV, which spreads through synaptically connected neurons, to express a fluorescent calcium indicator protein and monitor neuronal activity in a living animal. Fluorescence signals were proportional to action potential number and could reliably detect single action potentials in vitro. With two-photon imaging in vivo, we observed both spontaneous and stimulated activity in neurons of infected murine peripheral autonomic submandibular ganglia (SMG. We optically recorded the SMG response in the salivary circuit to direct electrical stimulation of the presynaptic axons and to physiologically relevant sensory stimulation of the oral cavity. During a time window of 48 hours after inoculation, few spontaneous transients occurred. By 72 hours, we identified more frequent and prolonged spontaneous calcium transients, suggestive of neuronal or tissue responses to infection that influence calcium signaling. Our work establishes in vivo investigation of physiological neuronal circuit activity and subsequent effects of infection with single cell resolution.

  11. A multichannel integrated circuit for electrical recording of neural activity, with independent channel programmability.

    Science.gov (United States)

    Mora Lopez, Carolina; Prodanov, Dimiter; Braeken, Dries; Gligorijevic, Ivan; Eberle, Wolfgang; Bartic, Carmen; Puers, Robert; Gielen, Georges

    2012-04-01

    Since a few decades, micro-fabricated neural probes are being used, together with microelectronic interfaces, to get more insight in the activity of neuronal networks. The need for higher temporal and spatial recording resolutions imposes new challenges on the design of integrated neural interfaces with respect to power consumption, data handling and versatility. In this paper, we present an integrated acquisition system for in vitro and in vivo recording of neural activity. The ASIC consists of 16 low-noise, fully-differential input channels with independent programmability of its amplification (from 100 to 6000 V/V) and filtering (1-6000 Hz range) capabilities. Each channel is AC-coupled and implements a fourth-order band-pass filter in order to steeply attenuate out-of-band noise and DC input offsets. The system achieves an input-referred noise density of 37 nV/√Hz, a NEF of 5.1, a CMRR > 60 dB, a THD < 1% and a sampling rate of 30 kS/s per channel, while consuming a maximum of 70 μA per channel from a single 3.3 V. The ASIC was implemented in a 0.35 μm CMOS technology and has a total area of 5.6 × 4.5 mm². The recording system was successfully validated in in vitro and in vivo experiments, achieving simultaneous multichannel recordings of cell activity with satisfactory signal-to-noise ratios.

  12. Illuminating neural circuits and behaviour in Caenorhabditis elegans with optogenetics.

    Science.gov (United States)

    Fang-Yen, Christopher; Alkema, Mark J; Samuel, Aravinthan D T

    2015-09-19

    The development of optogenetics, a family of methods for using light to control neural activity via light-sensitive proteins, has provided a powerful new set of tools for neurobiology. These techniques have been particularly fruitful for dissecting neural circuits and behaviour in the compact and transparent roundworm Caenorhabditis elegans. Researchers have used optogenetic reagents to manipulate numerous excitable cell types in the worm, from sensory neurons, to interneurons, to motor neurons and muscles. Here, we show how optogenetics applied to this transparent roundworm has contributed to our understanding of neural circuits.

  13. Contextual behavior and neural circuits

    Directory of Open Access Journals (Sweden)

    Inah eLee

    2013-05-01

    Full Text Available Animals including humans engage in goal-directed behavior flexibly in response to items and their background, which is called contextual behavior in this review. Although the concept of context has long been studied, there are differences among researchers in defining and experimenting with the concept. The current review aims to provide a categorical framework within which not only the neural mechanisms of contextual information processing but also the contextual behavior can be studied in more concrete ways. For this purpose, we categorize contextual behavior into three subcategories as follows by considering the types of interactions among context, item, and response: contextual response selection, contextual item selection, and contextual item-response selection. Contextual response selection refers to the animal emitting different types of responses to the same item depending on the context in the background. Contextual item selection occurs when there are multiple items that need to be chosen in a contextual manner. Finally, when multiple items and multiple contexts are involved, contextual item-response selection takes place whereby the animal either choose an item or inhibit such a response depending on item-context paired association. The literature suggests that the rhinal cortical regions and the hippocampal formation play key roles in mnemonically categorizing and recognizing contextual representations and the associated items. In addition, it appears that the fronto-striatal cortical loops in connection with the contextual information-processing areas critically control the flexible deployment of adaptive action sets and motor responses for maximizing goals. We suggest that contextual information processing should be investigated in experimental settings where contextual stimuli and resulting behaviors are clearly defined and measurable, considering the dynamic top-down and bottom-up interactions among the neural systems for

  14. Document analysis with neural net circuits

    Science.gov (United States)

    Graf, Hans Peter

    1994-01-01

    Document analysis is one of the main applications of machine vision today and offers great opportunities for neural net circuits. Despite more and more data processing with computers, the number of paper documents is still increasing rapidly. A fast translation of data from paper into electronic format is needed almost everywhere, and when done manually, this is a time consuming process. Markets range from small scanners for personal use to high-volume document analysis systems, such as address readers for the postal service or check processing systems for banks. A major concern with present systems is the accuracy of the automatic interpretation. Today's algorithms fail miserably when noise is present, when print quality is poor, or when the layout is complex. A common approach to circumvent these problems is to restrict the variations of the documents handled by a system. In our laboratory, we had the best luck with circuits implementing basic functions, such as convolutions, that can be used in many different algorithms. To illustrate the flexibility of this approach, three applications of the NET32K circuit are described in this short viewgraph presentation: locating address blocks, cleaning document images by removing noise, and locating areas of interest in personal checks to improve image compression. Several of the ideas realized in this circuit that were inspired by neural nets, such as analog computation with a low resolution, resulted in a chip that is well suited for real-world document analysis applications and that compares favorably with alternative, 'conventional' circuits.

  15. [Dual neural circuit model of reading and writing].

    Science.gov (United States)

    Iwata, Makoto

    2011-08-01

    In the hypothetical neural circuit model of reading and writing that was initially proposed by Dejerine and subsequently confirmed by Geschwind, the left angular gyrus was considered as a unique center for processing letters. Japanese investigators, however, have repeatedly pointed out that this angular gyrus model cannot fully explain the disturbances observed in reading and writing Kanji letters in Japanese patients with various types of alexia with or without agraphia. In 1982, I proposed a dual neural circuit model of reading and writing Japanese on the basis of neuropsychological studies on the various types of alexia with or without agraphia without aphasia. This dual neural circuit model proposes that apart from the left angular gyrus which was thought to be a node for phonological processing of letters, the left posterior inferior temporal area, also acts as a node for semantic processing of letters. Further investigations using O15-PET activation on normal subjects revealed that the left middle occipital gyrus (area 19 of Brodmann) and the posterior portion of the left inferior temporal gyrus (area 37 of Brodmann) are the cortical areas responsible for reading Japanese letters; the former serving for phonological reading and the latter for semantic reading. This duality of the neural circuit in processing letters was later applied to explain disturbances in reading English, and was finally accepted as a valid model for other alphabetic letter systems too.

  16. Electronic circuits modeling using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Andrejević Miona V.

    2003-01-01

    Full Text Available In this paper artificial neural networks (ANN are applied to modeling of electronic circuits. ANNs are used for application of the black-box modeling concept in the time domain. Modeling process is described, so the topology of the ANN, the testing signal used for excitation, together with the complexity of ANN are considered. The procedure is first exemplified in modeling of resistive circuits. MOS transistor, as a four-terminal device, is modeled. Then nonlinear negative resistive characteristic is modeled in order to be used as a piece-wise linear resistor in Chua's circuit. Examples of modeling nonlinear dynamic circuits are given encompassing a variety of modeling problems. A nonlinear circuit containing quartz oscillator is considered for modeling. Verification of the concept is performed by verifying the ability of the model to generalize i.e. to create acceptable responses to excitations not used during training. Implementation of these models within a behavioral simulator is exemplified. Every model is implemented in realistic surrounding in order to show its interaction, and of course, its usage and purpose.

  17. Circuit design and exponential stabilization of memristive neural networks.

    Science.gov (United States)

    Wen, Shiping; Huang, Tingwen; Zeng, Zhigang; Chen, Yiran; Li, Peng

    2015-03-01

    This paper addresses the problem of circuit design and global exponential stabilization of memristive neural networks with time-varying delays and general activation functions. Based on the Lyapunov-Krasovskii functional method and free weighting matrix technique, a delay-dependent criteria for the global exponential stability and stabilization of memristive neural networks are derived in form of linear matrix inequalities (LMIs). Two numerical examples are elaborated to illustrate the characteristics of the results. It is noteworthy that the traditional assumptions on the boundness of the derivative of the time-varying delays are removed.

  18. Noise Delays Bifurcation in a Positively Coupled Neural Circuit

    OpenAIRE

    Gutkin, Boris; Hely, Tim; Jost, Juergen

    2000-01-01

    We report a noise induced delay of bifurcation in a simple pulse-coupled neural circuit. We study the behavior of two neural oscillators, each individually governed by saddle-node dynamics, with reciprocal excitatory synaptic connections. In the deterministic circuit, the synaptic current amplitude acts as a control parameter to move the circuit from a mono-stable regime through a bifurcation into a bistable regime. In this regime stable sustained anti-phase oscillations in both neurons coexi...

  19. Sparse and silent coding in neural circuits

    CERN Document Server

    L\\Horincz, András; Szirtes, Gábor

    2010-01-01

    Sparse coding algorithms are about finding a linear basis in which signals can be represented by a small number of active (non-zero) coefficients. Such coding has many applications in science and engineering and is believed to play an important role in neural information processing. However, due to the computational complexity of the task, only approximate solutions provide the required efficiency (in terms of time). As new results show, under particular conditions there exist efficient solutions by minimizing the magnitude of the coefficients (`$l_1$-norm') instead of minimizing the size of the active subset of features (`$l_0$-norm'). Straightforward neural implementation of these solutions is not likely, as they require \\emph{a priori} knowledge of the number of active features. Furthermore, these methods utilize iterative re-evaluation of the reconstruction error, which in turn implies that final sparse forms (featuring `population sparseness') can only be reached through the formation of a series of non-...

  20. Neural dynamics and circuit mechanisms of decision-making.

    Science.gov (United States)

    Wang, Xiao-Jing

    2012-12-01

    In this review, I briefly summarize current neurobiological studies of decision-making that bear on two general themes. The first focuses on the nature of neural representation and dynamics in a decision circuit. Experimental and computational results suggest that ramping-to-threshold in the temporal domain and trajectory of population activity in the state space represent a duality of perspectives on a decision process. Moreover, a decision circuit can display several different dynamical regimes, such as the ramping mode and the jumping mode with distinct defining properties. The second is concerned with the relationship between biologically-based mechanistic models and normative-type models. A fruitful interplay between experiments and these models at different levels of abstraction have enabled investigators to pose increasingly refined questions and gain new insights into the neural basis of decision-making. In particular, recent work on multi-alternative decisions suggests that deviations from rational models of choice behavior can be explained by established neural mechanisms.

  1. Implantable neurotechnologies: a review of integrated circuit neural amplifiers.

    Science.gov (United States)

    Ng, Kian Ann; Greenwald, Elliot; Xu, Yong Ping; Thakor, Nitish V

    2016-01-01

    Neural signal recording is critical in modern day neuroscience research and emerging neural prosthesis programs. Neural recording requires the use of precise, low-noise amplifier systems to acquire and condition the weak neural signals that are transduced through electrode interfaces. Neural amplifiers and amplifier-based systems are available commercially or can be designed in-house and fabricated using integrated circuit (IC) technologies, resulting in very large-scale integration or application-specific integrated circuit solutions. IC-based neural amplifiers are now used to acquire untethered/portable neural recordings, as they meet the requirements of a miniaturized form factor, light weight and low power consumption. Furthermore, such miniaturized and low-power IC neural amplifiers are now being used in emerging implantable neural prosthesis technologies. This review focuses on neural amplifier-based devices and is presented in two interrelated parts. First, neural signal recording is reviewed, and practical challenges are highlighted. Current amplifier designs with increased functionality and performance and without penalties in chip size and power are featured. Second, applications of IC-based neural amplifiers in basic science experiments (e.g., cortical studies using animal models), neural prostheses (e.g., brain/nerve machine interfaces) and treatment of neuronal diseases (e.g., DBS for treatment of epilepsy) are highlighted. The review concludes with future outlooks of this technology and important challenges with regard to neural signal amplification.

  2. Grand Research Plan for Neural Circuits of Emotion and Memory-Current status of neural circuit studies in China

    Institute of Scientific and Technical Information of China (English)

    Yuan-Gui Zhu; He-Qi Cao; Er-Dan Dong

    2013-01-01

    During recent years,major advances have been made in neuroscience,i.e.,asynchronous release,three-dimensional structural data sets,saliency maps,magnesium in brain research,and new functional roles of long non-coding RNAs.Especially,the development of optogenetic technology provides access to important information about relevant neural circuits by allowing the activation of specific neurons in awake mammals and directly observing the resulting behavior.The Grand Research Plan for Neural Circuits of Emotion and Memory was launched by the National Natural Science Foundation of China.It takes emotion and memory as its main objects,making the best use of cutting-edge technologies from medical science,life science and information science.In this paper,we outline the current status of neural circuit studies in China and the technologies and methodologies being applied,as well as studies related to the impairments of emotion and memory.In this phase,we are making efforts to repair the current deficiencies by making adjustments,mainly involving four aspects of core scientific issues to investigate these circuits at multiple levels.Five research directions have been taken to solve important scientific problems while the Grand Research Plan is implemented.Future research into this area will be multimodal,incorporating a range of methods and sciences into each project.Addressing these issues will ensure a bright future,major discoveries,and a higher level of treatment for all affected by debilitating brain illnesses.

  3. Improved Estimation and Interpretation of Correlations in Neural Circuits

    Science.gov (United States)

    Yatsenko, Dimitri; Josić, Krešimir; Ecker, Alexander S.; Froudarakis, Emmanouil; Cotton, R. James; Tolias, Andreas S.

    2015-01-01

    Ambitious projects aim to record the activity of ever larger and denser neuronal populations in vivo. Correlations in neural activity measured in such recordings can reveal important aspects of neural circuit organization. However, estimating and interpreting large correlation matrices is statistically challenging. Estimation can be improved by regularization, i.e. by imposing a structure on the estimate. The amount of improvement depends on how closely the assumed structure represents dependencies in the data. Therefore, the selection of the most efficient correlation matrix estimator for a given neural circuit must be determined empirically. Importantly, the identity and structure of the most efficient estimator informs about the types of dominant dependencies governing the system. We sought statistically efficient estimators of neural correlation matrices in recordings from large, dense groups of cortical neurons. Using fast 3D random-access laser scanning microscopy of calcium signals, we recorded the activity of nearly every neuron in volumes 200 μm wide and 100 μm deep (150–350 cells) in mouse visual cortex. We hypothesized that in these densely sampled recordings, the correlation matrix should be best modeled as the combination of a sparse graph of pairwise partial correlations representing local interactions and a low-rank component representing common fluctuations and external inputs. Indeed, in cross-validation tests, the covariance matrix estimator with this structure consistently outperformed other regularized estimators. The sparse component of the estimate defined a graph of interactions. These interactions reflected the physical distances and orientation tuning properties of cells: The density of positive ‘excitatory’ interactions decreased rapidly with geometric distances and with differences in orientation preference whereas negative ‘inhibitory’ interactions were less selective. Because of its superior performance, this

  4. A neural circuit for angular velocity computation

    Directory of Open Access Journals (Sweden)

    Samuel B Snider

    2010-12-01

    Full Text Available In one of the most remarkable feats of motor control in the animal world, some Diptera, such as the housefly, can accurately execute corrective flight maneuvers in tens of milliseconds. These reflexive movements are achieved by the halteres, gyroscopic force sensors, in conjunction with rapidly-tunable wing-steering muscles. Specifically, the mechanosensory campaniform sensilla located at the base of the halteres transduce and transform rotation-induced gyroscopic forces into information about the angular velocity of the fly's body. But how exactly does the fly's neural architecture generate the angular velocity from the lateral strain forces on the left and right halteres? To explore potential algorithms, we built a neuro-mechanical model of the rotation detection circuit. We propose a neurobiologically plausible method by which the fly could accurately separate and measure the three-dimensional components of an imposed angular velocity. Our model assumes a single sign-inverting synapse and formally resembles some models of directional selectivity by the retina. Using multidimensional error analysis, we demonstrate the robustness of our model under a variety of input conditions. Our analysis reveals the maximum information available to the fly given its physical architecture and the mathematics governing the rotation-induced forces at the haltere's end knob.

  5. A neural circuit for angular velocity computation.

    Science.gov (United States)

    Snider, Samuel B; Yuste, Rafael; Packer, Adam M

    2010-01-01

    In one of the most remarkable feats of motor control in the animal world, some Diptera, such as the housefly, can accurately execute corrective flight maneuvers in tens of milliseconds. These reflexive movements are achieved by the halteres, gyroscopic force sensors, in conjunction with rapidly tunable wing steering muscles. Specifically, the mechanosensory campaniform sensilla located at the base of the halteres transduce and transform rotation-induced gyroscopic forces into information about the angular velocity of the fly's body. But how exactly does the fly's neural architecture generate the angular velocity from the lateral strain forces on the left and right halteres? To explore potential algorithms, we built a neuromechanical model of the rotation detection circuit. We propose a neurobiologically plausible method by which the fly could accurately separate and measure the three-dimensional components of an imposed angular velocity. Our model assumes a single sign-inverting synapse and formally resembles some models of directional selectivity by the retina. Using multidimensional error analysis, we demonstrate the robustness of our model under a variety of input conditions. Our analysis reveals the maximum information available to the fly given its physical architecture and the mathematics governing the rotation-induced forces at the haltere's end knob. PMID:21228902

  6. Neural Control of Energy Balance: Translating Circuits to Therapies

    OpenAIRE

    Gautron, Laurent; Elmquist, Joel K.; Williams, Kevin W

    2015-01-01

    Recent insights into the neural circuits controlling energy balance and glucose homeostasis have rekindled the hope for development of novel treatments for obesity and diabetes. However, many therapies contribute relatively modest beneficial gains with accompanying side effects, and the mechanisms of action for other interventions remain undefined. This Review summarizes current knowledge linking the neural circuits regulating energy and glucose balance with current and potential pharmacother...

  7. Progress in understanding mood disorders: optogenetic dissection of neural circuits.

    Science.gov (United States)

    Lammel, S; Tye, K M; Warden, M R

    2014-01-01

    Major depression is characterized by a cluster of symptoms that includes hopelessness, low mood, feelings of worthlessness and inability to experience pleasure. The lifetime prevalence of major depression approaches 20%, yet current treatments are often inadequate both because of associated side effects and because they are ineffective for many people. In basic research, animal models are often used to study depression. Typically, experimental animals are exposed to acute or chronic stress to generate a variety of depression-like symptoms. Despite its clinical importance, very little is known about the cellular and neural circuits that mediate these symptoms. Recent advances in circuit-targeted approaches have provided new opportunities to study the neuropathology of mood disorders such as depression and anxiety. We review recent progress and highlight some studies that have begun tracing a functional neuronal circuit diagram that may prove essential in establishing novel treatment strategies in mood disorders. First, we shed light on the complexity of mesocorticolimbic dopamine (DA) responses to stress by discussing two recent studies reporting that optogenetic activation of midbrain DA neurons can induce or reverse depression-related behaviors. Second, we describe the role of the lateral habenula circuitry in the pathophysiology of depression. Finally, we discuss how the prefrontal cortex controls limbic and neuromodulatory circuits in mood disorders.

  8. Neural circuit dysfunction in schizophrenia: Insights from animal models.

    Science.gov (United States)

    Sigurdsson, T

    2016-05-01

    Despite decades of research, the neural circuit abnormalities underlying schizophrenia remain elusive. Although studies on schizophrenia patients have yielded important insights they have not been able to fully reveal the details of how neural circuits are disrupted in the disease, which is essential for understanding its pathophysiology and developing new treatment strategies. Animal models of schizophrenia are likely to play an important role in this effort. Such models allow neural circuit dysfunction to be investigated in detail and the role of risk factors and pathophysiological mechanisms to be experimentally assessed. The goal of this review is to summarize what we have learned from electrophysiological studies that have examined neural circuit function in animal models of schizophrenia. Although these studies have revealed diverse manifestations of neural circuit dysfunction spanning multiple levels of analysis, common themes have nevertheless emerged across different studies and animal models, revealing a core set of neural circuit abnormalities. These include an imbalance between excitation and inhibition, deficits in synaptic plasticity, disruptions in local and long-range synchrony and abnormalities in dopaminergic signaling. The relevance of these findings to the pathophysiology of the disease is discussed, as well as outstanding questions for future research.

  9. The neural circuit basis of learning

    Science.gov (United States)

    Patrick, Kaifosh William John

    The astounding capacity for learning ranks among the nervous system's most impressive features. This thesis comprises studies employing varied approaches to improve understanding, at the level of neural circuits, of the brain's capacity for learning. The first part of the thesis contains investigations of hippocampal circuitry -- both theoretical work and experimental work in the mouse Mus musculus -- as a model system for declarative memory. To begin, Chapter 2 presents a theory of hippocampal memory storage and retrieval that reflects nonlinear dendritic processing within hippocampal pyramidal neurons. As a prelude to the experimental work that comprises the remainder of this part, Chapter 3 describes an open source software platform that we have developed for analysis of data acquired with in vivo Ca2+ imaging, the main experimental technique used throughout the remainder of this part of the thesis. As a first application of this technique, Chapter 4 characterizes the content of signaling at synapses between GABAergic neurons of the medial septum and interneurons in stratum oriens of hippocampal area CA1. Chapter 5 then combines these techniques with optogenetic, pharmacogenetic, and pharmacological manipulations to uncover inhibitory circuit mechanisms underlying fear learning. The second part of this thesis focuses on the cerebellum-like electrosensory lobe in the weakly electric mormyrid fish Gnathonemus petersii, as a model system for non-declarative memory. In Chapter 6, we study how short-duration EOD motor commands are recoded into a complex temporal basis in the granule cell layer, which can be used to cancel Purkinje-like cell firing to the longer duration and temporally varying EOD-driven sensory responses. In Chapter 7, we consider not only the temporal aspects of the granule cell code, but also the encoding of body position provided from proprioceptive and efference copy sources. Together these studies clarify how the cerebellum-like circuitry of the

  10. FUZZY NEURAL NETWORK FOR OBJECT IDENTIFICATION ON INTEGRATED CIRCUIT LAYOUTS

    Directory of Open Access Journals (Sweden)

    A. A. Doudkin

    2015-01-01

    Full Text Available Fuzzy neural network model based on neocognitron is proposed to identify layout objects on images of topological layers of integrated circuits. Testing of the model on images of real chip layouts was showed a highеr degree of identification of the proposed neural network in comparison to base neocognitron.

  11. FUZZY NEURAL NETWORK FOR OBJECT IDENTIFICATION ON INTEGRATED CIRCUIT LAYOUTS

    OpenAIRE

    Doudkin, A. A.

    2016-01-01

    Fuzzy neural network model based on neocognitron is proposed to identify layout objects on images of topological layers of integrated circuits. Testing of the model on images of real chip layouts was showed a highеr degree of identification of the proposed neural network in comparison to base neocognitron.

  12. Complexity and competition in appetitive and aversive neural circuits

    Directory of Open Access Journals (Sweden)

    Crista L. Barberini

    2012-11-01

    Full Text Available Decision-making often involves using sensory cues to predict possible rewarding or punishing reinforcement outcomes before selecting a course of action. Recent work has revealed complexity in how the brain learns to predict rewards and punishments. Analysis of neural signaling during and after learning in the amygdala and orbitofrontal cortex, two brain areas that process appetitive and aversive stimuli, reveals a dynamic relationship between appetitive and aversive circuits. Specifically, the relationship between signaling in appetitive and aversive circuits in these areas shifts as a function of learning. Furthermore, although appetitive and aversive circuits may often drive opposite behaviors – approaching or avoiding reinforcement depending upon its valence – these circuits can also drive similar behaviors, such as enhanced arousal or attention; these processes also may influence choice behavior. These data highlight the formidable challenges ahead in dissecting how appetitive and aversive neural circuits interact to produce a complex and nuanced range of behaviors.

  13. Complexity and competition in appetitive and aversive neural circuits.

    Science.gov (United States)

    Barberini, Crista L; Morrison, Sara E; Saez, Alex; Lau, Brian; Salzman, C Daniel

    2012-01-01

    Decision-making often involves using sensory cues to predict possible rewarding or punishing reinforcement outcomes before selecting a course of action. Recent work has revealed complexity in how the brain learns to predict rewards and punishments. Analysis of neural signaling during and after learning in the amygdala and orbitofrontal cortex, two brain areas that process appetitive and aversive stimuli, reveals a dynamic relationship between appetitive and aversive circuits. Specifically, the relationship between signaling in appetitive and aversive circuits in these areas shifts as a function of learning. Furthermore, although appetitive and aversive circuits may often drive opposite behaviors - approaching or avoiding reinforcement depending upon its valence - these circuits can also drive similar behaviors, such as enhanced arousal or attention; these processes also may influence choice behavior. These data highlight the formidable challenges ahead in dissecting how appetitive and aversive neural circuits interact to produce a complex and nuanced range of behaviors.

  14. Photovoltaic Pixels for Neural Stimulation: Circuit Models and Performance.

    Science.gov (United States)

    Boinagrov, David; Lei, Xin; Goetz, Georges; Kamins, Theodore I; Mathieson, Keith; Galambos, Ludwig; Harris, James S; Palanker, Daniel

    2016-02-01

    Photovoltaic conversion of pulsed light into pulsed electric current enables optically-activated neural stimulation with miniature wireless implants. In photovoltaic retinal prostheses, patterns of near-infrared light projected from video goggles onto subretinal arrays of photovoltaic pixels are converted into patterns of current to stimulate the inner retinal neurons. We describe a model of these devices and evaluate the performance of photovoltaic circuits, including the electrode-electrolyte interface. Characteristics of the electrodes measured in saline with various voltages, pulse durations, and polarities were modeled as voltage-dependent capacitances and Faradaic resistances. The resulting mathematical model of the circuit yielded dynamics of the electric current generated by the photovoltaic pixels illuminated by pulsed light. Voltages measured in saline with a pipette electrode above the pixel closely matched results of the model. Using the circuit model, our pixel design was optimized for maximum charge injection under various lighting conditions and for different stimulation thresholds. To speed discharge of the electrodes between the pulses of light, a shunt resistor was introduced and optimized for high frequency stimulation.

  15. Two-photon holographic optogenetics of neural circuits (Conference Presentation)

    Science.gov (United States)

    Yang, Weijian; Carrillo-Reid, Luis; Peterka, Darcy S.; Yuste, Rafael

    2016-03-01

    Optical manipulation of in vivo neural circuits with cellular resolution could be important for understanding cortical function. Despite recent progress, simultaneous optogenetic activation with cellular precision has either been limited to 2D planes, or a very small numbers of neurons over a limited volume. Here we demonstrate a novel paradigm for simultaneous 3D activation using a low repetition rate pulse-amplified fiber laser system and a spatial light modulator (SLM) to project 3D holographic excitation patterns on the cortex of mice in vivo for targeted volumetric 3D photoactivation. This method is compatible with two-photon imaging, and enables the simultaneous activation of multiple cells in 3D, using red-shifted opsins, such as C1V1 or ReaChR, while simultaneously imaging GFP-based sensors such as GCaMP6. This all-optical imaging and 3D manipulation approach achieves simultaneous reading and writing of cortical activity, and should be a powerful tool for the study of neuronal circuits.

  16. Fully integrated circuit chip of microelectronic neural bridge

    International Nuclear Information System (INIS)

    Nerve tracts interruption is one of the major reasons for dysfunction after spiral cord injury. The microelectronic neural bridge is a method to restore function of interrupted neural pathways, by making use of microelectronic chips to bypass the injured nerve tracts. A low-power fully integrated microelectronic neural bridge chip is designed, using CSMC 0.5-μm CMOS technology. The structure and the key points in the circuit design will be introduced in detail. In order to meet the requirement for implantation, the circuit was modified to avoid the use of off-chip components, and fully monolithic integration is achieved. The operating voltage of the circuit is ±2.5 V, and the chip area is 1.21 × 1.18 mm2. According to the characteristic of neural signal, the time-domain method is used in testing. The pass bandwidth of the microelectronic neural bridge system covers the whole frequency range of the neural signal, power consumption is 4.33 mW, and the gain is adjustable. The design goals are achieved. (semiconductor integrated circuits)

  17. Sleep quality and neural circuit function supporting emotion regulation

    Directory of Open Access Journals (Sweden)

    Minkel Jared D

    2012-12-01

    Full Text Available Abstract Background Recent laboratory studies employing an extended sleep deprivation model have mapped sleep-related changes in behavior onto functional alterations in specific brain regions supporting emotion, suggesting possible biological mechanisms for an association between sleep difficulties and deficits in emotion regulation. However, it is not yet known if similar behavioral and neural changes are associated with the more modest variability in sleep observed in daily life. Methods We examined relationships between sleep and neural circuitry of emotion using the Pittsburgh Sleep Quality Index and fMRI data from a widely used emotion regulation task focusing on cognitive reappraisal of negative emotional stimuli in an unselected sample of 97 adult volunteers (48 women; mean age 42.78±7.37 years, range 30–54 years old. Results Emotion regulation was associated with greater activation in clusters located in the dorsomedial prefrontal cortex (dmPFC, left dorsolateral prefrontal cortex (dlPFC, and inferior parietal cortex. Only one subscale from the Pittsburgh Sleep Quality Index, use of sleep medications, was related to BOLD responses in the dmPFC and dlPFC during cognitive reappraisal. Use of sleep medications predicted lesser BOLD responses during reappraisal, but other aspects of sleep, including sleep duration and subjective sleep quality, were not related to neural activation in this paradigm. Conclusions The relatively modest variability in sleep that is common in the general community is unlikely to cause significant disruption in neural circuits supporting reactivity or regulation by cognitive reappraisal of negative emotion. Use of sleep medication however, may influence emotion regulation circuitry, but additional studies are necessary to determine if such use plays a causal role in altering emotional responses.

  18. Synchrony and neural coding in cerebellar circuits

    Directory of Open Access Journals (Sweden)

    Abigail L Person

    2012-12-01

    Full Text Available The cerebellum regulates complex movements and is also implicated in cognitive tasks, and cerebellar dysfunction is consequently associated not only with movement disorders, but also with conditions like autism and dyslexia. How information is encoded by specific cerebellar firing patterns remains debated, however. A central question is how the cerebellar cortex transmits its integrated output to the cerebellar nuclei via GABAergic synapses from Purkinje neurons. Possible answers come from accumulating evidence that subsets of Purkinje cells synchronize their firing during behaviors that require the cerebellum. Consistent with models predicting that coherent activity of inhibitory networks has the capacity to dictate firing patterns of target neurons, recent experimental work supports the idea that inhibitory synchrony may regulate the response of cerebellar nuclear cells to Purkinje inputs, owing to the interplay between unusually fast inhibitory synaptic responses and high rates of intrinsic activity. Data from multiple laboratories lead to a working hypothesis that synchronous inhibitory input from Purkinje cells can set the timing and rate of action potentials produced by cerebellar nuclear cells, thereby relaying information out of the cerebellum. If so, then changing spatiotemporal patterns of Purkinje activity would allow different subsets of inhibitory neurons to control cerebellar output at different times. Here we explore the evidence for and against the idea that a synchrony code defines, at least in part, the input-output function between the cerebellar cortex and nuclei. We consider the literature on the existence of simple spike synchrony, convergence of Purkinje neurons onto nuclear neurons, and intrinsic properties of nuclear neurons that contribute to responses to inhibition. Finally, we discuss factors that may disrupt or modulate a synchrony code and describe the potential contributions of inhibitory synchrony to other motor

  19. Computational Aspects of Feedback in Neural Circuits

    OpenAIRE

    Wolfgang Maass; Prashant Joshi; Sontag, Eduardo D.

    2007-01-01

    It has previously been shown that generic cortical microcircuit models can perform complex real-time computations on continuous input streams, provided that these computations can be carried out with a rapidly fading memory. We investigate the computational capability of such circuits in the more realistic case where not only readout neurons, but in addition a few neurons within the circuit, have been trained for specific tasks. This is essentially equivalent to the case where the output of t...

  20. Reconstruction of virtual neural circuits in an insect brain

    Directory of Open Access Journals (Sweden)

    Shigehiro Namiki

    2009-09-01

    Full Text Available The reconstruction of large-scale nervous systems represents a major scientific and engineering challenge in current neuroscience research that needs to be resolved in order to understand the emergent properties of such systems. We focus on insect nervous systems because they represent a good compromise between architectural simplicity and the ability to generate a rich behavioral repertoire. In insects, several sensory maps have been reconstructed so far. We provide an overview over this work including our reconstruction of population activity in the primary olfactory network, the antennal lobe. Our reconstruction approach, that also provides functional connectivity data, will be refined and extended to allow the building of larger scale neural circuits up to entire insect brains, from sensory input to motor output.

  1. Wavelet neural network based fault diagnosis in nonlinear analog circuits

    Institute of Scientific and Technical Information of China (English)

    Yin Shirong; Chen Guangju; Xie Yongle

    2006-01-01

    The theories of diagnosing nonlinear analog circuits by means of the transient response testing are studied. Wavelet analysis is made to extract the transient response signature of nonlinear circuits and compress the signature dada. The best wavelet function is selected based on the between-category total scatter of signature. The fault dictionary of nonlinear circuits is constructed based on improved back-propagation(BP) neural network. Experimental results demonstrate that the method proposed has high diagnostic sensitivity and fast fault identification and deducibility.

  2. Immune Cells Exploit a Neural Circuit to Enter the CNS

    OpenAIRE

    Kevin J Tracey

    2012-01-01

    Multiple sclerosis (MS) is associated with the appearance of autoreactive T cells in the central nervous system. Using a mouse model of MS, Arima et al. now show that this attack begins at a specific spinal cord location. T cell entry into the CNS is regulated by a reflex neural circuit originating from leg muscle contractions.

  3. Classical Conditioning with Pulsed Integrated Neural Networks: Circuits and System

    DEFF Research Database (Denmark)

    Lehmann, Torsten

    1998-01-01

    In this paper we investigate on-chip learning for pulsed, integrated neural networks. We discuss the implementational problems the technology imposes on learning systems and we find that abiologically inspired approach using simple circuit structures is most likely to bring success. We develop a ...... chip to solve simple classical conditioning tasks, thus verifying the design methodologies put forward in the paper....

  4. Implementing neural architectures using analog VLSI circuits

    OpenAIRE

    Maher, Mary Ann C.; DeWeerth, Stephen P.; Mahowald, Misha A.; Mead, Carver A.

    1989-01-01

    Analog very large-scale integrated (VLSI) technology can be used not only to study and simulate biological systems, but also to emulate them in designing artificial sensory systems. A methodology for building these systems in CMOS VLSI technology has been developed using analog micropower circuit elements that can be hierarchically combined. Using this methodology, experimental VLSI chips of visual and motor subsystems have been designed and fabricated. These chips exhibit behavior similar to...

  5. Circuit Models and Experimental Noise Measurements of Micropipette Amplifiers for Extracellular Neural Recordings from Live Animals

    OpenAIRE

    Chang Hao Chen; Sio Hang Pun; Peng Un Mak; Vai, Mang I.; Achim Klug; Lei, Tim C.

    2014-01-01

    Glass micropipettes are widely used to record neural activity from single neurons or clusters of neurons extracellularly in live animals. However, to date, there has been no comprehensive study of noise in extracellular recordings with glass micropipettes. The purpose of this work was to assess various noise sources that affect extracellular recordings and to create model systems in which novel micropipette neural amplifier designs can be tested. An equivalent circuit of the glass micropipett...

  6. Construction of implantable optical fibers for long-term optogenetic manipulation of neural circuits.

    Science.gov (United States)

    Sparta, Dennis R; Stamatakis, Alice M; Phillips, Jana L; Hovelsø, Nanna; van Zessen, Ruud; Stuber, Garret D

    2012-01-01

    In vivo optogenetic strategies have redefined our ability to assay how neural circuits govern behavior. Although acutely implanted optical fibers have previously been used in such studies, long-term control over neuronal activity has been largely unachievable. Here we describe a method to construct implantable optical fibers to readily manipulate neural circuit elements with minimal tissue damage or change in light output over time (weeks to months). Implanted optical fibers readily interface with in vivo electrophysiological arrays or electrochemical detection electrodes. The procedure described here, from implant construction to the start of behavioral experimentation, can be completed in approximately 2-6 weeks. Successful use of implantable optical fibers will allow for long-term control of mammalian neural circuits in vivo, which is integral to the study of the neurobiology of behavior. PMID:22157972

  7. Neural circuit mechanisms of short-term memory

    Science.gov (United States)

    Goldman, Mark

    Memory over time scales of seconds to tens of seconds is thought to be maintained by neural activity that is triggered by a memorized stimulus and persists long after the stimulus is turned off. This presents a challenge to current models of memory-storing mechanisms, because the typical time scales associated with cellular and synaptic dynamics are two orders of magnitude smaller than this. While such long time scales can easily be achieved by bistable processes that toggle like a flip-flop between a baseline and elevated-activity state, many neuronal systems have been observed experimentally to be capable of maintaining a continuum of stable states. For example, in neural integrator networks involved in the accumulation of evidence for decision making and in motor control, individual neurons have been recorded whose activity reflects the mathematical integral of their inputs; in the absence of input, these neurons sustain activity at a level proportional to the running total of their inputs. This represents an analog form of memory whose dynamics can be conceptualized through an energy landscape with a continuum of lowest-energy states. Such continuous attractor landscapes are structurally non-robust, in seeming violation of the relative robustness of biological memory systems. In this talk, I will present and compare different biologically motivated circuit motifs for the accumulation and storage of signals in short-term memory. Challenges to generating robust memory maintenance will be highlighted and potential mechanisms for ameliorating the sensitivity of memory networks to perturbations will be discussed. Funding for this work was provided by NIH R01 MH065034, NSF IIS-1208218, Simons Foundation 324260, and a UC Davis Ophthalmology Research to Prevent Blindness Grant.

  8. TrakEM2 software for neural circuit reconstruction.

    Directory of Open Access Journals (Sweden)

    Albert Cardona

    Full Text Available A key challenge in neuroscience is the expeditious reconstruction of neuronal circuits. For model systems such as Drosophila and C. elegans, the limiting step is no longer the acquisition of imagery but the extraction of the circuit from images. For this purpose, we designed a software application, TrakEM2, that addresses the systematic reconstruction of neuronal circuits from large electron microscopical and optical image volumes. We address the challenges of image volume composition from individual, deformed images; of the reconstruction of neuronal arbors and annotation of synapses with fast manual and semi-automatic methods; and the management of large collections of both images and annotations. The output is a neural circuit of 3d arbors and synapses, encoded in NeuroML and other formats, ready for analysis.

  9. Controlling chaos in balanced neural circuits with input spike trains

    Science.gov (United States)

    Engelken, Rainer; Wolf, Fred

    The cerebral cortex can be seen as a system of neural circuits driving each other with spike trains. Here we study how the statistics of these spike trains affects chaos in balanced target circuits.Earlier studies of chaos in balanced neural circuits either used a fixed input [van Vreeswijk, Sompolinsky 1996, Monteforte, Wolf 2010] or white noise [Lajoie et al. 2014]. We study dynamical stability of balanced networks driven by input spike trains with variable statistics. The analytically obtained Jacobian enables us to calculate the complete Lyapunov spectrum. We solved the dynamics in event-based simulations and calculated Lyapunov spectra, entropy production rate and attractor dimension. We vary correlations, irregularity, coupling strength and spike rate of the input and action potential onset rapidness of recurrent neurons.We generally find a suppression of chaos by input spike trains. This is strengthened by bursty and correlated input spike trains and increased action potential onset rapidness. We find a link between response reliability and the Lyapunov spectrum. Our study extends findings in chaotic rate models [Molgedey et al. 1992] to spiking neuron models and opens a novel avenue to study the role of projections in shaping the dynamics of large neural circuits.

  10. Micropower circuits for bidirectional wireless telemetry in neural recording applications.

    Science.gov (United States)

    Neihart, Nathan M; Harrison, Reid R

    2005-11-01

    State-of-the art neural recording systems require electronics allowing for transcutaneous, bidirectional data transfer. As these circuits will be implanted near the brain, they must be small and low power. We have developed micropower integrated circuits for recovering clock and data signals over a transcutaneous power link. The data recovery circuit produces a digital data signal from an ac power waveform that has been amplitude modulated. We have also developed an FM transmitter with the lowest power dissipation reported for biosignal telemetry. The FM transmitter consists of a low-noise biopotential amplifier and a voltage controlled oscillator used to transmit amplified neural signals at a frequency near 433 MHz. All circuits were fabricated in a standard 0.5-microm CMOS VLSI process. The resulting chip is powered through a wireless inductive link. The power consumption of the clock and data recovery circuits is measured to be 129 microW; the power consumption of the transmitter is measured to be 465 microW when using an external surface mount inductor. Using a parasitic antenna less than 2 mm long, a received power level was measured to be -59.73 dBm at a distance of one meter. PMID:16285399

  11. Chaotic phenomena in Josephson circuits coupled quantum cellular neural networks

    Institute of Scientific and Technical Information of China (English)

    Wang Sen; Cai Li; Li Qin; Wu Gang

    2007-01-01

    In this paper the nonlinear dynamical behaviour of a quantum cellular neural network (QCNN) by coupling Josephson circuits was investigated and it was shown that the QCNN using only two of them can cause the onset of chaotic oscillation. The theoretical analysis and simulation for the two Josephson-circuits-coupled QCNN have been done by using the amplitude and phase as state variables. The complex chaotic behaviours can be observed and then proved by calculating Lyapunov exponents. The study provides valuable information about QCNNs for future application in high-parallel signal processing and novel chaotic generators.

  12. A Neural Network Appraoch to Fault Diagnosis in Analog Circuits

    Institute of Scientific and Technical Information of China (English)

    尉乃红; 杨士元; 等

    1996-01-01

    Thia paper presents a neural network based fault diagnosis approach for analog circuits,taking the tolerances of circuit elements into account.Specifically,a normalization rule of input information,a pseudo-fault domain border(PFDB)pattern selection method and a new output error function are proposed for training the backpropagation(BP) network to be a fault diagnoser.Experimental results demonstrate that the diagnoser performs as well as or better than any classical approaches in terms of accuracy,and provides at least an order-of-magnitude improvement in post-fault diagnostic speed.

  13. Optogenetic dissection of neural circuits underlying emotional valence and motivated behaviors.

    Science.gov (United States)

    Nieh, Edward H; Kim, Sung-Yon; Namburi, Praneeth; Tye, Kay M

    2013-05-20

    The neural circuits underlying emotional valence and motivated behaviors are several synapses away from both defined sensory inputs and quantifiable motor outputs. Electrophysiology has provided us with a suitable means for observing neural activity during behavior, but methods for controlling activity for the purpose of studying motivated behaviors have been inadequate: electrical stimulation lacks cellular specificity and pharmacological manipulation lacks temporal resolution. The recent emergence of optogenetic tools provides a new means for establishing causal relationships between neural activity and behavior. Optogenetics, the use of genetically-encodable light-activated proteins, permits the modulation of specific neural circuit elements with millisecond precision. The ability to control individual cell types, and even projections between distal regions, allows us to investigate functional connectivity in a causal manner. The greatest consequence of controlling neural activity with finer precision has been the characterization of individual neural circuits within anatomical brain regions as defined functional units. Within the mesolimbic dopamine system, optogenetics has helped separate subsets of dopamine neurons with distinct functions for reward, aversion and salience processing, elucidated GABA neuronal effects on behavior, and characterized connectivity with forebrain and cortical structures. Within the striatum, optogenetics has confirmed the opposing relationship between direct and indirect pathway medium spiny neurons (MSNs), in addition to characterizing the inhibition of MSNs by cholinergic interneurons. Within the hypothalamus, optogenetics has helped overcome the heterogeneity in neuronal cell-type and revealed distinct circuits mediating aggression and feeding. Within the amygdala, optogenetics has allowed the study of intra-amygdala microcircuitry as well as interconnections with distal regions involved in fear and anxiety. In this review, we

  14. A neural circuit covarying with social hierarchy in macaques.

    Directory of Open Access Journals (Sweden)

    MaryAnn P Noonan

    2014-09-01

    Full Text Available Despite widespread interest in social dominance, little is known of its neural correlates in primates. We hypothesized that social status in primates might be related to individual variation in subcortical brain regions implicated in other aspects of social and emotional behavior in other mammals. To examine this possibility we used magnetic resonance imaging (MRI, which affords the taking of quantitative measurements noninvasively, both of brain structure and of brain function, across many regions simultaneously. We carried out a series of tests of structural and functional MRI (fMRI data in 25 group-living macaques. First, a deformation-based morphometric (DBM approach was used to show that gray matter in the amygdala, brainstem in the vicinity of the raphe nucleus, and reticular formation, hypothalamus, and septum/striatum of the left hemisphere was correlated with social status. Second, similar correlations were found in the same areas in the other hemisphere. Third, similar correlations were found in a second data set acquired several months later from a subset of the same animals. Fourth, the strength of coupling between fMRI-measured activity in the same areas was correlated with social status. The network of subcortical areas, however, had no relationship with the sizes of individuals' social networks, suggesting the areas had a simple and direct relationship with social status. By contrast a second circuit in cortex, comprising the midsuperior temporal sulcus and anterior and dorsal prefrontal cortex, covaried with both individuals' social statuses and the social network sizes they experienced. This cortical circuit may be linked to the social cognitive processes that are taxed by life in more complex social networks and that must also be used if an animal is to achieve a high social status.

  15. Neural Networks Integrated Circuit for Biomimetics MEMS Microrobot

    OpenAIRE

    Ken Saito; Kazuaki Maezumi; Yuka Naito; Tomohiro Hidaka; Kei Iwata; Yuki Okane; Hirozumi Oku; Minami Takato; Fumio Uchikoba

    2014-01-01

    In this paper, we will propose the neural networks integrated circuit (NNIC) which is the driving waveform generator of the 4.0, 2.7, 2.5 mm, width, length, height in size biomimetics microelectromechanical systems (MEMS) microrobot. The microrobot was made from silicon wafer fabricated by micro fabrication technology. The mechanical system of the robot was equipped with small size rotary type actuators, link mechanisms and six legs to realize the ant-like switching behavior. The NNIC generat...

  16. Derivation of Neural Circuits from the Similarity Matching Principle

    Science.gov (United States)

    Pehlevan, Cengiz; Chklovskii, Dmitri

    Our brains analyze high-dimensional datasets streamed by our sensory organs in multiple stages. Sensory cortices, for example, perform tasks like dimensionality reduction, sparse feature discovery and clustering. To model these tasks we pursue an approach analogous to use of action principles in physics and propose a new family of objective functions based on the principle of similarity matching. From these objective functions we derive online distributed algorithms that can be implemented by biological neural networks resembling cortical circuits. Our networks can adapt to changes in the number of latent dimensions or the number of clusters in the input dataset. Furthermore, we formulate minimax optimization problems from which we derive online algorithms with two classes of neurons identified with principal neurons and interneurons in biological circuits. In addition to bearing resemblance to biological circuits, our algorithms are competitive for Big Data applications.

  17. Stochastic interpolation model of the medial superior olive neural circuit.

    Science.gov (United States)

    Sanda, Pavel; Marsalek, Petr

    2012-01-24

    This article presents a stochastic model of binaural hearing in the medial superior olive (MSO) circuit. This model is a variant of the slope encoding models. First, a general framework is developed describing the elementary neural operations realized on spike trains in individual parts of the circuit and how the neurons converging onto the MSO are connected. Random delay, coincidence detection of spikes, divergence and convergence of spike trains are operations implemented by the following modules: spike generator, jitter generator, and coincidence detector. Subsequent processing of spike trains computes the sound azimuth in the circuit. The circuit parameters that influence efficiency of slope encoding are studied. In order to measure the overall circuit performance the concept of an ideal observer is used instead of a detailed model of higher relays in the auditory pathway. This makes it possible to bridge the gap between psychophysical observations in humans and recordings taken of small rodents. Most of the results are obtained through numerical simulations of the model. PMID:21920505

  18. Precision psychiatry: a neural circuit taxonomy for depression and anxiety.

    Science.gov (United States)

    Williams, Leanne M

    2016-05-01

    Although there have been tremendous advances in the understanding of human dysfunctions in the brain circuitry for self-reflection, emotion, and cognitive control, a brain-based taxonomy for mental disease is still lacking. As a result, these advances have not been translated into actionable clinical tools, and the language of brain circuits has not been incorporated into training programmes. To address this gap, I present this synthesis of published work, with a focus on functional imaging of circuit dysfunctions across the spectrum of mood and anxiety disorders. This synthesis provides the foundation for a taxonomy of putative types of dysfunction, which cuts across traditional diagnostic boundaries for depression and anxiety and includes instead distinct types of neural circuit dysfunction that together reflect the heterogeneity of depression and anxiety. This taxonomy is suited to specifying symptoms in terms of underlying neural dysfunction at the individual level and is intended as the foundation for building mechanistic research and ultimately guiding clinical practice. PMID:27150382

  19. Oscillation-induced signal transmission and gating in neural circuits.

    Science.gov (United States)

    Jahnke, Sven; Memmesheimer, Raoul-Martin; Timme, Marc

    2014-12-01

    Reliable signal transmission constitutes a key requirement for neural circuit function. The propagation of synchronous pulse packets through recurrent circuits is hypothesized to be one robust form of signal transmission and has been extensively studied in computational and theoretical works. Yet, although external or internally generated oscillations are ubiquitous across neural systems, their influence on such signal propagation is unclear. Here we systematically investigate the impact of oscillations on propagating synchrony. We find that for standard, additive couplings and a net excitatory effect of oscillations, robust propagation of synchrony is enabled in less prominent feed-forward structures than in systems without oscillations. In the presence of non-additive coupling (as mediated by fast dendritic spikes), even balanced oscillatory inputs may enable robust propagation. Here, emerging resonances create complex locking patterns between oscillations and spike synchrony. Interestingly, these resonances make the circuits capable of selecting specific pathways for signal transmission. Oscillations may thus promote reliable transmission and, in co-action with dendritic nonlinearities, provide a mechanism for information processing by selectively gating and routing of signals. Our results are of particular interest for the interpretation of sharp wave/ripple complexes in the hippocampus, where previously learned spike patterns are replayed in conjunction with global high-frequency oscillations. We suggest that the oscillations may serve to stabilize the replay. PMID:25503492

  20. Analog integrated circuits for the Lotka-Volterra competitive neural networks.

    Science.gov (United States)

    Asai, T; Ohtani, M; Yonezu, H

    1999-01-01

    A subthreshold MOS integrated circuit (IC) is designed and fabricated for implementing a competitive neural network of the Lotka-Volterra (LV) type which is derived from conventional membrane dynamics of neurons and is used for the selection of external inputs. The steady-state solutions to the LV equation can be classified into three types, each of which represents qualitatively different selection behavior. Among the solutions, the winners-share-all (WSA) solution in which a certain number of neurons remain activated in steady states is particularly useful owing to robustness in the selection of inputs from a noisy environment. The measured results of the fabricated LV IC's agree well with the theoretical prediction as long as the influence of device mismatches is small. Furthermore, results of extensive circuit simulations prove that the large-scale LV circuit producing the WSA solution does exhibit a reliable selection compared with winner-take-all circuits, in the possible presence of device mismatches.

  1. Wireless Neural Recording With Single Low-Power Integrated Circuit

    Science.gov (United States)

    Harrison, Reid R.; Kier, Ryan J.; Chestek, Cynthia A.; Gilja, Vikash; Nuyujukian, Paul; Ryu, Stephen; Greger, Bradley; Solzbacher, Florian; Shenoy, Krishna V.

    2010-01-01

    We present benchtop and in vivo experimental results from an integrated circuit designed for wireless implantable neural recording applications. The chip, which was fabricated in a commercially available 0.6-μm 2P3M BiCMOS process, contains 100 amplifiers, a 10-bit analog-to-digital converter (ADC), 100 threshold-based spike detectors, and a 902–928 MHz frequency-shift-keying (FSK) transmitter. Neural signals from a selected amplifier are sampled by the ADC at 15.7 kSps and telemetered over the FSK wireless data link. Power, clock, and command signals are sent to the chip wirelessly over a 2.765-MHz inductive (coil-to-coil) link. The chip is capable of operating with only two off-chip components: a power/command receiving coil and a 100-nF capacitor. PMID:19497825

  2. Nanowire electrodes for high-density stimulation and measurement of neural circuits

    Directory of Open Access Journals (Sweden)

    Jacob T. Robinson

    2013-03-01

    Full Text Available Brain-machine interfaces (BMIs that can precisely monitor and control neural activity will likely require new hardware with improved resolution and specificity. New nanofabricated electrodes with feature sizes and densities comparable to neural circuits may lead to such improvements. In this perspective, we review the recent development of vertical nanowire (NW electrodes that could provide highly parallel single-cell recording and stimulation for future BMIs. We compare the advantages of these devices and discuss some of the technical challenges that must be overcome for this technology to become a platform for next-generation closed-loop BMIs.

  3. Enhancement of synchronization in a hybrid neural circuit by spike timing dependent plasticity

    OpenAIRE

    Nowotny, Thomas; Zhigulin, Valentin P.; Selverston, Allan I; Abarbanel, Henry D.I.; Rabinovich, Mikhail I.

    2003-01-01

    Synchronization of neural activity is fundamental for many functions of the brain. We demonstrate that spike-timing dependent plasticity (STDP) enhances synchronization (entrainment) in a hybrid circuit composed of a spike generator, a dynamic clamp emulating an excitatory plastic synapse, and a chemically isolated neuron from the Aplysia abdominal ganglion. Fixed-phase entrainment of the Aplysia neuron to the spike generator is possible for a much wider range of frequency ratios and is more ...

  4. Continuous or discrete attractors in neural circuits? A self-organized switch at maximal entropy

    CERN Document Server

    Bernacchia, Alberto

    2007-01-01

    A recent experiment suggests that neural circuits may alternatively implement continuous or discrete attractors, depending on the training set up. In recurrent neural network models, continuous and discrete attractors are separately modeled by distinct forms of synaptic prescriptions (learning rules). Here, we report a solvable network model, endowed with Hebbian synaptic plasticity, which is able to learn either discrete or continuous attractors, depending on the frequency of presentation of stimuli and on the structure of sensory coding. A continuous attractor is learned when experience matches sensory coding, i.e. when the distribution of experienced stimuli matches the distribution of preferred stimuli of neurons. In that case, there is no processing of sensory information and neural activity displays maximal entropy. If experience goes beyond sensory coding, processing is initiated and the continuous attractor is destabilized into a set of discrete attractors.

  5. Neural circuits mediating olfactory-driven behavior in fish

    Directory of Open Access Journals (Sweden)

    Florence eKermen

    2013-04-01

    Full Text Available The fish olfactory system processes odor signals and mediates behaviors that are crucial for survival such as foraging, courtship and alarm response. Although the upstream olfactory brain areas (olfactory epithelium and olfactory bulb are well studied, less is known about their target brain areas and the role they play in generating odor-driven behaviors. Here we review a broad range of literature on the anatomy, physiology and behavioral output of the olfactory system and its target areas in a wide range of teleost fish. Additionally, we discuss how applying recent technological advancements to the zebrafish (Danio rerio could help in understanding the function of these target areas. We hope to provide a framework for elucidating the neural circuit computations underlying the odor-driven behaviors in this small, transparent and genetically amenable vertebrate.

  6. Circuit Models and Experimental Noise Measurements of Micropipette Amplifiers for Extracellular Neural Recordings from Live Animals

    Directory of Open Access Journals (Sweden)

    Chang Hao Chen

    2014-01-01

    Full Text Available Glass micropipettes are widely used to record neural activity from single neurons or clusters of neurons extracellularly in live animals. However, to date, there has been no comprehensive study of noise in extracellular recordings with glass micropipettes. The purpose of this work was to assess various noise sources that affect extracellular recordings and to create model systems in which novel micropipette neural amplifier designs can be tested. An equivalent circuit of the glass micropipette and the noise model of this circuit, which accurately describe the various noise sources involved in extracellular recordings, have been developed. Measurement schemes using dead brain tissue as well as extracellular recordings from neurons in the inferior colliculus, an auditory brain nucleus of an anesthetized gerbil, were used to characterize noise performance and amplification efficacy of the proposed micropipette neural amplifier. According to our model, the major noise sources which influence the signal to noise ratio are the intrinsic noise of the neural amplifier and the thermal noise from distributed pipette resistance. These two types of noise were calculated and measured and were shown to be the dominating sources of background noise for in vivo experiments.

  7. PCSIM: A Parallel Simulation Environment for Neural Circuits Fully Integrated with Python

    OpenAIRE

    Pecevski, Dejan; Natschläger, Thomas; Schuch, Klaus

    2009-01-01

    The Parallel Circuit SIMulator (PCSIM) is a software package for simulation of neural circuits. It is primarily designed for distributed simulation of large scale networks of spiking point neurons. Although its computational core is written in C++, PCSIM's primary interface is implemented in the Python programming language, which is a powerful programming environment and allows the user to easily integrate the neural circuit simulator with data analysis and visualization tools to manage the f...

  8. PCSIM: a parallel simulation environment for neural circuits fully integrated with Python

    OpenAIRE

    Dejan Pecevski; Thomas Natschläger; Klaus Schuch

    2009-01-01

    The Parallel Circuit SIMulator (PCSIM) is a software package for simulation of neural circuits. It is primarily designed for distributed simulation of large scale networks of spiking point neurons. Although its computational core is written in C++, PCSIM's primary interface is implemented in the Python programming language, which is a powerful programming environment and allows the user to easily integrate the neural circuit simulator with data analysis and visualization tools to manage...

  9. Extracellular space preservation aids the connectomic analysis of neural circuits

    Science.gov (United States)

    Pallotto, Marta; Watkins, Paul V; Fubara, Boma; Singer, Joshua H; Briggman, Kevin L

    2015-01-01

    Dense connectomic mapping of neuronal circuits is limited by the time and effort required to analyze 3D electron microscopy (EM) datasets. Algorithms designed to automate image segmentation suffer from substantial error rates and require significant manual error correction. Any improvement in segmentation error rates would therefore directly reduce the time required to analyze 3D EM data. We explored preserving extracellular space (ECS) during chemical tissue fixation to improve the ability to segment neurites and to identify synaptic contacts. ECS preserved tissue is easier to segment using machine learning algorithms, leading to significantly reduced error rates. In addition, we observed that electrical synapses are readily identified in ECS preserved tissue. Finally, we determined that antibodies penetrate deep into ECS preserved tissue with only minimal permeabilization, thereby enabling correlated light microscopy (LM) and EM studies. We conclude that preservation of ECS benefits multiple aspects of the connectomic analysis of neural circuits. DOI: http://dx.doi.org/10.7554/eLife.08206.001 PMID:26650352

  10. Long-Lasting Neural Circuit Dysfunction Following Developmental Ethanol Exposure

    Directory of Open Access Journals (Sweden)

    Mariko Saito

    2013-04-01

    Full Text Available Fetal Alcohol Spectrum Disorder (FASD is a general diagnosis for those exhibiting long-lasting neurobehavioral and cognitive deficiencies as a result of fetal alcohol exposure. It is among the most common causes of mental deficits today. Those impacted are left to rely on advances in our understanding of the nature of early alcohol-induced disorders toward human therapies. Research findings over the last decade have developed a model where ethanol-induced neurodegeneration impacts early neural circuit development, thereby perpetuating subsequent integration and plasticity in vulnerable brain regions. Here we review our current knowledge of FASD neuropathology based on discoveries of long-lasting neurophysiological effects of acute developmental ethanol exposure in animal models. We discuss the important balance between synaptic excitation and inhibition in normal neural network function, and relate the significance of that balance to human FASD as well as related disease states. Finally, we postulate that excitation/inhibition imbalance caused by early ethanol-induced neurodegeneration results in perturbed local and regional network signaling and therefore neurobehavioral pathology.

  11. Shaping Neural Circuits by High Order Synaptic Interactions

    Science.gov (United States)

    Ravid Tannenbaum, Neta; Burak, Yoram

    2016-01-01

    Spike timing dependent plasticity (STDP) is believed to play an important role in shaping the structure of neural circuits. Here we show that STDP generates effective interactions between synapses of different neurons, which were neglected in previous theoretical treatments, and can be described as a sum over contributions from structural motifs. These interactions can have a pivotal influence on the connectivity patterns that emerge under the influence of STDP. In particular, we consider two highly ordered forms of structure: wide synfire chains, in which groups of neurons project to each other sequentially, and self connected assemblies. We show that high order synaptic interactions can enable the formation of both structures, depending on the form of the STDP function and the time course of synaptic currents. Furthermore, within a certain regime of biophysical parameters, emergence of the ordered connectivity occurs robustly and autonomously in a stochastic network of spiking neurons, without a need to expose the neural network to structured inputs during learning. PMID:27517461

  12. Neural Networks Integrated Circuit for Biomimetics MEMS Microrobot

    Directory of Open Access Journals (Sweden)

    Ken Saito

    2014-06-01

    Full Text Available In this paper, we will propose the neural networks integrated circuit (NNIC which is the driving waveform generator of the 4.0, 2.7, 2.5 mm, width, length, height in size biomimetics microelectromechanical systems (MEMS microrobot. The microrobot was made from silicon wafer fabricated by micro fabrication technology. The mechanical system of the robot was equipped with small size rotary type actuators, link mechanisms and six legs to realize the ant-like switching behavior. The NNIC generates the driving waveform using synchronization phenomena such as biological neural networks. The driving waveform can operate the actuators of the MEMS microrobot directly. Therefore, the NNIC bare chip realizes the robot control without using any software programs or A/D converters. The microrobot performed forward and backward locomotion, and also changes direction by inputting an external single trigger pulse. The locomotion speed of the microrobot was 26.4 mm/min when the step width was 0.88 mm. The power consumption of the system was 250 mWh when the room temperature was 298 K.

  13. Active components for integrated plasmonic circuits

    DEFF Research Database (Denmark)

    Krasavin, A.V.; Bolger, P.M.; Zayats, A.V.;

    2009-01-01

    We present a comprehensive study of highly efficient and compact passive and active components for integrated plasmonic circuit based on dielectric-loaded surface plasmon polariton waveguides.......We present a comprehensive study of highly efficient and compact passive and active components for integrated plasmonic circuit based on dielectric-loaded surface plasmon polariton waveguides....

  14. Distinct neural circuits underlie assessment of a diversity of natural dangers by American crows.

    Science.gov (United States)

    Cross, Donna J; Marzluff, John M; Palmquist, Ila; Minoshima, Satoshi; Shimizu, Toru; Miyaoka, Robert

    2013-08-22

    Social animals encountering natural dangers face decisions such as whether to freeze, flee or harass the threat. The American crow, Corvus brachyrhynchos, conspicuously mobs dangers. We used positron emission tomography to test the hypothesis that distinct neuronal substrates underlie the crow's consistent behavioural response to different dangers. We found that crows activated brain regions associated with attention and arousal (nucleus isthmo-opticus/locus coeruleus), and with motor response (arcopallium), as they fixed their gaze on a threat. However, despite this consistent behavioural and neural response, the sight of a person who previously captured the crow, a person holding a dead crow and a taxidermy-mounted hawk activated distinct forebrain regions (amygdala, hippocampus and portion of the caudal nidopallium, respectively). We suggest that aspects of mobbing behaviour are guided by unique neural circuits that respond to differences in mental processing-learning, memory formation and multisensory discrimination-required to appropriately nuance a risky behaviour to specific dangers. PMID:23825209

  15. Technologies for imaging neural activity in large volumes.

    Science.gov (United States)

    Ji, Na; Freeman, Jeremy; Smith, Spencer L

    2016-08-26

    Neural circuitry has evolved to form distributed networks that act dynamically across large volumes. Conventional microscopy collects data from individual planes and cannot sample circuitry across large volumes at the temporal resolution relevant to neural circuit function and behaviors. Here we review emerging technologies for rapid volume imaging of neural circuitry. We focus on two critical challenges: the inertia of optical systems, which limits image speed, and aberrations, which restrict the image volume. Optical sampling time must be long enough to ensure high-fidelity measurements, but optimized sampling strategies and point-spread function engineering can facilitate rapid volume imaging of neural activity within this constraint. We also discuss new computational strategies for processing and analyzing volume imaging data of increasing size and complexity. Together, optical and computational advances are providing a broader view of neural circuit dynamics and helping elucidate how brain regions work in concert to support behavior. PMID:27571194

  16. Feature evaluation and extraction based on neural network in analog circuit fault diagnosis

    Institute of Scientific and Technical Information of China (English)

    Yuan Haiying; Chen Guangju; Xie Yongle

    2007-01-01

    Choosing the right characteristic parameter is the key to fault diagnosis in analog circuit.The feature evaluation and extraction methods based on neural network are presented.Parameter evaluation of circuit features is realized by training results from neural network; the superior nonlinear mapping capability is competent for extracting fault features which are normalized and compressed subsequently.The complex classification problem on fault pattern recognition in analog circuit is transferred into feature processing stage by feature extraction based on neural network effectively, which improves the diagnosis efficiency.A fault diagnosis illustration validated this method.

  17. [Synapse elimination and functional neural circuit formation in the cerebellum].

    Science.gov (United States)

    Kano, Masanobu

    2013-06-01

    Neuronal connections are initially redundant, but unnecessary connections are eliminated subsequently during postnatal development. This process, known as 'synapse elimination', is thought to be crucial for establishing functionally mature neural circuits. The climbing fiber (CF) to the Purkinje cell (PC) synapse in the cerebellum is a representative model of synapse elimination. We disclose that one-to-one connection from CF to PC is established through four distinct phases: (1) strengthening of a single CF among multiple CFs in each PC at P3-P7, (2) translocation of a single strengthened CF to PC dendrites from around P9, and (3) early phase (P7 to around P11) and (4) late phase (around P12 to P17) of elimination of weak CF synapses from PC somata. Mice with PC-selective deletion of P/Q-type voltage-dependent Ca2+ channel (VDCC) exhibit severe defects in strengthening of single CFs, dendritic translocation of single CFs and CF elimination from P7. In contrast, mice with a mutation of a single allele for the GABA-synthesizing enzyme GAD67 have a selective impairment of CF elimination from P10 due to reduced inhibition and elevated Ca2+ influx to PC somata. Thus, regulation of Ca2+ influx to PCs is crucial for the four phases of CF synapse elimination. PMID:25069248

  18. Energy efficient neural stimulation: coupling circuit design and membrane biophysics.

    Directory of Open Access Journals (Sweden)

    Thomas J Foutz

    Full Text Available The delivery of therapeutic levels of electrical current to neural tissue is a well-established treatment for numerous indications such as Parkinson's disease and chronic pain. While the neuromodulation medical device industry has experienced steady clinical growth over the last two decades, much of the core technology underlying implanted pulse generators remain unchanged. In this study we propose some new methods for achieving increased energy-efficiency during neural stimulation. The first method exploits the biophysical features of excitable tissue through the use of a centered-triangular stimulation waveform. Neural activation with this waveform is achieved with a statistically significant reduction in energy compared to traditional rectangular waveforms. The second method demonstrates energy savings that could be achieved by advanced circuitry design. We show that the traditional practice of using a fixed compliance voltage for constant-current stimulation results in substantial energy loss. A portion of this energy can be recuperated by adjusting the compliance voltage to real-time requirements. Lastly, we demonstrate the potential impact of axon fiber diameter on defining the energy-optimal pulse-width for stimulation. When designing implantable pulse generators for energy efficiency, we propose that the future combination of a variable compliance system, a centered-triangular stimulus waveform, and an axon diameter specific stimulation pulse-width has great potential to reduce energy consumption and prolong battery life in neuromodulation devices.

  19. The universal fuzzy logical framework of neural circuits and its application in modeling primary visual cortex.

    Science.gov (United States)

    Hu, Hong; Li, Su; Wang, YunJiu; Qi, XiangLin; Shi, ZhongZhi

    2008-10-01

    Analytical study of large-scale nonlinear neural circuits is a difficult task. Here we analyze the function of neural systems by probing the fuzzy logical framework of the neural cells' dynamical equations. Although there is a close relation between the theories of fuzzy logical systems and neural systems and many papers investigate this subject, most investigations focus on finding new functions of neural systems by hybridizing fuzzy logical and neural system. In this paper, the fuzzy logical framework of neural cells is used to understand the nonlinear dynamic attributes of a common neural system by abstracting the fuzzy logical framework of a neural cell. Our analysis enables the educated design of network models for classes of computation. As an example, a recurrent network model of the primary visual cortex has been built and tested using this approach.

  20. The universal fuzzy logical framework of neural circuits and its application in modeling primary visual cortex

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Analytical study of large-scale nonlinear neural circuits is a difficult task. Here we analyze the function of neural systems by probing the fuzzy logical framework of the neural cells’ dynamical equations. Al- though there is a close relation between the theories of fuzzy logical systems and neural systems and many papers investigate this subject, most investigations focus on finding new functions of neural systems by hybridizing fuzzy logical and neural system. In this paper, the fuzzy logical framework of neural cells is used to understand the nonlinear dynamic attributes of a common neural system by abstracting the fuzzy logical framework of a neural cell. Our analysis enables the educated design of network models for classes of computation. As an example, a recurrent network model of the primary visual cortex has been built and tested using this approach.

  1. The universal fuzzy Logical framework of neural circuits and its application in modeling primary visual cortex

    Institute of Scientific and Technical Information of China (English)

    HU Hong; LI Su; WANG YunJiu; QI XiangLin; SHI ZhongZhi

    2008-01-01

    Analytical study of large-scale nonlinear neural circuits is a difficult task. Here we analyze the function of neural systems by probing the fuzzy logical framework of the neural cells' dynamical equations. Al-though there is a close relation between the theories of fuzzy logical systems and neural systems and many papers investigate this subject, most investigations focus on finding new functions of neural systems by hybridizing fuzzy logical and neural system. In this paper, the fuzzy logical framework of neural cells is used to understand the nonlinear dynamic attributes of a common neural system by abstracting the fuzzy logical framework of a neural cell. Our analysis enables the educated design of network models for classes of computation. As an example, a recurrent network model of the primary visual cortex has been built and tested using this approach.

  2. Nonlocal mechanism for cluster synchronization in neural circuits

    Science.gov (United States)

    Kanter, I.; Kopelowitz, E.; Vardi, R.; Zigzag, M.; Kinzel, W.; Abeles, M.; Cohen, D.

    2011-03-01

    The interplay between the topology of cortical circuits and synchronized activity modes in distinct cortical areas is a key enigma in neuroscience. We present a new nonlocal mechanism governing the periodic activity mode: the greatest common divisor (GCD) of network loops. For a stimulus to one node, the network splits into GCD-clusters in which cluster neurons are in zero-lag synchronization. For complex external stimuli, the number of clusters can be any common divisor. The synchronized mode and the transients to synchronization pinpoint the type of external stimuli. The findings, supported by an information mixing argument and simulations of Hodgkin-Huxley population dynamic networks with unidirectional connectivity and synaptic noise, call for reexamining sources of correlated activity in cortex and shorter information processing time scales.

  3. EM-based optimization of microwave circuits using artificial neural networks: the state of the art

    OpenAIRE

    Rayas-Sánchez, José E.

    2004-01-01

    This paper reviews the current state-of-the-art in electromagnetic (EM)-based design and optimization of microwave circuits using artificial neural networks (ANNs). Measurement-based design of microwave circuits using ANNs is also reviewed. The conventional microwave neural optimization approach is surveyed, along with typical enhancing techniques, such as segmentation, decomposition, hierarchy, design of experiments and clusterization. Innovative strategies for ANN-based design exploiting...

  4. Equivalent Circuit Parameters Estimation for PEM Fuel Cell Using RBF Neural Network and Enhanced Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Wen-Yeau Chang

    2013-01-01

    Full Text Available This paper proposes an equivalent circuit parameters measurement and estimation method for proton exchange membrane fuel cell (PEMFC. The parameters measurement method is based on current loading technique; in current loading test a no load PEMFC is suddenly turned on to obtain the waveform of the transient terminal voltage. After the equivalent circuit parameters were measured, a hybrid method that combines a radial basis function (RBF neural network and enhanced particle swarm optimization (EPSO algorithm is further employed for the equivalent circuit parameters estimation. The RBF neural network is adopted such that the estimation problem can be effectively processed when the considered data have different features and ranges. In the hybrid method, EPSO algorithm is used to tune the connection weights, the centers, and the widths of RBF neural network. Together with the current loading technique, the proposed hybrid estimation method can effectively estimate the equivalent circuit parameters of PEMFC. To verify the proposed approach, experiments were conducted to demonstrate the equivalent circuit parameters estimation of PEMFC. A practical PEMFC stack was purposely created to produce the common current loading activities of PEMFC for the experiments. The practical results of the proposed method were studied in accordance with the conditions for different loading conditions.

  5. Self-control of chaos in neural circuits with plastic electrical synapses

    Science.gov (United States)

    Zhigulin, V. P.; Rabinovich, M. I.

    2004-10-01

    Two kinds of connections are known to exist in neural circuits: electrical (also called gap junctions) and chemical. Whereas chemical synapses are known to be plastic (i. e., modifiable), but slow, electrical transmission through gap junctions is not modifiable, but is very fast. We suggest the new artificial synapse that combines the best properties of both: the fast reaction of a gap junction and the plasticity of a chemical synapse. Such a plastic electrical synapse can be used in hybrid neural circuits and for the development of neural prosthetics, i.e., implanted devices that can interact with the real nervous system. Based on the computer modelling we show that such a plastic electrical synapse regularizes chaos in the minimal neural circuit consisting of two chaotic bursting neurons.

  6. Technology for integrated circuit micropackages for neural interfaces, based on gold–silicon wafer bonding

    International Nuclear Information System (INIS)

    Progress in the development of active neural interface devices requires a very compact method for protecting integrated circuits (ICs). In this paper, a method of forming micropackages is described in detail. The active areas of the chips are sealed in gas-filled cavities of the cap wafer in a wafer-bonding process using Au–Si eutectic. We describe the simple additions to the design of the IC, the post-processing of the active wafer and the required features of the cap wafer. The bonds, which were made at pressure and temperature levels within the range of the tolerance of complementary metal–oxide–semiconductor ICs, are strong enough to meet MIL STD 883G, Method 2019.8 (shear force test). We show results that suggest a method for wafer-scale gross leak testing using FTIR. This micropackaging method requires no special fabrication process and is based on using IC compatible or conventional fabrication steps. (paper)

  7. Hierarchical Neural Networks Method for Fault Diagnosis of Large-Scale Analog Circuits

    Institute of Scientific and Technical Information of China (English)

    TAN Yanghong; HE Yigang; FANG Gefeng

    2007-01-01

    A novel hierarchical neural networks (HNNs) method for fault diagnosis of large-scale circuits is proposed. The presented techniques using neural networks(NNs) approaches require a large amount of computation for simulating various faulty component possibilities. For large scale circuits, the number of possible faults, and hence the simulations, grow rapidly and become tedious and sometimes even impractical. Some NNs are distributed to the torn sub-blocks according to the proposed torn principles of large scale circuits. And the NNs are trained in batches by different patterns in the light of the presented rules of various patterns when the DC, AC and transient responses of the circuit are available. The method is characterized by decreasing the over-lapped feasible domains of responses of circuits with tolerance and leads to better performance and higher correct classification. The methodology is illustrated by means of diagnosis examples.

  8. An implantable wireless neural interface for recording cortical circuit dynamics in moving primates

    Science.gov (United States)

    Borton, David A.; Yin, Ming; Aceros, Juan; Nurmikko, Arto

    2013-04-01

    Objective. Neural interface technology suitable for clinical translation has the potential to significantly impact the lives of amputees, spinal cord injury victims and those living with severe neuromotor disease. Such systems must be chronically safe, durable and effective. Approach. We have designed and implemented a neural interface microsystem, housed in a compact, subcutaneous and hermetically sealed titanium enclosure. The implanted device interfaces the brain with a 510k-approved, 100-element silicon-based microelectrode array via a custom hermetic feedthrough design. Full spectrum neural signals were amplified (0.1 Hz to 7.8 kHz, 200× gain) and multiplexed by a custom application specific integrated circuit, digitized and then packaged for transmission. The neural data (24 Mbps) were transmitted by a wireless data link carried on a frequency-shift-key-modulated signal at 3.2 and 3.8 GHz to a receiver 1 m away by design as a point-to-point communication link for human clinical use. The system was powered by an embedded medical grade rechargeable Li-ion battery for 7 h continuous operation between recharge via an inductive transcutaneous wireless power link at 2 MHz. Main results. Device verification and early validation were performed in both swine and non-human primate freely-moving animal models and showed that the wireless implant was electrically stable, effective in capturing and delivering broadband neural data, and safe for over one year of testing. In addition, we have used the multichannel data from these mobile animal models to demonstrate the ability to decode neural population dynamics associated with motor activity. Significance. We have developed an implanted wireless broadband neural recording device evaluated in non-human primate and swine. The use of this new implantable neural interface technology can provide insight into how to advance human neuroprostheses beyond the present early clinical trials. Further, such tools enable mobile

  9. In search of the neural circuits of intrinsic motivation

    Directory of Open Access Journals (Sweden)

    Frederic Kaplan

    2007-10-01

    Full Text Available Children seem to acquire new know-how in a continuous and open-ended manner. In this paper, we hypothesize that an intrinsic motivation to progress in learning is at the origins of the remarkable structure of children's developmental trajectories. In this view, children engage in exploratory and playful activities for their own sake, not as steps toward other extrinsic goals. The central hypothesis of this paper is that intrinsically motivating activities correspond to expected decrease in prediction error. This motivation system pushes the infant to avoid both predictable and unpredictable situations in order to focus on the ones that are expected to maximize progress in learning. Based on a computational model and a series of robotic experiments, we show how this principle can lead to organized sequences of behavior of increasing complexity characteristic of several behavioral and developmental patterns observed in humans. We then discuss the putative circuitry underlying such an intrinsic motivation system in the brain and formulate two novel hypotheses. The first one is that tonic dopamine acts as a learning progress signal. The second is that this progress signal is directly computed through a hierarchy of microcortical circuits that act both as prediction and metaprediction systems.

  10. Information processing in micro and meso-scale neural circuits during normal and disease states

    Science.gov (United States)

    Luongo, Francisco

    Neural computation can occur at multiple spatial and temporal timescales. The sum total of all of these processes is to guide optimal behaviors within the context of the constraints imposed by the physical world. How the circuits of the brain achieves this goal represents a central question in systems neuroscience. Here I explore the many ways in which the circuits of the brain can process information at both the micro and meso scale. Understanding the way information is represented and processed in the brain could shed light on the neuropathology underlying complex neuropsychiatric diseases such as autism and schizophrenia. Chapter 2 establishes an experimental paradigm for assaying patterns of microcircuit activity and examines the role of dopaminergic modulation on prefrontal microcircuits. We find that dopamine type 2 (D2) receptor activation results in an increase in spontaneous activity while dopamine type 1 (D1) activation does not. Chapter 3 of this dissertation presents a study that illustrates how cholingergic activation normally produces what has been suggested as a neural substrate of attention; pairwise decorrelation in microcircuit activity. This study also shows that in two etiologicall distinct mouse models of autism, FMR1 knockout mice and Valproic Acid exposed mice, this ability to decorrelate in the presence of cholinergic activation is lost. This represents a putative microcircuit level biomarker of autism. Chapter 4 examines the structure/function relationship within the prefrontal microcircuit. Spontaneous activity in prefrontal microcircuits is shown to be organized according to a small world architecture. Interestingly, this architecture is important for one concrete function of neuronal microcircuits; the ability to produce temporally stereotyped patterns of activation. In the final chapter, we identify subnetworks in chronic intracranial electrocorticographic (ECoG) recordings using pairwise electrode coherence and dimensionality reduction

  11. Design of 3D Active Multichannel Silicon Neural Microelectrode

    Institute of Scientific and Technical Information of China (English)

    WANG Di; ZHANG Guoxiong; LI Xingfei

    2006-01-01

    To find a design method for 3D active multichannel silicon microelectrode,a microstructure of active neural recording system is presented,where two 2D probes,two integrated circuits and two spacers are microassembled on a 5 mm ×7 mm silicon platform,and 32 sites neural signals can be operated simultaneously.A theoretical model for measuring the neural signal by the silicon microelectrode is proposed based on the structure and fabrication process of a single-shank probe.The method of determining the dimensional parameters of the probe shank is discussed in the following three aspects,i.e.the structures of pallium and endocranium,coupled interconnecters noise,and strength characteristic of neural probe.The design criterion is to minimize the size of the neural probe as well as that the probe has enough stiffness to pierce the endocranium.The on-chip unity-gain bandpass amplifier has an overall gain of 42 dB over a bandwidth from 60 Hz to 10 kHz;and the DC-baseline stability circuit is of high input resistance above 30 MΩ to guarantee a cutoff frequency below 100 Hz.The circuit works in stimulating or recording modes.The conversion of the modes depends on the stimulating control signal.

  12. A neural command circuit for grooming movement control.

    Science.gov (United States)

    Hampel, Stefanie; Franconville, Romain; Simpson, Julie H; Seeds, Andrew M

    2015-09-07

    Animals perform many stereotyped movements, but how nervous systems are organized for controlling specific movements remains unclear. Here we use anatomical, optogenetic, behavioral, and physiological techniques to identify a circuit in Drosophila melanogaster that can elicit stereotyped leg movements that groom the antennae. Mechanosensory chordotonal neurons detect displacements of the antennae and excite three different classes of functionally connected interneurons, which include two classes of brain interneurons and different parallel descending neurons. This multilayered circuit is organized such that neurons within each layer are sufficient to specifically elicit antennal grooming. However, we find differences in the durations of antennal grooming elicited by neurons in the different layers, suggesting that the circuit is organized to both command antennal grooming and control its duration. As similar features underlie stimulus-induced movements in other animals, we infer the possibility of a common circuit organization for movement control that can be dissected in Drosophila.

  13. An Implantable Mixed Analog/Digital Neural Stimulator Circuit

    DEFF Research Database (Denmark)

    Gudnason, Gunnar; Bruun, Erik; Haugland, Morten

    1999-01-01

    This paper describes a chip for a multichannel neural stimulator for functional electrical stimulation. The chip performs all the signal processing required in an implanted neural stimulator. The power and signal transmission to the stimulator is carried out via an inductive link. From the signals...... electrical stimulation is to restore various bodily functions (e.g. motor functions) in patients who have lost them due to injury or disease....

  14. Circuit Design of On-Chip BP Learning Neural Network with Programmable Neuron Characteristics

    Institute of Scientific and Technical Information of China (English)

    卢纯; 石秉学; 陈卢

    2000-01-01

    A circuit system of on chip BP(Back-Propagation) learning neural network with pro grammable neurons has been designed,which comprises a feedforward network,an error backpropagation network and a weight updating circuit. It has the merits of simplicity,programmability, speedness,low power-consumption and high density. A novel neuron circuit with pro grammable parameters has been proposed. It generates not only the sigmoidal function but also its derivative. HSPICE simulations are done to a neuron circuit with level 47 transistor models as a standard 1.2tμm CMOS process. The results show that both functions are matched with their respec ive ideal functions very well. The non-linear partition problem is used to verify the operation of the network. The simulation result shows the superior performance of this BP neural network with on-chip learning.

  15. Artificial Neural Network-Based Fault Distance Locator for Double-Circuit Transmission Lines

    Directory of Open Access Journals (Sweden)

    Anamika Jain

    2013-01-01

    Full Text Available This paper analyses two different approaches of fault distance location in a double circuit transmission lines, using artificial neural networks. The single and modular artificial neural networks were developed for determining the fault distance location under varying types of faults in both the circuits. The proposed method uses the voltages and currents signals available at only the local end of the line. The model of the example power system is developed using Matlab/Simulink software. Effects of variations in power system parameters, for example, fault inception angle, CT saturation, source strength, its X/R ratios, fault resistance, fault type and distance to fault have been investigated extensively on the performance of the neural network based protection scheme (for all ten faults in both the circuits. Additionally, the effects of network changes: namely, double circuit operation and single circuit operation, have also been considered. Thus, the present work considers the entire range of possible operating conditions, which has not been reported earlier. The comparative results of single and modular neural network indicate that the modular approach gives correct fault location with better accuracy. It is adaptive to variation in power system parameters, network changes and works successfully under a variety of operating conditions.

  16. Longitudinal evidence for functional specialization of the neural circuit supporting working memory in the human brain

    OpenAIRE

    Finn, Amy S.; Sheridan, Margaret A.; Hudson Kam, Carla L.; Hinshaw, Stephen; D’Esposito, Mark

    2010-01-01

    Although children perform more poorly than adults on many cognitive measures, they are better able to learn things such as language and music. These differences could result from the delayed specialization of neural circuits and asynchronies in the maturation of neural substrates required for learning. Working memory—the ability to hold information in mind that is no longer present in the environment—comprises a set of cognitive processes required for many, if not all, forms of learning. A cr...

  17. Fault Diagnosis of Mixed-Signal Analog Circuit using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Ashwani Kumar Narula

    2015-06-01

    Full Text Available This paper presents parametric fault diagnosis in mixed-signal analog circuit using artificial neural networks. Single parametric faults are considered in this study. A benchmark R2R digital to analog converter circuit has been used as an example circuit for experimental validations. The input test pattern required for testing are reduced to optimum value using sensitivity analysis of the circuit under test. The effect of component tolerances has also been taken care of by performing the Monte-Carlo analysis. In this study parametric fault models are defined for the R2R network of the digital to analog converter. The input test patterns are applied to the circuit under test and the output responses are measured for each fault model covering all the Monte-Carlo runs. The classification of the parametric faults is done using artificial neural networks. The fault diagnosis system is developed in LabVIEW environment in the form of a virtual instrument. The artificial neural network is designed using MATLAB and finally embedded in the virtual instrument. The fault diagnosis is validated with simulated data and with the actual data acquired from the circuit hardware.

  18. Activity transport models for PWR primary circuits

    International Nuclear Information System (INIS)

    The corrosion products activated in the primary circuit form a major source of occupational radiation dose in the PWR reactors. Transport of corrosion activity is a complex process including chemistry, reactor physics, thermodynamics and hydrodynamics. All the mechanisms involved are not known and there is no comprehensive theory for the process, so experimental test loops and plant data are very important in research efforts. Several activity transport modelling attempts have been made to improve the water chemistry control and to minimise corrosion in PWR's. In this research report some of these models are reviewed with special emphasis on models designed for Soviet VVER type reactors. (51 refs., 16 figs., 4 tabs.)

  19. Doublesex Regulates the Connectivity of a Neural Circuit Controlling Drosophila Male Courtship Song.

    Science.gov (United States)

    Shirangi, Troy R; Wong, Allan M; Truman, James W; Stern, David L

    2016-06-20

    It is unclear how regulatory genes establish neural circuits that compose sex-specific behaviors. The Drosophila melanogaster male courtship song provides a powerful model to study this problem. Courting males vibrate a wing to sing bouts of pulses and hums, called pulse and sine song, respectively. We report the discovery of male-specific thoracic interneurons-the TN1A neurons-that are required specifically for sine song. The TN1A neurons can drive the activity of a sex-non-specific wing motoneuron, hg1, which is also required for sine song. The male-specific connection between the TN1A neurons and the hg1 motoneuron is regulated by the sexual differentiation gene doublesex. We find that doublesex is required in the TN1A neurons during development to increase the density of the TN1A arbors that interact with dendrites of the hg1 motoneuron. Our findings demonstrate how a sexual differentiation gene can build a sex-specific circuit motif by modulating neuronal arborization. PMID:27326931

  20. The neural circuit and synaptic dynamics underlying perceptual decision-making

    Science.gov (United States)

    Liu, Feng

    2015-03-01

    Decision-making with several choice options is central to cognition. To elucidate the neural mechanisms of multiple-choice motion discrimination, we built a continuous recurrent network model to represent a local circuit in the lateral intraparietal area (LIP). The network is composed of pyramidal cells and interneurons, which are directionally tuned. All neurons are reciprocally connected, and the synaptic connectivity strength is heterogeneous. Specifically, we assume two types of inhibitory connectivity to pyramidal cells: opposite-feature and similar-feature inhibition. The model accounted for both physiological and behavioral data from monkey experiments. The network is endowed with slow excitatory reverberation, which subserves the buildup and maintenance of persistent neural activity, and predominant feedback inhibition, which underlies the winner-take-all competition and attractor dynamics. The opposite-feature and opposite-feature inhibition have different effects on decision-making, and only their combination allows for a categorical choice among 12 alternatives. Together, our work highlights the importance of structured synaptic inhibition in multiple-choice decision-making processes.

  1. Segregation of neural circuits involved in spatial learning in reaching and navigational space.

    Science.gov (United States)

    Nemmi, Federico; Boccia, Maddalena; Piccardi, Laura; Galati, Gaspare; Guariglia, Cecilia

    2013-07-01

    Recent behavioral and neuropsychological studies suggest that visuo-spatial memory for reaching and navigational space is dissociated. In the present fMRI study, we investigated the hypothesis that learning spatial sequences in reaching and navigational space is processed by partially segregated neural systems. To this aim, we adapted the Corsi block tapping test (CBT) and the walking Corsi test (WalCT); the latter is a modification of the CBT in which subjects observe and reproduce spatial sequences by walking in a room instead of tapping wooden blocks on a table. Subjects were scanned while learning supra-span sequences of spatial locations through observation of video clips in which an actor tapped the blocks within reaching space (CBT) or walked on tiles placed on a carpet (WalCT). A large cerebral network spanning from visual occipital to parietal to frontal areas was activated during learning of both the CBT and the WalCT sequences. Within this network right lingual gyrus, calcarine sulcus and dorsolateral prefrontal cortex were specifically associated with learning in navigational space, whereas left inferior temporal gyrus, lingual and fusiform gyrus and middle occipital gyrus were associated with learning sequences in reaching space. These results support the idea of a partial segregation between neural circuits for reaching and navigational space not only in the domain of perception and action planning but also in spatial learning and long-term memory. PMID:23615031

  2. Laser programmable integrated circuit for forming synapses in neural networks

    Science.gov (United States)

    Fu, C.Y.

    1997-02-11

    Customizable neural network in which one or more resistors form each synapse is disclosed. All the resistors in the synaptic array are identical, thus simplifying the processing issues. Highly doped, amorphous silicon is used as the resistor material, to create extremely high resistances occupying very small spaces. Connected in series with each resistor in the array is at least one severable conductor whose uppermost layer has a lower reflectivity of laser energy than typical metal conductors at a desired laser wavelength. 5 figs.

  3. Design and analysis of an active power factor correction circuit

    OpenAIRE

    Zhou, Zhen

    1989-01-01

    The design of an active-unity power factor correction circuit with variable-hysteresis control for off-line dc-to-dc switching power supplies is described. Design equations relating the boost inductor current ripple to the circuit components selection and circuit performance arc discussed. A computer-aided design program (CADO) is developed to give the optimal circuit components selection. A 500 watt, 300 volt experimental circuit is built to verify the simulation and analysis ...

  4. Low Temperature Performance of High-Speed Neural Network Circuits

    Science.gov (United States)

    Duong, T.; Tran, M.; Daud, T.; Thakoor, A.

    1995-01-01

    Artificial neural networks, derived from their biological counterparts, offer a new and enabling computing paradigm specially suitable for such tasks as image and signal processing with feature classification/object recognition, global optimization, and adaptive control. When implemented in fully parallel electronic hardware, it offers orders of magnitude speed advantage. Basic building blocks of the new architecture are the processing elements called neurons implemented as nonlinear operational amplifiers with sigmoidal transfer function, interconnected through weighted connections called synapses implemented using circuitry for weight storage and multiply functions either in an analog, digital, or hybrid scheme.

  5. The neural circuits recruited for the production of signs and fingerspelled words.

    Science.gov (United States)

    Emmorey, Karen; Mehta, Sonya; McCullough, Stephen; Grabowski, Thomas J

    2016-09-01

    Signing differs from typical non-linguistic hand actions because movements are not visually guided, finger movements are complex (particularly for fingerspelling), and signs are not produced as holistic gestures. We used positron emission tomography to investigate the neural circuits involved in the production of American Sign Language (ASL). Different types of signs (one-handed (articulated in neutral space), two-handed (neutral space), and one-handed body-anchored signs) were elicited by asking deaf native signers to produce sign translations of English words. Participants also fingerspelled (one-handed) printed English words. For the baseline task, participants indicated whether a word contained a descending letter. Fingerspelling engaged ipsilateral motor cortex and cerebellar cortex in contrast to both one-handed signs and the descender baseline task, which may reflect greater timing demands and complexity of handshape sequences required for fingerspelling. Greater activation in the visual word form area was also observed for fingerspelled words compared to one-handed signs. Body-anchored signs engaged bilateral superior parietal cortex to a greater extent than the descender baseline task and neutral space signs, reflecting the motor control and proprioceptive monitoring required to direct the hand toward a specific location on the body. Less activation in parts of the motor circuit was observed for two-handed signs compared to one-handed signs, possibly because, for half of the signs, handshape and movement goals were spread across the two limbs. Finally, the conjunction analysis comparing each sign type with the descender baseline task revealed common activation in the supramarginal gyrus bilaterally, which we interpret as reflecting phonological retrieval and encoding processes. PMID:27459390

  6. Visualization and manipulation of neural activity in the developing vertebrate nervous system

    Directory of Open Access Journals (Sweden)

    Jiayi eZhang

    2011-11-01

    Full Text Available Neural activity during vertebrate development has been unambiguously shown to play a critical role in sculpting circuit formation and function. Patterned neural activity in various parts of the developing nervous system is thought to modulate neurite outgrowth, axon targeting and synapse refinement. The nature and role of patterned neural activity during development has been classically studied with in vitro preparations using pharmacological manipulations. In this review we discuss newly available and developing molecular genetic tools for the visualization and manipulation of neural activity patterns specifically during development.

  7. Ontogeny of neural circuits underlying spatial memory in the rat

    Directory of Open Access Journals (Sweden)

    James Alexander Ainge

    2012-03-01

    Full Text Available Spatial memory is a well characterised psychological function in both humans and rodents. The combined computations of a network of systems including place cells in the hippocampus, grid cells in the medial entorhinal cortex and head direction cells found in numerous structures in the brain have been suggested to form the neural instantiation of the cognitive map as first described by Tolman in 1948. However, while our understanding of the neural mechanisms underlying spatial representations in adults is relatively sophisticated, we know substantially less about how this network develops in young animals. In this article we review studies examining the developmental timescale that these systems follow. Electrophysiological recordings from very young rats show that directional information is at adult levels at the outset of navigational experience. The systems supporting allocentric memory, however, take longer to mature. This is consistent with behavioural studies of young rats which show that spatial memory based on head direction develops very early but that allocentric spatial memory takes longer to mature. We go on to report new data demonstrating that memory for associations between objects and their spatial locations is slower to develop than memory for objects alone. This is again consistent with previous reports suggesting that adult like spatial representations have a protracted development in rats and also suggests that the systems involved in processing non-spatial stimuli come online earlier.

  8. PCSIM: a parallel simulation environment for neural circuits fully integrated with Python

    Directory of Open Access Journals (Sweden)

    Dejan Pecevski

    2009-05-01

    Full Text Available The Parallel Circuit SIMulator (PCSIM is a software package for simulation of neural circuits. It is primarily designed for distributed simulation of large scale networks of spiking point neurons. Although its computational core is written in C++, PCSIM's primary interface is implemented in the Python programming language, which is a powerful programming environment and allows the user to easily integrate the neural circuit simulator with data analysis and visualization tools to manage the full neural modeling life cycle. The main focus of this paper is to describe PCSIM's full integration into Python and the benefits thereof. In particular we will investigate how the automatically generated bidirectional interface and PCSIM's object-oriented modular framework enable the user to adopt a hybrid modeling approach: using and extending PCSIM's functionality either employing pure Python or C++ and thus combining the advantages of both worlds. Furthermore, we describe several supplementary PCSIM packages written in pure Python and tailored towards setting up and analyzing neural simulations.

  9. Massively parallel neural circuits for stereoscopic color vision: encoding, decoding and identification.

    Science.gov (United States)

    Lazar, Aurel A; Slutskiy, Yevgeniy B; Zhou, Yiyin

    2015-03-01

    Past work demonstrated how monochromatic visual stimuli could be faithfully encoded and decoded under Nyquist-type rate conditions. Color visual stimuli were then traditionally encoded and decoded in multiple separate monochromatic channels. The brain, however, appears to mix information about color channels at the earliest stages of the visual system, including the retina itself. If information about color is mixed and encoded by a common pool of neurons, how can colors be demixed and perceived? We present Color Video Time Encoding Machines (Color Video TEMs) for encoding color visual stimuli that take into account a variety of color representations within a single neural circuit. We then derive a Color Video Time Decoding Machine (Color Video TDM) algorithm for color demixing and reconstruction of color visual scenes from spikes produced by a population of visual neurons. In addition, we formulate Color Video Channel Identification Machines (Color Video CIMs) for functionally identifying color visual processing performed by a spiking neural circuit. Furthermore, we derive a duality between TDMs and CIMs that unifies the two and leads to a general theory of neural information representation for stereoscopic color vision. We provide examples demonstrating that a massively parallel color visual neural circuit can be first identified with arbitrary precision and its spike trains can be subsequently used to reconstruct the encoded stimuli. We argue that evaluation of the functional identification methodology can be effectively and intuitively performed in the stimulus space. In this space, a signal reconstructed from spike trains generated by the identified neural circuit can be compared to the original stimulus.

  10. Statistical Estimation of the ,Switching Activity in VLSI Circuits

    OpenAIRE

    Farid N. Najm; Michael G. Xakellis

    1998-01-01

    Higher levels of integration have led to a generation of integrated circuits for which power dissipation and reliability are major design concerns. In CMOS circuits, both of these problems are directly related to the extent of circuit switching activity. The average number of transitions per second at a circuit node is a measure of switching activity that has been called the transition density. This paper presents a statistical simulation technique to estimate individual node transition densi...

  11. Computing with a canonical neural circuits model with pool normalization and modulating feedback.

    Science.gov (United States)

    Brosch, Tobias; Neumann, Heiko

    2014-12-01

    Evidence suggests that the brain uses an operational set of canonical computations like normalization, input filtering, and response gain enhancement via reentrant feedback. Here, we propose a three-stage columnar architecture of cascaded model neurons to describe a core circuit combining signal pathways of feedforward and feedback processing and the inhibitory pooling of neurons to normalize the activity. We present an analytical investigation of such a circuit by first reducing its detail through the lumping of initial feedforward response filtering and reentrant modulating signal amplification. The resulting excitatory-inhibitory pair of neurons is analyzed in a 2D phase-space. The inhibitory pool activation is treated as a separate mechanism exhibiting different effects. We analyze subtractive as well as divisive (shunting) interaction to implement center-surround mechanisms that include normalization effects in the characteristics of real neurons. Different variants of a core model architecture are derived and analyzed--in particular, individual excitatory neurons (without pool inhibition), the interaction with an inhibitory subtractive or divisive (i.e., shunting) pool, and the dynamics of recurrent self-excitation combined with divisive inhibition. The stability and existence properties of these model instances are characterized, which serve as guidelines to adjust these properties through proper model parameterization. The significance of the derived results is demonstrated by theoretical predictions of response behaviors in the case of multiple interacting hypercolumns in a single and in multiple feature dimensions. In numerical simulations, we confirm these predictions and provide some explanations for different neural computational properties. Among those, we consider orientation contrast-dependent response behavior, different forms of attentional modulation, contrast element grouping, and the dynamic adaptation of the silent surround in extraclassical

  12. Diversity of Dopaminergic Neural Circuits in Response to Drug Exposure.

    Science.gov (United States)

    Juarez, Barbara; Han, Ming-Hu

    2016-09-01

    Addictive substances are known to increase dopaminergic signaling in the mesocorticolimbic system. The origin of this dopamine (DA) signaling originates in the ventral tegmental area (VTA), which sends afferents to various targets, including the nucleus accumbens, the medial prefrontal cortex, and the basolateral amygdala. VTA DA neurons mediate stimuli saliency and goal-directed behaviors. These neurons undergo robust drug-induced intrinsic and extrinsic synaptic mechanisms following acute and chronic drug exposure, which are part of brain-wide adaptations that ultimately lead to the transition into a drug-dependent state. Interestingly, recent investigations of the differential subpopulations of VTA DA neurons have revealed projection-specific functional roles in mediating reward, aversion, and stress. It is now critical to view drug-induced neuroadaptations from a circuit-level perspective to gain insight into how differential dopaminergic adaptations and signaling to targets of the mesocorticolimbic system mediates drug reward. This review hopes to describe the projection-specific intrinsic characteristics of these subpopulations, the differential afferent inputs onto these VTA DA neuron subpopulations, and consolidate findings of drug-induced plasticity of VTA DA neurons and highlight the importance of future projection-based studies of this system. PMID:26934955

  13. Impairments of neural circuit function in Alzheimer's disease.

    Science.gov (United States)

    Busche, Marc Aurel; Konnerth, Arthur

    2016-08-01

    An essential feature of Alzheimer's disease (AD) is the accumulation of amyloid-β (Aβ) peptides in the brain, many years to decades before the onset of overt cognitive symptoms. We suggest that during this very extended early phase of the disease, soluble Aβ oligomers and amyloid plaques alter the function of local neuronal circuits and large-scale networks by disrupting the balance of synaptic excitation and inhibition (E/I balance) in the brain. The analysis of mouse models of AD revealed that an Aβ-induced change of the E/I balance caused hyperactivity in cortical and hippocampal neurons, a breakdown of slow-wave oscillations, as well as network hypersynchrony. Remarkably, hyperactivity of hippocampal neurons precedes amyloid plaque formation, suggesting that hyperactivity is one of the earliest dysfunctions in the pathophysiological cascade initiated by abnormal Aβ accumulation. Therapeutics that correct the E/I balance in early AD may prevent neuronal dysfunction, widespread cell loss and cognitive impairments associated with later stages of the disease.This article is part of the themed issue 'Evolution brings Ca(2+) and ATP together to control life and death'. PMID:27377723

  14. The use of brain imaging to elucidate neural circuit changes in cocaine addiction

    Directory of Open Access Journals (Sweden)

    Hanlon CA

    2012-09-01

    Full Text Available Colleen A Hanlon,1,2 Melanie Canterberry11Department of Psychiatry and Behavioral Sciences, 2Department of Neurosciences Medical University of South Carolina, Charleston, SC, USAAbstract: Within substance abuse, neuroimaging has experienced tremendous growth as both a research method and a clinical tool in the last decade. The application of functional imaging methods to cocaine dependent patients and individuals in treatment programs, has revealed that the effects of cocaine are not limited to dopamine-rich subcortical structures, but that the cortical projection areas are also disrupted in cocaine dependent patients. In this review, we will first describe several of the imaging methods that are actively being used to address functional and structural abnormalities in addiction. This will be followed by an overview of the cortical and subcortical brain regions that are most often cited as dysfunctional in cocaine users. We will also introduce functional connectivity analyses currently being used to investigate interactions between these cortical and subcortical areas in cocaine users and abstainers. Finally, this review will address recent research which demonstrates that alterations in the functional connectivity in cocaine users may be associated with structural pathology in these circuits, as demonstrated through diffusion tensor imaging. Through the use of these tools in both a basic science setting and as applied to treatment seeking individuals, we now have a greater understanding of the complex cortical and subcortical networks which contribute to the stages of initial craving, dependence, abstinence, and relapse. Although the ability to use neuroimaging to predict treatment response or identify vulnerable populations is still in its infancy, the next decade holds tremendous promise for using neuroimaging to tailor either behavioral or pharmacologic treatment interventions to the individual.Keywords: addiction, neural circuit, functional

  15. Homology and homoplasy of swimming behaviors and neural circuits in the Nudipleura (Mollusca, Gastropoda, Opisthobranchia).

    Science.gov (United States)

    Newcomb, James M; Sakurai, Akira; Lillvis, Joshua L; Gunaratne, Charuni A; Katz, Paul S

    2012-06-26

    How neural circuit evolution relates to behavioral evolution is not well understood. Here the relationship between neural circuits and behavior is explored with respect to the swimming behaviors of the Nudipleura (Mollusca, Gastropoda, Opithobranchia). Nudipleura is a diverse monophyletic clade of sea slugs among which only a small percentage of species can swim. Swimming falls into a limited number of categories, the most prevalent of which are rhythmic left-right body flexions (LR) and rhythmic dorsal-ventral body flexions (DV). The phylogenetic distribution of these behaviors suggests a high degree of homoplasy. The central pattern generator (CPG) underlying DV swimming has been well characterized in Tritonia diomedea and in Pleurobranchaea californica. The CPG for LR swimming has been elucidated in Melibe leonina and Dendronotus iris, which are more closely related. The CPGs for the categorically distinct DV and LR swimming behaviors consist of nonoverlapping sets of homologous identified neurons, whereas the categorically similar behaviors share some homologous identified neurons, although the exact composition of neurons and synapses in the neural circuits differ. The roles played by homologous identified neurons in categorically distinct behaviors differ. However, homologous identified neurons also play different roles even in the swim CPGs of the two LR swimming species. Individual neurons can be multifunctional within a species. Some of those functions are shared across species, whereas others are not. The pattern of use and reuse of homologous neurons in various forms of swimming and other behaviors further demonstrates that the composition of neural circuits influences the evolution of behaviors. PMID:22723353

  16. Soft Fault Diagnosis for Analog Circuits Based on Slope Fault Feature and BP Neural Networks

    Institute of Scientific and Technical Information of China (English)

    HU Mei; WANG Hong; HU Geng; YANG Shiyuan

    2007-01-01

    Fault diagnosis is very important for development and maintenance of safe and reliable electronic circuits and systems. This paper describes an approach of soft fault diagnosis for analog circuits based on slope fault feature and back propagation neural networks (BPNN). The reported approach uses the voltage relation function between two nodes as fault features; and for linear analog circuits, the voltage relation function is a linear function, thus the slope is invariant as fault feature. Therefore, a unified fault feature for both hard fault (open or short fault) and soft fault (parametric fault) is extracted. Unlike other NN-based diagnosis methods which utilize node voltages or frequency response as fault features, the reported BPNN is trained by the extracted feature vectors, the slope features are calculated by just simulating once for each component, and the trained BPNN can achieve all the soft faults diagnosis of the component. Experiments show that our approach is promising.

  17. Two-photon multiplane imaging of neural circuits (Conference Presentation)

    Science.gov (United States)

    Yang, Weijian; Miller, Jae-eun K.; Carrillo-Reid, Luis; Pnevmatikakis, Eftychios; Paninski, Liam; Peterka, Darcy S.; Yuste, Rafael

    2016-03-01

    Imaging the neuronal activity throughout the brain with high temporal and spatial resolution is an important step in understanding how the brain works. Two-photon laser scanning microscopy with fluorescent calcium indicators has enabled this type of experiments in vivo. Most of these microscopes acquire images serially, with a single laser beam, limiting the overall imaging speed. To overcome this limit, multiple beamlets can be used to image in parallel multiple regions. Here, we demonstrate a novel scheme of a two-photon laser-scanning microscope that can simultaneously record neuronal activity at multiple planes of the sample with a single photomultiplier tube. A spatial light modulator is used to generate the designated multiple beamlets, and a constrained non-negative matrix factorization algorithm is used to demix the signals from multiple scanned planes. We simultaneously record neuronal activity of multiple layers of a mouse cortex at 10 fps in vivo. This novel imaging scheme provides a powerful tool for mapping the brain activity.

  18. Multi-point optical manipulation and simultaneous imaging of neural circuits through wavefront phase modulation (Presentation Recording)

    Science.gov (United States)

    Aghayee, Samira; Winkowski, Dan; Kanold, Patrick; Losert, Wolfgang

    2015-08-01

    The spatial connectivity of neural circuits and the various activity patterns they exert is what forms the brain function. How these patterns link to a certain perception or a behavior is a key question in neuroscience. Recording the activity of neural circuits while manipulating arbitrary neurons leads to answering this question. That is why acquiring a fast and reliable method of stimulation and imaging a population of neurons at a single cell resolution is of great importance. Owing to the recent advancements in calcium imaging and optogenetics, tens to hundreds of neurons in a living system can be imaged and manipulated optically. We describe the adaptation of a multi-point optical method that can be used to address the specific challenges faced in the in-vivo study of neuronal networks in the cerebral cortex. One specific challenge in the cerebral cortex is that the information flows perpendicular to the surface. Therefore, addressing multiple points in a three dimensional space simultaneously is of great interest. Using a liquid crystal spatial light modulator, the wavefront of the input laser beam is modified to produce multiple focal points at different depths of the sample for true multipoint two-photon excitation.

  19. New Active Digital Pixel Circuit for CMOS Image Sensor

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    A new active digital pixel circuit for CMOS image sensor is designed consisting of four components: a photo-transducer, a preamplifier, a sample & hold (S & H) circuit and an A/D converter with an inverter. It is optimized by simulation and adjustment based on 2μm standard CMOS process. Each circuit of the components is designed with specific parameters. The simulation results of the whole pixel circuits show that the circuit has such advantages as low distortion, low power consumption, and improvement of the output performances by using an inverter.

  20. Fiberless multicolor neural optoelectrode for in vivo circuit analysis

    Science.gov (United States)

    Kampasi, Komal; Stark, Eran; Seymour, John; Na, Kyounghwan; Winful, Herbert G.; Buzsáki, György; Wise, Kensall D.; Yoon, Euisik

    2016-08-01

    Maximizing the potential of optogenetic approaches in deep brain structures of intact animals requires optical manipulation of neurons at high spatial and temporal resolutions, while simultaneously recording electrical data from those neurons. Here, we present the first fiber-less optoelectrode with a monolithically integrated optical waveguide mixer that can deliver multicolor light at a common waveguide port to achieve multicolor modulation of the same neuronal population in vivo. We demonstrate successful device implementation by achieving efficient coupling between a side-emitting injection laser diode (ILD) and a dielectric optical waveguide mixer via a gradient-index (GRIN) lens. The use of GRIN lenses attains several design features, including high optical coupling and thermal isolation between ILDs and waveguides. We validated the packaged devices in the intact brain of anesthetized mice co-expressing Channelrhodopsin-2 and Archaerhodopsin in pyramidal cells in the hippocampal CA1 region, achieving high quality recording, activation and silencing of the exact same neurons in a given local region. This fully-integrated approach demonstrates the spatial precision and scalability needed to enable independent activation and silencing of the same or different groups of neurons in dense brain regions while simultaneously recording from them, thus considerably advancing the capabilities of currently available optogenetic toolsets.

  1. Incorporating Artificial Neural Networks in the dynamic thermal-hydraulic model of a controlled cryogenic circuit

    Science.gov (United States)

    Carli, S.; Bonifetto, R.; Savoldi, L.; Zanino, R.

    2015-09-01

    A model based on Artificial Neural Networks (ANNs) is developed for the heated line portion of a cryogenic circuit, where supercritical helium (SHe) flows and that also includes a cold circulator, valves, pipes/cryolines and heat exchangers between the main loop and a saturated liquid helium (LHe) bath. The heated line mimics the heat load coming from the superconducting magnets to their cryogenic cooling circuits during the operation of a tokamak fusion reactor. An ANN is trained, using the output from simulations of the circuit performed with the 4C thermal-hydraulic (TH) code, to reproduce the dynamic behavior of the heated line, including for the first time also scenarios where different types of controls act on the circuit. The ANN is then implemented in the 4C circuit model as a new component, which substitutes the original 4C heated line model. For different operational scenarios and control strategies, a good agreement is shown between the simplified ANN model results and the original 4C results, as well as with experimental data from the HELIOS facility confirming the suitability of this new approach which, extended to an entire magnet systems, can lead to real-time control of the cooling loops and fast assessment of control strategies for heat load smoothing to the cryoplant.

  2. Neural circuits of disgust induced by sexual stimuli in homosexual and heterosexual men: An fMRI study

    International Nuclear Information System (INIS)

    Few studies demonstrated neural circuits related to disgust were influenced by internal sexual orientation in male. Here we used fMRI to study the neural responses to disgust in homosexual and heterosexual men to investigate that issue. Thirty-two healthy male volunteers (sixteen homosexual and sixteen heterosexual) were scanned while viewing alternating blocks of three types of erotic film: heterosexual couples (F-M), male homosexual couples (M-M), and female homosexual couples (F-F) engaged in sexual activity. All the participants rated their level of disgust and sexual arousal as well. The F-F and M-M stimuli induced disgust in homosexual and heterosexual men, respectively. The common activations related to disgusting stimuli included: bilateral frontal gyrus and occipital gyrus, right middle temporal gyrus, left superior temporal gyrus, right cerebellum, and right thalamus. Homosexual men had greater neural responses in the left medial frontal gyrus than did heterosexual men to the sexual disgusting stimuli; in contrast, heterosexual men showed significantly greater activation than homosexual men in the left cuneus. ROI analysis showed that negative correlation were found between the magnitude of MRI signals in the left medial frontal gyrus and scores of disgust in homosexual subjects (p < 0.05). This study indicated that there were regions in common as well as regions specific for each type of erotic stimuli during disgust of homosexual and heterosexual men.

  3. Neural circuits of disgust induced by sexual stimuli in homosexual and heterosexual men: An fMRI study

    Energy Technology Data Exchange (ETDEWEB)

    Zhang Minming [Department of Radiology, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou (China); Hu Shaohua [Department of Mental Health, First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qing Chun Road, Hangzhou, Zhejiang Province 310003 (China); Xu Lijuan [National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing (China); Wang Qidong [Department of Radiology, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou (China); Xu Xiaojun [Department of Radiology, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou (China); Wei Erqing [College of Pharmacology, Zhejiang University (China); Yan Leqin [MD Anderson Cancer Center, Virginia Harris Cockrell Cancer Research Center, University of Texas, Austin (United States); Hu Jianbo; Wei Ning; Zhou Weihua; Huang Manli [Department of Mental Health, First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qing Chun Road, Hangzhou, Zhejiang Province 310003 (China); Xu Yi, E-mail: xuyi61@yahoo.com.cn [Department of Mental Health, First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qing Chun Road, Hangzhou, Zhejiang Province 310003 (China)

    2011-11-15

    Few studies demonstrated neural circuits related to disgust were influenced by internal sexual orientation in male. Here we used fMRI to study the neural responses to disgust in homosexual and heterosexual men to investigate that issue. Thirty-two healthy male volunteers (sixteen homosexual and sixteen heterosexual) were scanned while viewing alternating blocks of three types of erotic film: heterosexual couples (F-M), male homosexual couples (M-M), and female homosexual couples (F-F) engaged in sexual activity. All the participants rated their level of disgust and sexual arousal as well. The F-F and M-M stimuli induced disgust in homosexual and heterosexual men, respectively. The common activations related to disgusting stimuli included: bilateral frontal gyrus and occipital gyrus, right middle temporal gyrus, left superior temporal gyrus, right cerebellum, and right thalamus. Homosexual men had greater neural responses in the left medial frontal gyrus than did heterosexual men to the sexual disgusting stimuli; in contrast, heterosexual men showed significantly greater activation than homosexual men in the left cuneus. ROI analysis showed that negative correlation were found between the magnitude of MRI signals in the left medial frontal gyrus and scores of disgust in homosexual subjects (p < 0.05). This study indicated that there were regions in common as well as regions specific for each type of erotic stimuli during disgust of homosexual and heterosexual men.

  4. Dynamical systems, attractors, and neural circuits [version 1; referees: 3 approved

    Directory of Open Access Journals (Sweden)

    Paul Miller

    2016-05-01

    Full Text Available Biology is the study of dynamical systems. Yet most of us working in biology have limited pedagogical training in the theory of dynamical systems, an unfortunate historical fact that can be remedied for future generations of life scientists. In my particular field of systems neuroscience, neural circuits are rife with nonlinearities at all levels of description, rendering simple methodologies and our own intuition unreliable. Therefore, our ideas are likely to be wrong unless informed by good models. These models should be based on the mathematical theories of dynamical systems since functioning neurons are dynamic—they change their membrane potential and firing rates with time. Thus, selecting the appropriate type of dynamical system upon which to base a model is an important first step in the modeling process. This step all too easily goes awry, in part because there are many frameworks to choose from, in part because the sparsely sampled data can be consistent with a variety of dynamical processes, and in part because each modeler has a preferred modeling approach that is difficult to move away from. This brief review summarizes some of the main dynamical paradigms that can arise in neural circuits, with comments on what they can achieve computationally and what signatures might reveal their presence within empirical data. I provide examples of different dynamical systems using simple circuits of two or three cells, emphasizing that any one connectivity pattern is compatible with multiple, diverse functions.

  5. A neural circuit transforming temporal periodicity information into a rate-based representation in the mammalian auditory system

    DEFF Research Database (Denmark)

    Dicke, Ulrike; Ewert, Stephan D.; Dau, Torsten;

    2007-01-01

    . In order to investigate the compatibility of the neural circuit with a linear modulation filterbank analysis as proposed in psychophysical studies, complex stimuli such as tones modulated by the sum of two sinusoids, narrowband noise, and iterated rippled noise were processed by the model. The model....... The present study suggests a neural circuit for the transformation from the temporal to the rate-based code. Due to the neural connectivity of the circuit, bandpass shaped rate modulation transfer functions are obtained that correspond to recorded functions of inferior colliculus IC neurons. In contrast...... to previous modeling studies, the present circuit does not employ a continuously changing temporal parameter to obtain different best modulation frequencies BMFs of the IC bandpass units. Instead, different BMFs are yielded from varying the number of input units projecting onto different bandpass units...

  6. Alzheimer's disease Braak Stage progressions: reexamined and redefined as Borrelia infection transmission through neural circuits.

    Science.gov (United States)

    MacDonald, Alan B

    2007-01-01

    Brain structure in health is a dynamic energized equation incorporating chemistry, neuronal structure, and circuitry components. The chemistry "piece" is represented by multiple neurotransmitters such as Acetylcholine, Serotonin, and Dopamine. The neuronal structure "piece" incorporates synapses and their connections. And finally circuits of neurons establish "architectural blueprints" of anatomic wiring diagrams of the higher order of brain neuron organizations. In Alzheimer's disease, there are progressive losses in all of these components. Brain structure crumbles. The deterioration in Alzheimer's is ordered, reproducible, and stepwise. Drs. Braak and Braak have described stages in the Alzheimer disease continuum. "Progressions" through Braak Stages benchmark "Regressions" in Cognitive function. Under the microscope, the Stages of Braak commence in brain regions near to the hippocampus, and over time, like a tsunami wave of destruction, overturn healthy brain regions, with neurofibrillary tangle damaged neurons "marching" through the temporal lobe, neocortex and occipital cortex. In effect the destruction ascends from the limbic regions to progressively destroy the higher brain centers. Rabies infection also "begins low and finishes high" in its wave of destruction of brain tissue. Herpes Zoster infections offer the paradigm of clinical latency of infection inside of nerves before the "marching commences". Varicella Zoster virus enters neurons in the pediatric years. Dormant virus remains inside the neurons for 50-80 years, tissue damage late in life (shingles) demonstrates the "march of the infection" down neural pathways (dermatomes) as linear areas of painful blisters loaded with virus from a childhood infection. Amalgamation of Zoster with Rabies models produces a hybrid model to explain all of the Braak Stages of Alzheimer's disease under a new paradigm, namely "Alzheimer's neuroborreliosis" in which latent Borrelia infections ascend neural circuits through

  7. Neural networks with discontinuous/impact activations

    CERN Document Server

    Akhmet, Marat

    2014-01-01

    This book presents as its main subject new models in mathematical neuroscience. A wide range of neural networks models with discontinuities are discussed, including impulsive differential equations, differential equations with piecewise constant arguments, and models of mixed type. These models involve discontinuities, which are natural because huge velocities and short distances are usually observed in devices modeling the networks. A discussion of the models, appropriate for the proposed applications, is also provided. This book also: Explores questions related to the biological underpinning for models of neural networks\\ Considers neural networks modeling using differential equations with impulsive and piecewise constant argument discontinuities Provides all necessary mathematical basics for application to the theory of neural networks Neural Networks with Discontinuous/Impact Activations is an ideal book for researchers and professionals in the field of engineering mathematics that have an interest in app...

  8. Neural CMOS-integrated circuit and its application to data classification.

    Science.gov (United States)

    Göknar, Izzet Cem; Yildiz, Merih; Minaei, Shahram; Deniz, Engin

    2012-05-01

    Implementation and new applications of a tunable complementary metal-oxide-semiconductor-integrated circuit (CMOS-IC) of a recently proposed classifier core-cell (CC) are presented and tested with two different datasets. With two algorithms-one based on Fisher's linear discriminant analysis and the other based on perceptron learning, used to obtain CCs' tunable parameters-the Haberman and Iris datasets are classified. The parameters so obtained are used for hard-classification of datasets with a neural network structured circuit. Classification performance and coefficient calculation times for both algorithms are given. The CC has 6-ns response time and 1.8-mW power consumption. The fabrication parameters used for the IC are taken from CMOS AMS 0.35-μm technology.

  9. Image-processing algorithms realized by discrete-time cellular neural networks and their circuit implementations

    International Nuclear Information System (INIS)

    In this study, eight image tasks: connected component detection (CCD) with down, right, +45o and -45o directions, edge detection, shadow projection with left and right directions and point removal are analyzed. These tasks are solved using the binary input and binary output discrete-time cellular neural networks (DTCNNs) associated with suitable templates. Furthermore, the behavior of the DTCNNs can be realized using Boolean functions, and the corresponding equivalent logic circuits are derived. An 8 x 8 DTCNNs-based image-processing chip is implemented by the FPGA technology. A simulation of the chip developed for the CCD task is also presented

  10. A subthreshold MOS circuit for the Lotka-Volterra neural network producing the winners-share-all solution.

    Science.gov (United States)

    Asai, T; Fukai, T; Tanaka, S

    1999-03-01

    An analog MOS circuit is proposed for implementing a Lotka-Volterra (LV) competitive neural network which produces winners-share-all solutions. The solutions give multiple winners receiving large inputs and are particularly useful for selecting a set of inputs through "decision by majority". We show that the LV network can easily be implemented using subthreshold MOS transistors. Results of extensive circuit simulations prove that the proposed circuit does exhibit a reliable selection compared with winner-take-all circuits, in the possible presence of device mismatches. These results pave a way to future implementation on a real device.

  11. Analog Programmable Distance Calculation Circuit for Winner Takes All Neural Network Realized in the CMOS Technology.

    Science.gov (United States)

    Talaśka, Tomasz; Kolasa, Marta; Długosz, Rafał; Pedrycz, Witold

    2016-03-01

    This paper presents a programmable analog current-mode circuit used to calculate the distance between two vectors of currents, following two distance measures. The Euclidean (L2) distance is commonly used. However, in many situations, it can be replaced with the Manhattan (L1) one, which is computationally less intensive, whose realization comes with less power dissipation and lower hardware complexity. The presented circuit can be easily reprogrammed to operate with one of these distances. The circuit is one of the components of an analog winner takes all neural network (NN) implemented in the complementary metal-oxide-semiconductor 0.18- [Formula: see text] technology. The learning process of the realized NN has been successfully verified by the laboratory tests of the fabricated chip. The proposed distance calculation circuit (DCC) features a simple structure, which makes it suitable for networks with a relatively large number of neurons realized in hardware and operating in parallel. For example, the network with three inputs occupies a relatively small area of 3900 μm(2). When operating in the L2 mode, the circuit dissipates 85 [Formula: see text] of power from the 1.5 V voltage supply, at maximum data rate of 10 MHz. In the L1 mode, an average dissipated power is reduced to 55 [Formula: see text] from 1.2 V voltage supply, while data rate is 12 MHz in this case. The given data rates are provided for the worst case scenario, where input currents differ by 1%-2% only. In this case, the settling time of the comparators used in the DCC is quite long. However, that kind of situation is very rare in the overall learning process. PMID:26087501

  12. A low-power 32-channel digitally programmable neural recording integrated circuit.

    Science.gov (United States)

    Wattanapanitch, W; Sarpeshkar, R

    2011-12-01

    We report the design of an ultra-low-power 32-channel neural-recording integrated circuit (chip) in a 0.18 μ m CMOS technology. The chip consists of eight neural recording modules where each module contains four neural amplifiers, an analog multiplexer, an A/D converter, and a serial programming interface. Each amplifier can be programmed to record either spikes or LFPs with a programmable gain from 49-66 dB. To minimize the total power consumption, an adaptive-biasing scheme is utilized to adjust each amplifier's input-referred noise to suit the background noise at the recording site. The amplifier's input-referred noise can be adjusted from 11.2 μVrms (total power of 5.4 μW) down to 5.4 μVrms (total power of 20 μW) in the spike-recording setting. The ADC in each recording module digitizes the a.c. signal input to each amplifier at 8-bit precision with a sampling rate of 31.25 kS/s per channel, with an average power consumption of 483 nW per channel, and, because of a.c. coupling, allows d.c. operation over a wide dynamic range. It achieves an ENOB of 7.65, resulting in a net efficiency of 77 fJ/State, making it one of the most energy-efficient designs for neural recording applications. The presented chip was successfully tested in an in vivo wireless recording experiment from a behaving primate with an average power dissipation per channel of 10.1 μ W. The neural amplifier and the ADC occupy areas of 0.03 mm(2) and 0.02 mm(2) respectively, making our design simultaneously area efficient and power efficient, thus enabling scaling to high channel-count systems.

  13. A multichannel integrated circuit for neural spike detection based on EC-PC threshold estimation.

    Science.gov (United States)

    Wu, Tong; Yang, Zhi

    2013-01-01

    In extracellular neural recording experiments, spike detection is an important step for information decoding of neuronal activities. An ASIC implementation of detection algorithms can provide substantial data-rate reduction and facilitate wireless operations. In this paper, we present a 16-channel neural spike detection ASIC. The chip takes raw data as inputs, and outputs three data streams simultaneously: field potentials down sampled at 1.25 KHz, band-pass filtered neural data, and spiking probability maps sampled at 40 KHz. The functionality and the performance of the chip have been verified in both in-vivo and benchtop experiments. Fabricated in a 0.13 µm CMOS process, the chip has a peak power dissipation of 85 µW per channel and achieves a data-rate reduction of 98.44%.

  14. Circuit Design: An inquiry lab activity at Maui Community College

    OpenAIRE

    Morzinski, Katie; Azucena, Oscar; Downs, Cooper; Favaloro, Tela; Park, Jung; U, Vivian

    2010-01-01

    We present an inquiry lab activity on Circuit Design that was conducted in Fall 2009 with first-year community college students majoring in Electrical Engineering Technology. This inquiry emphasized the use of engineering process skills, including circuit assembly and problem solving, while learning technical content. Content goals of the inquiry emphasized understanding voltage dividers (Kirchoff's voltage law) and analysis and optimization of resistive networks (Thevenin equivalence). We as...

  15. Recruitment of Polysynaptic Connections Underlies Functional Recovery of a Neural Circuit after Lesion

    Science.gov (United States)

    Tamvacakis, Arianna N.

    2016-01-01

    Abstract The recruitment of additional neurons to neural circuits often occurs in accordance with changing functional demands. Here we found that synaptic recruitment plays a key role in functional recovery after neural injury. Disconnection of a brain commissure in the nudibranch mollusc, Tritonia diomedea, impairs swimming behavior by eliminating particular synapses in the central pattern generator (CPG) underlying the rhythmic swim motor pattern. However, the CPG functionally recovers within a day after the lesion. The strength of a spared inhibitory synapse within the CPG from Cerebral Neuron 2 (C2) to Ventral Swim Interneuron B (VSI) determines the level of impairment caused by the lesion, which varies among individuals. In addition to this direct synaptic connection, there are polysynaptic connections from C2 and Dorsal Swim Interneurons to VSI that provide indirect excitatory drive but play only minor roles under normal conditions. After disconnecting the pedal commissure (Pedal Nerve 6), the recruitment of polysynaptic excitation became a major source of the excitatory drive to VSI. Moreover, the amount of polysynaptic recruitment, which changed over time, differed among individuals and correlated with the degree of recovery of the swim motor pattern. Thus, functional recovery was mediated by an increase in the magnitude of polysynaptic excitatory drive, compensating for the loss of direct excitation. Since the degree of susceptibility to injury corresponds to existing individual variation in the C2 to VSI synapse, the recovery relied upon the extent to which the network reorganized to incorporate additional synapses.

  16. Measurements of the Effects of Smoke on Active Circuits

    Energy Technology Data Exchange (ETDEWEB)

    Tanaka, T.J.

    1999-02-09

    Smoke has long been recognized as the most common source of fire damage to electrical equipment; however, most failures have been analyzed after the fire was out and the smoke vented. The effects caused while the smoke is still in the air have not been explored. Such effects have implications for new digital equipment being installed in nuclear reactors. The U.S. Nuclear Regulatory Commission is sponsoring work to determine the impact of smoke on digital instrumentation and control. As part of this program, Sandia National Laboratories has tested simple active circuits to determine how smoke affects them. These tests included the study of three possible failure modes on a functional board: (1) circuit bridging, (2) corrosion (metal loss), and (3) induction of stray capacitance. The performance of nine different circuits was measured continuously on bare and conformably coated boards during smoke exposures lasting 1 hour each and continued for 24 hours after the exposure started. The circuit that was most affected by smoke (100% change in measured values) was the one most sensitive to circuit bridging. Its high impedance (50 M{Omega}) was shorted during the exposure, but in some cases recovered after the smoke was vented. The other two failure modes, corrosion and induced stray capacitance, caused little change in the function of the circuits. The smoke permanently increased resistance of the circuit tested for corrosion, implying that the cent acts were corroded. However, the change was very small (< 2%). The stray-capacitance test circuit showed very little change after a smoke exposure in either the short or long term. The results of the tests suggest that conformal coatings and type of circuit are major considerations when designing digital circuitry to be used in critical control systems.

  17. Measurements of the effects of smoke on active circuits

    International Nuclear Information System (INIS)

    Smoke has long been recognized as the most common source of fire damage to electrical equipment; however, most failures have been analyzed after the fire was out and the smoke vented. The effects caused while the smoke is still in the air have not been explored. Such effects have implications for new digital equipment being installed in nuclear reactors. The US Nuclear Regulatory Commission is sponsoring work to determine the impact of smoke on digital instrumentation and control. As part of this program, Sandia National Laboratories has tested simple active circuits to determine how smoke affects them. These tests included the study of three possible failure modes on a functional board: (1) circuit bridging, (2) corrosion (metal loss), and (3) induction of stray capacitance. The performance of nine different circuits was measured continuously on bare and conformally coated boards during smoke exposures lasting 1 hour each and continued for 24 hours after the exposure started. The circuit that was most affected by smoke (100% change in measured values) was the one most sensitive to circuit bridging. Its high impedance (50 Mohm) was shorted during the exposure, but in some cases recovered after the smoke was vented. The other two failure modes, corrosion and induced stray capacitance, caused little change in the function of the circuits. The smoke permanently increased resistance of the circuit tested for corrosion, implying that the contacts were corroded. However, the change was very small (< 2%). The stray capacitance test circuit showed very little change after a smoke exposure in either the short or long term. The results of the tests suggest that conformal coatings and type of circuit are major considerations when designing digital circuitry to be used in critical control systems

  18. Circuit Design: An inquiry lab activity at Maui Community College

    CERN Document Server

    Morzinski, Katie; Downs, Cooper; Favaloro, Tela; Park, Jung; U, Vivian

    2010-01-01

    We present an inquiry lab activity on Circuit Design that was conducted in Fall 2009 with first-year community college students majoring in Electrical Engineering Technology. This inquiry emphasized the use of engineering process skills, including circuit assembly and problem solving, while learning technical content. Content goals of the inquiry emphasized understanding voltage dividers (Kirchoff's voltage law) and analysis and optimization of resistive networks (Thevenin equivalence). We assumed prior exposure to series and parallel circuits and Ohm's law (the relationship between voltage, current, and resistance) and designed the inquiry to develop these skills. The inquiry utilized selection of engineering challenges on a specific circuit (the Wheatstone Bridge) to realize these learning goals. Students generated questions and observations during the starters, which were categorized into four engineering challenges or design goals. The students formed teams and chose one challenge to focus on during the i...

  19. Local circuit neurons in the striatum regulate neural and behavioral responses to dopaminergic stimulation

    OpenAIRE

    Saka, E.; Iadarola, M.; FitzGerald, D J; Graybiel, A M

    2002-01-01

    Interneurons are critical for shaping neuronal circuit activity in many parts of the central nervous system. To study interneuron function in the basal ganglia, we tested and characterized an NK-1 receptor-based method for targeted ablation of specific classes of interneuron in the striatum. Our findings demonstrate that the neurotoxin SP-PE35, a substance P–Pseudomonas exotoxin conjugate, selectively targets striatal cholinergic and nitric oxide synthase/somatostatinergic interneurons when i...

  20. MOUSE VISION AS A GATEWAY FOR UNDERSTANDING HOW EXPERIENCE SHAPES NEURAL CIRCUITS

    Directory of Open Access Journals (Sweden)

    Nicholas ePriebe

    2014-10-01

    Full Text Available Genetic programs controlling ontogeny drive many of the essential connectivity patterns within the brain. Yet it is activity, derived from the experience of interacting with the world, that sculpts the precise circuitry of the central nervous system. Such experience-dependent plasticity has been observed throughout the brain but has been most extensively studied in the neocortex. A prime example of this refinement of neural circuitry is found in primary visual cortex (V1, where functional connectivity changes have been observed both during development and in adulthood. The mouse visual system has become a predominant model for investigating the principles that underlie experience-dependent plasticity, given the general conservation of visual neural circuitry across mammals as well as the powerful tools and techniques recently developed for use in rodent. The genetic tractability of mice has permitted the identification of signaling pathways that translate experience-driven activity patterns into changes in circuitry. Further, the accessibility of visual cortex has allowed neural activity to be manipulated with optogenetics and observed with genetically-encoded calcium sensors. Consequently, mouse visual cortex has become one of the dominant platforms to study experience-dependent plasticity.

  1. Mouse vision as a gateway for understanding how experience shapes neural circuits

    Science.gov (United States)

    Priebe, Nicholas J.; McGee, Aaron W.

    2014-01-01

    Genetic programs controlling ontogeny drive many of the essential connectivity patterns within the brain. Yet it is activity, derived from the experience of interacting with the world, that sculpts the precise circuitry of the central nervous system. Such experience-dependent plasticity has been observed throughout the brain but has been most extensively studied in the neocortex. A prime example of this refinement of neural circuitry is found in primary visual cortex (V1), where functional connectivity changes have been observed both during development and in adulthood. The mouse visual system has become a predominant model for investigating the principles that underlie experience-dependent plasticity, given the general conservation of visual neural circuitry across mammals as well as the powerful tools and techniques recently developed for use in rodent. The genetic tractability of mice has permitted the identification of signaling pathways that translate experience-driven activity patterns into changes in circuitry. Further, the accessibility of visual cortex has allowed neural activity to be manipulated with optogenetics and observed with genetically-encoded calcium sensors. Consequently, mouse visual cortex has become one of the dominant platforms to study experience-dependent plasticity. PMID:25324730

  2. Information transmission in oscillatory neural activity

    CERN Document Server

    Koepsell, Kilian

    2008-01-01

    Periodic neural activity not locked to the stimulus or to motor responses is usually ignored. Here, we present new tools for modeling and quantifying the information transmission based on periodic neural activity that occurs with quasi-random phase relative to the stimulus. We propose a model to reproduce characteristic features of oscillatory spike trains, such as histograms of inter-spike intervals and phase locking of spikes to an oscillatory influence. The proposed model is based on an inhomogeneous Gamma process governed by a density function that is a product of the usual stimulus-dependent rate and a quasi-periodic function. Further, we present an analysis method generalizing the direct method (Rieke et al, 1999; Brenner et al, 2000) to assess the information content in such data. We demonstrate these tools on recordings from relay cells in the lateral geniculate nucleus of the cat.

  3. Neural activity associated with self-reflection

    OpenAIRE

    Herwig Uwe; Kaffenberger Tina; Schell Caroline; Jäncke Lutz; Brühl Annette B

    2012-01-01

    Abstract Background Self-referential cognitions are important for self-monitoring and self-regulation. Previous studies have addressed the neural correlates of self-referential processes in response to or related to external stimuli. We here investigated brain activity associated with a short, exclusively mental process of self-reflection in the absence of external stimuli or behavioural requirements. Healthy subjects reflected either on themselves, a personally known or an unknown person dur...

  4. Uncertainty-Dependent Extinction of Fear Memory in an Amygdala-mPFC Neural Circuit Model.

    Science.gov (United States)

    Li, Yuzhe; Nakae, Ken; Ishii, Shin; Naoki, Honda

    2016-09-01

    Uncertainty of fear conditioning is crucial for the acquisition and extinction of fear memory. Fear memory acquired through partial pairings of a conditioned stimulus (CS) and an unconditioned stimulus (US) is more resistant to extinction than that acquired through full pairings; this effect is known as the partial reinforcement extinction effect (PREE). Although the PREE has been explained by psychological theories, the neural mechanisms underlying the PREE remain largely unclear. Here, we developed a neural circuit model based on three distinct types of neurons (fear, persistent and extinction neurons) in the amygdala and medial prefrontal cortex (mPFC). In the model, the fear, persistent and extinction neurons encode predictions of net severity, of unconditioned stimulus (US) intensity, and of net safety, respectively. Our simulation successfully reproduces the PREE. We revealed that unpredictability of the US during extinction was represented by the combined responses of the three types of neurons, which are critical for the PREE. In addition, we extended the model to include amygdala subregions and the mPFC to address a recent finding that the ventral mPFC (vmPFC) is required for consolidating extinction memory but not for memory retrieval. Furthermore, model simulations led us to propose a novel procedure to enhance extinction learning through re-conditioning with a stronger US; strengthened fear memory up-regulates the extinction neuron, which, in turn, further inhibits the fear neuron during re-extinction. Thus, our models increased the understanding of the functional roles of the amygdala and vmPFC in the processing of uncertainty in fear conditioning and extinction. PMID:27617747

  5. Uncertainty-Dependent Extinction of Fear Memory in an Amygdala-mPFC Neural Circuit Model

    Science.gov (United States)

    Li, Yuzhe; Nakae, Ken; Ishii, Shin; Naoki, Honda

    2016-01-01

    Uncertainty of fear conditioning is crucial for the acquisition and extinction of fear memory. Fear memory acquired through partial pairings of a conditioned stimulus (CS) and an unconditioned stimulus (US) is more resistant to extinction than that acquired through full pairings; this effect is known as the partial reinforcement extinction effect (PREE). Although the PREE has been explained by psychological theories, the neural mechanisms underlying the PREE remain largely unclear. Here, we developed a neural circuit model based on three distinct types of neurons (fear, persistent and extinction neurons) in the amygdala and medial prefrontal cortex (mPFC). In the model, the fear, persistent and extinction neurons encode predictions of net severity, of unconditioned stimulus (US) intensity, and of net safety, respectively. Our simulation successfully reproduces the PREE. We revealed that unpredictability of the US during extinction was represented by the combined responses of the three types of neurons, which are critical for the PREE. In addition, we extended the model to include amygdala subregions and the mPFC to address a recent finding that the ventral mPFC (vmPFC) is required for consolidating extinction memory but not for memory retrieval. Furthermore, model simulations led us to propose a novel procedure to enhance extinction learning through re-conditioning with a stronger US; strengthened fear memory up-regulates the extinction neuron, which, in turn, further inhibits the fear neuron during re-extinction. Thus, our models increased the understanding of the functional roles of the amygdala and vmPFC in the processing of uncertainty in fear conditioning and extinction. PMID:27617747

  6. The atmospheric electric global circuit. [thunderstorm activity

    Science.gov (United States)

    Kasemir, H. W.

    1979-01-01

    The hypothesis that world thunderstorm activity represents the generator for the atmospheric electric current flow in the earth atmosphere between ground and the ionosphere is based on a close correlation between the magnitude and the diurnal variation of the supply current (thunderstorm generator current) and the load current (fair weather air-earth current density integrated over the earth surface). The advantages of using lightning survey satellites to furnish a base for accepting or rejecting the thunderstorm generator hypothesis are discussed.

  7. The primary visual cortex in the neural circuit for visual orienting

    Science.gov (United States)

    Zhaoping, Li

    The primary visual cortex (V1) is traditionally viewed as remote from influencing brain's motor outputs. However, V1 provides the most abundant cortical inputs directly to the sensory layers of superior colliculus (SC), a midbrain structure to command visual orienting such as shifting gaze and turning heads. I will show physiological, anatomical, and behavioral data suggesting that V1 transforms visual input into a saliency map to guide a class of visual orienting that is reflexive or involuntary. In particular, V1 receives a retinotopic map of visual features, such as orientation, color, and motion direction of local visual inputs; local interactions between V1 neurons perform a local-to-global computation to arrive at a saliency map that highlights conspicuous visual locations by higher V1 responses. The conspicuous location are usually, but not always, where visual input statistics changes. The population V1 outputs to SC, which is also retinotopic, enables SC to locate, by lateral inhibition between SC neurons, the most salient location as the saccadic target. Experimental tests of this hypothesis will be shown. Variations of the neural circuit for visual orienting across animal species, with more or less V1 involvement, will be discussed. Supported by the Gatsby Charitable Foundation.

  8. Serial Section Registration of Axonal Confocal Microscopy Datasets for Long-Range Neural Circuit Reconstruction

    Energy Technology Data Exchange (ETDEWEB)

    Hogrebe, Luke; Paiva, Antonio R.; Jurrus, Elizabeth R.; Christensen, Cameron; Bridge, Michael; Dai, Li; Pfeiffer, Rebecca; Hof, Patrick; Roysam, Badrinath; Korenberg, Julie; Tasdizen, Tolga

    2012-06-15

    In the context of long-range digital neural circuit reconstruction, this paper investigates an approach for registering axons across histological serial sections. Tracing distinctly labeled axons over large distances allows neuroscientists to study very explicit relationships between the brain's complex interconnects and, for example, diseases or aberrant development. Large scale histological analysis requires, however, that the tissue be cut into sections. In immunohistochemical studies thin sections are easily distorted due to the cutting, preparation, and slide mounting processes. In this work we target the registration of thin serial sections containing axons. Sections are first traced to extract axon centerlines, and these traces are used to define registration landmarks where they intersect section boundaries. The trace data also provides distinguishing information regarding an axon's size and orientation within a section. We propose the use of these features when pairing axons across sections in addition to utilizing the spatial relationships amongst the landmarks. The global rotation and translation of an unregistered section are accounted for using a random sample consensus (RANSAC) based technique. An iterative nonrigid refinement process using B-spline warping is then used to reconnect axons and produce the sought after connectivity information.

  9. A Dual Infection Pseudorabies Virus Conditional Reporter Approach to Identify Projections to Collateralized Neurons in Complex Neural Circuits

    OpenAIRE

    J Patrick Card; Oren Kobiler; Ludmir, Ethan B.; Vedant Desai; Sved, Alan F.; Enquist, Lynn W.

    2011-01-01

    Replication and transneuronal transport of pseudorabies virus (PRV) are widely used to define the organization of neural circuits in rodent brain. Here we report a dual infection approach that highlights connections to neurons that collateralize within complex networks. The method combines Cre recombinase (Cre) expression from a PRV recombinant (PRV-267) and Cre-dependent reporter gene expression from a second infecting strain of PRV (PRV-263). PRV-267 expresses both Cre and a monomeric red f...

  10. Neural circuits underlying mother's voice perception predict social communication abilities in children.

    Science.gov (United States)

    Abrams, Daniel A; Chen, Tianwen; Odriozola, Paola; Cheng, Katherine M; Baker, Amanda E; Padmanabhan, Aarthi; Ryali, Srikanth; Kochalka, John; Feinstein, Carl; Menon, Vinod

    2016-05-31

    The human voice is a critical social cue, and listeners are extremely sensitive to the voices in their environment. One of the most salient voices in a child's life is mother's voice: Infants discriminate their mother's voice from the first days of life, and this stimulus is associated with guiding emotional and social function during development. Little is known regarding the functional circuits that are selectively engaged in children by biologically salient voices such as mother's voice or whether this brain activity is related to children's social communication abilities. We used functional MRI to measure brain activity in 24 healthy children (mean age, 10.2 y) while they attended to brief (social function. Compared to female control voices, mother's voice elicited greater activity in primary auditory regions in the midbrain and cortex; voice-selective superior temporal sulcus (STS); the amygdala, which is crucial for processing of affect; nucleus accumbens and orbitofrontal cortex of the reward circuit; anterior insula and cingulate of the salience network; and a subregion of fusiform gyrus associated with face perception. The strength of brain connectivity between voice-selective STS and reward, affective, salience, memory, and face-processing regions during mother's voice perception predicted social communication skills. Our findings provide a novel neurobiological template for investigation of typical social development as well as clinical disorders, such as autism, in which perception of biologically and socially salient voices may be impaired. PMID:27185915

  11. Cholinergic interneurons control local circuit activity and cocaine conditioning.

    Science.gov (United States)

    Witten, Ilana B; Lin, Shih-Chun; Brodsky, Matthew; Prakash, Rohit; Diester, Ilka; Anikeeva, Polina; Gradinaru, Viviana; Ramakrishnan, Charu; Deisseroth, Karl

    2010-12-17

    Cholinergic neurons are widespread, and pharmacological modulation of acetylcholine receptors affects numerous brain processes, but such modulation entails side effects due to limitations in specificity for receptor type and target cell. As a result, causal roles of cholinergic neurons in circuits have been unclear. We integrated optogenetics, freely moving mammalian behavior, in vivo electrophysiology, and slice physiology to probe the cholinergic interneurons of the nucleus accumbens by direct excitation or inhibition. Despite representing less than 1% of local neurons, these cholinergic cells have dominant control roles, exerting powerful modulation of circuit activity. Furthermore, these neurons could be activated by cocaine, and silencing this drug-induced activity during cocaine exposure (despite the fact that the manipulation of the cholinergic interneurons was not aversive by itself) blocked cocaine conditioning in freely moving mammals.

  12. New exponential synchronization criteria for time-varying delayed neural networks with discontinuous activations.

    Science.gov (United States)

    Cai, Zuowei; Huang, Lihong; Zhang, Lingling

    2015-05-01

    This paper investigates the problem of exponential synchronization of time-varying delayed neural networks with discontinuous neuron activations. Under the extended Filippov differential inclusion framework, by designing discontinuous state-feedback controller and using some analytic techniques, new testable algebraic criteria are obtained to realize two different kinds of global exponential synchronization of the drive-response system. Moreover, we give the estimated rate of exponential synchronization which depends on the delays and system parameters. The obtained results extend some previous works on synchronization of delayed neural networks not only with continuous activations but also with discontinuous activations. Finally, numerical examples are provided to show the correctness of our analysis via computer simulations. Our method and theoretical results have a leading significance in the design of synchronized neural network circuits involving discontinuous factors and time-varying delays.

  13. Neural circuits containing olfactory neurons are involved in prepulse inhibition of the startle reflex in rats

    Directory of Open Access Journals (Sweden)

    Haichen eNiu

    2015-03-01

    Full Text Available Many neuropsychiatric disorders, such as schizophrenia, have been associated with abnormalities in the function of the olfactory system and prepulse inhibition (PPI of the startle reflex. However, whether these two abnormalities are related is unclear. The present study was designed to determine whether inhibiting olfactory sensory input via the infusion of zinc sulfate (ZnE, 0.17 M, 0.5 ml into the olfactory naris disrupts PPI. Furthermore, lidocaine/MK801 was bilaterally microinjected into the olfactory bulb (OB to examine whether the blockade of olfactory sensory input impairs PPI. To identify the neural projections that connect the olfaction- and PPI-related areas of the CNS, trans-synaptic retrograde tracing using a recombinant pseudorabies virus (PRV was performed. Our results demonstrated that blocking olfactory sensory input altered olfaction-related behavior. At the functional level, we demonstrated that the inhibition of olfactory sensory input impaired PPI of the startle response subsequent to a decrease in c-fos expression in relevant brain regions. Furthermore, the results of a similar and more robust experiment indicated that blocking olfactory sensory input via the microinjection of lidocaine/MK801 into the OB impaired PPI. At the circuit level, based on trans-synaptic retrograde tracing using PRV, we demonstrated that a large portion of the labeled neurons in several regions of the olfactory cortices connected to the pedunculopontine tegmental nucleus (PPTg. Thus, these data suggest that the olfactory system participates in the regulation of PPI and plays a role in the effect of PPI on the startle response in rats.

  14. Peripheral neural activity recording and stimulation system.

    Science.gov (United States)

    Loi, D; Carboni, C; Angius, G; Angotzi, G N; Barbaro, M; Raffo, L; Raspopovic, S; Navarro, X

    2011-08-01

    This paper presents a portable, embedded, microcontroller-based system for bidirectional communication (recording and stimulation) between an electrode, implanted in the peripheral nervous system, and a host computer. The device is able to record and digitize spontaneous and/or evoked neural activities and store them in data files on a PC. In addition, the system has the capability of providing electrical stimulation of peripheral nerves, injecting biphasic current pulses with programmable duration, intensity, and frequency. The recording system provides a highly selective band-pass filter from 800 Hz to 3 kHz, with a gain of 56 dB. The amplification range can be further extended to 96 dB with a variable gain amplifier. The proposed acquisition/stimulation circuitry has been successfully tested through in vivo measurements, implanting a tf-LIFE electrode in the sciatic nerve of a rat. Once implanted, the device showed an input referred noise of 0.83 μVrms, was capable of recording signals below 10 μ V, and generated muscle responses to injected stimuli. The results demonstrate the capability of processing and transmitting neural signals with very low distortion and with a power consumption lower than 1 W. A graphic, user-friendly interface has been developed to facilitate the configuration of the entire system, providing the possibility to activate stimulation and monitor recordings in real time.

  15. Long-range neural activity evoked by premotor cortex stimulation: a TMS/EEG co-registration study

    Directory of Open Access Journals (Sweden)

    Marco eZanon

    2013-11-01

    Full Text Available The premotor cortex is one of the fundamental structures composing the neural networks of the human brain. It is implicated in many behaviors and cognitive tasks, ranging from movement to attention and eye-related activity. Therefore, neural circuits that are related to premotor cortex have been studied to clarify their connectivity and/or role in different tasks. In the present work, we aimed to investigate the propagation of the neural activity evoked in the dorsal premotor cortex using transcranial magnetic stimulation/electroencephalography (TMS/EEG. Towards this end, interest was focused on the neural dynamics elicited in long-ranging temporal and spatial networks. Twelve healthy volunteers underwent a single-pulse TMS protocol in a resting condition with eyes closed, and the evoked activity, measured by EEG, was compared to a sham condition in a time window ranging from 45 msec to about 200 msec after TMS. Spatial and temporal investigations were carried out with sLORETA. TMS was found to induce propagation of neural activity mainly in the contralateral sensorimotor and frontal cortices, at about 130 msec after delivery of the stimulus. Different types of analyses showed propagated activity also in posterior, mainly visual, regions, in a time window between 70 and 130 msec. Finally, a likely rebounding activation of the sensorimotor and frontal regions, was observed in various time ranges. Taken together, the present findings further characterize the neural circuits that are driven by dorsal premotor cortex activation in healthy humans.

  16. Single-Cell Memory Regulates a Neural Circuit for Sensory Behavior

    Directory of Open Access Journals (Sweden)

    Kyogo Kobayashi

    2016-01-01

    Full Text Available Unveiling the molecular and cellular mechanisms underlying memory has been a challenge for the past few decades. Although synaptic plasticity is proven to be essential for memory formation, the significance of “single-cell memory” still remains elusive. Here, we exploited a primary culture system for the analysis of C. elegans neurons and show that a single thermosensory neuron has an ability to form, retain, and reset a temperature memory. Genetic and proteomic analyses found that the expression of the single-cell memory exhibits inter-individual variability, which is controlled by the evolutionarily conserved CaMKI/IV and Raf pathway. The variable responses of a sensory neuron influenced the neural activity of downstream interneurons, suggesting that modulation of the sensory neurons ultimately determines the behavioral output in C. elegans. Our results provide proof of single-cell memory and suggest that the individual differences in neural responses at the single-cell level can confer individuality.

  17. A dopamine-modulated neural circuit regulating aversive taste memory in Drosophila.

    Science.gov (United States)

    Masek, Pavel; Worden, Kurtresha; Aso, Yoshinori; Rubin, Gerald M; Keene, Alex C

    2015-06-01

    Taste memories allow animals to modulate feeding behavior in accordance with past experience and avoid the consumption of potentially harmful food [1]. We have developed a single-fly taste memory assay to functionally interrogate the neural circuitry encoding taste memories [2]. Here, we screen a collection of Split-GAL4 lines that label small populations of neurons associated with the fly memory center-the mushroom bodies (MBs) [3]. Genetic silencing of PPL1 dopamine neurons disrupts conditioned, but not naive, feeding behavior, suggesting these neurons are selectively involved in the conditioned taste response. We identify two PPL1 subpopulations that innervate the MB α lobe and are essential for aversive taste memory. Thermogenetic activation of these dopamine neurons during training induces memory, indicating these neurons are sufficient for the reinforcing properties of bitter tastant to the MBs. Silencing of either the intrinsic MB neurons or the output neurons from the α lobe disrupts taste conditioning. Thermogenetic manipulation of these output neurons alters naive feeding response, suggesting that dopamine neurons modulate the threshold of response to appetitive tastants. Taken together, these findings detail a neural mechanism underlying the formation of taste memory and provide a functional model for dopamine-dependent plasticity in Drosophila.

  18. Distinct rhythmic locomotor patterns can be generated by a simple adaptive neural circuit: biology, simulation, and VLSI implementation.

    Science.gov (United States)

    Ryckebusch, S; Wehr, M; Laurent, G

    1994-12-01

    Rhythmic motor patterns can be induced in leg motor neurons of isolated locust thoracic ganglia by bath application of pilocarpine. We observed that the relative phases of levators and depressors differed in the three thoracic ganglia. Assuming that the central pattern generating circuits underlying these three segmental rhythms are probably very similar, we developed a simple model circuit that can produce any one of the three activity patterns and characteristic phase relationships by modifying a single synaptic weight. We show results of a computer simulation of this circuit using the neuronal simulator NeuraLOG/Spike. We built and tested an analog VLSI circuit implementation of this model circuit that exhibits the same range of "behaviors" as the computer simulation. This multidisciplinary strategy will be useful to explore the dynamics of central pattern generating networks coupled to physical actuators, and ultimately should allow the design of biologically realistic walking robots.

  19. Exposure to Forced Swim Stress Alters Local Circuit Activity and Plasticity in the Dentate Gyrus of the Hippocampus

    Directory of Open Access Journals (Sweden)

    Mouna Maroun

    2008-02-01

    Full Text Available Studies have shown that, depending on its severity and context, stress can affect neural plasticity. Most related studies focused on synaptic plasticity and long-term potentiation (LTP of principle cells. However, evidence suggests that following high-frequency stimulation, which induces LTP in principal cells, modifications also take place at the level of complex interactions with interneurons within the dentate gyrus, that is, at the local circuit level. So far, the possible effects of stress on local circuit activity and plasticity were not studied. Therefore, we set out to examine the possible alterations in local circuit activity and plasticity following exposure to stress. Local circuit activity and plasticity were measured by using frequency dependant inhibition (FDI and commissural modulation protocols following exposure to a 15 minute-forced swim trial. Exposure to stress did not alter FDI. The application of theta-burst stimulation (TBS reduced FDI in both control and stressed rats, but this type of plasticity was greater in stressed rats. Commissural-induced inhibition was significantly higher in stressed rats both before and after applying theta-burst stimulation. These findings indicate that the exposure to acute stress affects aspects of local circuit activity and plasticity in the dentate gyrus. It is possible that these alterations underlie some of the behavioral consequences of the stress experience.

  20. Exposure to forced swim stress alters local circuit activity and plasticity in the dentate gyrus of the hippocampus.

    Science.gov (United States)

    Yarom, Orli; Maroun, Mouna; Richter-Levin, Gal

    2008-01-01

    Studies have shown that, depending on its severity and context, stress can affect neural plasticity. Most related studies focused on synaptic plasticity and long-term potentiation (LTP) of principle cells. However, evidence suggests that following high-frequency stimulation, which induces LTP in principal cells, modifications also take place at the level of complex interactions with interneurons within the dentate gyrus, that is, at the local circuit level. So far, the possible effects of stress on local circuit activity and plasticity were not studied. Therefore, we set out to examine the possible alterations in local circuit activity and plasticity following exposure to stress. Local circuit activity and plasticity were measured by using frequency dependant inhibition (FDI) and commissural modulation protocols following exposure to a 15 minute-forced swim trial. Exposure to stress did not alter FDI. The application of theta-burst stimulation (TBS) reduced FDI in both control and stressed rats, but this type of plasticity was greater in stressed rats. Commissural-induced inhibition was significantly higher in stressed rats both before and after applying theta-burst stimulation. These findings indicate that the exposure to acute stress affects aspects of local circuit activity and plasticity in the dentate gyrus. It is possible that these alterations underlie some of the behavioral consequences of the stress experience.

  1. Successful reconstruction of a physiological circuit with known connectivity from spiking activity alone.

    Directory of Open Access Journals (Sweden)

    Felipe Gerhard

    Full Text Available Identifying the structure and dynamics of synaptic interactions between neurons is the first step to understanding neural network dynamics. The presence of synaptic connections is traditionally inferred through the use of targeted stimulation and paired recordings or by post-hoc histology. More recently, causal network inference algorithms have been proposed to deduce connectivity directly from electrophysiological signals, such as extracellularly recorded spiking activity. Usually, these algorithms have not been validated on a neurophysiological data set for which the actual circuitry is known. Recent work has shown that traditional network inference algorithms based on linear models typically fail to identify the correct coupling of a small central pattern generating circuit in the stomatogastric ganglion of the crab Cancer borealis. In this work, we show that point process models of observed spike trains can guide inference of relative connectivity estimates that match the known physiological connectivity of the central pattern generator up to a choice of threshold. We elucidate the necessary steps to derive faithful connectivity estimates from a model that incorporates the spike train nature of the data. We then apply the model to measure changes in the effective connectivity pattern in response to two pharmacological interventions, which affect both intrinsic neural dynamics and synaptic transmission. Our results provide the first successful application of a network inference algorithm to a circuit for which the actual physiological synapses between neurons are known. The point process methodology presented here generalizes well to larger networks and can describe the statistics of neural populations. In general we show that advanced statistical models allow for the characterization of effective network structure, deciphering underlying network dynamics and estimating information-processing capabilities.

  2. An active control synchronization for two modified Chua circuits

    Science.gov (United States)

    Li, Guo-Hui

    2005-03-01

    From modern control theory, an active control method to synchronize two modified Chua circuits with each other, which exhibit chaos, is presented. Some sufficient conditions of linear stability of the chaotic synchronization are obtained from rigorous mathematic justification. On the basis of the state-observer, the controller is analytically deduced using the active control. It is shown that this technique can be applied to achieve synchronization of the two systems with each other, whether they are identical or not. Finally, numerical simulations show the effectiveness of the proposed control scheme.

  3. An active control synchronization for two modified Chua circuits

    Institute of Scientific and Technical Information of China (English)

    Li Guo-Hui

    2005-01-01

    From modern control theory, an active control method to synchronize two modified Chua circuits with each other, which exhibit chaos, is presented. Some sufficient conditions of linear stability of the chaotic synchronization are obtained from rigorous mathematic justification. On the basis of the state-observer, the controller is analytically deduced using the active control. It is shown that this technique can be applied to achieve synchroniztion of the tow systems with each other, whether they are identical or not. Finally, numerical simulations show the effectiveness of the proposed control scheme.

  4. Altered behavioral performance and live imaging of circuit-specific neural deficiencies in a zebrafish model for psychomotor retardation.

    Directory of Open Access Journals (Sweden)

    David Zada

    2014-09-01

    Full Text Available The mechanisms and treatment of psychomotor retardation, which includes motor and cognitive impairment, are indefinite. The Allan-Herndon-Dudley syndrome (AHDS is an X-linked psychomotor retardation characterized by delayed development, severe intellectual disability, muscle hypotonia, and spastic paraplegia, in combination with disturbed thyroid hormone (TH parameters. AHDS has been associated with mutations in the monocarboxylate transporter 8 (mct8/slc16a2 gene, which is a TH transporter. In order to determine the pathophysiological mechanisms of AHDS, MCT8 knockout mice were intensively studied. Although these mice faithfully replicated the abnormal serum TH levels, they failed to exhibit the neurological and behavioral symptoms of AHDS patients. Here, we generated an mct8 mutant (mct8-/- zebrafish using zinc-finger nuclease (ZFN-mediated targeted gene editing system. The elimination of MCT8 decreased the expression levels of TH receptors; however, it did not affect the expression of other TH-related genes. Similar to human patients, mct8-/- larvae exhibited neurological and behavioral deficiencies. High-throughput behavioral assays demonstrated that mct8-/- larvae exhibited reduced locomotor activity, altered response to external light and dark transitions and an increase in sleep time. These deficiencies in behavioral performance were associated with altered expression of myelin-related genes and neuron-specific deficiencies in circuit formation. Time-lapse imaging of single-axon arbors and synapses in live mct8-/- larvae revealed a reduction in filopodia dynamics and axon branching in sensory neurons and decreased synaptic density in motor neurons. These phenotypes enable assessment of the therapeutic potential of three TH analogs that can enter the cells in the absence of MCT8. The TH analogs restored the myelin and axon outgrowth deficiencies in mct8-/- larvae. These findings suggest a mechanism by which MCT8 regulates neural circuit

  5. Modiifcation of tenascin-R expression following unilateral labyrinthectomy in rats indicates its possible role in neural plasticity of the vestibular neural circuit

    Institute of Scientific and Technical Information of China (English)

    Botond Gaal; Einar rn Jhannesson; Amit Dattani; Agnes Magyar; Ildik Wber; Clara Matesz

    2015-01-01

    We have previously found that unilateral labyrinthectomy is accompanied by modiifcation of hyaluronan and chondroitin sulfate proteoglycan staining in the lateral vestibular nucleus of rats and the time course of subsequent reorganization of extracellular matrix assembly cor-relates to the restoration of impaired vestibular function. The tenascin-R has repelling effect on pathfinding during axonal growth/regrowth, and thus inhibits neural circuit repair. By using immunohistochemical method, we studied the modification of tenascin-R expression in the superior, medial, lateral, and descending vestibular nuclei of the rat following unilateral labyrin-thectomy. On postoperative day 1, tenascin-R reaction in the perineuronal nets disappeared on the side of labyrinthectomy in the superior, lateral, medial, and rostral part of the descending vestibular nuclei. On survival day 3, the staining intensity of tenascin-R reaction in perineuronal nets recovered on the operated side of the medial vestibular nucleus, whereas it was restored by the time of postoperative day 7 in the superior, lateral and rostral part of the descending vestib-ular nuclei. The staining intensity of tenascin-R reaction remained unchanged in the caudal part of the descending vestibular nucleus bilaterally. Regional differences in the modiifcation of tena-scin-R expression presented here may be associated with different roles of individual vestibular nuclei in the compensatory processes. The decreased expression of the tenascin-R may suggest the extracellular facilitation of plastic modiifcations in the vestibular neural circuit after lesion of the labyrinthine receptors.

  6. Active Snubber Circuit for High Power Inverter Leg

    DEFF Research Database (Denmark)

    Rasmussen, Tonny Wederberg; Johansen, Morten Holst

    2009-01-01

    Abstract— High power converters in the conventional 6 pulse configuration with 6 switching elements IGBTs (Insulated Gate Bipolar Transistor) are pushed to the limit of power. Especially the switching loss is high. This reduces the switching frequency due to cooling problems. Passive snubber...... circuits have been introduced to reduce the loss even though some of the loss is removed from the IGBT to the snubber resistance. This paper takes also the next step to introduce the active Undeland snubber which in principle is lossless. The paper describes this solution together with some simulations...

  7. Active Match Load Circuit Intended for Testing Piezoelectric Transformers

    DEFF Research Database (Denmark)

    Andersen, Thomas; Rødgaard, Martin Schøler; Andersen, Michael A. E.

    2012-01-01

    famous Rosen type design back in the 1950s. After the discovered of new piezoelectric materials and new PT designs have been invented, the PT based power converters are in the area where they can outperform tradition electromagnetic based converters in certain applications. The performance of PTs can......An adjustable high voltage active load circuit for voltage amplitudes above 100 volts, especially intended for resistive matching the output impedance of a piezoelectric transformer (PT) is proposed in this paper. PTs have been around for over 50 years, were C. A. Rosen is common known for his...

  8. The use of brain imaging to elucidate neural circuit changes in cocaine addiction.

    Science.gov (United States)

    Hanlon, Colleen A; Canterberry, Melanie

    2012-09-01

    Within substance abuse, neuroimaging has experienced tremendous growth as both a research method and a clinical tool in the last decade. The application of functional imaging methods to cocaine dependent patients and individuals in treatment programs, has revealed that the effects of cocaine are not limited to dopamine-rich subcortical structures, but that the cortical projection areas are also disrupted in cocaine dependent patients. In this review, we will first describe several of the imaging methods that are actively being used to address functional and structural abnormalities in addiction. This will be followed by an overview of the cortical and subcortical brain regions that are most often cited as dysfunctional in cocaine users. We will also introduce functional connectivity analyses currently being used to investigate interactions between these cortical and subcortical areas in cocaine users and abstainers. Finally, this review will address recent research which demonstrates that alterations in the functional connectivity in cocaine users may be associated with structural pathology in these circuits, as demonstrated through diffusion tensor imaging. Through the use of these tools in both a basic science setting and as applied to treatment seeking individuals, we now have a greater understanding of the complex cortical and subcortical networks which contribute to the stages of initial craving, dependence, abstinence, and relapse. Although the ability to use neuroimaging to predict treatment response or identify vulnerable populations is still in its infancy, the next decade holds tremendous promise for using neuroimaging to tailor either behavioral or pharmacologic treatment interventions to the individual. PMID:23162375

  9. Activity patterns of cultured neural networks on micro electrode arrays

    NARCIS (Netherlands)

    Rutten, W.L.C.; Pelt, van J.

    2001-01-01

    A hybrid neuro-electronic interface is a cell-cultured micro electrode array, acting as a neural information transducer for stimulation and/or recording of neural activity in the brain or the spinal cord (ventral motor region or dorsal sensory region). It consists of an array of micro electrodes on

  10. Comprehensive figures of merit for passive and active plasmonic circuits

    CERN Document Server

    Krasavin, Alexey V

    2015-01-01

    In this article a comprehensive figures of merit for both passive and active plasmonic circuit components are introduced, benchmarking their performance for the realisation of high-bandwidth optical data communication in electronic chips. For the first time the figure of merit for passive plasmonic interconnects has been derived in terms of ultimate global characteristics of the plasmonic circuitry, particularly bandwidth and power consumption densities. Then, these parameters were linked to the local waveguide characteristics, such as mode propagation length, bending radius, etc. The figure has been applied to provide a comprehensive comparison to the main types of the plasmonics waveguides and can serve as an excellent benchmark for future designs. Completing the development of broadband optical on-chip data communication, we developed an all-inclusive figure of merit for active photonic- or plasmonic-based electro-optic modulators, establishing the communication between electronic and photonic chip domains...

  11. Synaptic plasticity, neural circuits and the emerging role of altered short-term information processing in schizophrenia

    Directory of Open Access Journals (Sweden)

    Gregg W. Crabtree

    2014-11-01

    Full Text Available Synaptic plasticity alters the strength of information flow between presynaptic and postsynaptic neurons and thus modifies the likelihood that action potentials in a presynaptic neuron will lead to an action potential in a postsynaptic neuron. As such, synaptic plasticity and pathological changes in synaptic plasticity impact the synaptic computation which controls the information flow through the neural microcircuits responsible for the complex information processing necessary to drive adaptive behaviors. As current theories of neuropsychiatric disease suggest that distinct dysfunctions in neural circuit performance may critically underlie the unique symptoms of these diseases, pathological alterations in synaptic plasticity mechanisms may be fundamental to the disease process. Here we consider mechanisms of both short-term and long-term plasticity of synaptic transmission and their possible roles in information processing by neural microcircuits in both health and disease. As paradigms of neuropsychiatric diseases with strongly implicated risk genes, we discuss the findings in schizophrenia and autism and consider the alterations in synaptic plasticity and network function observed in both human studies and genetic mouse models of these diseases. Together these studies have begun to point towards a likely dominant role of short-term synaptic plasticity alterations in schizophrenia while dysfunction in autism spectrum disorders may be due to a combination of both short-term and long-term synaptic plasticity alterations.

  12. Embedding responses in spontaneous neural activity shaped through sequential learning.

    Directory of Open Access Journals (Sweden)

    Tomoki Kurikawa

    Full Text Available Recent experimental measurements have demonstrated that spontaneous neural activity in the absence of explicit external stimuli has remarkable spatiotemporal structure. This spontaneous activity has also been shown to play a key role in the response to external stimuli. To better understand this role, we proposed a viewpoint, "memories-as-bifurcations," that differs from the traditional "memories-as-attractors" viewpoint. Memory recall from the memories-as-bifurcations viewpoint occurs when the spontaneous neural activity is changed to an appropriate output activity upon application of an input, known as a bifurcation in dynamical systems theory, wherein the input modifies the flow structure of the neural dynamics. Learning, then, is a process that helps create neural dynamical systems such that a target output pattern is generated as an attractor upon a given input. Based on this novel viewpoint, we introduce in this paper an associative memory model with a sequential learning process. Using a simple hebbian-type learning, the model is able to memorize a large number of input/output mappings. The neural dynamics shaped through the learning exhibit different bifurcations to make the requested targets stable upon an increase in the input, and the neural activity in the absence of input shows chaotic dynamics with occasional approaches to the memorized target patterns. These results suggest that these dynamics facilitate the bifurcations to each target attractor upon application of the corresponding input, which thus increases the capacity for learning. This theoretical finding about the behavior of the spontaneous neural activity is consistent with recent experimental observations in which the neural activity without stimuli wanders among patterns evoked by previously applied signals. In addition, the neural networks shaped by learning properly reflect the correlations of input and target-output patterns in a similar manner to those designed in

  13. Visual motion imagery neurofeedback based on the hMT+/V5 complex: evidence for a feedback-specific neural circuit involving neocortical and cerebellar regions

    Science.gov (United States)

    Banca, Paula; Sousa, Teresa; Catarina Duarte, Isabel; Castelo-Branco, Miguel

    2015-12-01

    Objective. Current approaches in neurofeedback/brain-computer interface research often focus on identifying, on a subject-by-subject basis, the neural regions that are best suited for self-driven modulation. It is known that the hMT+/V5 complex, an early visual cortical region, is recruited during explicit and implicit motion imagery, in addition to real motion perception. This study tests the feasibility of training healthy volunteers to regulate the level of activation in their hMT+/V5 complex using real-time fMRI neurofeedback and visual motion imagery strategies. Approach. We functionally localized the hMT+/V5 complex to further use as a target region for neurofeedback. An uniform strategy based on motion imagery was used to guide subjects to neuromodulate hMT+/V5. Main results. We found that 15/20 participants achieved successful neurofeedback. This modulation led to the recruitment of a specific network as further assessed by psychophysiological interaction analysis. This specific circuit, including hMT+/V5, putative V6 and medial cerebellum was activated for successful neurofeedback runs. The putamen and anterior insula were recruited for both successful and non-successful runs. Significance. Our findings indicate that hMT+/V5 is a region that can be modulated by focused imagery and that a specific cortico-cerebellar circuit is recruited during visual motion imagery leading to successful neurofeedback. These findings contribute to the debate on the relative potential of extrinsic (sensory) versus intrinsic (default-mode) brain regions in the clinical application of neurofeedback paradigms. This novel circuit might be a good target for future neurofeedback approaches that aim, for example, the training of focused attention in disorders such as ADHD.

  14. Downstream effect of ramping neural activity through synapses with short-term plasticity

    Science.gov (United States)

    Wei, Wei; Wang, Xiao-Jing

    2016-01-01

    Ramping neuronal activity has been observed in multiple cortical areas correlated with evidence accumulation processes or timing. In this work we investigate the downstream effect of ramping neuronal activity through synapses that display short-term facilitation (STF) or depression (STD). We obtain an analytical result for a synapse driven by deterministic linear ramping input that exhibits pure STF or STD, and investigate the general case when both STF and STD exist numerically. In neural circuits, the ramping inputs usually have strong fluctuation and each downstream neuron receives converging inputs from many presynaptic neurons. We show that the analytical deterministic solution gives an accurate description of the averaging synaptic activation that a postsynaptic neuron receives in a neural circuit, even when the fluctuation in ramping input is strong. Therefore our work provides insights on the impact of ramping neuronal activity on downstream neurons through synapses displaying short-term plasticity. Specifically, activation of a synapse with STF shows a sublinear increase with time and is insensitive to the slopes of ramping inputs during the initial period, followed by a linear ramping similar to a synapse without STF. Activation of a synapse with STD, on the other hand, develops a local maximum before reaching a steady state, which is independent of the slope of ramping input. For a synapse displaying both STF and STD, increase of the depression time constant from a value much smaller than the facilitation time constant τF to a value much larger than τF leads to a transition from facilitation domination to depression domination. By utilizing STD in the corticostriatal synapses, our work provides an understanding of the saturation of striatal activity as observed for monkeys performing evidence accumulation. Our work also predicts that in the fixed duration version of motion discrimination tasks the stationary state of neuronal activity downstream to the

  15. Neural progenitor cells regulate microglia functions and activity.

    Science.gov (United States)

    Mosher, Kira I; Andres, Robert H; Fukuhara, Takeshi; Bieri, Gregor; Hasegawa-Moriyama, Maiko; He, Yingbo; Guzman, Raphael; Wyss-Coray, Tony

    2012-11-01

    We found mouse neural progenitor cells (NPCs) to have a secretory protein profile distinct from other brain cells and to modulate microglial activation, proliferation and phagocytosis. NPC-derived vascular endothelial growth factor was necessary and sufficient to exert at least some of these effects in mice. Thus, neural precursor cells may not only be shaped by microglia, but also regulate microglia functions and activity.

  16. Neural Activity Reveals Preferences Without Choices

    Science.gov (United States)

    Smith, Alec; Bernheim, B. Douglas; Camerer, Colin

    2014-01-01

    We investigate the feasibility of inferring the choices people would make (if given the opportunity) based on their neural responses to the pertinent prospects when they are not engaged in actual decision making. The ability to make such inferences is of potential value when choice data are unavailable, or limited in ways that render standard methods of estimating choice mappings problematic. We formulate prediction models relating choices to “non-choice” neural responses and use them to predict out-of-sample choices for new items and for new groups of individuals. The predictions are sufficiently accurate to establish the feasibility of our approach. PMID:25729468

  17. Macro-micro imaging of cardiac-neural circuits in co-cultures from normal and diseased hearts.

    Science.gov (United States)

    Bub, Gil; Burton, Rebecca-Ann B

    2015-07-15

    The autonomic nervous system plays an important role in the modulation of normal cardiac rhythm, but is also implicated in modulating the heart's susceptibility to re-entrant ventricular and atrial arrhythmias. The mechanisms by which the autonomic nervous system is pro-arrhythmic or anti-arrhythmic is multifaceted and varies for different types of arrhythmia and their cardiac substrates. Despite decades of research in this area, fundamental questions related to how neuron density and spatial organization modulate cardiac wave dynamics remain unanswered. These questions may be ill-posed in intact tissues where the activity of individual cells is often experimentally inaccessible. Development of simplified biological models that would allow us to better understand the influence of neural activation on cardiac activity can be beneficial. This Symposium Review summarizes the development of in vitro cardiomyocyte cell culture models of re-entrant activity, as well as challenges associated with extending these models to include the effects of neural activation.

  18. Matching tutor to student: rules and mechanisms for efficient two-stage learning in neural circuits

    CERN Document Server

    Tesileanu, Tiberiu; Balasubramanian, Vijay

    2016-01-01

    Existing models of birdsong learning assume that brain area LMAN introduces variability into song for trial-and-error learning. Recent data suggest that LMAN also encodes a corrective bias driving short-term improvements in song. These later consolidate in area RA, a motor cortex analogue downstream of LMAN. We develop a new model of such two-stage learning. Using a stochastic gradient descent approach, we derive how 'tutor' circuits should match plasticity mechanisms in 'student' circuits for efficient learning. We further describe a reinforcement learning framework with which the tutor can build its teaching signal. We show that mismatching the tutor signal and plasticity mechanism can impair or abolish learning. Applied to birdsong, our results predict the temporal structure of the corrective bias from LMAN given a plasticity rule in RA. Our framework can be applied predictively to other paired brain areas showing two-stage learning.

  19. Next-generation transgenic mice for optogenetic analysis of neural circuits

    Directory of Open Access Journals (Sweden)

    Brent eAsrican

    2013-11-01

    Full Text Available Here we characterize several new lines of transgenic mice useful for optogenetic analysis of brain circuit function. These mice express optogenetic probes, such as enhanced halorhodopsin or several different versions of channelrhodopsins, behind various neuron-specific promoters. These mice permit photoinhibition or photostimulation both in vitro and in vivo. Our results also reveal the important influence of fluorescent tags on optogenetic probe expression and function in transgenic mice.

  20. Active Engine Mounting Control Algorithm Using Neural Network

    Directory of Open Access Journals (Sweden)

    Fadly Jashi Darsivan

    2009-01-01

    Full Text Available This paper proposes the application of neural network as a controller to isolate engine vibration in an active engine mounting system. It has been shown that the NARMA-L2 neurocontroller has the ability to reject disturbances from a plant. The disturbance is assumed to be both impulse and sinusoidal disturbances that are induced by the engine. The performance of the neural network controller is compared with conventional PD and PID controllers tuned using Ziegler-Nichols. From the result simulated the neural network controller has shown better ability to isolate the engine vibration than the conventional controllers.

  1. Altered neural connectivity in excitatory and inhibitory cortical circuits in autism

    OpenAIRE

    Basilis Zikopoulos

    2013-01-01

    Converging evidence from diverse studies suggests that atypical brain connectivity in autism affects in distinct ways short- and long-range cortical pathways, disrupting neural communication and the balance of excitation and inhibition. This hypothesis is based mostly on functional non-invasive studies that show atypical synchronization and connectivity patterns between cortical areas in children and adults with autism. Indirect methods to study the course and integrity of major brain pathway...

  2. A neural circuit mechanism integrating motivational state with memory expression in Drosophila

    OpenAIRE

    Krashes, Michael J.; DasGupta, Shamik; Vreede, Andrew; White, Benjamin; Armstrong, J. Douglas; Waddell, Scott

    2009-01-01

    Motivational states are important determinants of behavior. In fruit flies appetitive memory expression is constrained by satiety and promoted by hunger. Here we identify a neural mechanism that integrates the motivational state of hunger and memory. We show that stimulation of neurons that express Neuropeptide F (dNPF), an ortholog of mammalian NPY, mimicks food-deprivation and promotes memory performance in satiated flies. Robust appetitive memory performance requires the dNPF receptor in s...

  3. Performance Evaluation a Developed Energy Harvesting Interface Circuit in Active Technique

    Directory of Open Access Journals (Sweden)

    Ramizi Mohamed

    2014-10-01

    Full Text Available This study presents the performance evaluation a developed energy harvesting interface circuit in active technique. The energy harvesting interface circuit for micro-power applications uses equivalent voltage of the piezoelectric materials have been developed and simulated. Circuit designs and simulation results are presented for a conventional diode rectifier with voltage doubler in passive technique. Most of the existing techniques are mainly passive-based energy harvesting circuits. Generally, the power harvesting capability of the passive technique is very low. To increase the harvested energy, the active technique and its components such as MOSFET, thyristor and transistor have chosen to design the proposed energy harvesting interface circuit. In this study, it has simulated both the conventional in passive circuit and developed energy harvester in active technique. The developed interface circuits consisting of piezoelectric element with input source of vibration, AC-DC thyristor doubler rectifier circuit and DC-DC boost converter using thyristor with storage device. In the development circuits, it is noted that the components thyristor instead of mainly diode available in conventional circuits have chosen. Because the forward voltage potential (0.7 V is higher than the incoming input voltage (0.2 V. Finally, the complete energy harvester using PSPICE software have designed and simulated. The proposed circuits in PSPICE generate the boost-up DC voltage up to 2 V. The overall efficiency of the developed circuit is 70%, followed by the software simulation, which is greater than conventional circuit efficiency of 20% in performance evaluator. It is concluded that the developed circuit output voltage can be used to operate for the applications in autonomous devices.

  4. Neural activity during encoding predicts false memories created by misinformation

    OpenAIRE

    Okado, Yoko; Stark, Craig E.L.

    2005-01-01

    False memories are often demonstrated using the misinformation paradigm, in which a person's recollection of a witnessed event is altered after exposure to misinformation about the event. The neural basis of this phenomenon, however, remains unknown. We used fMRI to investigate encoding processes during the viewing of an event and misinformation to see whether neural activity during either encoding phase could predict what would be remembered. fMRI data were collected as participants studied ...

  5. Learning to Discern Images Modifies Neural Activity

    OpenAIRE

    Gregor Rainer; Han Lee; Logothetis, Nikos K.

    2004-01-01

    One of the most remarkable capabilities of the adult brain is its ability to learn and continuously adapt to an ever-changing environment. While many studies have documented how learning improves the perception and identification of visual stimuli, relatively little is known about how it modifies the underlying neural mechanisms. We trained monkeys to identify natural images that were degraded by interpolation with visual noise. We found that learning led to an improvement in monkeys' ability...

  6. Novel Low Loss Active Voltage Clamp Circuit for Series Connection of RCGCT thyristors

    Science.gov (United States)

    Ito, Hiroshi; Suzuki, Akihiro; Iwata, Akihiko

    This paper describes novel low loss active voltage clamp circuits for the series connection of RCGCT thyristors. For high voltage converters the series connection of power semiconductor devices is an essential technique for direct switching of high voltages. Several protection circuits have been applied to the series connection of RCGCT thyristors such as CRD snubber circuits which suppress over-voltages across RCGCT thyristors, and voltage balancing resistors to equalize voltage sharing in steady states. However, significant losses in these protection circuits lower the converter’s efficiency. We propose novel low-loss protection circuits, which have active voltage clamp snubber circuits and static voltage balancing circuits. The clamp capacitor voltage of the active voltage clamp snubber circuits are designed to be higher than the equally divided DC-Link voltage. This method can reduce the loss of the clamp circuit to no more than 1/10 of that of the conventional CRD snubber. Also the static voltage balancing circuits compensate for the voltage imbalance generated by the difference in the leakage current between the series connection RCGCT thyristors.

  7. Interactivity and reward-related neural activation during a serious videogame.

    Directory of Open Access Journals (Sweden)

    Steven W Cole

    Full Text Available This study sought to determine whether playing a "serious" interactive digital game (IDG--the Re-Mission videogame for cancer patients--activates mesolimbic neural circuits associated with incentive motivation, and if so, whether such effects stem from the participatory aspects of interactive gameplay, or from the complex sensory/perceptual engagement generated by its dynamic event-stream. Healthy undergraduates were randomized to groups in which they were scanned with functional magnetic resonance imaging (FMRI as they either actively played Re-Mission or as they passively observed a gameplay audio-visual stream generated by a yoked active group subject. Onset of interactive game play robustly activated mesolimbic projection regions including the caudate nucleus and nucleus accumbens, as well as a subregion of the parahippocampal gyrus. During interactive gameplay, subjects showed extended activation of the thalamus, anterior insula, putamen, and motor-related regions, accompanied by decreased activation in parietal and medial prefrontal cortex. Offset of interactive gameplay activated the anterior insula and anterior cingulate. Between-group comparisons of within-subject contrasts confirmed that mesolimbic activation was significantly more pronounced in the active playgroup than in the passive exposure control group. Individual difference analyses also found the magnitude of parahippocampal activation following gameplay onset to correlate with positive attitudes toward chemotherapy assessed both at the end of the scanning session and at an unannounced one-month follow-up. These findings suggest that IDG-induced activation of reward-related mesolimbic neural circuits stems primarily from participatory engagement in gameplay (interactivity, rather than from the effects of vivid and dynamic sensory stimulation.

  8. Interactivity and reward-related neural activation during a serious videogame.

    Science.gov (United States)

    Cole, Steven W; Yoo, Daniel J; Knutson, Brian

    2012-01-01

    This study sought to determine whether playing a "serious" interactive digital game (IDG)--the Re-Mission videogame for cancer patients--activates mesolimbic neural circuits associated with incentive motivation, and if so, whether such effects stem from the participatory aspects of interactive gameplay, or from the complex sensory/perceptual engagement generated by its dynamic event-stream. Healthy undergraduates were randomized to groups in which they were scanned with functional magnetic resonance imaging (FMRI) as they either actively played Re-Mission or as they passively observed a gameplay audio-visual stream generated by a yoked active group subject. Onset of interactive game play robustly activated mesolimbic projection regions including the caudate nucleus and nucleus accumbens, as well as a subregion of the parahippocampal gyrus. During interactive gameplay, subjects showed extended activation of the thalamus, anterior insula, putamen, and motor-related regions, accompanied by decreased activation in parietal and medial prefrontal cortex. Offset of interactive gameplay activated the anterior insula and anterior cingulate. Between-group comparisons of within-subject contrasts confirmed that mesolimbic activation was significantly more pronounced in the active playgroup than in the passive exposure control group. Individual difference analyses also found the magnitude of parahippocampal activation following gameplay onset to correlate with positive attitudes toward chemotherapy assessed both at the end of the scanning session and at an unannounced one-month follow-up. These findings suggest that IDG-induced activation of reward-related mesolimbic neural circuits stems primarily from participatory engagement in gameplay (interactivity), rather than from the effects of vivid and dynamic sensory stimulation.

  9. Neurometabolic coupling between neural activity, glucose, and lactate in activated visual cortex.

    Science.gov (United States)

    Li, Baowang; Freeman, Ralph D

    2015-11-01

    Neural activity is closely coupled with energy metabolism but details of the association remain to be identified. One basic area involves the relationships between neural activity and the main supportive substrates of glucose and lactate. This is of fundamental significance for the interpretation of non-invasive neural imaging. Here, we use microelectrodes with high spatial and temporal resolution to determine simultaneous co-localized changes in glucose, lactate, and neural activity during visual activation of the cerebral cortex in the cat. Tissue glucose and lactate concentration levels are measured with electrochemical microelectrodes while neural spiking activity and local field potentials are sampled by a microelectrode. These measurements are performed simultaneously while neurons are activated by visual stimuli of different contrast levels, orientations, and sizes. We find immediate decreases in tissue glucose concentration and simultaneous increases in lactate during neural activation. Both glucose and lactate signals return to their baseline levels instantly as neurons cease firing. No sustained changes or initial dips in glucose or lactate signals are elicited by visual stimulation. However, co-localized measurements of cerebral blood flow and neural activity demonstrate a clear delay in the cerebral blood flow signal such that it does not correlate temporally with the neural response. These results provide direct real-time evidence regarding the coupling between co-localized energy metabolism and neural activity during physiological stimulation. They are also relevant to a current question regarding the role of lactate in energy metabolism in the brain during neural activation. Dynamic changes in energy metabolites can be measured directly with high spatial and temporal resolution by use of enzyme-based microelectrodes. Here, to examine neuro-metabolic coupling during brain activation, we use combined microelectrodes to simultaneously measure

  10. Dopamine-induced dissociation of BOLD and neural activity in macaque visual cortex.

    Science.gov (United States)

    Zaldivar, Daniel; Rauch, Alexander; Whittingstall, Kevin; Logothetis, Nikos K; Goense, Jozien

    2014-12-01

    Neuromodulators determine how neural circuits process information during cognitive states such as wakefulness, attention, learning, and memory. fMRI can provide insight into their function and dynamics, but their exact effect on BOLD responses remains unclear, limiting our ability to interpret the effects of changes in behavioral state using fMRI. Here, we investigated the effects of dopamine (DA) injections on neural responses and haemodynamic signals in macaque primary visual cortex (V1) using fMRI (7T) and intracortical electrophysiology. Aside from DA's involvement in diseases such as Parkinson's and schizophrenia, it also plays a role in visual perception. We mimicked DAergic neuromodulation by systemic injection of L-DOPA and Carbidopa (LDC) or by local application of DA in V1 and found that systemic application of LDC increased the signal-to-noise ratio (SNR) and amplitude of the visually evoked neural responses in V1. However, visually induced BOLD responses decreased, whereas cerebral blood flow (CBF) responses increased. This dissociation of BOLD and CBF suggests that dopamine increases energy metabolism by a disproportionate amount relative to the CBF response, causing the reduced BOLD response. Local application of DA in V1 had no effect on neural activity, suggesting that the dopaminergic effects are mediated by long-range interactions. The combination of BOLD-based and CBF-based fMRI can provide a signature of dopaminergic neuromodulation, indicating that the application of multimodal methods can improve our ability to distinguish sensory processing from neuromodulatory effects. PMID:25456449

  11. Dynamic changes in single unit activity and γ oscillations in a thalamocortical circuit during rapid instrumental learning.

    Science.gov (United States)

    Yu, Chunxiu; Fan, David; Lopez, Alberto; Yin, Henry H

    2012-01-01

    The medial prefrontal cortex (mPFC) and mediodorsal thalamus (MD) together form a thalamocortical circuit that has been implicated in the learning and production of goal-directed actions. In this study we measured neural activity in both regions simultaneously, as rats learned to press a lever to earn food rewards. In both MD and mPFC, instrumental learning was accompanied by dramatic changes in the firing patterns of the neurons, in particular the rapid emergence of single-unit neural activity reflecting the completion of the action and reward delivery. In addition, we observed distinct patterns of changes in the oscillatory LFP response in MD and mPFC. With learning, there was a significant increase in theta band oscillations (6-10 Hz) in the MD, but not in the mPFC. By contrast, gamma band oscillations (40-55 Hz) increased in the mPFC, but not in the MD. Coherence between these two regions also changed with learning: gamma coherence in relation to reward delivery increased, whereas theta coherence did not. Together these results suggest that, as rats learned the instrumental contingency between action and outcome, the emergence of task related neural activity is accompanied by enhanced functional interaction between MD and mPFC in response to the reward feedback.

  12. Optical imaging of neural and hemodynamic brain activity

    Science.gov (United States)

    Schei, Jennifer Lynn

    Optical imaging technologies can be used to record neural and hemodynamic activity. Neural activity elicits physiological changes that alter the optical tissue properties. Specifically, changes in polarized light are concomitant with neural depolarization. We measured polarization changes from an isolated lobster nerve during action potential propagation using both reflected and transmitted light. In transmission mode, polarization changes were largest throughout the center of the nerve, suggesting that most of the optical signal arose from the inner nerve bundle. In reflection mode, polarization changes were largest near the edges, suggesting that most of the optical signal arose from the outer sheath. To overcome irregular cell orientation found in the brain, we measured polarization changes from a nerve tied in a knot. Our results show that neural activation produces polarization changes that can be imaged even without regular cell orientations. Neural activation expends energy resources and elicits metabolic delivery through blood vessel dilation, increasing blood flow and volume. We used spectroscopic imaging techniques combined with electrophysiological measurements to record evoked neural and hemodynamic responses from the auditory cortex of the rat. By using implantable optics, we measured responses across natural wake and sleep states, as well as responses following different amounts of sleep deprivation. During quiet sleep, evoked metabolic responses were larger compared to wake, perhaps because blood vessels were more compliant. When animals were sleep deprived, evoked hemodynamic responses were smaller following longer periods of deprivation. These results suggest that prolonged neural activity through sleep deprivation may diminish vascular compliance as indicated by the blunted vascular response. Subsequent sleep may allow vessels to relax, restoring their ability to deliver blood. These results also suggest that severe sleep deprivation or chronic

  13. Neural Network-Based Active Control for Offshore Platforms

    Institute of Scientific and Technical Information of China (English)

    周亚军; 赵德有

    2003-01-01

    A new active control scheme, based on neural network, for the suppression of oscillation in multiple-degree-of-freedom (MDOF) offshore platforms, is studied in this paper. With the main advantages of neural network, i.e. the inherent robustness, fault tolerance, and generalized capability of its parallel massive interconnection structure, the active structural control of offshore platforms under random waves is accomplished by use of the BP neural network model. The neural network is trained offline with the data generated from numerical analysis, and it simulates the process of Classical Linear Quadratic Regular Control for the platform under random waves. After the learning phase, the trained network has learned about the nonlinear dynamic behavior of the active control system, and is capable of predicting the active control forces of the next time steps. The results obtained show that the active control is feasible and effective, and it finally overcomes time delay owing to the robustness, fault tolerance, and generalized capability of artificial neural network.

  14. Neural circuits in anxiety and stress disorders: a focused review

    Directory of Open Access Journals (Sweden)

    Duval ER

    2015-01-01

    Full Text Available Elizabeth R Duval, Arash Javanbakht, Israel LiberzonDepartment of Psychiatry, University of Michigan Health System, Ann Arbor, MI, USAAbstract: Anxiety and stress disorders are among the most prevalent neuropsychiatric disorders. In recent years, multiple studies have examined brain regions and networks involved in anxiety symptomatology in an effort to better understand the mechanisms involved and to develop more effective treatments. However, much remains unknown regarding the specific abnormalities and interactions between networks of regions underlying anxiety disorder presentations. We examined recent neuroimaging literature that aims to identify neural mechanisms underlying anxiety, searching for patterns of neural dysfunction that might be specific to different anxiety disorder categories. Across different anxiety and stress disorders, patterns of hyperactivation in emotion-generating regions and hypoactivation in prefrontal/regulatory regions are common in the literature. Interestingly, evidence of differential patterns is also emerging, such that within a spectrum of disorders ranging from more fear-based to more anxiety-based, greater involvement of emotion-generating regions is reported in panic disorder and specific phobia, and greater involvement of prefrontal regions is reported in generalized anxiety disorder and posttraumatic stress disorder. We summarize the pertinent literature and suggest areas for continued investigation.Keywords: fear, anxiety, neuroimaging

  15. High Accuracy Human Activity Monitoring using Neural network

    CERN Document Server

    Sharma, Annapurna; Chung, Wan-Young

    2011-01-01

    This paper presents the designing of a neural network for the classification of Human activity. A Triaxial accelerometer sensor, housed in a chest worn sensor unit, has been used for capturing the acceleration of the movements associated. All the three axis acceleration data were collected at a base station PC via a CC2420 2.4GHz ISM band radio (zigbee wireless compliant), processed and classified using MATLAB. A neural network approach for classification was used with an eye on theoretical and empirical facts. The work shows a detailed description of the designing steps for the classification of human body acceleration data. A 4-layer back propagation neural network, with Levenberg-marquardt algorithm for training, showed best performance among the other neural network training algorithms.

  16. Advanced active quenching circuits for single-photon avalanche photodiodes

    Science.gov (United States)

    Stipčević, M.; Christensen, B. G.; Kwiat, P. G.; Gauthier, D. J.

    2016-05-01

    Commercial photon-counting modules, often based on actively quenched solid-state avalanche photodiode sensors, are used in wide variety of applications. Manufacturers characterize their detectors by specifying a small set of parameters, such as detection efficiency, dead time, dark counts rate, afterpulsing probability and single photon arrival time resolution (jitter), however they usually do not specify the conditions under which these parameters are constant or present a sufficient description. In this work, we present an in-depth analysis of the active quenching process and identify intrinsic limitations and engineering challenges. Based on that, we investigate the range of validity of the typical parameters used by two commercial detectors. We identify an additional set of imperfections that must be specified in order to sufficiently characterize the behavior of single-photon counting detectors in realistic applications. The additional imperfections include rate-dependence of the dead time, jitter, detection delay shift, and "twilighting." Also, the temporal distribution of afterpulsing and various artifacts of the electronics are important. We find that these additional non-ideal behaviors can lead to unexpected effects or strong deterioration of the system's performance. Specifically, we discuss implications of these new findings in a few applications in which single-photon detectors play a major role: the security of a quantum cryptographic protocol, the quality of single-photon-based random number generators and a few other applications. Finally, we describe an example of an optimized avalanche quenching circuit for a high-rate quantum key distribution system based on time-bin entangled photons.

  17. Altered regional and circuit resting-state activity associated with unilateral hearing loss.

    Directory of Open Access Journals (Sweden)

    Xingchao Wang

    Full Text Available The deprivation of sensory input after hearing damage results in functional reorganization of the brain including cross-modal plasticity in the sensory cortex and changes in cognitive processing. However, it remains unclear whether partial deprivation from unilateral auditory loss (UHL would similarly affect the neural circuitry of cognitive processes in addition to the functional organization of sensory cortex. Here, we used resting-state functional magnetic resonance imaging to investigate intrinsic activity in 34 participants with UHL from acoustic neuroma in comparison with 22 matched normal controls. In sensory regions, we found decreased regional homogeneity (ReHo in the bilateral calcarine cortices in UHL. However, there was an increase of ReHo in the right anterior insular cortex (rAI, the key node of cognitive control network (CCN and multimodal sensory integration, as well as in the left parahippocampal cortex (lPHC, a key node in the default mode network (DMN. Moreover, seed-based resting-state functional connectivity analysis showed an enhanced relationship between rAI and several key regions of the DMN. Meanwhile, lPHC showed more negative relationship with components in the CCN and greater positive relationship in the DMN. Such reorganizations of functional connectivity within the DMN and between the DMN and CCN were confirmed by a graph theory analysis. These results suggest that unilateral sensory input damage not only alters the activity of the sensory areas but also reshapes the regional and circuit functional organization of the cognitive control network.

  18. Anabolic steroids alter the physiological activity of aggression circuits in the lateral anterior hypothalamus.

    Science.gov (United States)

    Morrison, T R; Sikes, R W; Melloni, R H

    2016-02-19

    Syrian hamsters exposed to anabolic/androgenic steroids (AAS) during adolescence consistently show increased aggressive behavior across studies. Although the behavioral and anatomical profiles of AAS-induced alterations have been well characterized, there is a lack of data describing physiological changes that accompany these alterations. For instance, behavioral pharmacology and neuroanatomical studies show that AAS-induced changes in the vasopressin (AVP) neural system within the latero-anterior hypothalamus (LAH) interact with the serotonin (5HT) and dopamine (DA) systems to modulate aggression. To characterize the electrophysiological profile of the AAS aggression circuit, we recorded LAH neurons in adolescent male hamsters in vivo and microiontophoretically applied agonists and antagonists of aggressive behavior. The interspike interval (ISI) of neurons from AAS-treated animals correlated positively with aggressive behaviors, and adolescent AAS exposure altered parameters of activity in regular firing neurons while also changing the proportion of neuron types (i.e., bursting, regular, irregular). AAS-treated animals had more responsive neurons that were excited by AVP application, while cells from control animals showed the opposite effect and were predominantly inhibited by AVP. Both DA D2 antagonists and 5HT increased the firing frequency of AVP-responsive cells from AAS animals and dual application of AVP and D2 antagonists doubled the excitatory effect of AVP or D2 antagonist administration alone. These data suggest that multiple DA circuits in the LAH modulate AAS-induced aggressive responding. More broadly, these data show that multiple neurochemical interactions at the neurophysiological level are altered by adolescent AAS exposure. PMID:26691962

  19. A Framework for Quantitative Modeling of Neural Circuits Involved in Sleep-to-Wake Transition

    Directory of Open Access Journals (Sweden)

    Siamak eSorooshyari

    2015-02-01

    Full Text Available Identifying the neuronal circuits and dynamics of sleep-to-wake transition is essential to understanding brain regulation of behavioral states, including sleep-wake cycles, arousal, and hyperarousal. Recent work by different laboratories has used optogenetics to determine the role of individual neuromodulators in state transitions. The optogenetically-driven data does not yet provide a multi-dimensional schematic of the mechanisms underlying changes in vigilance states. This work presents a modeling framework to interpret, assist, and drive research on the sleep-regulatory network. We identify feedback, redundancy, and gating hierarchy as three fundamental aspects of this model. The presented model is expected to expand as additional data on the contribution of each transmitter to a vigilance state becomes available. Incorporation of conductance-based models of neuronal ensembles into this model and existing models of cortical excitability will provide more comprehensive insight into sleep dynamics as well as sleep and arousal-related disorders.

  20. SEMICONDUCTOR INTEGRATED CIRCUITS: A four-channel microelectronic system for neural signal regeneration

    Science.gov (United States)

    Shushan, Xie; Zhigong, Wang; Xiaoying, Lü; Wenyuan, Li; Haixian, Pan

    2009-12-01

    This paper presents a microelectronic system which is capable of making a signal record and functional electric stimulation of an injured spinal cord. As a requirement of implantable engineering for the regeneration microelectronic system, the system is of low noise, low power, small size and high performance. A front-end circuit and two high performance OPAs (operational amplifiers) have been designed for the system with different functions, and the two OPAs are a low-noise low-power two-stage OPA and a constant-gm RTR input and output OPA. The system has been realized in CSMC 0.5-μm CMOS technology. The test results show that the system satisfies the demands of neuron signal regeneration.

  1. Internal models for interpreting neural population activity during sensorimotor control.

    Science.gov (United States)

    Golub, Matthew D; Yu, Byron M; Chase, Steven M

    2015-01-01

    To successfully guide limb movements, the brain takes in sensory information about the limb, internally tracks the state of the limb, and produces appropriate motor commands. It is widely believed that this process uses an internal model, which describes our prior beliefs about how the limb responds to motor commands. Here, we leveraged a brain-machine interface (BMI) paradigm in rhesus monkeys and novel statistical analyses of neural population activity to gain insight into moment-by-moment internal model computations. We discovered that a mismatch between subjects' internal models and the actual BMI explains roughly 65% of movement errors, as well as long-standing deficiencies in BMI speed control. We then used the internal models to characterize how the neural population activity changes during BMI learning. More broadly, this work provides an approach for interpreting neural population activity in the context of how prior beliefs guide the transformation of sensory input to motor output.

  2. Altered neural connectivity in excitatory and inhibitory cortical circuits in autism

    Directory of Open Access Journals (Sweden)

    Basilis eZikopoulos

    2013-09-01

    Full Text Available Converging evidence from diverse studies suggests that atypical brain connectivity in autism affects in distinct ways short- and long-range cortical pathways, disrupting neural communication and the balance of excitation and inhibition. This hypothesis is based mostly on functional non-invasive studies that show atypical synchronization and connectivity patterns between cortical areas in children and adults with autism. Indirect methods to study the course and integrity of major brain pathways at low resolution show changes in fractional anisotropy or diffusivity of the white matter in autism. Findings in post-mortem brains of adults with autism provide evidence of changes in the fine structure of axons below prefrontal cortices, which communicate over short- or long-range pathways with other cortices and subcortical structures. Here we focus on evidence of cellular and axon features that likely underlie the changes in short- and long-range communication in autism. We review recent findings of changes in the shape, thickness, and volume of brain areas, cytoarchitecture, neuronal morphology, cellular elements, and structural and neurochemical features of individual axons in the white matter, where pathology is evident even in gross images. We relate cellular and molecular features to imaging and genetic studies that highlight a variety of polymorphisms and epigenetic factors that primarily affect neurite growth and synapse formation and function in autism. We report preliminary findings of changes in autism in the ratio of distinct types of inhibitory neurons in prefrontal cortex, known to shape network dynamics and the balance of excitation and inhibition. Finally we present a model that synthesizes diverse findings by relating them to developmental events, with a goal to identify common processes that perturb development in autism and affect neural communication, reflected in altered patterns of attention, social interactions, and language.

  3. Improved AC pixel electrode circuit for active matrix of organic light-emitting display

    Science.gov (United States)

    Si, Yujuan; Lang, Liuqi; Chen, Wanzhong; Liu, Shiyong

    2004-05-01

    In this paper, a modified four-transistor pixel circuit for active-matrix organic light-emitting displays (AMOLED) was developed to improve the performance of OLED device. This modified pixel circuit can provide an AC driving mode to make the OLED working in a reversed-biased voltage during the certain cycle. The optimized values of the reversed-biased voltage and the characteristics of the pixel circuit were investigated using AIM-SPICE. The simulated results reveal that this circuit can provide a suitable output current and voltage characteristic, and little change was made in luminance current.

  4. Remediation of Childhood Math Anxiety and Associated Neural Circuits through Cognitive Tutoring.

    Science.gov (United States)

    Supekar, Kaustubh; Iuculano, Teresa; Chen, Lang; Menon, Vinod

    2015-09-01

    Math anxiety is a negative emotional reaction that is characterized by feelings of stress and anxiety in situations involving mathematical problem solving. High math-anxious individuals tend to avoid situations involving mathematics and are less likely to pursue science, technology, engineering, and math-related careers than those with low math anxiety. Math anxiety during childhood, in particular, has adverse long-term consequences for academic and professional success. Identifying cognitive interventions and brain mechanisms by which math anxiety can be ameliorated in children is therefore critical. Here we investigate whether an intensive 8 week one-to-one cognitive tutoring program designed to improve mathematical skills reduces childhood math anxiety, and we identify the neurobiological mechanisms by which math anxiety can be reduced in affected children. Forty-six children in grade 3, a critical early-onset period for math anxiety, participated in the cognitive tutoring program. High math-anxious children showed a significant reduction in math anxiety after tutoring. Remarkably, tutoring remediated aberrant functional responses and connectivity in emotion-related circuits anchored in the basolateral amygdala. Crucially, children with greater tutoring-induced decreases in amygdala reactivity had larger reductions in math anxiety. Our study demonstrates that sustained exposure to mathematical stimuli can reduce math anxiety and highlights the key role of the amygdala in this process. Our findings are consistent with models of exposure-based therapy for anxiety disorders and have the potential to inform the early treatment of a disability that, if left untreated in childhood, can lead to significant lifelong educational and socioeconomic consequences in affected individuals. Significance statement: Math anxiety during early childhood has adverse long-term consequences for academic and professional success. It is therefore important to identify ways to alleviate

  5. Remediation of Childhood Math Anxiety and Associated Neural Circuits through Cognitive Tutoring.

    Science.gov (United States)

    Supekar, Kaustubh; Iuculano, Teresa; Chen, Lang; Menon, Vinod

    2015-09-01

    Math anxiety is a negative emotional reaction that is characterized by feelings of stress and anxiety in situations involving mathematical problem solving. High math-anxious individuals tend to avoid situations involving mathematics and are less likely to pursue science, technology, engineering, and math-related careers than those with low math anxiety. Math anxiety during childhood, in particular, has adverse long-term consequences for academic and professional success. Identifying cognitive interventions and brain mechanisms by which math anxiety can be ameliorated in children is therefore critical. Here we investigate whether an intensive 8 week one-to-one cognitive tutoring program designed to improve mathematical skills reduces childhood math anxiety, and we identify the neurobiological mechanisms by which math anxiety can be reduced in affected children. Forty-six children in grade 3, a critical early-onset period for math anxiety, participated in the cognitive tutoring program. High math-anxious children showed a significant reduction in math anxiety after tutoring. Remarkably, tutoring remediated aberrant functional responses and connectivity in emotion-related circuits anchored in the basolateral amygdala. Crucially, children with greater tutoring-induced decreases in amygdala reactivity had larger reductions in math anxiety. Our study demonstrates that sustained exposure to mathematical stimuli can reduce math anxiety and highlights the key role of the amygdala in this process. Our findings are consistent with models of exposure-based therapy for anxiety disorders and have the potential to inform the early treatment of a disability that, if left untreated in childhood, can lead to significant lifelong educational and socioeconomic consequences in affected individuals. Significance statement: Math anxiety during early childhood has adverse long-term consequences for academic and professional success. It is therefore important to identify ways to alleviate

  6. Aging is associated with changes in the neural circuits underlying empathy.

    Science.gov (United States)

    Chen, Yao-Chu; Chen, Cheng-Chiang; Decety, Jean; Cheng, Yawei

    2014-04-01

    Although the neurodevelopment of empathy from childhood to adolescence has been documented, no study has yet examined it across a life span aging perspective. Sixty-five healthy participants from 3 age groups (young, middle-aged, old) underwent functional magnetic resonance imaging while presented with visual stimuli depicting body parts being injured, either accidentally by oneself or intentionally by another, thus isolating pain and agency as 2 variables of interest. Older adults reported less dispositional emotional empathy as assessed by the interpersonal reactivity index, and their unpleasantness ratings were more sensitive to intentional harm. The response in anterior insula and anterior mid-cingulate cortex to others' pain, indicative of emotional empathy, showed an age-related decline, whereas the response in medial prefrontal cortex and posterior superior temporal sulcus to perceived agency did not change with age. Dynamic causal modeling demonstrated that their effective connectivity remained stable. The pattern of hemodynamic response was not related to regional gray matter volume loss. These findings suggest that the neural response associated with emotional empathy lessened with age, whereas the response to perceived agency is preserved. PMID:24211010

  7. Disrupted insula-based neural circuit organization and conflict interference in trauma-exposed youth.

    Science.gov (United States)

    Marusak, Hilary A; Etkin, Amit; Thomason, Moriah E

    2015-01-01

    Childhood trauma exposure is a potent risk factor for psychopathology. Emerging research suggests that aberrant saliency processing underlies the link between early trauma exposure and later cognitive and socioemotional deficits that are hallmark of several psychiatric disorders. Here, we examine brain and behavioral responses during a face categorization conflict task, and relate these to intrinsic connectivity of the salience network (SN). The results demonstrate a unique pattern of SN dysfunction in youth exposed to trauma (n = 14) relative to comparison youth (n = 19) matched on age, sex, IQ, and sociodemographic risk. We find that trauma-exposed youth are more susceptible to conflict interference and this correlates with higher fronto-insular responses during conflict. Resting-state functional connectivity data collected in the same participants reveal increased connectivity of the insula to SN seed regions that is associated with diminished reward sensitivity, a critical risk/resilience trait following stress. In addition to altered intrinsic connectivity of the SN, we observed altered connectivity between the SN and default mode network (DMN) in trauma-exposed youth. These data uncover network-level disruptions in brain organization following one of the strongest predictors of illness, early life trauma, and demonstrate the relevance of observed neural effects for behavior and specific symptom dimensions. SN dysfunction may serve as a diathesis that contributes to illness and negative outcomes following childhood trauma.

  8. Disrupted insula-based neural circuit organization and conflict interference in trauma-exposed youth

    Directory of Open Access Journals (Sweden)

    Hilary A. Marusak

    2015-01-01

    Full Text Available Childhood trauma exposure is a potent risk factor for psychopathology. Emerging research suggests that aberrant saliency processing underlies the link between early trauma exposure and later cognitive and socioemotional deficits that are hallmark of several psychiatric disorders. Here, we examine brain and behavioral responses during a face categorization conflict task, and relate these to intrinsic connectivity of the salience network (SN. The results demonstrate a unique pattern of SN dysfunction in youth exposed to trauma (n = 14 relative to comparison youth (n = 19 matched on age, sex, IQ, and sociodemographic risk. We find that trauma-exposed youth are more susceptible to conflict interference and this correlates with higher fronto-insular responses during conflict. Resting-state functional connectivity data collected in the same participants reveal increased connectivity of the insula to SN seed regions that is associated with diminished reward sensitivity, a critical risk/resilience trait following stress. In addition to altered intrinsic connectivity of the SN, we observed altered connectivity between the SN and default mode network (DMN in trauma-exposed youth. These data uncover network-level disruptions in brain organization following one of the strongest predictors of illness, early life trauma, and demonstrate the relevance of observed neural effects for behavior and specific symptom dimensions. SN dysfunction may serve as a diathesis that contributes to illness and negative outcomes following childhood trauma.

  9. Cultured Neural Networks: Optimization of Patterned Network Adhesiveness and Characterization of their Neural Activity

    Directory of Open Access Journals (Sweden)

    W. L. C. Rutten

    2006-01-01

    Full Text Available One type of future, improved neural interface is the “cultured probe”. It is a hybrid type of neural information transducer or prosthesis, for stimulation and/or recording of neural activity. It would consist of a microelectrode array (MEA on a planar substrate, each electrode being covered and surrounded by a local circularly confined network (“island” of cultured neurons. The main purpose of the local networks is that they act as biofriendly intermediates for collateral sprouts from the in vivo system, thus allowing for an effective and selective neuron–electrode interface. As a secondary purpose, one may envisage future information processing applications of these intermediary networks. In this paper, first, progress is shown on how substrates can be chemically modified to confine developing networks, cultured from dissociated rat cortex cells, to “islands” surrounding an electrode site. Additional coating of neurophobic, polyimide-coated substrate by triblock-copolymer coating enhances neurophilic-neurophobic adhesion contrast. Secondly, results are given on neuronal activity in patterned, unconnected and connected, circular “island” networks. For connected islands, the larger the island diameter (50, 100 or 150 μm, the more spontaneous activity is seen. Also, activity may show a very high degree of synchronization between two islands. For unconnected islands, activity may start at 22 days in vitro (DIV, which is two weeks later than in unpatterned networks.

  10. An Efficiency Improved Active Power Decoupling Circuit with Minimized Implementation Cost

    DEFF Research Database (Denmark)

    Tang, Yi; Blaabjerg, Frede

    2014-01-01

    Active power decoupling techniques are promising solutions for capacitance reduction in single-phase AC/DC or DC/AC systems. This paper proposes a novel circuit topology which can realize the power decoupling function without adding additional active switches into the circuit. Also, the proposed...... is that the current stress of power semiconductors can be reduced as compared to a conventional single-phase converter under high load operation. Therefore, the conversion efficiency can be improved and this is impossible for other existing active power decoupling circuits. The operational principle of the proposed...

  11. Conflict Resolution as Near-Threshold Decision-Making: A Spiking Neural Circuit Model with Two-Stage Competition for Antisaccadic Task

    Science.gov (United States)

    Wang, Xiao-Jing

    2016-01-01

    Automatic responses enable us to react quickly and effortlessly, but they often need to be inhibited so that an alternative, voluntary action can take place. To investigate the brain mechanism of controlled behavior, we investigated a biologically-based network model of spiking neurons for inhibitory control. In contrast to a simple race between pro- versus anti-response, our model incorporates a sensorimotor remapping module, and an action-selection module endowed with a “Stop” process through tonic inhibition. Both are under the modulation of rule-dependent control. We tested the model by applying it to the well known antisaccade task in which one must suppress the urge to look toward a visual target that suddenly appears, and shift the gaze diametrically away from the target instead. We found that the two-stage competition is crucial for reproducing the complex behavior and neuronal activity observed in the antisaccade task across multiple brain regions. Notably, our model demonstrates two types of errors: fast and slow. Fast errors result from failing to inhibit the quick automatic responses and therefore exhibit very short response times. Slow errors, in contrast, are due to incorrect decisions in the remapping process and exhibit long response times comparable to those of correct antisaccade responses. The model thus reveals a circuit mechanism for the empirically observed slow errors and broad distributions of erroneous response times in antisaccade. Our work suggests that selecting between competing automatic and voluntary actions in behavioral control can be understood in terms of near-threshold decision-making, sharing a common recurrent (attractor) neural circuit mechanism with discrimination in perception. PMID:27551824

  12. Neural circuit architecture defects in a Drosophila model of Fragile X syndrome are alleviated by minocycline treatment and genetic removal of matrix metalloproteinase

    Directory of Open Access Journals (Sweden)

    Saul S. Siller

    2011-09-01

    Fragile X syndrome (FXS, caused by loss of the fragile X mental retardation 1 (FMR1 product (FMRP, is the most common cause of inherited intellectual disability and autism spectrum disorders. FXS patients suffer multiple behavioral symptoms, including hyperactivity, disrupted circadian cycles, and learning and memory deficits. Recently, a study in the mouse FXS model showed that the tetracycline derivative minocycline effectively remediates the disease state via a proposed matrix metalloproteinase (MMP inhibition mechanism. Here, we use the well-characterized Drosophila FXS model to assess the effects of minocycline treatment on multiple neural circuit morphological defects and to investigate the MMP hypothesis. We first treat Drosophila Fmr1 (dfmr1 null animals with minocycline to assay the effects on mutant synaptic architecture in three disparate locations: the neuromuscular junction (NMJ, clock neurons in the circadian activity circuit and Kenyon cells in the mushroom body learning and memory center. We find that minocycline effectively restores normal synaptic structure in all three circuits, promising therapeutic potential for FXS treatment. We next tested the MMP hypothesis by assaying the effects of overexpressing the sole Drosophila tissue inhibitor of MMP (TIMP in dfmr1 null mutants. We find that TIMP overexpression effectively prevents defects in the NMJ synaptic architecture in dfmr1 mutants. Moreover, co-removal of dfmr1 similarly rescues TIMP overexpression phenotypes, including cellular tracheal defects and lethality. To further test the MMP hypothesis, we generated dfmr1;mmp1 double null mutants. Null mmp1 mutants are 100% lethal and display cellular tracheal defects, but co-removal of dfmr1 allows adult viability and prevents tracheal defects. Conversely, co-removal of mmp1 ameliorates the NMJ synaptic architecture defects in dfmr1 null mutants, despite the lack of detectable difference in MMP1 expression or gelatinase activity between the single

  13. Neural circuit competition in cocaine-seeking: Roles of the infralimbic cortex and nucleus accumbens shell

    OpenAIRE

    LaLumiere, Ryan T.; Smith, Kyle C.; Kalivas, Peter W.

    2012-01-01

    Following cocaine self-administration and extinction training, activity in the infralimbic cortex (IL) suppresses cocaine-seeking behavior. IL inactivation induces cocaine-seeking, whereas activation suppresses cocaine-reinstated drug-seeking. We asked how the suppression of cocaine-seeking induced by IL activation integrates with the circuitry promoting reinstated cocaine-seeking. Following cocaine self-administration and extinction training, rats underwent cue-induced reinstatement. In orde...

  14. Controlling neural activity in Caenorhabditis elegans to evoke chemotactic behavior

    Science.gov (United States)

    Kocabas, Askin; Shen, Ching-Han; Guo, Zengcai V.; Ramanathan, Sharad

    2013-03-01

    Animals locate and track chemoattractive gradients in the environment to find food. With its simple nervous system, Caenorhabditis elegans is a good model system in which to understand how the dynamics of neural activity control this search behavior. To understand how the activity in its interneurons coordinate different motor programs to lead the animal to food, here we used optogenetics and new optical tools to manipulate neural activity directly in freely moving animals to evoke chemotactic behavior. By deducing the classes of activity patterns triggered during chemotaxis and exciting individual neurons with these patterns, we identified interneurons that control the essential locomotory programs for this behavior. Notably, we discovered that controlling the dynamics of activity in just one interneuron pair was sufficient to force the animal to locate, turn towards and track virtual light gradients.

  15. Analytical criteria for local activity of CNN with five state variables and application to Hyper-chaos synchronization Chua's circuit

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    The analytic criteria for the local activity theory in one-port cellular neural network (CNN) with five local state variables are presented. The application to a Hyper-chaos synchronization Chua's circuit (HCSCC) CNN with 1125 variables is studied. The bifurcation diagrams of the HCSCC CNN show that they are slightly different from the smoothed CNN with one or two ports and four state variables calculated earlier. The evolution of the patterns of the state variables of the HCSCC CNN is stimulated. Oscillatory patterns, chaotic patterns, convergent or divergent patterns may emerge if the selected cell parameters are located in the locally active domains but nearby or in the edge of chaos domain.

  16. A High Step-Down Interleaved Buck Converter with Active-Clamp Circuits for Wind Turbines

    Directory of Open Access Journals (Sweden)

    Chih-Lung Shen

    2012-12-01

    Full Text Available In this paper, a high step-down interleaved buck coupled-inductor converter (IBCC with active-clamp circuits for wind energy conversion has been studied. In high step-down voltage applications, an IBCC can extend duty ratio and reduce voltage stresses on active switches. In order to reduce switching losses of active switches to improve conversion efficiency, a IBCC with soft-switching techniques is usually required. Compared with passive-clamp circuits, the IBCC with active-clamp circuits have lower switching losses and minimum ringing voltage of the active switches. Thus, the proposed IBCC with active-clamp circuits for wind energy conversion can significantly increase conversion efficiency. Finally, a 240 W prototype of the proposed IBCC with active-clamp circuits was built and implemented. Experimental results have shown that efficiency can reach as high as 91%. The proposed IBCC with active-clamp circuits is presented in high step-down voltage applications to verify the performance and the feasibility for energy conversion of wind turbines.

  17. Development of a computational model on the neural activity patterns of a visual working memory in a hierarchical feedforward Network

    Science.gov (United States)

    An, Soyoung; Choi, Woochul; Paik, Se-Bum

    2015-11-01

    Understanding the mechanism of information processing in the human brain remains a unique challenge because the nonlinear interactions between the neurons in the network are extremely complex and because controlling every relevant parameter during an experiment is difficult. Therefore, a simulation using simplified computational models may be an effective approach. In the present study, we developed a general model of neural networks that can simulate nonlinear activity patterns in the hierarchical structure of a neural network system. To test our model, we first examined whether our simulation could match the previously-observed nonlinear features of neural activity patterns. Next, we performed a psychophysics experiment for a simple visual working memory task to evaluate whether the model could predict the performance of human subjects. Our studies show that the model is capable of reproducing the relationship between memory load and performance and may contribute, in part, to our understanding of how the structure of neural circuits can determine the nonlinear neural activity patterns in the human brain.

  18. Biomimetics Micro Robot with Active Hardware Neural Networks Locomotion Control and Insect-Like Switching Behaviour

    Directory of Open Access Journals (Sweden)

    Ken Saito

    2012-11-01

    Full Text Available In this paper, we presented the 4.0, 2.7, 2.5 mm, width, length, height size biomimetics micro robot system which was inspired by insects. The micro robot system was made from silicon wafer fabricated by micro electro mechanical systems (MEMS technology. The mechanical system of the robot was equipped with small size rotary type actuators, link mechanisms and six legs to realize the insect‐like switching behaviour. In addition, we constructed the active hardware neural networks (HNN by analogue CMOS circuits as a locomotion controlling system. The HNN utilized the pulse‐type hardware neuron model (P‐HNM as a basic component. The HNN outputs the driving pulses using synchronization phenomena such as biological neural networks. The driving pulses can operate the actuators of the biomimetics micro robot directly. Therefore, the HNN realized the robot control without using any software programs or A/D converters. The micro robot emulated the locomotion method and the neural networks of an insect with rotary type actuators, link mechanisms and HNN. The micro robot performed forward and backward locomotion, and also changed direction by inputting an external trigger pulse. The locomotion speed was 26.4 mm/min when the step width was 0.88 mm.

  19. Passive and active elements using fractional Lβ C α circuit

    KAUST Repository

    Radwan, Ahmed Gomaa

    2011-10-01

    This paper introduces a qualitative revision of the traditional LC tank circuit in the fractional domain. The paper can be divided into six major parts, aiming in turn to establish the various conditions under which L βCα impedance may act as a resistor, negative resistor, or a positive or negative pure imaginary inductor or capacitor, in accordance to new frequency definitions; illustrate the process by which the phase response chooses the shortest path from initial to final phase, and use this illustration to verify the cases discussed in part one; develop the generalized parameters for the bandpass filter response of the L βCα circuit, such as the resonance frequency and quality factor versus α-β plane; discuss sensitivity analyses with respect to the fractional orders, as well as the time domain analyses for the impulse and step responses with their analytical formulas; and lastly, to propose some possible applications for this generalized circuit. Mathematical and PSpice simulation results are included to validate the discussion. © 2011 IEEE.

  20. 78 FR 37203 - Authorization of Production Activity; Subzone 196A; TTI, Inc. (Electromechanical and Circuit...

    Science.gov (United States)

    2013-06-20

    ... comment (78 FR 15683, 03-12-2013). The FTZ Board has determined that no further review of the activity is... and Circuit Protection Devices Production/Kitting); Fort Worth, Texas On February 13, 2013, TTI,...

  1. Feasibility of Tunable Amplifier and Bandpass Filter for Mobile Handsets Using Active Inductor Circuits

    OpenAIRE

    Majeed, B.; N. T. Ali; J. Rodriguez Tellez

    2002-01-01

    In this paper active inductor circuits are employed to assess their suitability for providing a tuning function in GaAs MMIC circuits. The specifications for a mobile handset amplifier and a bandpass filter operating from a 3 V supply rail are used as test vehicles. The design and simulation of the circuits employs a low-cost commercially available low pinch-off GaAs MESFET process. The suitability of active inductors for tuning in such applications considers issues such as frequency tuning r...

  2. Fast ventral stream neural activity enables rapid visual categorization.

    Science.gov (United States)

    Cauchoix, Maxime; Crouzet, Sébastien M; Fize, Denis; Serre, Thomas

    2016-01-15

    Primates can recognize objects embedded in complex natural scenes in a glimpse. Rapid categorization paradigms have been extensively used to study our core perceptual abilities when the visual system is forced to operate under strong time constraints. However, the neural underpinning of rapid categorization remains to be understood, and the incredible speed of sight has yet to be reconciled with modern ventral stream cortical theories of object recognition. Here we recorded multichannel subdural electrocorticogram (ECoG) signals from intermediate areas (V4/PIT) of the ventral stream of the visual cortex while monkeys were actively engaged in a rapid animal/non-animal categorization task. A traditional event-related potential (ERP) analysis revealed short visual latencies (<50-70ms) followed by a rapidly developing visual selectivity (within ~20-30ms) for most electrodes. A multi-variate pattern analysis (MVPA) technique further confirmed that reliable animal/non-animal category information was possible from this initial ventral stream neural activity (within ~90-100ms). Furthermore, this early category-selective neural activity was (a) unaffected by the presentation of a backward (pattern) mask, (b) generalized to novel (unfamiliar) stimuli and (c) co-varied with behavioral responses (both accuracy and reaction times). Despite the strong prevalence of task-related information on the neural signal, task-irrelevant visual information could still be decoded independently of monkey behavior. Monkey behavioral responses were also found to correlate significantly with human behavioral responses for the same set of stimuli. Together, the present study establishes that rapid ventral stream neural activity induces a visually selective signal subsequently used to drive rapid visual categorization and that this visual strategy may be shared between human and non-human primates. PMID:26477655

  3. Understanding Emergent Dynamics: Using a Collective Activity Coordinate of a Neural Network to Recognize Time-Varying Patterns.

    Science.gov (United States)

    Hopfield, John J

    2015-10-01

    In higher animals, complex and robust behaviors are produced by the microscopic details of large structured ensembles of neurons. I describe how the emergent computational dynamics of a biologically based neural network generates a robust natural solution to the problem of categorizing time-varying stimulus patterns such as spoken words or animal stereotypical behaviors. The recognition of these patterns is made difficult by their substantial variation in cadence and duration. The neural circuit behaviors used are similar to those associated with brain neural integrators. In the larger context described here, this kind of circuit becomes a building block of an entirely different computational algorithm for solving complex problems. While the network behavior is simulated in detail, a collective view is essential to understanding the results. A closed equation of motion for the collective variable describes an algorithm that quantitatively accounts for many aspects of the emergent network computation. The feedback connections and ongoing activity in the network shape the collective dynamics onto a reduced dimensionality manifold of activity space, which defines the algorithm and computation actually performed. The external inputs are weak and are not the dominant drivers of network activity.

  4. Early interfaced neural activity from chronic amputated nerves

    Directory of Open Access Journals (Sweden)

    Kshitija Garde

    2009-05-01

    Full Text Available Direct interfacing of transected peripheral nerves with advanced robotic prosthetic devices has been proposed as a strategy for achieving natural motor control and sensory perception of such bionic substitutes, thus fully functionally replacing missing limbs in amputees. Multi-electrode arrays placed in the brain and peripheral nerves have been used successfully to convey neural control of prosthetic devices to the user. However, reactive gliosis, micro hemorrhages, axonopathy and excessive inflammation, currently limit their long-term use. Here we demonstrate that enticement of peripheral nerve regeneration through a non-obstructive multi-electrode array, after either acute or chronic nerve amputation, offers a viable alternative to obtain early neural recordings and to enhance long-term interfacing of nerve activity. Non restrictive electrode arrays placed in the path of regenerating nerve fibers allowed the recording of action potentials as early as 8 days post-implantation with high signal-to-noise ratio, as long as 3 months in some animals, and with minimal inflammation at the nerve tissue-metal electrode interface. Our findings suggest that regenerative on-dependent multi-electrode arrays of open design allow the early and stable interfacing of neural activity from amputated peripheral nerves and might contribute towards conveying full neural control and sensory feedback to users of robotic prosthetic devices. .

  5. Forgetting the best when predicting the worst: Preliminary observations on neural circuit function in adolescent social anxiety

    Directory of Open Access Journals (Sweden)

    Johanna M. Jarcho

    2015-06-01

    Full Text Available Social anxiety disorder typically begins in adolescence, a sensitive period for brain development, when increased complexity and salience of peer relationships requires novel forms of social learning. Disordered social learning in adolescence may explain how brain dysfunction promotes social anxiety. Socially anxious adolescents (n = 15 and adults (n = 19 and non-anxious adolescents (n = 24 and adults (n = 32 predicted, then received, social feedback from high and low-value peers while undergoing functional magnetic resonance imaging (fMRI. A surprise recall task assessed memory biases for feedback. Neural correlates of social evaluation prediction errors (PEs were assessed by comparing engagement to expected and unexpected positive and negative feedback. For socially anxious adolescents, but not adults or healthy participants of either age group, PEs elicited heightened striatal activity and negative fronto-striatal functional connectivity. This occurred selectively to unexpected positive feedback from high-value peers and corresponded with impaired memory for social feedback. While impaired memory also occurred in socially-anxious adults, this impairment was unrelated to brain-based PE activity. Thus, social anxiety in adolescence may relate to altered neural correlates of PEs that contribute to impaired learning about social feedback. Small samples necessitate replication. Nevertheless, results suggest that the relationship between learning and fronto-striatal function may attenuate as development progresses.

  6. Active Noise Feedback Control Using a Neural Network

    Directory of Open Access Journals (Sweden)

    Zhang Qizhi

    2001-01-01

    Full Text Available The active noise control (ANC is discussed. Many digital ANC systems often based on the filter-x algorithm for finite impulse response (FIR filter use adaptive filtering techniques. But if the primary noise path is nonlinear, the control system based on adaptive filter technology will be invalid. In this paper, an adaptive active nonlinear noise feedback control approach using a neural network is derived. The feedback control system drives a secondary signal to destructively interfere with the original noise to cut down the noise power. An on-line learning algorithm based on the error gradient descent method was proposed, and the local stability of closed loop system is proved using the discrete Lyapunov function. A nonlinear simulation example shows that the adaptive active noise feedback control method based on a neural network is very effective to the nonlinear noise control.

  7. Perception Neural Networks for Active Noise Control Systems

    Directory of Open Access Journals (Sweden)

    Wang Xiaoli

    2012-11-01

    Full Text Available In a response to a growing demand for environments of 70dB or less noise levels, many industrial sectors have focused with some form of noise control system. Active noise control (ANC has proven to be the most effective technology. This paper mainly investigates application of neural network on self-adaptation system in active noise control (ANC. An active silencing control system is made which adopts a motional feedback loudspeaker as not a noise controlling source but a detecting sensor. The working fundamentals and the characteristics of the motional feedback loudspeaker are analyzed in detail. By analyzing each acoustical path, identification based adaptive linear neural network is built. This kind of identifying method can be achieved conveniently. The estimated result of each sound channel matches well with its real sound character, respectively.

  8. Power-Integrated Circuit Active Leakage Current Detector

    Directory of Open Access Journals (Sweden)

    M. F. Bulacio

    2012-01-01

    Full Text Available Most of the failures of induction motors become insulation faults, causing a permanent damage. Using differential current transformers, a system capable of insulation fault detection was developed, based on the differential relay protection scheme. Both signal injection and fault detection circuitry were integrated in a single chip. The proposed scheme is faster than other existing protection and not restricted to protect induction motors, but several other devices (such as IGBTs and systems. This paper explains the principle of operation of fault protection scheme and analyzes an integrated implementation through simulations and experimental results. A power-integrated circuit (PIC implementation is presented.

  9. Neural circuits underlying hyperactivity in an animal model for anorexia nervosa

    OpenAIRE

    Verhagen, L.A.W.

    2009-01-01

    Anorexia nervosa (AN) means literally “a nervous loss of appetite” and is characterized by reduced food intake, extreme body weight loss, hypothermia, amenorrhea and emaciation. The average prevalence of AN has been reported to be 0.3% and has the highest mortality rate (>10%) of all psychiatric disorders. Excessive physical activity is demonstrated by many, if not most, patients with AN at some point in the course of the disorder, and has been described as a hallmark feature of the syndrome....

  10. Simulation of Aorta Artery Aneurysms Using Active Electronic Circuit

    Directory of Open Access Journals (Sweden)

    Kamran Hassani

    2007-01-01

    Full Text Available The fusiform and saccular aneurysms in different aorta artery sections were studied using an electronic circuit of cardiovascular system. The geometrical model of each artery section including thoracic and abdominal were generated in accordance with original anatomical data. By increasing the rate of aneurysm in each studied section, the pressure drop were calculated using CFD method, furthermore the compliance variations due to aneurysms were determined by mathematical method. The equivalent electronic circuit was then used to study the effects of the pressure drops and compliance variations on whole cardiovascular system. The results of the simulation exhibited the features of the pathology, including hypertension, the increase of the pulse pressure with the rate of aneurysm and the large magnitude of back flow during systole. Finally, the obtained results were compared with relevant clinical data .We have concluded from the study that aorta aneurysms in both fusiform and saccular, especially at highest diameters, may be the most important determinant of the artery rapture and heart failure.

  11. Where's the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network.

    Science.gov (United States)

    Hartmann, Christoph; Lazar, Andreea; Nessler, Bernhard; Triesch, Jochen

    2015-12-01

    Even in the absence of sensory stimulation the brain is spontaneously active. This background "noise" seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experimental observations have extended our knowledge of trial-to-trial variability and spontaneous activity in several directions: 1. Trial-to-trial variability systematically decreases following the onset of a sensory stimulus or the start of a motor act. 2. Spontaneous activity states in sensory cortex outline the region of evoked sensory responses. 3. Across development, spontaneous activity aligns itself with typical evoked activity patterns. 4. The spontaneous brain activity prior to the presentation of an ambiguous stimulus predicts how the stimulus will be interpreted. At present it is unclear how these observations relate to each other and how they arise in cortical circuits. Here we demonstrate that all of these phenomena can be accounted for by a deterministic self-organizing recurrent neural network model (SORN), which learns a predictive model of its sensory environment. The SORN comprises recurrently coupled populations of excitatory and inhibitory threshold units and learns via a combination of spike-timing dependent plasticity (STDP) and homeostatic plasticity mechanisms. Similar to balanced network architectures, units in the network show irregular activity and variable responses to inputs. Additionally, however, the SORN exhibits sequence learning abilities matching recent findings from visual cortex and the network's spontaneous activity reproduces the experimental findings mentioned above. Intriguingly, the network's behaviour is reminiscent of sampling-based probabilistic inference, suggesting that correlates of sampling-based inference can develop from the interaction of STDP and homeostasis in deterministic networks. We conclude that key observations on spontaneous brain activity and the variability of neural responses can be

  12. Persistent activity in neural networks with dynamic synapses.

    Directory of Open Access Journals (Sweden)

    Omri Barak

    2007-02-01

    Full Text Available Persistent activity states (attractors, observed in several neocortical areas after the removal of a sensory stimulus, are believed to be the neuronal basis of working memory. One of the possible mechanisms that can underlie persistent activity is recurrent excitation mediated by intracortical synaptic connections. A recent experimental study revealed that connections between pyramidal cells in prefrontal cortex exhibit various degrees of synaptic depression and facilitation. Here we analyze the effect of synaptic dynamics on the emergence and persistence of attractor states in interconnected neural networks. We show that different combinations of synaptic depression and facilitation result in qualitatively different network dynamics with respect to the emergence of the attractor states. This analysis raises the possibility that the framework of attractor neural networks can be extended to represent time-dependent stimuli.

  13. Optical imaging of neuronal activity and visualization of fine neural structures in non-desheathed nervous systems.

    Directory of Open Access Journals (Sweden)

    Christopher John Goldsmith

    Full Text Available Locating circuit neurons and recording from them with single-cell resolution is a prerequisite for studying neural circuits. Determining neuron location can be challenging even in small nervous systems because neurons are densely packed, found in different layers, and are often covered by ganglion and nerve sheaths that impede access for recording electrodes and neuronal markers. We revisited the voltage-sensitive dye RH795 for its ability to stain and record neurons through the ganglion sheath. Bath-application of RH795 stained neuronal membranes in cricket, earthworm and crab ganglia without removing the ganglion sheath, revealing neuron cell body locations in different ganglion layers. Using the pyloric and gastric mill central pattern generating neurons in the stomatogastric ganglion (STG of the crab, Cancer borealis, we found that RH795 permeated the ganglion without major residue in the sheath and brightly stained somatic, axonal and dendritic membranes. Visibility improved significantly in comparison to unstained ganglia, allowing the identification of somata location and number of most STG neurons. RH795 also stained axons and varicosities in non-desheathed nerves, and it revealed the location of sensory cell bodies in peripheral nerves. Importantly, the spike activity of the sensory neuron AGR, which influences the STG motor patterns, remained unaffected by RH795, while desheathing caused significant changes in AGR activity. With respect to recording neural activity, RH795 allowed us to optically record membrane potential changes of sub-sheath neuronal membranes without impairing sensory activity. The signal-to-noise ratio was comparable with that previously observed in desheathed preparations and sufficiently high to identify neurons in single-sweep recordings and synaptic events after spike-triggered averaging. In conclusion, RH795 enabled staining and optical recording of neurons through the ganglion sheath and is therefore both a

  14. Optical Imaging of Neuronal Activity and Visualization of Fine Neural Structures in Non-Desheathed Nervous Systems

    Science.gov (United States)

    Stein, Wolfgang

    2014-01-01

    Locating circuit neurons and recording from them with single-cell resolution is a prerequisite for studying neural circuits. Determining neuron location can be challenging even in small nervous systems because neurons are densely packed, found in different layers, and are often covered by ganglion and nerve sheaths that impede access for recording electrodes and neuronal markers. We revisited the voltage-sensitive dye RH795 for its ability to stain and record neurons through the ganglion sheath. Bath-application of RH795 stained neuronal membranes in cricket, earthworm and crab ganglia without removing the ganglion sheath, revealing neuron cell body locations in different ganglion layers. Using the pyloric and gastric mill central pattern generating neurons in the stomatogastric ganglion (STG) of the crab, Cancer borealis, we found that RH795 permeated the ganglion without major residue in the sheath and brightly stained somatic, axonal and dendritic membranes. Visibility improved significantly in comparison to unstained ganglia, allowing the identification of somata location and number of most STG neurons. RH795 also stained axons and varicosities in non-desheathed nerves, and it revealed the location of sensory cell bodies in peripheral nerves. Importantly, the spike activity of the sensory neuron AGR, which influences the STG motor patterns, remained unaffected by RH795, while desheathing caused significant changes in AGR activity. With respect to recording neural activity, RH795 allowed us to optically record membrane potential changes of sub-sheath neuronal membranes without impairing sensory activity. The signal-to-noise ratio was comparable with that previously observed in desheathed preparations and sufficiently high to identify neurons in single-sweep recordings and synaptic events after spike-triggered averaging. In conclusion, RH795 enabled staining and optical recording of neurons through the ganglion sheath and is therefore both a good anatomical

  15. Optimal Coding Predicts Attentional Modulation of Activity in Neural Systems

    OpenAIRE

    Jaramillo, Santiago; Pearlmutter, Barak A.

    2007-01-01

    Neuronal activity in response to a fixed stimulus has been shown to change as a function of attentional state, implying that the neural code also changes with attention. We propose an information-theoretic account of such modulation: that the nervous system adapts to optimally encode sensory stimuli while taking into account the changing relevance of different features. We show using computer simulation that such modulation emerges in a coding system informed about the uneven relevance of ...

  16. Activated sludge process based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    张文艺; 蔡建安

    2002-01-01

    Considering the difficulty of creating water quality model for activated sludge system, a typical BP artificial neural network model has been established to simulate the operation of a waste water treatment facilities. The comparison of prediction results with the on-spot measurements shows the model, the model is accurate and this model can also be used to realize intelligentized on-line control of the wastewater processing process.

  17. Efficient universal computing architectures for decoding neural activity.

    Directory of Open Access Journals (Sweden)

    Benjamin I Rapoport

    Full Text Available The ability to decode neural activity into meaningful control signals for prosthetic devices is critical to the development of clinically useful brain- machine interfaces (BMIs. Such systems require input from tens to hundreds of brain-implanted recording electrodes in order to deliver robust and accurate performance; in serving that primary function they should also minimize power dissipation in order to avoid damaging neural tissue; and they should transmit data wirelessly in order to minimize the risk of infection associated with chronic, transcutaneous implants. Electronic architectures for brain- machine interfaces must therefore minimize size and power consumption, while maximizing the ability to compress data to be transmitted over limited-bandwidth wireless channels. Here we present a system of extremely low computational complexity, designed for real-time decoding of neural signals, and suited for highly scalable implantable systems. Our programmable architecture is an explicit implementation of a universal computing machine emulating the dynamics of a network of integrate-and-fire neurons; it requires no arithmetic operations except for counting, and decodes neural signals using only computationally inexpensive logic operations. The simplicity of this architecture does not compromise its ability to compress raw neural data by factors greater than [Formula: see text]. We describe a set of decoding algorithms based on this computational architecture, one designed to operate within an implanted system, minimizing its power consumption and data transmission bandwidth; and a complementary set of algorithms for learning, programming the decoder, and postprocessing the decoded output, designed to operate in an external, nonimplanted unit. The implementation of the implantable portion is estimated to require fewer than 5000 operations per second. A proof-of-concept, 32-channel field-programmable gate array (FPGA implementation of this portion

  18. Neural sensing of electrical activity with stretchable microelectrode arrays.

    Science.gov (United States)

    Yu, Zhe; Graudejus, Oliver; Lacour, Stéphanie P; Wagner, Sigurd; Morrison, Barclay

    2009-01-01

    Sensing neural activity within mechanically active tissues poses particular hurdles because most electrodes are much stiffer than biological tissues. As the tissue deforms, the rigid electrodes may damage the surrounding tissue. The problem is exacerbated when sensing neural activity in experimental models of traumatic brain injury (TBI) which is caused by the rapid and large deformation of brain tissue. We have developed a stretchable microelectrode array (SMEA) that can withstand large elastic deformations (>5% biaxial strain) while continuing to function. The SMEA were fabricated from thin metal conductors patterned on polydimethylsiloxane (PDMS) and encapsulated with a photo-patternable silicone. SMEA were used to record spontaneous activity from brain slice cultures, as well as evoked activity after stimulating through SMEA electrodes. Slices of brain tissue were grown on SMEA in long-term culture and then mechanically injured with our well-characterized in vitro injury model by stretching the SMEA and the adherent culture, which was confirmed by image analysis. Because brain tissue was grown on the substrate-integrated SMEA, post-injury changes in electrophysiological function were normalized to pre-injury function since the SMEA deformed with the tissue and remained in place during mechanical stimulation. The combination of our injury model and SMEA could help elucidate mechanisms responsible for post-traumatic neuronal dysfunction in the quest for TBI therapies. The SMEA may have additional sensing applications in other mechanically active tissues such as peripheral nerve and heart. PMID:19964344

  19. A neural network model for olfactory glomerular activity prediction

    Science.gov (United States)

    Soh, Zu; Tsuji, Toshio; Takiguchi, Noboru; Ohtake, Hisao

    2012-12-01

    Recently, the importance of odors and methods for their evaluation have seen increased emphasis, especially in the fragrance and food industries. Although odors can be characterized by their odorant components, their chemical information cannot be directly related to the flavors we perceive. Biological research has revealed that neuronal activity related to glomeruli (which form part of the olfactory system) is closely connected to odor qualities. Here we report on a neural network model of the olfactory system that can predict glomerular activity from odorant molecule structures. We also report on the learning and prediction ability of the proposed model.

  20. Experiences of activity measurements of primary circuit materials in a WWR-SM research reactor

    International Nuclear Information System (INIS)

    The activity of water and gas samples taken from the primary circuit have been measured nondestructively for more than two years to monitor the technological parameters of the reactor. In the primary water samples 17 fission products and seven activated traces, as well as six radioactive conponents in the gas samples were determined routinely by Ge/Li gamma-spectrometry. (author)

  1. Sustained activity in hierarchical modular neural networks: self-organized criticality and oscillations

    Directory of Open Access Journals (Sweden)

    Sheng-Jun Wang

    2011-06-01

    Full Text Available Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. They are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality. We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. It was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We find that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and self-organized criticality, which are not present in the respective random networks. The underlying mechanism is that each dense module cannot sustain activity on its own, but displays self-organized criticality in the presence of weak perturbations. The hierarchical modular networks provide the coupling among subsystems with self-organized criticality. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivityof critical state and predictability and timing of oscillations for efficient

  2. Multiswitching Synchronization of a Driven Hyperchaotic Circuit Using Active Backstepping

    Directory of Open Access Journals (Sweden)

    A. Ayotunde Ajayi

    2014-01-01

    Full Text Available An active backstepping technique is proposed for the realization of multiswitching synchronization of periodically forced hyperchaotic Van der Pol-Duffing oscillators. The active backstepping technique is a systematic design approach with recursive procedures that skillfully optimizes the choice of Lyapunov functions and active control technique. Using the active backstepping technique, the usual master-slave synchronization scheme is extended to study the synchronization of systems with different combinations of the slave states variables with master state variables. Our numerical results confirm the effectiveness of the proposed analytical technique.

  3. Amygdala-ventral striatum circuit activation decreases long-term fear

    Science.gov (United States)

    Correia, Susana S; McGrath, Anna G; Lee, Allison; Graybiel, Ann M; Goosens, Ki A

    2016-01-01

    In humans, activation of the ventral striatum, a region associated with reward processing, is associated with the extinction of fear, a goal in the treatment of fear-related disorders. This evidence suggests that extinction of aversive memories engages reward-related circuits, but a causal relationship between activity in a reward circuit and fear extinction has not been demonstrated. Here, we identify a basolateral amygdala (BLA)-ventral striatum (NAc) pathway that is activated by extinction training. Enhanced recruitment of this circuit during extinction learning, either by pairing reward with fear extinction training or by optogenetic stimulation of this circuit during fear extinction, reduces the return of fear that normally follows extinction training. Our findings thus identify a specific BLA-NAc reward circuit that can regulate the persistence of fear extinction and point toward a potential therapeutic target for disorders in which the return of fear following extinction therapy is an obstacle to treatment. DOI: http://dx.doi.org/10.7554/eLife.12669.001 PMID:27671733

  4. Striatal plasticity and basal ganglia circuit function

    OpenAIRE

    Kreitzer, Anatol C.; Malenka, Robert C.

    2008-01-01

    The dorsal striatum, which consists of the caudate and putamen, is the gateway to the basal ganglia. It receives convergent excitatory afferents from cortex and thalamus and forms the origin of the direct and indirect pathways—distinct basal ganglia circuits involved in motor control. It is also a major site of activity-dependent synaptic plasticity. Striatal plasticity alters the transfer of information throughout basal ganglia circuits and may represent a key neural substrate for adaptive m...

  5. Estimation of spatiotemporal neural activity using radial basis function networks.

    Science.gov (United States)

    Anderson, R W; Das, S; Keller, E L

    1998-12-01

    We report a method using radial basis function (RBF) networks to estimate the time evolution of population activity in topologically organized neural structures from single-neuron recordings. This is an important problem in neuroscience research, as such estimates may provide insights into systems-level function of these structures. Since single-unit neural data tends to be unevenly sampled and highly variable under similar behavioral conditions, obtaining such estimates is a difficult task. In particular, a class of cells in the superior colliculus called buildup neurons can have very narrow regions of saccade vectors for which they discharge at high rates but very large surround regions over which they discharge at low, but not zero, levels. Estimating the dynamic movement fields for these cells for two spatial dimensions at closely spaced timed intervals is a difficult problem, and no general method has been described that can be applied to all buildup cells. Estimation of individual collicular cells' spatiotemporal movement fields is a prerequisite for obtaining reliable two-dimensional estimates of the population activity on the collicular motor map during saccades. Therefore, we have developed several computational-geometry-based algorithms that regularize the data before computing a surface estimation using RBF networks. The method is then expanded to the problem of estimating simultaneous spatiotemporal activity occurring across the superior colliculus during a single movement (the inverse problem). In principle, this methodology could be applied to any neural structure with a regular, two-dimensional organization, provided a sufficient spatial distribution of sampled neurons is available.

  6. Transcriptional Regulatory Circuits Controlling Brown Fat Development and Activation

    OpenAIRE

    Seale, Patrick

    2015-01-01

    Brown and beige adipose tissue is specialized for heat production and can be activated to reduce obesity and metabolic dysfunction in animals. Recent studies also have indicated that human brown fat activity levels correlate with leanness. This has revitalized interest in brown fat biology and has driven the discovery of many new regulators of brown fat development and function. This review summarizes recent advances in our understanding of the transcriptional mechanisms that control brown an...

  7. Activational and effort-related aspects of motivation: neural mechanisms and implications for psychopathology.

    Science.gov (United States)

    Salamone, John D; Yohn, Samantha E; López-Cruz, Laura; San Miguel, Noemí; Correa, Mercè

    2016-05-01

    Motivation has been defined as the process that allows organisms to regulate their internal and external environment, and control the probability, proximity and availability of stimuli. As such, motivation is a complex process that is critical for survival, which involves multiple behavioural functions mediated by a number of interacting neural circuits. Classical theories of motivation suggest that there are both directional and activational aspects of motivation, and activational aspects (i.e. speed and vigour of both the instigation and persistence of behaviour) are critical for enabling organisms to overcome work-related obstacles or constraints that separate them from significant stimuli. The present review discusses the role of brain dopamine and related circuits in behavioural activation, exertion of effort in instrumental behaviour, and effort-related decision-making, based upon both animal and human studies. Impairments in behavioural activation and effort-related aspects of motivation are associated with psychiatric symptoms such as anergia, fatigue, lassitude and psychomotor retardation, which cross multiple pathologies, including depression, schizophrenia, and Parkinson's disease. Therefore, this review also attempts to provide an interdisciplinary approach that integrates findings from basic behavioural neuroscience, behavioural economics, clinical neuropsychology, psychiatry, and neurology, to provide a coherent framework for future research and theory in this critical field. Although dopamine systems are a critical part of the brain circuitry regulating behavioural activation, exertion of effort, and effort-related decision-making, mesolimbic dopamine is only one part of a distributed circuitry that includes multiple neurotransmitters and brain areas. Overall, there is a striking similarity between the brain areas involved in behavioural activation and effort-related processes in rodents and in humans. Animal models of effort-related decision

  8. Multifractal detrended fluctuation analysis of optogenetic modulation of neural activity

    Science.gov (United States)

    Kumar, S.; Gu, L.; Ghosh, N.; Mohanty, S. K.

    2013-02-01

    Here, we introduce a computational procedure to examine whether optogenetically activated neuronal firing recordings could be characterized as multifractal series. Optogenetics is emerging as a valuable experimental tool and a promising approach for studying a variety of neurological disorders in animal models. The spiking patterns from cortical region of the brain of optogenetically-stimulated transgenic mice were analyzed using a sophisticated fluctuation analysis method known as multifractal detrended fluctuation analysis (MFDFA). We observed that the optogenetically-stimulated neural firings are consistent with a multifractal process. Further, we used MFDFA to monitor the effect of chemically induced pain (formalin injection) and optogenetic treatment used to relieve the pain. In this case, dramatic changes in parameters characterizing a multifractal series were observed. Both the generalized Hurst exponent and width of singularity spectrum effectively differentiates the neural activities during control and pain induction phases. The quantitative nature of the analysis equips us with better measures to quantify pain. Further, it provided a measure for effectiveness of the optogenetic stimulation in inhibiting pain. MFDFA-analysis of spiking data from other deep regions of the brain also turned out to be multifractal in nature, with subtle differences in the parameters during pain-induction by formalin injection and inhibition by optogenetic stimulation. Characterization of neuronal firing patterns using MFDFA will lead to better understanding of neuronal response to optogenetic activation and overall circuitry involved in the process.

  9. Telencephalic neural activation following passive avoidance learning in a terrestrial toad.

    Science.gov (United States)

    Puddington, Martín M; Daneri, M Florencia; Papini, Mauricio R; Muzio, Rubén N

    2016-12-15

    The present study explores passive avoidance learning and its neural basis in toads (Rhinella arenarum). In Experiment 1, two groups of toads learned to move from a lighted compartment into a dark compartment. After responding, animals in the experimental condition were exposed to an 800-mM strongly hypertonic NaCl solution that leads to weight loss. Control animals received exposure to a 300-mM slightly hypertonic NaCl solution that leads to neither weight gain nor loss. After 10 daily acquisition trials, animals in the experimental group showed significantly longer latency to enter the dark compartment. Additionally, 10 daily trials in which both groups received the 300-mM NaCl solution after responding eliminated this group effect. Thus, experimental animals showed gradual acquisition and extinction of a passive avoidance respond. Experiment 2 replicated the gradual acquisition effect, but, after the last trial, animals were sacrificed and neural activation was assessed in five brain regions using AgNOR staining for nucleoli-an index of brain activity. Higher activation in the experimental animals, relative to controls, was observed in the amygdala and striatum. Group differences in two other regions, lateral pallium and septum, were borderline, but nonsignificant, whereas group differences in the medial pallium were nonsignificant. These preliminary results suggest that a striatal-amygdala activation could be a key component of the brain circuit controlling passive avoidance learning in amphibians. The results are discussed in relation to the results of analogous experiments with other vertebrates. PMID:27498147

  10. Properties and application of a multichannel integrated circuit for low-artifact, patterned electrical stimulation of neural tissue

    Science.gov (United States)

    Hottowy, Paweł; Skoczeń, Andrzej; Gunning, Deborah E.; Kachiguine, Sergei; Mathieson, Keith; Sher, Alexander; Wiącek, Piotr; Litke, Alan M.; Dąbrowski, Władysław

    2012-01-01

    Objective Modern multielectrode array (MEA) systems can record the neuronal activity from thousands of electrodes, but their ability to provide spatio-temporal patterns of electrical stimulation is very limited. Furthermore, the stimulus-related artifacts significantly limit the ability to record the neuronal responses to the stimulation. To address these issues, we designed a multichannel integrated circuit for patterned MEA-based electrical stimulation and evaluated its performance in experiments with isolated mouse and rat retina. Approach The Stimchip includes 64 independent stimulation channels. Each channel comprises an internal digital-to-analog converter that can be configured as a current or voltage source. The shape of the stimulation waveform is defined independently for each channel by the real-time data stream. In addition, each channel is equipped with circuitry for reduction of the stimulus artifact. Main results Using a high-density MEA stimulation/recording system, we effectively stimulated individual retinal ganglion cells (RGCs) and recorded the neuronal responses with minimal distortion, even on the stimulating electrodes. We independently stimulated a population of RGCs in rat retina and, using a complex spatio-temporal pattern of electrical stimulation pulses, we replicated visually-evoked spiking activity of a subset of these cells with high fidelity. Significance Compared with current state-of-the-art MEA systems, the Stimchip is able to stimulate neuronal cells with much more complex sequences of electrical pulses and with significantly reduced artifacts. This opens up new possibilities for studies of neuronal responses to electrical stimulation, both in the context of neuroscience research and in the development of neuroprosthetic devices. PMID:23160018

  11. Reduced parahippocampal connectivity produces schizophrenia-like memory deficits in simulated neural circuits with reduced parahippocampal connectivity

    NARCIS (Netherlands)

    L. Talamini; M. Meeter; B. Elvevåg; J.M.J. Murre; T.E. Goldberg

    2005-01-01

    Episodic memory impairments are well characterized in schizophrenia, but their neural origin is unclear. The objective of this experiment is to determine whether the episodic memory impairments in schizophrenia may originate from reduced parahippocampal connectivity. The experimental design used was

  12. Practical aspects of 13C surface receive coils with active decoupling and tuning circuit

    DEFF Research Database (Denmark)

    Nilsson, Daniel; Mohr, Johan Jacob; Zhurbenko, Vitaliy

    2012-01-01

    is based on application-specified coil profile and includes impedance matching and balancing circuits. Active decoupling is implemented in order to minimize the influence of the receiving coil on the homogeneity of the transmit-coil field. Measurement results for a coil prototype are presented, including...

  13. Dopamine Modulation of Emotional Processing in Cortical and Subcortical Neural Circuits: Evidence for a Final Common Pathway in Schizophrenia?

    OpenAIRE

    Laviolette, Steven R

    2007-01-01

    The neural regulation of emotional perception, learning, and memory is essential for normal behavioral and cognitive functioning. Many of the symptoms displayed by individuals with schizophrenia may arise from fundamental disturbances in the ability to accurately process emotionally salient sensory information. The neurotransmitter dopamine (DA) and its ability to modulate neural regions involved in emotional learning, perception, and memory formation has received considerable research attent...

  14. Classification of the extracellular fields produced by activated neural structures

    Directory of Open Access Journals (Sweden)

    Perry Danielle

    2005-09-01

    Full Text Available Abstract Background Classifying the types of extracellular potentials recorded when neural structures are activated is an important component in understanding nerve pathophysiology. Varying definitions and approaches to understanding the factors that influence the potentials recorded during neural activity have made this issue complex. Methods In this article, many of the factors which influence the distribution of electric potential produced by a traveling action potential are discussed from a theoretical standpoint with illustrative simulations. Results For an axon of arbitrary shape, it is shown that a quadrupolar potential is generated by action potentials traveling along a straight axon. However, a dipole moment is generated at any point where an axon bends or its diameter changes. Next, it is shown how asymmetric disturbances in the conductivity of the medium surrounding an axon produce dipolar potentials, even during propagation along a straight axon. Next, by studying the electric fields generated by a dipole source in an insulating cylinder, it is shown that in finite volume conductors, the extracellular potentials can be very different from those in infinite volume conductors. Finally, the effects of impulses propagating along axons with inhomogeneous cable properties are analyzed. Conclusion Because of the well-defined factors affecting extracellular potentials, the vague terms far-field and near-field potentials should be abandoned in favor of more accurate descriptions of the potentials.

  15. 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.

  16. Integrated Brain Circuits: Astrocytic Networks Modulate Neuronal Activity and Behavior

    Science.gov (United States)

    Halassa, Michael M.; Haydon, Philip G.

    2011-01-01

    The past decade has seen an explosion of research on roles of neuron-astrocyte interactions in the control of brain function. We highlight recent studies performed on the tripartite synapse, the structure consisting of pre- and postsynaptic elements of the synapse and an associated astrocytic process. Astrocytes respond to neuronal activity and neuro-transmitters, through the activation of metabotropic receptors, and can release the gliotransmitters ATP, D-serine, and glutamate, which act on neurons. Astrocyte-derived ATP modulates synaptic transmission, either directly or through its metabolic product adenosine. D-serine modulates NMDA receptor function, whereas glia-derived glutamate can play important roles in relapse following withdrawal from drugs of abuse. Cell type–specific molecular genetics has allowed a new level of examination of the function of astrocytes in brain function and has revealed an important role of these glial cells that is mediated by adenosine accumulation in the control of sleep and in cognitive impairments that follow sleep deprivation. PMID:20148679

  17. Neural oscillations: beta band activity across motor networks.

    Science.gov (United States)

    Khanna, Preeya; Carmena, Jose M

    2015-06-01

    Local field potential (LFP) activity in motor cortical and basal ganglia regions exhibits prominent beta (15-40Hz) oscillations during reaching and grasping, muscular contraction, and attention tasks. While in vitro and computational work has revealed specific mechanisms that may give rise to the frequency and duration of this oscillation, there is still controversy about what behavioral processes ultimately drive it. Here, simultaneous behavioral and large-scale neural recording experiments from non-human primate and human subjects are reviewed in the context of specific hypotheses about how beta band activity is generated. Finally, a new experimental paradigm utilizing operant conditioning combined with motor tasks is proposed as a way to further investigate this oscillation. PMID:25528615

  18. Cat's medullary reticulospinal and subnucleus reticularis dorsalis noxious neurons form a coupled neural circuit through collaterals of descending axons.

    Science.gov (United States)

    Leiras, Roberto; Martín-Cora, Francisco; Velo, Patricia; Liste, Tania; Canedo, Antonio

    2016-01-01

    Animals and human beings sense and react to real/potential dangerous stimuli. However, the supraspinal mechanisms relating noxious sensing and nocifensive behavior are mostly unknown. The collateralization and spatial organization of interrelated neurons are important determinants of coordinated network function. Here we electrophysiologically studied medial medullary reticulospinal neurons (mMRF-RSNs) antidromically identified from the cervical cord of anesthetized cats and found that 1) more than 40% (79/183) of the sampled mMRF-RSNs emitted bifurcating axons running within the dorsolateral (DLF) and ventromedial (VMF) ipsilateral fascicles; 2) more than 50% (78/151) of the tested mMRF-RSNs with axons running in the VMF collateralized to the subnucleus reticularis dorsalis (SRD) that also sent ipsilateral descending fibers bifurcating within the DLF and the VMF. This percentage of mMRF collateralization to the SRD increased to more than 81% (53/65) when considering the subpopulation of mMRF-RSNs responsive to noxiously heating the skin; 3) reciprocal monosynaptic excitatory relationships were electrophysiologically demonstrated between noxious sensitive mMRF-RSNs and SRD cells; and 4) injection of the anterograde tracer Phaseolus vulgaris leucoagglutinin evidenced mMRF to SRD and SRD to mMRF projections contacting the soma and proximal dendrites. The data demonstrated a SRD-mMRF network interconnected mainly through collaterals of descending axons running within the VMF, with the subset of noxious sensitive cells forming a reverberating circuit probably amplifying mutual outputs simultaneously regulating motor activity and spinal noxious afferent input. The results provide evidence that noxious stimulation positively engages a reticular SRD-mMRF-SRD network involved in pain-sensory-to-motor transformation and modulation. PMID:26581870

  19. 秀丽线虫接触感知神经网络的电路实现%Analog circuit implementation and application of neural network for touch sensitivity in Caenorhabditis elegans

    Institute of Scientific and Technical Information of China (English)

    申蛟隆; 陈焕文; 刘泽文

    2014-01-01

    To overcome bad real time effect and weak parallel processing ability of neural network simulated by software,using analog circuit to implement a neural network for touch sensitivity in Caenorhabditis elegans was proposed,and the nematode’s withdrawal behaviour was reproduced with analog circuit at the same time.All parameters included in the neural network imple-mented by analog circuit were converted from the parameters acquired by using the real-coded genetic algorithm to train the neu-ral network for touch sensitivity in Caenorhabditis elegans.The analog circuit was simulated by Hspice.The results of circuit simulated by Hspice were consistent with the numerical results obtained from the neural network model,which showed the vali-dity and the correctness of analog circuit.%为克服神经网络软件仿真实时性差、并行处理能力弱等缺点,提出了采用电路的方法实现秀丽线虫的接触感知神经网络,模拟秀丽线虫的回撤行为。其中所有参数由实数编码遗传算法训练秀丽线虫接触感知神经网络模型所得参数转化而来。通过Hspice仿真器进行仿真,Hspice仿真结果和秀丽线虫接触感知神经网络模型的数值仿真结果相符,验证了该电路的有效性和正确性。

  20. The Zebrafish Brain in Research and Teaching: A Simple in Vivo and in Vitro Model for the Study of Spontaneous Neural Activity

    Science.gov (United States)

    Vargas, R.; Johannesdottir, I. P.; Sigurgeirsson, B.; Porsteinsson, H.; Karlsson, K. AE.

    2011-01-01

    Recently, the zebrafish ("Danio rerio") has been established as a key animal model in neuroscience. Behavioral, genetic, and immunohistochemical techniques have been used to describe the connectivity of diverse neural circuits. However, few studies have used zebrafish to understand the function of cerebral structures or to study neural circuits.…

  1. Effects of Near-Infrared Laser on Neural Cell Activity

    International Nuclear Information System (INIS)

    Near-infrared laser has been used to relieve patients from various kinds of pain caused by postherpetic neuralgesia, myofascial dysfunction, surgical and traumatic wound, cancer, and rheumatoid arthritis. Clinically, He-Ne (λ=632.8 nm, 780 nm) and Ga-Al-As (805 ± 25 nm) lasers are used to irradiate trigger points or nerve ganglion. However the precise mechanisms of such biological actions of the laser have not yet been resolved. Since laser therapy is often effective to suppress the pain caused by hyperactive excitation of sensory neurons, interactions with laser light and neural cells are suggested. As neural excitation requires large amount of energy liberated from adenosine triphosphate (ATP), we examined the effect of 830-nm laser irradiation on the energy metabolism of the rat central nervous system and isolated mitochondria from brain. The diode laser was applied for 15 min with irradiance of 4.8 W/cm2 on a 2 mm-diameter spot at the brain surface. Tissue ATP content of the irradiated area in the cerebral cortex was 19% higher than that of the non-treated area (opposite side of the cortex), whereas the ADP content showed no significant difference. Irradiation at another wavelength (652 nm) had no effect on either ATP or ADP contents. The temperature of the brain tissue was increased 4.5-5.0 deg. C during the irradiation of both 830-nm and 652-nm laser light. Direct irradiation of the mitochondrial suspension did not show any wavelength-dependent acceleration of respiration rate nor ATP synthesis. These results suggest that the increase in tissue ATP content did not result from the thermal effect, but from specific effect of the laser operated at 830 nm. Electrophysiological studies showed the hyperpolarization of membrane potential of isolated neurons and decrease in membrane resistance with irradiation of the laser, suggesting an activation of potassium channels. Intracellular ATP is reported to regulate some kinds of potassium channels. Possible mechanisms

  2. Studying modulation on simultaneously activated SSVEP neural networks by a cognitive task.

    Science.gov (United States)

    Wu, Zhenghua

    2014-01-01

    Since the discovery of steady-state visually evoked potential (SSVEP), it has been used in many fields. Numerous studies suggest that there exist three SSVEP neural networks in different frequency bands. An obvious phenomenon has been observed, that the amplitude and phase of SSVEP can be modulated by a cognitive task. Previous works have studied this modulation on separately activated SSVEP neural networks by a cognitive task. If two or more SSVEP neural networks are activated simultaneously in the process of a cognitive task, is the modulation on different SSVEP neural networks the same? In this study, two different SSVEP neural networks were activated simultaneously by two different frequency flickers, with a working memory task irrelevant to the flickers being conducted at the same time. The modulated SSVEP waves were compared with each other and to those only under one flicker in previous studies. The comparison results show that the cognitive task can modulate different SSVEP neural networks with a similar style.

  3. Can simple interactions capture complex features of neural activity underlying behavior in a virtual reality environment?

    Science.gov (United States)

    Meshulam, Leenoy; Gauthier, Jeffrey; Brody, Carlos; Tank, David; Bialek, William

    The complex neural interactions which are abundant in most recordings of neural activity are relatively poorly understood. A prime example of such interactions can be found in the in vivo neural activity which underlies complex behaviors of mice, imaged in brain regions such as hippocampus and parietal cortex. Experimental techniques now allow us to accurately follow these neural interactions in the simultaneous activity of large neuronal populations of awake behaving animals. Here, we demonstrate that pairwise maximum entropy models can predict a surprising number of properties of the neural activity. The models, that are constrained with activity rates and interactions between pairs of neurons, are well fit to the activity `states' in the hippocampus and cortex of mice performing cognitive tasks while navigating in a virtual reality environment.

  4. Identification of non-linear models of neural activity in bold fmri

    DEFF Research Database (Denmark)

    Jacobsen, Daniel Jakup; Madsen, Kristoffer Hougaard; Hansen, Lars Kai

    2006-01-01

    Non-linear hemodynamic models express the BOLD signal as a nonlinear, parametric functional of the temporal sequence of local neural activity. Several models have been proposed for this neural activity. We identify one such parametric model by estimating the distribution of its parameters...

  5. Evidence-Based Systematic Review: Effects of Neuromuscular Electrical Stimulation on Swallowing and Neural Activation

    Science.gov (United States)

    Clark, Heather; Lazarus, Cathy; Arvedson, Joan; Schooling, Tracy; Frymark, Tobi

    2009-01-01

    Purpose: To systematically review the literature examining the effects of neuromuscular electrical stimulation (NMES) on swallowing and neural activation. The review was conducted as part of a series examining the effects of oral motor exercises (OMEs) on speech, swallowing, and neural activation. Method: A systematic search was conducted to…

  6. Neural activity changes underlying the working memory deficit in alpha-CaMKII heterozygous knockout mice

    Directory of Open Access Journals (Sweden)

    Naoki Matsuo

    2009-09-01

    Full Text Available The alpha-isoform of calcium/calmodulin-dependent protein kinase II (α-CaMKII is expressed abundantly in the forebrain and is considered to have an essential role in synaptic plasticity and cognitive function. Previously, we reported that mice heterozygous for a null mutation of α-CaMKII (α-CaMKII+/- have profoundly dysregulated behaviors including a severe working memory deficit, which is an endophenotype of schizophrenia and other psychiatric disorders. In addition, we found that almost all the neurons in the dentate gyrus (DG of the mutant mice failed to mature at molecular, morphological and electrophysiological levels. In the present study, to identify the brain substrates of the working memory deficit in the mutant mice, we examined the expression of the immediate early genes (IEGs, c-Fos and Arc, in the brain after a working memory version of the eight-arm radial maze test. c-Fos expression was abolished almost completely in the DG and was reduced significantly in neurons in the CA1 and CA3 areas of the hippocampus, central amygdala, and medial prefrontal cortex (mPFC. However, c-Fos expression was intact in the entorhinal and visual cortices. Immunohistochemical studies using arc promoter driven dVenus transgenic mice demonstrated that arc gene activation after the working memory task occurred in mature, but not immature neurons in the DG of wild-type mice. These results suggest crucial insights for the neural circuits underlying spatial mnemonic processing during a working memory task and suggest the involvement of α-CaMKII in the proper maturation and integration of DG neurons into these circuits.

  7. Self healing of open circuit faults: With active re-configurability and mimicry of synaptic plasticity

    Science.gov (United States)

    Yaswant, Vaddi; Kumar, Amit; Sambandan, Sanjiv

    2016-07-01

    We discuss the self-repair of open faults in circuits using electrically conductive particles dispersed in an insulating fluid. The repair is triggered by the electric field developed across the open circuit in a current carrying interconnect and results in the formation of a bridge of particles across the gap. We illustrate and model the dynamics of the resistance of the self-healed route, Rb, in low field conditions. Furthermore, active control of Rb and active re-wiring are also demonstrated. Considering Rb to be akin to weights between nodes, the formation and re-wiring of routes and the control of Rb mimic synaptic plasticity in biological systems and open interesting possibilities for computing.

  8. Determination of Activation Functions in A Feedforward Neural Network by using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Oğuz ÜSTÜN

    2009-03-01

    Full Text Available In this study, activation functions of all layers of the multilayered feedforward neural network have been determined by using genetic algorithm. The main criteria that show the efficiency of the neural network is to approximate to the desired output with the same number nodes and connection weights. One of the important parameter to determine this performance is to choose a proper activation function. In the classical neural network designing, a network is designed by choosing one of the generally known activation function. In the presented study, a table has been generated for the activation functions. The ideal activation function for each node has been chosen from this table by using the genetic algorithm. Two dimensional regression problem clusters has been used to compare the performance of the classical static neural network and the genetic algorithm based neural network. Test results reveal that the proposed method has a high level approximation capacity.

  9. Can Neural Activity Propagate by Endogenous Electrical Field?

    Science.gov (United States)

    Qiu, Chen; Shivacharan, Rajat S; Zhang, Mingming; Durand, Dominique M

    2015-12-01

    It is widely accepted that synaptic transmissions and gap junctions are the major governing mechanisms for signal traveling in the neural system. Yet, a group of neural waves, either physiological or pathological, share the same speed of ∼0.1 m/s without synaptic transmission or gap junctions, and this speed is not consistent with axonal conduction or ionic diffusion. The only explanation left is an electrical field effect. We tested the hypothesis that endogenous electric fields are sufficient to explain the propagation with in silico and in vitro experiments. Simulation results show that field effects alone can indeed mediate propagation across layers of neurons with speeds of 0.12 ± 0.09 m/s with pathological kinetics, and 0.11 ± 0.03 m/s with physiologic kinetics, both generating weak field amplitudes of ∼2-6 mV/mm. Further, the model predicted that propagation speed values are inversely proportional to the cell-to-cell distances, but do not significantly change with extracellular resistivity, membrane capacitance, or membrane resistance. In vitro recordings in mice hippocampi produced similar speeds (0.10 ± 0.03 m/s) and field amplitudes (2.5-5 mV/mm), and by applying a blocking field, the propagation speed was greatly reduced. Finally, osmolarity experiments confirmed the model's prediction that cell-to-cell distance inversely affects propagation speed. Together, these results show that despite their weak amplitude, electric fields can be solely responsible for spike propagation at ∼0.1 m/s. This phenomenon could be important to explain the slow propagation of epileptic activity and other normal propagations at similar speeds. PMID:26631463

  10. A Component-Minimized Single-Phase Active Power Decoupling Circuit with Reduced Current Stress to Semiconductor Switches

    DEFF Research Database (Denmark)

    Tang, Yi; Blaabjerg, Frede

    2015-01-01

    This letter proposes a novel circuit topology which can realize the power decoupling function without adding additional active switches into the circuit. The dc-link capacitor of a full bridge rectifier is split into two identical parts and the midpoint is connected to one leg through a filter in...

  11. SPR imaging combined with cyclic voltammetry for the detection of neural activity

    Directory of Open Access Journals (Sweden)

    Hui Li

    2014-03-01

    Full Text Available Surface plasmon resonance (SPR detects changes in refractive index at a metal-dielectric interface. In this study, SPR imaging (SPRi combined with cyclic voltammetry (CV was applied to detect neural activity in isolated bullfrog sciatic nerves. The neural activities induced by chemical and electrical stimulation led to an SPR response, and the activities were recorded in real time. The activities of different parts of the sciatic nerve were recorded and compared. The results demonstrated that SPR imaging combined with CV is a powerful tool for the investigation of neural activity.

  12. The social emotion of embarrassment: Modulations of neural circuits in response to own and others’ social predicaments

    OpenAIRE

    Müller-Pinzler, L.

    2016-01-01

    Embarrassment is a so called social emotion arising during the interaction with our surrounding social world. It is present in various situations in our daily lives and holds a regulative function telling us how to perform according to prevalent norms and moral values. Due to the human ability to infer and share others' emotions, thoughts or intentions embarrassment is often also experienced vicariously for others. This thesis is focused on the neural and physiological correlates of embarrass...

  13. Synaptic plasticity, neural circuits, and the emerging role of altered short-term information processing in schizophrenia

    OpenAIRE

    Crabtree, Gregg W.; Gogos, Joseph A.

    2014-01-01

    Synaptic plasticity alters the strength of information flow between presynaptic and postsynaptic neurons and thus modifies the likelihood that action potentials in a presynaptic neuron will lead to an action potential in a postsynaptic neuron. As such, synaptic plasticity and pathological changes in synaptic plasticity impact the synaptic computation which controls the information flow through the neural microcircuits responsible for the complex information processing necessary to drive adapt...

  14. Circuit design of a LSI neural network using BP-GA algorithm%采用BP-GA算法的一种LSI神经网络的电路设计

    Institute of Scientific and Technical Information of China (English)

    卢纯; 石秉学

    2001-01-01

    A new algorithm is proposed to combine the Back-Propagation algorithm (BP) and the Genetic Algorithm (GA). The combined algorithm is used to design a Large Scale Integrated circuit (LSI) for a two-layer feedforward Artificial Neural Network (ANN). A novel neuron is proposed as the key element of the neural network. The neuron's activation function fit the sigmoid well and the bias weight and the gain factor of the neuron can be modulated. Further more, the saturation levels of the sigmoid remain constant for different gain values. HSPICE simulations were done using the neural network using transistor models for a standard 1.2μm CMOS process. Results using the exclusive or (XOR) benchmark demonstra te its effectiveness.%将误差反传(BP)算法和遗传算法(GA)有机地结合在一起,提出了一种新的算法BP-GA。采用BP-GA算法,设计了一个两层前向LSI神经网络。作为神经网络的关键部件,提出的新型神经元性能优越。它的激活函数与理想sigmoid函数拟合很好; 可实现对阈值及增益因子的编程并且不同增益因子下饱和输出电压值相同。采用标准1.2μm CMOS工艺的模型参数,对该两层前向神经网络电路进行的HSPICE模拟证明了它有解决异或(XOR)问 题的能力。

  15. Compassionate attitude towards others' suffering activates the mesolimbic neural system.

    Science.gov (United States)

    Kim, Ji-Woong; Kim, Sung-Eun; Kim, Jae-Jin; Jeong, Bumseok; Park, Chang-Hyun; Son, Ae Ree; Song, Ji Eun; Ki, Seon Wan

    2009-08-01

    Compassion is one of the essential components which enable individuals to enter into and maintain relationships of caring. Compassion tends to motivate us to help people who are emotionally suffering. It is also known that a feeling of intrinsic reward may occur as a result of experiencing compassion for others. We conducted this study to understand the neural nature of compassion for other people's emotional state. Twenty-one healthy normal volunteers participated in this study. We used a 2 x 2 factorial design in which each subject was asked to assume a compassionate attitude or passive attitude while viewing the sad or neutral facial affective pictures during functional magnetic imaging. The main effect of a compassionate attitude was observed in the medial frontal cortex, the subgenual frontal cortex, the inferior frontal cortex and the midbrain regions. A test of the interaction between a compassionate attitude and sad facial affect revealed significant activations in the midbrain-ventral striatum/septal network region. The results of this study suggest that taking a compassionate attitude towards other people's sad expressions modulate the activities of the midbrain-ventral striatum/septal region network, which is known to play a role in the prosocial/social approach motivation and its accompanied rewarding feeling. PMID:19428038

  16. Generalized activity equations for spiking neural network dynamics

    Directory of Open Access Journals (Sweden)

    Michael A Buice

    2013-11-01

    Full Text Available Much progress has been made in uncovering the computational capabilities of spiking neural networks. However, spiking neurons will always be more expensive to simulate compared to rate neurons because of the inherent disparity in time scales - the spike duration time is much shorter than the inter-spike time, which is much shorter than any learning time scale. In numerical analysis, this is a classic stiff problem. Spiking neurons are also much more difficult to study analytically. One possible approach to making spiking networks more tractable is to augment mean field activity models with some information about spiking correlations. For example, such a generalized activity model could carry information about spiking rates and correlations between spikes self-consistently. Here, we will show how this can be accomplished by constructing a complete formal probabilistic description of the network and then expanding around a small parameter such as the inverse of the number of neurons in the network. The mean field theory of the system gives a rate-like description. The first order terms in the perturbation expansion keep track of covariances.

  17. Analysis and Simple Circuit Design of Double Differential EMG Active Electrode.

    Science.gov (United States)

    Guerrero, Federico Nicolás; Spinelli, Enrique Mario; Haberman, Marcelo Alejandro

    2016-06-01

    In this paper we present an analysis of the voltage amplifier needed for double differential (DD) sEMG measurements and a novel, very simple circuit for implementing DD active electrodes. The three-input amplifier that standalone DD active electrodes require is inherently different from a differential amplifier, and general knowledge about its design is scarce in the literature. First, the figures of merit of the amplifier are defined through a decomposition of its input signal into three orthogonal modes. This analysis reveals a mode containing EMG crosstalk components that the DD electrode should reject. Then, the effect of finite input impedance is analyzed. Because there are three terminals, minimum bounds for interference rejection ratios due to electrode and input impedance unbalances with two degrees of freedom are obtained. Finally, a novel circuit design is presented, including only a quadruple operational amplifier and a few passive components. This design is nearly as simple as the branched electrode and much simpler than the three instrumentation amplifier design, while providing robust EMG crosstalk rejection and better input impedance using unity gain buffers for each electrode input. The interference rejection limits of this input stage are analyzed. An easily replicable implementation of the proposed circuit is described, together with a parameter design guideline to adjust it to specific needs. The electrode is compared with the established alternatives, and sample sEMG signals are obtained, acquired on different body locations with dry contacts, successfully rejecting interference sources.

  18. Analysis and Simple Circuit Design of Double Differential EMG Active Electrode.

    Science.gov (United States)

    Guerrero, Federico Nicolás; Spinelli, Enrique Mario; Haberman, Marcelo Alejandro

    2016-06-01

    In this paper we present an analysis of the voltage amplifier needed for double differential (DD) sEMG measurements and a novel, very simple circuit for implementing DD active electrodes. The three-input amplifier that standalone DD active electrodes require is inherently different from a differential amplifier, and general knowledge about its design is scarce in the literature. First, the figures of merit of the amplifier are defined through a decomposition of its input signal into three orthogonal modes. This analysis reveals a mode containing EMG crosstalk components that the DD electrode should reject. Then, the effect of finite input impedance is analyzed. Because there are three terminals, minimum bounds for interference rejection ratios due to electrode and input impedance unbalances with two degrees of freedom are obtained. Finally, a novel circuit design is presented, including only a quadruple operational amplifier and a few passive components. This design is nearly as simple as the branched electrode and much simpler than the three instrumentation amplifier design, while providing robust EMG crosstalk rejection and better input impedance using unity gain buffers for each electrode input. The interference rejection limits of this input stage are analyzed. An easily replicable implementation of the proposed circuit is described, together with a parameter design guideline to adjust it to specific needs. The electrode is compared with the established alternatives, and sample sEMG signals are obtained, acquired on different body locations with dry contacts, successfully rejecting interference sources. PMID:26841414

  19. Modeling and Optimization of Microwave Circuits Based on Neural Networks%基于神经网络的微波电路建模与优化

    Institute of Scientific and Technical Information of China (English)

    刘荧; 林嘉宇; 毛钧杰

    2000-01-01

    本文讨论用神经网络对微波电路进行建模、优化。借助电磁场理论计算或基于实际测量,可得到微波电路的输入、输出样本数据,从而可训练神经网络,在兼顾它的推广性能的基础上,对微波电路建模。进一步,通过优化神经网络对应参数,可优化微波电路。文章用RBF(RadialBasis Function)神经网络对微带变阻器建模、优化,以此为例,进行了较为详细的阐述。%[1] A.H. Zaabab. et al. A neural network model ing approach to circuit optimization and statis tical design, IEEE Trans. MTT , 1995; 43 (6): 1349~1358. [2] P.M. Watson,K. C. Gupta. EM-ANN models for microstrip vias and interconnects in dataset circuits. IEEE Trans. MTT, 1996; 44(12): 2495~2503. [3] P.M. Watson,K. C. Gupta. Design and opti mization of CPW circuits using EM-ANN models for CPW components. IEEE Trans. MTT, 1997 ; 45(12): 2515~2535. [4] D.C. Montgomery. Design and Analysis of Experiments. New York :Wiley, 1991. [5] Acosta F. RBF and related models: an overview. Signal Processing, 1995; 45:37~ 58. [6] D.R. Huh,B. G. Horne. Progress in super- vised neural networks :what′.s new since lipp mann?. IEEE SP Magazine, 1993 ;10(1 ):8~ 39. [7] J. Park,I. Sandberg. Approximation and RBF networks. Neural Comput, 1993; 5:305~316. [8] S. Chen,et al. Orthogonal least squares learn ing algorithm for radial basis function net works. IEEE Trans. Neural Networks, 1991; 2(2) :302~309. [9] 陈尚勤,李晓峰.快速自适应信息处理.北京:人民邮电出版社,1993. [10] I. Cha, S. A. Kassam. Channel equalization using adaptive complex radial basis function networks. IEEE J. SAC, 1995;13(1):122 ~131. [11] E.S. Chng, et al. Orthogonal least-square learning algorithm with local adaptation pro cess for the radial basis function networks. IEEE SP Letters, 1996;3(8):253~255. [12] M.J. Orr. Local Smoothing of RBF Net works. http://www. cns. ed. ac. uk/people/ mark

  20. Neuritin: A gene induced by neural activity and neurotrophins that promotes neuritogenesis

    OpenAIRE

    Naeve, Gregory S.; Ramakrishnan, Meena; Kramer, Rainer; Hevroni, Dana; Citri, Yoav; Theill, Lars E.

    1997-01-01

    Neural activity and neurotrophins induce synaptic remodeling in part by altering gene expression. A cDNA encoding a glycosylphoshatidylinositol-anchored protein was identified by screening for hippocampal genes that are induced by neural activity. This molecule, named neuritin, is expressed in postmitotic-differentiating neurons of the developing nervous system and neuronal structures associated with plasticity in the adult. Neuritin message is induced by neuronal activity and by the activity...

  1. Application of Artificial Neural Network in Active Vibration Control of Diesel Engine

    Institute of Scientific and Technical Information of China (English)

    SUN Cheng-shun; ZHANG Jian-wu

    2005-01-01

    Artificial Neural Network (ANN) is applied to diesel twostage vibration isolating system and an AVC (Active Vibration Control) system is developed. Both identifier and controller are constructed by three-layer BP neural network. Besides computer simulation, experiment research is carried out on both analog bench and diesel bench. The results of simulation and experiment show a diminished response of vibration.

  2. Patterns of Theta Activity in Limbic Anxiety Circuit Preceding Exploratory Behavior in Approach-Avoidance Conflict

    Science.gov (United States)

    Jacinto, Luis R.; Cerqueira, João J.; Sousa, Nuno

    2016-01-01

    Theta oscillations within the hippocampus-amygdala-medial prefrontal cortex (HPC-AMY-mPFC) circuit have been consistently implicated in the regulation of anxiety behaviors, including risk-assessment. To study if theta activity during risk-assessment was correlated with exploratory behavior in an approach/avoidance paradigm we recorded simultaneous local field potentials from this circuit in rats exploring the elevated-plus maze (EPM). Opposing patterns of power variations in the ventral hippocampus (vHPC), basolateral amygdala (BLA), and prelimbic (PrL) mPFC, but not in the dorsal hippocampus (dHPC), during exploratory risk-assessment of the open arms preceded further exploration of the open arms or retreat back to the safer closed arms. The same patterns of theta power variations in the HPC-BLA-mPFC(PrL) circuit were also displayed by animals submitted to chronic unpredictable stress protocol known to induce an anxious state. Diverging patterns of vHPC-mPFC(PrL) theta coherence were also significantly correlated with forthcoming approach or avoidance behavior in the conflict situation in both controls and stressed animals; interestingly, vHPC-BLA, and BLA-mPFC(PrL) theta coherence correlated with future behavior only in stressed animals, underlying the pivotal role of the amygdala on the stress response.

  3. SNW1 is a critical regulator of spatial BMP activity, neural plate border formation, and neural crest specification in vertebrate embryos.

    Directory of Open Access Journals (Sweden)

    Mary Y Wu

    Full Text Available Bone morphogenetic protein (BMP gradients provide positional information to direct cell fate specification, such as patterning of the vertebrate ectoderm into neural, neural crest, and epidermal tissues, with precise borders segregating these domains. However, little is known about how BMP activity is regulated spatially and temporally during vertebrate development to contribute to embryonic patterning, and more specifically to neural crest formation. Through a large-scale in vivo functional screen in Xenopus for neural crest fate, we identified an essential regulator of BMP activity, SNW1. SNW1 is a nuclear protein known to regulate gene expression. Using antisense morpholinos to deplete SNW1 protein in both Xenopus and zebrafish embryos, we demonstrate that dorsally expressed SNW1 is required for neural crest specification, and this is independent of mesoderm formation and gastrulation morphogenetic movements. By exploiting a combination of immunostaining for phosphorylated Smad1 in Xenopus embryos and a BMP-dependent reporter transgenic zebrafish line, we show that SNW1 regulates a specific domain of BMP activity in the dorsal ectoderm at the neural plate border at post-gastrula stages. We use double in situ hybridizations and immunofluorescence to show how this domain of BMP activity is spatially positioned relative to the neural crest domain and that of SNW1 expression. Further in vivo and in vitro assays using cell culture and tissue explants allow us to conclude that SNW1 acts upstream of the BMP receptors. Finally, we show that the requirement of SNW1 for neural crest specification is through its ability to regulate BMP activity, as we demonstrate that targeted overexpression of BMP to the neural plate border is sufficient to restore neural crest formation in Xenopus SNW1 morphants. We conclude that through its ability to regulate a specific domain of BMP activity in the vertebrate embryo, SNW1 is a critical regulator of neural plate

  4. Nonlinear dynamics of neural delayed feedback

    Energy Technology Data Exchange (ETDEWEB)

    Longtin, A.

    1990-01-01

    Neural delayed feedback is a property shared by many circuits in the central and peripheral nervous systems. The evolution of the neural activity in these circuits depends on their present state as well as on their past states, due to finite propagation time of neural activity along the feedback loop. These systems are often seen to undergo a change from a quiescent state characterized by low level fluctuations to an oscillatory state. We discuss the problem of analyzing this transition using techniques from nonlinear dynamics and stochastic processes. Our main goal is to characterize the nonlinearities which enable autonomous oscillations to occur and to uncover the properties of the noise sources these circuits interact with. The concepts are illustrated on the human pupil light reflex (PLR) which has been studied both theoretically and experimentally using this approach. 5 refs., 3 figs.

  5. Physical methods for generating and decoding neural activity in Hirudo verbana

    Science.gov (United States)

    Migliori, Benjamin John

    The interface between living nervous systems and hardware is an excellent proving ground for precision experimental methods and information classification systems. Nervous systems are complex (104 -- 10 15(!) connections), fragile, and highly active in intricate, constantly evolving patterns. However, despite the conveniently electrical nature of neural transmission, the interface between nervous systems and hardware poses significant experimental difficulties. As the desire for direct interfaces with neural signals continues to expand, the need for methods of generating and measuring neural activity with high spatiotemporal precision has become increasingly critical. In this thesis, I describe advances I have made in the ability to modify, generate, measure, and understand neural signals both in- and ex-vivo. I focus on methods developed for transmitting and extracting signals in the intact nervous system of Hirudo verbana (the medicinal leech), an animal with a minimally complex nervous system (10000 neurons distributed in packets along a nerve cord) that exhibits a diverse array of behaviors. To introduce artificial activity patterns, I developed a photothermal activation system in which a highly focused laser is used to irradiate carbon microparticles in contact with target neurons. The resulting local temperature increase generates an electrical current that forces the target neuron to fire neural signals, thereby providing a unique neural input mechanism. These neural signals can potentially be used to alter behavioral choice or generate specific behavioral output, and can be used endogenously in many animal models. I also describe new tools developed to expand the application of this method. In complement to this input system, I describe a new method of analyzing neural output signals involved in long-range coordination of behaviors. Leech behavioral signals are propagated between neural packets as electrical pulses in the nerve connective, a bundle of

  6. Filtered-X Radial Basis Function Neural Networks for Active Noise Control

    Directory of Open Access Journals (Sweden)

    Bambang Riyanto

    2004-05-01

    Full Text Available This paper presents active control of acoustic noise using radial basis function (RBF networks and its digital signal processor (DSP real-time implementation. The neural control system consists of two stages: first, identification (modeling of secondary path of the active noise control using RBF networks and its learning algorithm, and secondly neural control of primary path based on neural model obtained in the first stage. A tapped delay line is introduced in front of controller neural, and another tapped delay line is inserted between controller neural networks and model neural networks. A new algorithm referred to as Filtered X-RBF is proposed to account for secondary path effects of the control system arising in active noise control. The resulting algorithm turns out to be the filtered-X version of the standard RBF learning algorithm. We address centralized and decentralized controller configurations and their DSP implementation is carried out. Effectiveness of the neural controller is demonstrated by applying the algorithm to active noise control within a 3 dimension enclosure to generate quiet zones around error microphones. Results of the real-time experiments show that 10-23 dB noise attenuation is produced with moderate transient response.

  7. Implementing Neural Networks Using VLSI for Image Processing (compression)

    OpenAIRE

    Sindhu R; Dr Shilpa Mehta

    2015-01-01

    Biological systems process the analog signals such as image and sound efficiently. To process the information the way biological systems do we make use of ANN. (Artificial Neural Networks) The focus of this paper is to review the implementation of the neural network architecture using analog components like Gilbert cell multiplier, differential amplifier for neuron activation function and tan sigmoid function circuit using MOS transistor. The neural architecture is trained usin...

  8. Peculiarities of dynamics of the global electric circuit elements during very low solar activity

    International Nuclear Information System (INIS)

    Complete text of publication follows. Accumulated data about dynamics of various elements of the solar - terrestrial relationship allow us to approach the problem of the solar activity influence on the middle atmosphere with taking into account role of the ground surface electrical conductivity. A special importance of this problem appears in the 23 cycle of the solar activity (2006-2009 years). This period is characterized by unusually low values of solar UV radiation as well as of magnitudes of the solar wind magnetic field. It means that impact of the solar electromagnetic energy on the near - Earth space is much weaker than usually. The Earth global electric circuit which includes the ionosphere, the stratosphere and the ground surface as its vital components has its own specific features during considered period. In this paper we outline these peculiarities of the global electric circuit and its influence on the middle atmosphere. First of all, we will demonstrate that experimental values of the atmospheric electric field (observations at Vostok Station, Antarctica) are the lowest during the last 3 years. We claim that role of the electric conductivity of the ground surface begin to play more significant role in the dynamics of the global electric circuit. To confirm that suggestion we studied interaction between the stratospheric temperature distribution in the high latitudes in winters of 2008 - 2009 and the area of the old sea ice (pack ice) in the Arctic Ocean during the same period. We will show that the areas of the low temperatures in the polar stratosphere correspond pretty well to distribution of the pack ice in the Arctic. Our explanation of the phenomena is based on difference of electric conductivity of the ice and of the open ocean water.

  9. Enhanced Pixel-Driving Circuits for Active-Matrix Organic-Light-Emitting Diode Displays with Large Sizes

    Science.gov (United States)

    Yu, Sang Ho; Choi, Sung Wook; Shin, Hong Jae; Kwack, Kae Dal; Kim, Tae Whan

    2005-03-01

    Enhanced pixel-driving circuits for active-matrix organic-light-emitting diode (AM-OLED) displays with large sizes and highly uniform brightnesses were designed for system on panel. The driving method used the pre-charge functions of the data for a highly uniform brightness during a short time to program the current. The currents of the designed pixel-driving circuits were not significantly affected by variations in the threshold voltages, or by the mobilities of the driving thin-film transistors. These results indicate that the proposed pixel-driving circuits hold promise for potential applications in AM-OLED displays with large sizes and highly uniform brightnesses.

  10. An optogenetics- and imaging-assisted simultaneous multiple patch-clamp recording system for decoding complex neural circuits.

    Science.gov (United States)

    Wang, Guangfu; Wyskiel, Daniel R; Yang, Weiguo; Wang, Yiqing; Milbern, Lana C; Lalanne, Txomin; Jiang, Xiaolong; Shen, Ying; Sun, Qian-Quan; Zhu, J Julius

    2015-03-01

    Deciphering neuronal circuitry is central to understanding brain function and dysfunction, yet it remains a daunting task. To facilitate the dissection of neuronal circuits, a process requiring functional analysis of synaptic connections and morphological identification of interconnected neurons, we present here a method for stable simultaneous octuple patch-clamp recordings. This method allows physiological analysis of synaptic interconnections among 4-8 simultaneously recorded neurons and/or 10-30 sequentially recorded neurons, and it allows anatomical identification of >85% of recorded interneurons and >99% of recorded principal neurons. We describe how to apply the method to rodent tissue slices; however, it can be used on other model organisms. We also describe the latest refinements and optimizations of mechanics, electronics, optics and software programs that are central to the realization of a combined single- and two-photon microscopy-based, optogenetics- and imaging-assisted, stable, simultaneous quadruple-viguple patch-clamp recording system. Setting up the system, from the beginning of instrument assembly and software installation to full operation, can be completed in 3-4 d.

  11. Modeling electrocortical activity through improved local approximations of integral neural field equations

    OpenAIRE

    Coombes, Stephen; Venkov, Nikola; Shiau, LieJune; Bojak, Ingo; Liley, David; Laing, Carlo

    2007-01-01

    Neural field models of firing rate activity typically take the form of integral equations with space-dependent axonal delays. Under natural assumptions on the synaptic connectivity we show how one can derive an equivalent partial differential equation (PDE) model that properly treats the axonal delay terms of the integral formulation. Our analysis avoids the so-called long-wavelength approximation that has previously been used to formulate PDE models for neural activity in two spatial dime...

  12. Parasympathetic neural activity accounts for the lowering of exercise heart rate at high altitude

    DEFF Research Database (Denmark)

    Boushel, Robert Christopher; Calbet, J A; Rådegran, G;

    2001-01-01

    In chronic hypoxia, both heart rate (HR) and cardiac output (Q) are reduced during exercise. The role of parasympathetic neural activity in lowering HR is unresolved, and its influence on Q and oxygen transport at high altitude has never been studied.......In chronic hypoxia, both heart rate (HR) and cardiac output (Q) are reduced during exercise. The role of parasympathetic neural activity in lowering HR is unresolved, and its influence on Q and oxygen transport at high altitude has never been studied....

  13. The Physics of Decision Making:. Stochastic Differential Equations as Models for Neural Dynamics and Evidence Accumulation in Cortical Circuits

    Science.gov (United States)

    Holmes, Philip; Eckhoff, Philip; Wong-Lin, K. F.; Bogacz, Rafal; Zacksenhouse, Miriam; Cohen, Jonathan D.

    2010-03-01

    We describe how drift-diffusion (DD) processes - systems familiar in physics - can be used to model evidence accumulation and decision-making in two-alternative, forced choice tasks. We sketch the derivation of these stochastic differential equations from biophysically-detailed models of spiking neurons. DD processes are also continuum limits of the sequential probability ratio test and are therefore optimal in the sense that they deliver decisions of specified accuracy in the shortest possible time. This leaves open the critical balance of accuracy and speed. Using the DD model, we derive a speed-accuracy tradeoff that optimizes reward rate for a simple perceptual decision task, compare human performance with this benchmark, and discuss possible reasons for prevalent sub-optimality, focussing on the question of uncertain estimates of key parameters. We present an alternative theory of robust decisions that allows for uncertainty, and show that its predictions provide better fits to experimental data than a more prevalent account that emphasises a commitment to accuracy. The article illustrates how mathematical models can illuminate the neural basis of cognitive processes.

  14. Sensitivity analysis of an LCL-filter-based three-phase active rectifier via a virtual circuit approach

    DEFF Research Database (Denmark)

    Blaabjerg, Frede; Chiarantoni, Ernesto; Aquila, Antonio Dell’;

    2004-01-01

    , to the grid side stiffness and to the parameters of the controller has never been detailed considered. In this paper the experimental results of an LCL-filter-based three-phase active rectifier are analysed with the circuit theory approach. A ?virtual circuit? is synthesized in role of the digital controller...... and of the feedback filters to have an homogenous model that allows a sensitivity analysis which is rigorous and straightforward for the industry....

  15. Post-training, intrahippocampal HDAC inhibition differentially impacts neural circuits underlying spatial memory in adult and aged mice.

    Science.gov (United States)

    Dagnas, Malorie; Micheau, Jacques; Decorte, Laurence; Beracochea, Daniel; Mons, Nicole

    2015-07-01

    Converging evidence indicates that pharmacologically elevating histone acetylation using post-training, systemic or intrahippocampal, administration of histone deacetylase inhibitor (HDACi) can enhance memory consolidation processes in young rodents but it is not yet clear, whether such treatment is sufficient to prevent memory impairments associated with aging. To address this question, we used a 1-day massed spatial learning task in the water maze to investigate the effects of immediate post-training injection of the HDACi trichostatin A (TSA) into the dorsal hippocampus on long-term memory consolidation in 3-4 and 18-20 month-old mice. We show that TSA improved the 24 h-memory retention for the hidden platform location in young-adults, but failed to rescue memory impairments in older mice. The results further indicate that Young-TSA mice sacrificed 1 h after training had a robust increase in histone H4 acetylation in the dorsal hippocampal CA1 region (dCA1) and the dorsomedial part of the striatum (DMS), a structure important for spatial information processing. Importantly, TSA infusion in aged mice completely rescued altered H4 acetylation in the dCA1 but failed to alleviate age-associated decreased H4 acetylation in the DMS. Moreover, intrahippocampal TSA infusion produced concomitant decreases (in adults) or increases (in older mice) of acetylated histone levels in the ventral hippocampus (vCA1 and vCA3) and the lateral amygdala, two structures critically involved in stress and emotional responses. These data suggest that the failure of post-training, intrahippocampal TSA injection to reverse age-associated memory impairments may be related to an inability to recruit appropriate circuit-specific epigenetic patterns during early consolidation processes.

  16. A neural measure of behavioral engagement: task-residual low-frequency blood oxygenation level-dependent activity in the precuneus.

    Science.gov (United States)

    Zhang, Sheng; Li, Chiang-Shan Ray

    2010-01-15

    Brain imaging has provided a useful tool to examine the neural processes underlying human cognition. A critical question is whether and how task engagement influences the observed regional brain activations. Here we highlighted this issue and derived a neural measure of task engagement from the task-residual low-frequency blood oxygenation level-dependent (BOLD) activity in the precuneus. Using independent component analysis, we identified brain regions in the default circuit - including the precuneus and medial prefrontal cortex (mPFC) - showing greater activation during resting as compared to task residuals in 33 individuals. Time series correlations with the posterior cingulate cortex as the seed region showed that connectivity with the precuneus was significantly stronger during resting as compared to task residuals. We hypothesized that if the task-residual BOLD activity in the precuneus reflects engagement, it should account for a certain amount of variance in task-related regional brain activation. In an additional experiment of 59 individuals performing a stop signal task, we observed that the fractional amplitude of low-frequency fluctuation (fALFF) of the precuneus but not the mPFC accounted for approximately 10% of the variance in prefrontal activation related to attentional monitoring and response inhibition. Taken together, these results suggest that task-residual fALFF in the precuneus may be a potential indicator of task engagement. This measurement may serve as a useful covariate in identifying motivation-independent neural processes that underlie the pathogenesis of a psychiatric or neurological condition.

  17. Neural Activity during Encoding Predicts False Memories Created by Misinformation

    Science.gov (United States)

    Okado, Yoko; Stark, Craig E. L.

    2005-01-01

    False memories are often demonstrated using the misinformation paradigm, in which a person's recollection of a witnessed event is altered after exposure to misinformation about the event. The neural basis of this phenomenon, however, remains unknown. The authors used fMRI to investigate encoding processes during the viewing of an event and…

  18. Sociocultural patterning of neural activity during self-reflection

    DEFF Research Database (Denmark)

    Ma, Yina; Bang, Dan; Wang, Chenbo;

    2014-01-01

    Western cultures encourage self-construals independent of social contexts whereas East Asian cultures foster interdependent self-construals that rely on how others perceive the self. How are culturally specific self-construals mediated by the human brain? Using functional MRI, we monitored neural...

  19. Active Control of Sound based on Diagonal Recurrent Neural Network

    NARCIS (Netherlands)

    Jayawardhana, Bayu; Xie, Lihua; Yuan, Shuqing

    2002-01-01

    Recurrent neural network has been known for its dynamic mapping and better suited for nonlinear dynamical system. Nonlinear controller may be needed in cases where the actuators exhibit the nonlinear characteristics, or in cases when the structure to be controlled exhibits nonlinear behavior. The fe

  20. Lack of telomerase activity in rabbit bone marrow stromal cells during differentiation along neural pathway

    Institute of Scientific and Technical Information of China (English)

    CHEN Zhen-zhou; XU Ru-xiang; JIANG Xiao-dan; TENG Xiao-hua; LI Gui-tao; ZHOU Yü-xi

    2006-01-01

    Objective: To investigate telomerase activity in rabbit bone marrow stromal cells (BMSCs) during their committed differentiation in vitro along neural pathway and the effect of glial cell line-derived neurotrophic factor (GDNF) on the expression of telomerase.Methods: BMSCs were acquired from rabbit marrow and divided into control group, GDNF (10 ng/ml) group.No. ZL02134314. 4) supplemented with 10% fetal bovine serum (FBS) was used to induce BMSCs differentiation along neural pathway. Fluorescent immunocytochemistry was employed to identify the expressions of Nestin, neuronspecific endase (NSE), and gial fibrillary acidic protein (GFAP). The growth curves of the cells and the status of cell cycles were analyzed, respectively. During the differentiation, telomerase activitys were detected using the telomeric repeat amplification protocol-enzyme-linked immunosorbent assay (TRAP-ELISA).Results: BMSCs were successfully induced to differentiate along neural pathway and expressed specific markers of fetal neural epithelium, mature neuron and glial cells. Telomerase activities were undetectable in BMSCs during differentiation along neural pathway. Similar changes of cell growth curves, cell cycle status and telomerase expression were observed in the two groups.Conclusions: Rabbit BMSCs do not display telomerase activity during differentiation along neural pathway. GDNF shows little impact on proliferation and telomerase activity of BMSCs.

  1. The fiber-optic imaging and manipulation of neural activity during animal behavior.

    Science.gov (United States)

    Miyamoto, Daisuke; Murayama, Masanori

    2016-02-01

    Recent progress with optogenetic probes for imaging and manipulating neural activity has further increased the relevance of fiber-optic systems for neural circuitry research. Optical fibers, which bi-directionally transmit light between separate sites (even at a distance of several meters), can be used for either optical imaging or manipulating neural activity relevant to behavioral circuitry mechanisms. The method's flexibility and the specifications of the light structure are well suited for following the behavior of freely moving animals. Furthermore, thin optical fibers allow researchers to monitor neural activity from not only the cortical surface but also deep brain regions, including the hippocampus and amygdala. Such regions are difficult to target with two-photon microscopes. Optogenetic manipulation of neural activity with an optical fiber has the advantage of being selective for both cell-types and projections as compared to conventional electrophysiological brain tissue stimulation. It is difficult to extract any data regarding changes in neural activity solely from a fiber-optic manipulation device; however, the readout of data is made possible by combining manipulation with electrophysiological recording, or the simultaneous application of optical imaging and manipulation using a bundle-fiber. The present review introduces recent progress in fiber-optic imaging and manipulation methods, while also discussing fiber-optic system designs that are suitable for a given experimental protocol. PMID:26427958

  2. Neuritin: a gene induced by neural activity and neurotrophins that promotes neuritogenesis.

    Science.gov (United States)

    Naeve, G S; Ramakrishnan, M; Kramer, R; Hevroni, D; Citri, Y; Theill, L E

    1997-03-18

    Neural activity and neurotrophins induce synaptic remodeling in part by altering gene expression. A cDNA encoding a glycosylphoshatidylinositol-anchored protein was identified by screening for hippocampal genes that are induced by neural activity. This molecule, named neuritin, is expressed in postmitotic-differentiating neurons of the developing nervous system and neuronal structures associated with plasticity in the adult. Neuritin message is induced by neuronal activity and by the activity-regulated neurotrophins BDNF and NT-3. Purified recombinant neuritin promotes neurite outgrowth and arborization in primary embryonic hippocampal and cortical cultures. These data implicate neuritin as a downstream effector of activity-induced neurite outgrowth. PMID:9122250

  3. Progress in neural plasticity

    Institute of Scientific and Technical Information of China (English)

    POO; Mu-Ming

    2010-01-01

    One of the properties of the nervous system is the use-dependent plasticity of neural circuits.The structure and function of neural circuits are susceptible to changes induced by prior neuronal activity,as reflected by short-and long-term modifications of synaptic efficacy and neuronal excitability.Regarded as the most attractive cellular mechanism underlying higher cognitive functions such as learning and memory,activity-dependent synaptic plasticity has been in the spotlight of modern neuroscience since 1973 when activity-induced long-term potentiation(LTP) of hippocampal synapses was first discovered.Over the last 10 years,Chinese neuroscientists have made notable contributions to the study of the cellular and molecular mechanisms of synaptic plasticity,as well as of the plasticity beyond synapses,including activity-dependent changes in intrinsic neuronal excitability,dendritic integration functions,neuron-glia signaling,and neural network activity.This work highlight some of these significant findings.

  4. Striatal plasticity and basal ganglia circuit function.

    Science.gov (United States)

    Kreitzer, Anatol C; Malenka, Robert C

    2008-11-26

    The dorsal striatum, which consists of the caudate and putamen, is the gateway to the basal ganglia. It receives convergent excitatory afferents from cortex and thalamus and forms the origin of the direct and indirect pathways, which are distinct basal ganglia circuits involved in motor control. It is also a major site of activity-dependent synaptic plasticity. Striatal plasticity alters the transfer of information throughout basal ganglia circuits and may represent a key neural substrate for adaptive motor control and procedural memory. Here, we review current understanding of synaptic plasticity in the striatum and its role in the physiology and pathophysiology of basal ganglia function. PMID:19038213

  5. High baseline activity in inferior temporal cortex improves neural and behavioral discriminability during visual categorization

    Directory of Open Access Journals (Sweden)

    Nazli eEmadi

    2014-11-01

    Full Text Available Spontaneous firing is a ubiquitous property of neural activity in the brain. Recent literature suggests that this baseline activity plays a key role in perception. However, it is not known how the baseline activity contributes to neural coding and behavior. Here, by recording from the single neurons in the inferior temporal cortex of monkeys performing a visual categorization task, we thoroughly explored the relationship between baseline activity, the evoked response, and behavior. Specifically we found that a low-frequency (< 8 Hz oscillation in the spike train, prior and phase-locked to the stimulus onset, was correlated with increased gamma power and neuronal baseline activity. This enhancement of the baseline activity was then followed by an increase in the neural selectivity and the response reliability and eventually a higher behavioral performance.

  6. A fast learning algorithm of neural network with tunable activation function

    Institute of Scientific and Technical Information of China (English)

    SHEN Yanjun; WANG Bingwen

    2004-01-01

    This paper presents a modified structure of a neural network with tunable activation function and provides a new learning algorithm for the neural network training. Simulation results of XOR problem, Feigenbaum function, and Henon map show that the new algorithm has better performance than BP (back propagation) algorithm in terms of shorter convergence time and higher convergence accuracy. Further modifications of the structure of the neural network with the faster learning algorithm demonstrate simpler structure with even faster convergence speed and better convergence accuracy.

  7. Active quench and reset integrated circuit with novel hold-off time control logic for Geiger-mode avalanche photodiodes.

    Science.gov (United States)

    Deng, Shijie; Morrison, Alan P

    2012-09-15

    This Letter presents an active quench-and-reset circuit for Geiger-mode avalanche photodiodes (GM-APDs). The integrated circuit was fabricated using a conventional 0.35 μm complementary metal oxide semiconductor process. Experimental results show that the circuit is capable of linearly setting the hold-off time from several nanoseconds to microseconds with a resolution of 6.5 ns. This allows the selection of the optimal afterpulse-free hold-off time for the GM-APD via external digital inputs or additional signal processing circuitry. Moreover, this circuit resets the APD automatically following the end of the hold-off period, thus simplifying the control for the end user. Results also show that a minimum dead time of 28.4 ns is achieved, demonstrating a saturated photon-counting rate of 35.2 Mcounts/s.

  8. Eating in mice with gastric bypass surgery causes exaggerated activation of brainstem anorexia circuit

    Science.gov (United States)

    Mumphrey, Michael B.; Hao, Zheng; Townsend, R. Leigh; Patterson, Laurel M.; Münzberg, Heike; Morrison, Christopher C.; Ye, Jianping; Berthoud, Hans-Rudolf

    2016-01-01

    Background/Objective Obesity and metabolic diseases are at an alarming level globally and increasingly affect children and adolescents. Gastric bypass and other bariatric surgeries have proven remarkably successful and are increasingly performed worldwide. Reduced desire to eat and changes in eating behavior and food choice account for most of the initial weight loss and diabetes remission after surgery, but the underlying mechanisms of altered gut-brain communication are unknown. Subjects/Methods To explore the potential involvement of a powerful brainstem anorexia pathway centered around the lateral parabrachial nucleus (lPBN) we measured meal-induced neuronal activation by means of c-Fos immunohistochemistry in a new high-fat diet-induced obese mouse model of Roux-en-Y gastric bypass (RYGB) at 10 and 40 days after RYGB or sham surgery. Results Voluntary ingestion of a meal 10 days after RYGB, but not after sham surgery, strongly and selectively activates calcitonin gene-related peptide neurons in the external lPBN as well as neurons in the nucleus tractus solitaries, area postrema, and medial amygdala. At 40 days after surgery, meal-induced activation in all these areas was greatly diminished and did not reach statistical significance. Conclusions The neural activation pattern and dynamics suggest a role of the brainstem anorexia pathway in the early effects of RYGB on meal size and food intake that may lead to adaptive neural and behavioral changes involved in the control of food intake and body weight at a lower level. However, selective inhibition of this pathway will be required for a more causal implication. PMID:26984418

  9. Active Vibration Control of the Smart Plate Using Artificial Neural Network Controller

    Directory of Open Access Journals (Sweden)

    Mohit

    2015-01-01

    Full Text Available The active vibration control (AVC of a rectangular plate with single input and single output approach is investigated using artificial neural network. The cantilever plate of finite length, breadth, and thickness having piezoelectric patches as sensors/actuators fixed at the upper and lower surface of the metal plate is considered for examination. The finite element model of the cantilever plate is utilized to formulate the whole strategy. The compact RIO and MATLAB simulation software are exercised to get the appropriate results. The cantilever plate is subjected to impulse input and uniform white noise disturbance. The neural network is trained offline and tuned with LQR controller. The various training algorithms to tune the neural network are exercised. The best efficient algorithm is finally considered to tune the neural network controller designed for active vibration control of the smart plate.

  10. A Novel LTPS-TFT Pixel Circuit to Compensate the Electronic Degradation for Active-Matrix Organic Light-Emitting Diode Displays

    OpenAIRE

    Ching-Lin Fan; Fan-Ping Tseng; Hui-Lung Lai; Bo-Jhang Sun; Kuang-Chi Chao; Yi-Chiung Chen

    2013-01-01

    A novel pixel driving circuit for active-matrix organic light-emitting diode (AMOLED) displays with low-temperature polycrystalline-silicon thin-film transistors (LTPS-TFTs) is studied. The proposed compensation pixel circuit is driven by voltage programming scheme, which is composed of five TFTs and one capacitor, and has been certified to provide uniform output current by the Automatic Integrated Circuit Modeling Simulation Program with Integrated Circuit Emphasis (AIM-SPICE) simulator. The...

  11. DQ reference frame modeling and control of single-phase active power decoupling circuits

    DEFF Research Database (Denmark)

    Tang, Yi; Qin, Zian; Blaabjerg, Frede;

    2015-01-01

    Power decoupling circuits can compensate the inherent double line frequency ripple power in single-phase systems and greatly facilitate their dc-link capacitor design. Example applications of power decoupling circuit include photovoltaic, light-emitting diode, fuel cell, and motor drive systems....... This paper presents the dq synchronous reference frame modeling of single-phase power decoupling circuits and a complete model describing the dynamics of dc-link ripple voltage is presented. The proposed model is universal and valid for both inductive and capacitive decoupling circuits, and the input...... of decoupling circuits can be either dependent or independent of its front-end converters. Based on this model, a dq synchronous reference frame controller is designed which allows the decoupling circuit to operate in two different modes because of the circuit symmetry. Simulation and experimental results...

  12. A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects.

    Science.gov (United States)

    Truccolo, Wilson; Eden, Uri T; Fellows, Matthew R; Donoghue, John P; Brown, Emery N

    2005-02-01

    Multiple factors simultaneously affect the spiking activity of individual neurons. Determining the effects and relative importance of these factors is a challenging problem in neurophysiology. We propose a statistical framework based on the point process likelihood function to relate a neuron's spiking probability to three typical covariates: the neuron's own spiking history, concurrent ensemble activity, and extrinsic covariates such as stimuli or behavior. The framework uses parametric models of the conditional intensity function to define a neuron's spiking probability in terms of the covariates. The discrete time likelihood function for point processes is used to carry out model fitting and model analysis. We show that, by modeling the logarithm of the conditional intensity function as a linear combination of functions of the covariates, the discrete time point process likelihood function is readily analyzed in the generalized linear model (GLM) framework. We illustrate our approach for both GLM and non-GLM likelihood functions using simulated data and multivariate single-unit activity data simultaneously recorded from the motor cortex of a monkey performing a visuomotor pursuit-tracking task. The point process framework provides a flexible, computationally efficient approach for maximum likelihood estimation, goodness-of-fit assessment, residual analysis, model selection, and neural decoding. The framework thus allows for the formulation and analysis of point process models of neural spiking activity that readily capture the simultaneous effects of multiple covariates and enables the assessment of their relative importance. PMID:15356183

  13. Characterization of the feeding inhibition and neural activation produced by dorsomedial hypothalamic cholecystokinin administration

    OpenAIRE

    Chen, Jie; Scott, Karen A.; Zhao, Zhengyan; Moran, Timothy H.; BI, Sheng

    2008-01-01

    Within the dorsomedial hypothalamus (DMH), cholecystokinin (CCK) has been proposed to modulate neuropeptide Y (NPY) signaling to affect food intake. However, the neural circuitry underlying the actions of this CCK-NPY signaling system in the controls of food intake has yet to be determined. We sought to characterize the feeding inhibition and brain neural activation produced by CCK administration into the DMH of rats. We determined the time course of feeding inhibitory effects of exogenous DM...

  14. Variability of neural activation during walking in humans: short heels and big calves

    OpenAIRE

    Ahn, A. N.; Kang, J. K.; Quitt, M. A.; Davidson, B. C.; Nguyen, C. T.

    2011-01-01

    People come in different shapes and sizes. In particular, calf muscle size in humans varies considerably. One possible cause for the different shapes of calf muscles is the inherent difference in neural signals sent to these muscles during walking. In sedentary adults, the variability in neural control of the calf muscles was examined with muscle size, walking kinematics and limb morphometrics. Half the subjects walked while activating their medial gastrocnemius (MG) muscles more strongly tha...

  15. Functional PDF Signaling in the Drosophila Circadian Neural Circuit Is Gated by Ral A-Dependent Modulation.

    Science.gov (United States)

    Klose, Markus; Duvall, Laura B; Li, Weihua; Liang, Xitong; Ren, Chi; Steinbach, Joe Henry; Taghert, Paul H

    2016-05-18

    The neuropeptide PDF promotes the normal sequencing of circadian behavioral rhythms in Drosophila, but its signaling mechanisms are not well understood. We report daily rhythmicity in responsiveness to PDF in critical pacemakers called small LNvs. There is a daily change in potency, as great as 10-fold higher, around dawn. The rhythm persists in constant darkness and does not require endogenous ligand (PDF) signaling or rhythmic receptor gene transcription. Furthermore, rhythmic responsiveness reflects the properties of the pacemaker cell type, not the receptor. Dopamine responsiveness also cycles, in phase with that of PDF, in the same pacemakers, but does not cycle in large LNv. The activity of RalA GTPase in s-LNv regulates PDF responsiveness and behavioral locomotor rhythms. Additionally, cell-autonomous PDF signaling reversed the circadian behavioral effects of lowered RalA activity. Thus, RalA activity confers high PDF responsiveness, providing a daily gate around the dawn hours to promote functional PDF signaling. PMID:27161526

  16. The impact of cancer on the neural activity

    CERN Document Server

    Iarosz, Kelly Cristiane; Baptista, Murilo da Silva; Protachevicz, Paulo Ricardo

    2015-01-01

    We study the impact of the decrease in the neural population on the neuronal firing rate. We propose a cellular automaton model from cancerous growth in a brain tissue and the death of neurons due absence of cells that help support to neurons. We use this model to study how the firing rate changes when the neuronal networks is under different external stimuli and the cancerous cells have different proliferation rate. Our work shows that the cancer proliferation decreases the neuronal firing rate.

  17. A comparison of muscle damage, soreness and performance following a simulated contact and non-contact team sport activity circuit.

    Science.gov (United States)

    Singh, Tarveen K R; Guelfi, Kym J; Landers, Grant; Dawson, Brian; Bishop, David

    2011-09-01

    The aim was to compare the effect of a simulated team sport activity circuit (reflective of the activity demands of Australian football) either with or without body 'contact' on muscle soreness, damage, and performance when the circuit was repeated 48 h later. Eleven male, team-sport athletes completed a 'non-contact' (NCON) and a 'contact' (CON) version of the team sport activity circuit in a crossover design with at least 1 week between trials. The effect of CON and NCON on repeated 15m sprint and vertical jump performance was assessed by completing the same version of the circuit 48 h after the initial trial. The effect on perceived soreness and blood markers of muscle damage and inflammation was also determined. Subsequent performance was affected to a greater extent by CON, with both best and mean sprint times significantly slower 48h following CON (pprotein increased following CON but not NCON. In conclusion, Greater perceived soreness and decrements in performance of the simulated team sport activity circuit when repeated 48 h later were observed following CON.

  18. Integrative activity of neural networks may code virtual spaces with internal representations.

    Science.gov (United States)

    Strelnikov, Kuzma

    2014-10-01

    It was shown recently in neuroimaging that spatial differentiation of brain activity provides novel information about brain function. This confirms the integrative organisation of brain activity, but given present technical limitations of neuroimaging approaches, the exact role of integrative activity remains unclear. We trained a neural network to integrate information using random numbers so as to imitate the "centre-periphery" pattern of brain activity in neuroimaging. Only the hierarchical organisation of the network permitted the learning of fast and reliable integration. We presented images to the trained network and, by spatial differentiation of the network activity, obtained virtual spaces with the presented images. Thus, our study established the necessity of the hierarchical organisation of neural networks for integration and demonstrated that the role of neural integration in the brain may be to create virtual spaces with internal representations of the objects.

  19. Coupling Strength and System Size Induce Firing Activity of Globally Coupled Neural Network

    Institute of Scientific and Technical Information of China (English)

    WEI Du-Qu; LUO Xiao-Shu; ZOU Yan-Li

    2008-01-01

    We investigate how firing activity of globally coupled neural network depends on the coupling strength C and system size N.Network elements are described by space-clamped FitzHugh-Nagumo (SCFHN) neurons with the values of parameters at which no firing activity occurs.It is found that for a given appropriate coupling strength,there is an intermediate range of system size where the firing activity of globally coupled SCFHN neural network is induced and enhanced.On the other hand,for a given intermediate system size level,there ex/sts an optimal value of coupling strength such that the intensity of firing activity reaches its maximum.These phenomena imply that the coupling strength and system size play a vital role in firing activity of neural network.

  20. Active control of vibration using a neural network.

    Science.gov (United States)

    Snyder, S D; Tanaka, N

    1995-01-01

    Feedforward control of sound and vibration using a neural network-based control system is considered, with the aim being to derive an architecture/algorithm combination which is capable of supplanting the commonly used finite impulse response filter/filtered-x least mean square (LMS) linear arrangement for certain nonlinear problems. An adaptive algorithm is derived which enables stable adaptation of the neural controller for this purpose, while providing the capacity to maintain causality within the control scheme. The algorithm is shown to be simply a generalization of the linear filtered-x LMS algorithm. Experiments are undertaken which demonstrate the utility of the proposed arrangement, showing that it performs as well as a linear control system for a linear control problem and better for a nonlinear control problem. The experiments also lead to the conclusion that more work is required to improve the predictability and consistency of the performance before the neural network controller becomes a practical alternative to the current linear feedforward systems.

  1. Circuit Training.

    Science.gov (United States)

    Nelson, Jane B.

    1998-01-01

    Describes a research-based activity for high school physics students in which they build an LC circuit and find its resonant frequency of oscillation using an oscilloscope. Includes a diagram of the apparatus and an explanation of the procedures. (DDR)

  2. Dynamical Behaviors of Multiple Equilibria in Competitive Neural Networks With Discontinuous Nonmonotonic Piecewise Linear Activation Functions.

    Science.gov (United States)

    Nie, Xiaobing; Zheng, Wei Xing

    2016-03-01

    This paper addresses the problem of coexistence and dynamical behaviors of multiple equilibria for competitive neural networks. First, a general class of discontinuous nonmonotonic piecewise linear activation functions is introduced for competitive neural networks. Then based on the fixed point theorem and theory of strict diagonal dominance matrix, it is shown that under some conditions, such n -neuron competitive neural networks can have 5(n) equilibria, among which 3(n) equilibria are locally stable and the others are unstable. More importantly, it is revealed that the neural networks with the discontinuous activation functions introduced in this paper can have both more total equilibria and locally stable equilibria than the ones with other activation functions, such as the continuous Mexican-hat-type activation function and discontinuous two-level activation function. Furthermore, the 3(n) locally stable equilibria given in this paper are located in not only saturated regions, but also unsaturated regions, which is different from the existing results on multistability of neural networks with multiple level activation functions. A simulation example is provided to illustrate and validate the theoretical findings.

  3. Research on a new active power filter topology based on chopper circuit

    CERN Document Server

    Xiaoling, Guo; Bo, Zhang; Jing, Zhang

    2015-01-01

    The active power filter (APF) is attracting more and more attention for its outstanding performance in current and voltage ripple compensation. As modern high-energy accelerators are demanding much more stringent current ripple guideline, the APF is introduced to the magnet power supply (MPS) in accelerator system. However, the conventional APF has a lot of shortages and drawbacks due to its traditional topology, such as complex structure, nonadjustable working voltage, requirement of power supply, and so on. This paper proposes a new topology of APF, which is working as two types of chopper circuits. This APF need not extra electricity, but to use the power of the MPS current ripple to realize ripple depressing. At the end of this paper, the experiment result proves its feasibility and effect.

  4. Microwave amplifier and active circuit design using the real frequency technique

    CERN Document Server

    Jarry, Pierre

    2016-01-01

    This book focuses on the authors' Real Frequency Technique (RFT) and its application to a wide variety of multi-stage microwave amplifiers and active filters, and passive equalizers for radar pulse shaping and antenna return loss applications. The first two chapters review the fundamentals of microwave amplifier design and provide a description of the RFT. Each subsequent chapter introduces a new type of amplifier or circuit design, reviews its design problems, and explains how the RFT can be adapted to solve these problems. The authors take a practical approach by summarizing the design steps and giving numerous examples of amplifier realizations and measured responses. Provides a complete description of the RFT as it is first used to design multistage lumped amplifiers using a progressive optimization of the equalizers, leading to a small umber of parameters to optimize simultaneously Presents modifications to the RFT to design trans-impedance microwave amplifiers that are used for photodiodes acti...

  5. p53 activated by AND gate genetic circuit under radiation and hypoxia for targeted cancer gene therapy.

    Science.gov (United States)

    Ding, Miao; Li, Rong; He, Rong; Wang, Xingyong; Yi, Qijian; Wang, Weidong

    2015-09-01

    Radio-activated gene therapy has been developed as a novel therapeutic strategy against cancer; however, expression of therapeutic gene in peritumoral tissues will result in unacceptable toxicity to normal cells. To restrict gene expression in targeted tumor mass, we used hypoxia and radiation tolerance features of tumor cells to develop a synthetic AND gate genetic circuit through connecting radiation sensitivity promoter cArG6 , heat shock response elements SNF1, HSF1 and HSE4 with retroviral vector plxsn. Their construction and dynamic activity process were identified through downstream enhanced green fluorescent protein and wtp53 expression in non-small cell lung cancer A549 cells and in a nude mice model. The result showed that AND gate genetic circuit could be activated by lower required radiation dose (6 Gy) and after activated, AND gate could induce significant apoptosis effects and growth inhibition of cancer cells in vitro and in vivo. The radiation- and hypoxia-activated AND gate genetic circuit, which could lead to more powerful target tumoricidal activity represented a promising strategy for both targeted and effective gene therapy of human lung adenocarcinoma and low dose activation character of the AND gate genetic circuit implied that this model could be further exploited to decrease side-effects of clinical radiation therapy.

  6. Multistability and Instability of Neural Networks With Discontinuous Nonmonotonic Piecewise Linear Activation Functions.

    Science.gov (United States)

    Nie, Xiaobing; Zheng, Wei Xing

    2015-11-01

    In this paper, we discuss the coexistence and dynamical behaviors of multiple equilibrium points for recurrent neural networks with a class of discontinuous nonmonotonic piecewise linear activation functions. It is proved that under some conditions, such n -neuron neural networks can have at least 5(n) equilibrium points, 3(n) of which are locally stable and the others are unstable, based on the contraction mapping theorem and the theory of strict diagonal dominance matrix. The investigation shows that the neural networks with the discontinuous activation functions introduced in this paper can have both more total equilibrium points and more locally stable equilibrium points than the ones with continuous Mexican-hat-type activation function or discontinuous two-level activation functions. An illustrative example with computer simulations is presented to verify the theoretical analysis.

  7. VLSI circuits implementing computational models of neocortical circuits.

    Science.gov (United States)

    Wijekoon, Jayawan H B; Dudek, Piotr

    2012-09-15

    This paper overviews the design and implementation of three neuromorphic integrated circuits developed for the COLAMN ("Novel Computing Architecture for Cognitive Systems based on the Laminar Microcircuitry of the Neocortex") project. The circuits are implemented in a standard 0.35 μm CMOS technology and include spiking and bursting neuron models, and synapses with short-term (facilitating/depressing) and long-term (STDP and dopamine-modulated STDP) dynamics. They enable execution of complex nonlinear models in accelerated-time, as compared with biology, and with low power consumption. The neural dynamics are implemented using analogue circuit techniques, with digital asynchronous event-based input and output. The circuits provide configurable hardware blocks that can be used to simulate a variety of neural networks. The paper presents experimental results obtained from the fabricated devices, and discusses the advantages and disadvantages of the analogue circuit approach to computational neural modelling. PMID:22342970

  8. Neural networks with non-smooth and impact activations

    CERN Document Server

    Akhmet, M U

    2011-01-01

    In this paper, we consider a model of impulsive recurrent neural networks with piecewise constant delay. The dynamics are presented by differential equations with discontinuities such as impulses at fixed moments and piecewise constant argument of generalized type. Sufficient conditions ensuring the existence, uniqueness and global asymptotic stability of the equilibrium point are obtained. By employing Green's function we derive new result of existence of the periodic solution. The global asymptotic stability of the solution is investigated. Examples with numerical simulations are given to validate the theoretical results.

  9. The circuit designer's companion

    CERN Document Server

    Williams, Tim

    2013-01-01

    The Circuit Designer's Companion covers the theoretical aspects and practices in analogue and digital circuit design. Electronic circuit design involves designing a circuit that will fulfill its specified function and designing the same circuit so that every production model of it will fulfill its specified function, and no other undesired and unspecified function.This book is composed of nine chapters and starts with a review of the concept of grounding, wiring, and printed circuits. The subsequent chapters deal with the passive and active components of circuitry design. These topics are foll

  10. Strategies influence neural activity for feedback learning across child and adolescent development.

    Science.gov (United States)

    Peters, Sabine; Koolschijn, P Cédric M P; Crone, Eveline A; Van Duijvenvoorde, Anna C K; Raijmakers, Maartje E J

    2014-09-01

    Learning from feedback is an important aspect of executive functioning that shows profound improvements during childhood and adolescence. This is accompanied by neural changes in the feedback-learning network, which includes pre-supplementary motor area (pre- SMA)/anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), superior parietal cortex (SPC), and the basal ganglia. However, there can be considerable differences within age ranges in performance that are ascribed to differences in strategy use. This is problematic for traditional approaches of analyzing developmental data, in which age groups are assumed to be homogenous in strategy use. In this study, we used latent variable models to investigate if underlying strategy groups could be detected for a feedback-learning task and whether there were differences in neural activation patterns between strategies. In a sample of 268 participants between ages 8 to 25 years, we observed four underlying strategy groups, which were cut across age groups and varied in the optimality of executive functioning. These strategy groups also differed in neural activity during learning; especially the most optimal performing group showed more activity in DLPFC, SPC and pre-SMA/ACC compared to the other groups. However, age differences remained an important contributor to neural activation, even when correcting for strategy. These findings contribute to the debate of age versus performance predictors of neural development, and highlight the importance of studying individual differences in strategy use when studying development.

  11. Epigenetic activation of Sox2 gene in the developing vertebrate neural plate

    Science.gov (United States)

    Bouzas, Santiago O.; Marini, Melisa S.; Torres Zelada, Eliana; Buzzi, Ailín L.; Morales Vicente, David A.; Strobl-Mazzulla, Pablo H.

    2016-01-01

    One of the earliest manifestations of neural induction is onset of expression of the neural marker Sox2, mediated by the activation of the enhancers N1 and N2. By using loss and gain of function, we find that Sox2 expression requires the activity of JmjD2A and the Msk1 kinase, which can respectively demethylate the repressive H3K9me3 mark and phosphorylate the activating H3S10 (H3S10ph) mark. Bimolecular fluorescence complementation reveals that the adaptor protein 14-3-3, known to bind to H3S10ph, interacts with JMJD2A and may be involved in its recruitment to regulatory regions of the Sox2 gene. Chromatin immunoprecipitation reveals dynamic binding of JMJD2A to the Sox2 promoter and N-1 enhancer at the time of neural plate induction. Finally, we show a clear temporal antagonism on the occupancy of H3K9me3 and H3S10ph modifications at the promoter of the Sox2 locus before and after the neural plate induction. Taken together, our results propose a series of epigenetic events necessary for the early activation of the Sox2 gene in neural progenitor cells. PMID:27099369

  12. Decoding-Accuracy-Based Sequential Dimensionality Reduction of Spatio-Temporal Neural Activities

    Science.gov (United States)

    Funamizu, Akihiro; Kanzaki, Ryohei; Takahashi, Hirokazu

    Performance of a brain machine interface (BMI) critically depends on selection of input data because information embedded in the neural activities is highly redundant. In addition, properly selected input data with a reduced dimension leads to improvement of decoding generalization ability and decrease of computational efforts, both of which are significant advantages for the clinical applications. In the present paper, we propose an algorithm of sequential dimensionality reduction (SDR) that effectively extracts motor/sensory related spatio-temporal neural activities. The algorithm gradually reduces input data dimension by dropping neural data spatio-temporally so as not to undermine the decoding accuracy as far as possible. Support vector machine (SVM) was used as the decoder, and tone-induced neural activities in rat auditory cortices were decoded into the test tone frequencies. SDR reduced the input data dimension to a quarter and significantly improved the accuracy of decoding of novel data. Moreover, spatio-temporal neural activity patterns selected by SDR resulted in significantly higher accuracy than high spike rate patterns or conventionally used spatial patterns. These results suggest that the proposed algorithm can improve the generalization ability and decrease the computational effort of decoding.

  13. Neural Activation Underlying Cognitive Control in the Context of Neutral and Affectively Charged Pictures in Children

    Science.gov (United States)

    Lamm, Connie; White, Lauren K.; McDermott, Jennifer Martin; Fox, Nathan A.

    2012-01-01

    The neural correlates of cognitive control for typically developing 9-year-old children were examined using dense-array ERPs and estimates of cortical activation (LORETA) during a go/no-go task with two conditions: a neutral picture condition and an affectively charged picture condition. Activation was estimated for the entire cortex after which…

  14. Rett syndrome: genes, synapses, circuits and therapeutics

    Directory of Open Access Journals (Sweden)

    Abhishek eBanerjee

    2012-05-01

    Full Text Available Development of the nervous system proceeds through a set of complex checkpoints which arise from a combination of sequential gene expression and early neural activity sculpted by the environment. Genetic and environmental insults lead to neurodevelopmental disorders which encompass a large group of diseases that result from anatomical and physiological abnormalities during maturation and development of brain circuits. Rett syndrome (RTT is a postnatal neurological disorder of genetic origin, caused by mutations in the X-linked gene MECP2. It features neuropsychiatric abnormalities like motor dysfunctions and mild to severe cognitive impairment. This review discusses several key questions and attempts to evaluate recently developed animal models, cell-type specific function of MeCP2, defects in neural circuit plasticity and possible therapeutic strategies. Finally, we also discuss how genes, proteins and overlapping signaling pathways affect the molecular etiology of apparently unrelated neuropsychiatric disorders, an understanding of which can offer novel therapeutic strategies.

  15. Vascular Endothelial Growth Factor Receptor 3 Controls Neural Stem Cell Activation in Mice and Humans

    Directory of Open Access Journals (Sweden)

    Jinah Han

    2015-02-01

    Full Text Available Neural stem cells (NSCs continuously produce new neurons within the adult mammalian hippocampus. NSCs are typically quiescent but activated to self-renew or differentiate into neural progenitor cells. The molecular mechanisms of NSC activation remain poorly understood. Here, we show that adult hippocampal NSCs express vascular endothelial growth factor receptor (VEGFR 3 and its ligand VEGF-C, which activates quiescent NSCs to enter the cell cycle and generate progenitor cells. Hippocampal NSC activation and neurogenesis are impaired by conditional deletion of Vegfr3 in NSCs. Functionally, this is associated with compromised NSC activation in response to VEGF-C and physical activity. In NSCs derived from human embryonic stem cells (hESCs, VEGF-C/VEGFR3 mediates intracellular activation of AKT and ERK pathways that control cell fate and proliferation. These findings identify VEGF-C/VEGFR3 signaling as a specific regulator of NSC activation and neurogenesis in mammals.

  16. Alterations in Neuronal Activity in Basal Ganglia-Thalamocortical Circuits in the Parkinsonian State

    Directory of Open Access Journals (Sweden)

    Adriana eGalvan

    2015-02-01

    Full Text Available In patients with Parkinson’s disease and in animal models of this disorder, neurons in the basal ganglia and related regions in thalamus and cortex show changes that can be recorded by using electrophysiologic single-cell recording techniques, including altered firing rates and patterns, pathologic oscillatory activity and increased inter-neuronal synchronization. In addition, changes in synaptic potentials or in the joint spiking activities of populations of neurons can be monitored as alterations in local field potentials, electroencephalograms or electrocorticograms. Most of the mentioned electrophysiologic changes are probably related to the degeneration of diencephalic dopaminergic neurons, leading to dopamine loss in the striatum and other basal ganglia nuclei, although degeneration of non-dopaminergic cell groups may also have a role. The altered electrical activity of the basal ganglia and associated nuclei may contribute to some of the motor signs of the disease. We here review the current knowledge of the electrophysiologic changes at the single cell level, the level of local populations of neural elements, and the level of the entire basal ganglia-thalamocortical network in parkinsonism, and discuss the possible use of this information to optimize treatment approaches to Parkinson’s disease, such as deep brain stimulation therapy.

  17. Acute effects of three different circuit weight training protocols on blood lactate, heart rate, and rating of perceived exertion in recreationally active women.

    Science.gov (United States)

    Skidmore, Brook L; Jones, Margaret T; Blegen, Mark; Matthews, Tracey D

    2012-01-01

    Interval and circuit weight training are popular training methods for maximizing time-efficiency, and are purported to deliver greater physiological benefits faster than traditional training methods. Adding interval training into a circuit weight-training workout may further enhance the benefits of circuit weight training by placing increased demands upon the cardiovascular system. Our purpose was to compare acute effects of three circuit weight training protocols 1) traditional circuit weight training, 2) aerobic circuit weight training, and 3) combined circuit weight-interval training on blood lactate (BLA), heart rate (HR), and ratings of perceived exertion (RPE). Eleven recreationally active women completed 7 exercise sessions. Session 1 included measurements of height, weight, estimated VO2max, and 13 repetition maximum (RM) testing of the weight exercises. Sessions 2-4 were held on non-consecutive days for familiarization with traditional circuit weight training (TRAD), aerobic circuit weight training (ACWT), and combined circuit weight-interval training (CWIT) protocols. In sessions 5-7, TRAD, ACWT, and CWIT were performed in a randomized order ≥ 72 hr apart for measures of BLA, HR, and RPE at pre-exercise and following each of three mini-circuit weight training stations. Repeated-measures ANOVAs yielded significant interactions (p workouts into exercise programming may enhance fitness benefits and maximize time-efficiency more so than traditional circuit training methods. PMID:24150076

  18. The Electron Runaround: Understanding Electric Circuit Basics through a Classroom Activity

    Science.gov (United States)

    Singh, Vandana

    2010-01-01

    Several misconceptions abound among college students taking their first general physics course, and to some extent pre-engineering physics students, regarding the physics and applications of electric circuits. Analogies used in textbooks, such as those that liken an electric circuit to a piped closed loop of water driven by a water pump, do not…

  19. Information content of neural networks with self-control and variable activity

    Science.gov (United States)

    Bollé, D.; Amari, S. I.; Dominguez Carreta, D. R. C.; Massolo, G.

    2001-02-01

    A self-control mechanism for the dynamics of neural networks with variable activity is discussed using a recursive scheme for the time evolution of the local field. It is based upon the introduction of a self-adapting time-dependent threshold as a function of both the neural and pattern activity in the network. This mechanism leads to an improvement of the information content of the network as well as an increase of the storage capacity and the basins of attraction. Different architectures are considered and the results are compared with numerical simulations.

  20. Computational modeling of neural activities for statistical inference

    CERN Document Server

    Kolossa, Antonio

    2016-01-01

    This authored monograph supplies empirical evidence for the Bayesian brain hypothesis by modeling event-related potentials (ERP) of the human electroencephalogram (EEG) during successive trials in cognitive tasks. The employed observer models are useful to compute probability distributions over observable events and hidden states, depending on which are present in the respective tasks. Bayesian model selection is then used to choose the model which best explains the ERP amplitude fluctuations. Thus, this book constitutes a decisive step towards a better understanding of the neural coding and computing of probabilities following Bayesian rules. The target audience primarily comprises research experts in the field of computational neurosciences, but the book may also be beneficial for graduate students who want to specialize in this field. .

  1. Optimized circuit design for flexible 8-bit RFID transponders with active layer of ink-jet printed small molecule semiconductors

    NARCIS (Netherlands)

    Kjellander, B.K.C.; Smaal, W.T.T.; Myny, K.; Genoe, J.; Dehaene, W.; Heremans, P.; Gelinck, G.H.

    2013-01-01

    We ink-jet print a blend of 6,13-bis(triisopropyl-silylethynyl)pentacene and polystyrene as the active layer for flexible circuits. The discrete ink-jet printed transistors exhibit a saturation mobility of 0.5 cm2 V -1 s-1. The relative spread in transistor characteristics can be very large. This sp

  2. Implications of the Dependence of Neuronal Activity on Neural Network States for the Design of Brain-Machine Interfaces

    OpenAIRE

    Panzeri, Stefano; Safaai, Houman; De Feo, Vito; Vato, Alessandro

    2016-01-01

    Brain-machine interfaces (BMIs) can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stim...

  3. Implementing Neural Networks Using VLSI for Image Processing (compression

    Directory of Open Access Journals (Sweden)

    Sindhu R

    2015-04-01

    Full Text Available Biological systems process the analog signals such as image and sound efficiently. To process the information the way biological systems do we make use of ANN. (Artificial Neural Networks The focus of this paper is to review the implementation of the neural network architecture using analog components like Gilbert cell multiplier, differential amplifier for neuron activation function and tan sigmoid function circuit using MOS transistor. The neural architecture is trained using Back propagation algorithm for compressing the image. This paper surveys the methods of implementing the neural network using VLSI .Different CMOS technologies are used for implementing the circuits for arithmetic operations (i.e. 180nm, 45nm, 32nm.And the MOS transistors are working in sub threshold region. In this paper a review is made on how the VLSI architecture is used to implement neural networks and trained for compressing the image.

  4. Embedded Touch Sensing Circuit Using Mutual Capacitance for Active-Matrix Organic Light-Emitting Diode Display

    Science.gov (United States)

    Park, Young-Ju; Seok, Su-Jeong; Park, Sang-Ho; Kim, Ohyun

    2011-03-01

    We propose and simulate an embedded touch sensing circuit for active-matrix organic light-emitting diode (AMOLED) displays. The circuit consists of three thin-film transistors (TFTs), one fixed capacitor, and one variable capacitor. AMOLED displays do not have a variable capacitance characteristic, so we realized a variable capacitor to detect touches in the sensing pixel by exploiting the change in the mutual capacitance between two electrodes that is caused by touch. When a dielectric substance approaches two electrodes, the electric field is shunted so that the mutual capacitance decreases. We use the existing TFT process to form the variable capacitor, so no additional process is needed. We use advanced solid-phase-crystallization TFTs because of their stability and uniformity. The proposed circuit detects multi-touch points by a scanning process.

  5. Application of neural networks with orthogonal activation functions in control of dynamical systems

    Science.gov (United States)

    Nikolić, Saša S.; Antić, Dragan S.; Milojković, Marko T.; Milovanović, Miroslav B.; Perić, Staniša Lj.; Mitić, Darko B.

    2016-04-01

    In this article, we present a new method for the synthesis of almost and quasi-orthogonal polynomials of arbitrary order. Filters designed on the bases of these functions are generators of generalised quasi-orthogonal signals for which we derived and presented necessary mathematical background. Based on theoretical results, we designed and practically implemented generalised first-order (k = 1) quasi-orthogonal filter and proved its quasi-orthogonality via performed experiments. Designed filters can be applied in many scientific areas. In this article, generated functions were successfully implemented in Nonlinear Auto Regressive eXogenous (NARX) neural network as activation functions. One practical application of the designed orthogonal neural network is demonstrated through the example of control of the complex technical non-linear system - laboratory magnetic levitation system. Obtained results were compared with neural networks with standard activation functions and orthogonal functions of trigonometric shape. The proposed network demonstrated superiority over existing solutions in the sense of system performances.

  6. Critical phenomena at a first-order phase transition in a lattice of glow lamps: Experimental findings and analogy to neural activity.

    Science.gov (United States)

    Minati, Ludovico; de Candia, Antonio; Scarpetta, Silvia

    2016-07-01

    Networks of non-linear electronic oscillators have shown potential as physical models of neural dynamics. However, two properties of brain activity, namely, criticality and metastability, remain under-investigated with this approach. Here, we present a simple circuit that exhibits both phenomena. The apparatus consists of a two-dimensional square lattice of capacitively coupled glow (neon) lamps. The dynamics of lamp breakdown (flash) events are controlled by a DC voltage globally connected to all nodes via fixed resistors. Depending on this parameter, two phases having distinct event rate and degree of spatiotemporal order are observed. The transition between them is hysteretic, thus a first-order one, and it is possible to enter a metastability region, wherein, approaching a spinodal point, critical phenomena emerge. Avalanches of events occur according to power-law distributions having exponents ≈3/2 for size and ≈2 for duration, and fractal structure is evident as power-law scaling of the Fano factor. These critical exponents overlap observations in biological neural networks; hence, this circuit may have value as building block to realize corresponding physical models. PMID:27475063

  7. Critical phenomena at a first-order phase transition in a lattice of glow lamps: Experimental findings and analogy to neural activity

    Science.gov (United States)

    Minati, Ludovico; de Candia, Antonio; Scarpetta, Silvia

    2016-07-01

    Networks of non-linear electronic oscillators have shown potential as physical models of neural dynamics. However, two properties of brain activity, namely, criticality and metastability, remain under-investigated with this approach. Here, we present a simple circuit that exhibits both phenomena. The apparatus consists of a two-dimensional square lattice of capacitively coupled glow (neon) lamps. The dynamics of lamp breakdown (flash) events are controlled by a DC voltage globally connected to all nodes via fixed resistors. Depending on this parameter, two phases having distinct event rate and degree of spatiotemporal order are observed. The transition between them is hysteretic, thus a first-order one, and it is possible to enter a metastability region, wherein, approaching a spinodal point, critical phenomena emerge. Avalanches of events occur according to power-law distributions having exponents ≈3/2 for size and ≈2 for duration, and fractal structure is evident as power-law scaling of the Fano factor. These critical exponents overlap observations in biological neural networks; hence, this circuit may have value as building block to realize corresponding physical models.

  8. Neural Networks for Logic Circuits

    Institute of Scientific and Technical Information of China (English)

    1998-01-01

    TheneuralnetworksforNOT,AND,OR,NAND,NOR,XORandXNORgateswerepresentedin[1]and[4].ThelogicfunctionofanygatecanbedescribedusingB...

  9. Neural speech recognition: continuous phoneme decoding using spatiotemporal representations of human cortical activity

    Science.gov (United States)

    Moses, David A.; Mesgarani, Nima; Leonard, Matthew K.; Chang, Edward F.

    2016-10-01

    Objective. The superior temporal gyrus (STG) and neighboring brain regions play a key role in human language processing. Previous studies have attempted to reconstruct speech information from brain activity in the STG, but few of them incorporate the probabilistic framework and engineering methodology used in modern speech recognition systems. In this work, we describe the initial efforts toward the design of a neural speech recognition (NSR) system that performs continuous phoneme recognition on English stimuli with arbitrary vocabulary sizes using the high gamma band power of local field potentials in the STG and neighboring cortical areas obtained via electrocorticography. Approach. The system implements a Viterbi decoder that incorporates phoneme likelihood estimates from a linear discriminant analysis model and transition probabilities from an n-gram phonemic language model. Grid searches were used in an attempt to determine optimal parameterizations of the feature vectors and Viterbi decoder. Main results. The performance of the system was significantly improved by using spatiotemporal representations of the neural activity (as opposed to purely spatial representations) and by including language modeling and Viterbi decoding in the NSR system. Significance. These results emphasize the importance of modeling the temporal dynamics of neural responses when analyzing their variations with respect to varying stimuli and demonstrate that speech recognition techniques can be successfully leveraged when decoding speech from neural signals. Guided by the results detailed in this work, further development of the NSR system could have applications in the fields of automatic speech recognition and neural prosthetics.

  10. Benchmark of AC and DC Active Power Decoupling Circuits for Second-Order Harmonic Mitigation in Kilowatt-Scale Single-Phase Inverters

    DEFF Research Database (Denmark)

    Qin, Zian; Tang, Yi; Loh, Poh Chiang;

    2016-01-01

    This paper presents the benchmark study of ac and dc active power decoupling circuits for second order harmonic mitigation in kW scale single-phase inverters. First of all, a brief comparison of recently reported active power decoupling circuits is given, and the best solution that can achieve high...... results obtained on a 2 kW single-phase inverter....

  11. Can modular psychological concepts like affect and emotion be assigned to a distinct subset of regional neural circuits?. Comment on "The quartet theory of human emotions: An integrative and neurofunctional model" by S. Koelsch et al.

    Science.gov (United States)

    Fehr, Thorsten; Herrmann, Manfred

    2015-06-01

    The proposed Quartet Theory of Human Emotions by Koelsch and co-workers [11] adumbrates evidence from various scientific sources to integrate and assign the psychological concepts of 'affect' and 'emotion' to four brain circuits or to four neuronal core systems for affect-processing in the brain. The authors differentiate between affect and emotion and assign several facultative, or to say modular, psychological domains and principles of information processing, such as learning and memory, antecedents of affective activity, emotion satiation, cognitive complexity, subjective quality feelings, degree of conscious appraisal, to different affect systems. Furthermore, they relate orbito-frontal brain structures to moral affects as uniquely human, and the hippocampus to attachment-related affects. An additional feature of the theory describes 'emotional effector-systems' for motor-related processes (e.g., emotion-related actions), physiological arousal, attention and memory that are assumed to be cross-linked with the four proposed affect systems. Thus, higher principles of emotional information processing, but also modular affect-related issues, such as moral and attachment related affects, are thought to be handled by these four different physiological sub-systems that are on the other side assumed to be highly interwoven at both physiological and functional levels. The authors also state that the proposed sub-systems have many features in common, such as the selection and modulation of biological processes related to behaviour, perception, attention and memory. The latter aspect challenges an ongoing discussion about the mind-body problem: To which degree do the proposed sub-systems 'sufficiently' cover the processing of complex modular or facultative emotional/affective and/or cognitive phenomena? There are current models and scientific positions that almost completely reject the idea that modular psychological phenomena are handled by a distinct selection of

  12. Dextromethorphan mediated bitter taste receptor activation in the pulmonary circuit causes vasoconstriction.

    Directory of Open Access Journals (Sweden)

    Jasbir D Upadhyaya

    Full Text Available Activation of bitter taste receptors (T2Rs in human airway smooth muscle cells leads to muscle relaxation and bronchodilation. This finding led to our hypothesis that T2Rs are expressed in human pulmonary artery smooth muscle cells and might be involved in regulating the vascular tone. RT-PCR was performed to reveal the expression of T2Rs in human pulmonary artery smooth muscle cells. Of the 25 T2Rs, 21 were expressed in these cells. Functional characterization was done by calcium imaging after stimulating the cells with different bitter agonists. Increased calcium responses were observed with most of the agonists, the largest increase seen for dextromethorphan. Previously in site-directed mutational studies, we have characterized the response of T2R1 to dextromethorphan, therefore, T2R1 was selected for further analysis in this study. Knockdown with T2R1 specific shRNA decreased mRNA levels, protein levels and dextromethorphan-induced calcium responses in pulmonary artery smooth muscle cells by up to 50%. To analyze if T2Rs are involved in regulating the pulmonary vascular tone, ex vivo studies using pulmonary arterial and airway rings were pursued. Myographic studies using porcine pulmonary arterial and airway rings showed that stimulation with dextromethorphan led to contraction of the pulmonary arterial and relaxation of the airway rings. This study shows that dextromethorphan, acting through T2R1, causes vasoconstrictor responses in the pulmonary circuit and relaxation in the airways.

  13. Method of analog circuit fault diagnosis based on FOA-neural network%基于果蝇-构造小波神经网络模拟电路诊断方法

    Institute of Scientific and Technical Information of China (English)

    于文新; 何怡刚; 吴先明; 高坤

    2015-01-01

    利用果蝇算法优化构造小波神经网络,建立FOA-构造小波神经网络模型,并将模型应用于模拟电路故障分析当中,通过仿真试验可发现该方法在故障诊断中有较高的准确性。%In the paper, FOA and wavelet-neural network are applied to establish a FOA-structure wavelet neural network algorithm. The model is applied to an analog circuit fault analysis by simulation. The method has higher accuracy in fault diagnosis.

  14. Neural correlates underlying micrographia in Parkinson's disease.

    Science.gov (United States)

    Wu, Tao; Zhang, Jiarong; Hallett, Mark; Feng, Tao; Hou, Yanan; Chan, Piu

    2016-01-01

    Micrographia is a common symptom in Parkinson's disease, which manifests as either a consistent or progressive reduction in the size of handwriting or both. Neural correlates underlying micrographia remain unclear. We used functional magnetic resonance imaging to investigate micrographia-related neural activity and connectivity modulations. In addition, the effect of attention and dopaminergic administration on micrographia was examined. We found that consistent micrographia was associated with decreased activity and connectivity in the basal ganglia motor circuit; while progressive micrographia was related to the dysfunction of basal ganglia motor circuit together with disconnections between the rostral supplementary motor area, rostral cingulate motor area and cerebellum. Attention significantly improved both consistent and progressive micrographia, accompanied by recruitment of anterior putamen and dorsolateral prefrontal cortex. Levodopa improved consistent micrographia accompanied by increased activity and connectivity in the basal ganglia motor circuit, but had no effect on progressive micrographia. Our findings suggest that consistent micrographia is related to dysfunction of the basal ganglia motor circuit; while dysfunction of the basal ganglia motor circuit and disconnection between the rostral supplementary motor area, rostral cingulate motor area and cerebellum likely contributes to progressive micrographia. Attention improves both types of micrographia by recruiting additional brain networks. Levodopa improves consistent micrographia by restoring the function of the basal ganglia motor circuit, but does not improve progressive micrographia, probably because of failure to repair the disconnected networks. PMID:26525918

  15. Using Perfusion fMRI to Measure Continuous Changes in Neural Activity with Learning

    Science.gov (United States)

    Olson, Ingrid R.; Rao, Hengyi; Moore, Katherine Sledge; Wang, Jiongjiong; Detre, John A.; Aguirre, Geoffrey K.

    2006-01-01

    In this study, we examine the suitability of a relatively new imaging technique, "arterial spin labeled perfusion imaging," for the study of continuous, gradual changes in neural activity. Unlike BOLD imaging, the perfusion signal is stable over long time-scales, allowing for accurate assessment of continuous performance. In addition, perfusion…

  16. Specific and Nonspecific Neural Activity during Selective Processing of Visual Representations in Working Memory

    Science.gov (United States)

    Oh, Hwamee; Leung, Hoi-Chung

    2010-01-01

    In this fMRI study, we investigated prefrontal cortex (PFC) and visual association regions during selective information processing. We recorded behavioral responses and neural activity during a delayed recognition task with a cue presented during the delay period. A specific cue ("Face" or "Scene") was used to indicate which one of the two…

  17. Differential neural activity patterns for spatial relations in humans: a MEG study.

    Science.gov (United States)

    Scott, Nicole M; Leuthold, Arthur; Sera, Maria D; Georgopoulos, Apostolos P

    2016-02-01

    Children learn the words for above-below relations earlier than for left-right relations, despite treating these equally well in a simple visual categorization task. Even as adults--conflicts in congruency, such as when a stimulus is depicted in a spatially incongruent manner with respect to salient global cues--can be challenging. Here we investigated the neural correlates of encoding and maintaining in working memory above-below and left-right relational planes in 12 adults using magnetoencephalography in order to discover whether above-below relations are represented by the brain differently than left-right relations. Adults performed perfectly on the task behaviorally, so any differences in neural activity were attributed to the stimuli's cognitive attributes. In comparing above-below to left-right relations during stimulus encoding, we found the greatest differences in neural activity in areas associated with space and movement. In comparing congruent to incongruent trials, we found the greatest differential activity in premotor areas. For both contrasts, brain areas involved in the encoding phase were also involved in the maintenance phase, which provides evidence that those brain areas are particularly important in representing the relational planes or congruency types throughout the trial. When comparing neural activity associated with the relational planes during working memory, additional right posterior areas were implicated, whereas the congruent-incongruent contrast implicated additional bilateral frontal and temporal areas. These findings are consistent with the hypothesis left-right relations are represented differently than above-below relations. PMID:26514809

  18. Identification of children's activity type with accelerometer-based neural networks

    NARCIS (Netherlands)

    Vries, S.I. de; Engels, M.; Garre, F.G.

    2011-01-01

    Purpose: The study's purpose was to identify children's physical activity type using artificial neural network (ANN) models based on uniaxial or triaxial accelerometer data from the hip or the ankle. Methods: Fifty-eight children (31 boys and 27 girls, age range = 9-12 yr) performed the following ac

  19. Modeling electrocortical activity through improved local approximations of integral neural field equations.

    NARCIS (Netherlands)

    Coombes, S.; Venkov, N.A.; Shiau, L.; Bojak, I.; Liley, D.T.; Laing, C.R.

    2007-01-01

    Neural field models of firing rate activity typically take the form of integral equations with space-dependent axonal delays. Under natural assumptions on the synaptic connectivity we show how one can derive an equivalent partial differential equation (PDE) model that properly treats the axonal dela

  20. Differences in Neural Activation as a Function of Risk-taking Task Parameters

    Directory of Open Access Journals (Sweden)

    Eliza eCongdon

    2013-09-01

    Full Text Available Despite evidence supporting a relationship between impulsivity and naturalistic risk-taking, the relationship of impulsivity with laboratory-based measures of risky decision-making remains unclear. One factor contributing to this gap in our understanding is the degree to which different risky decision-making tasks vary in their details. We conducted an fMRI investigation of the Angling Risk Task (ART, which is an improved behavioral measure of risky decision-making. In order to examine whether the observed pattern of neural activation was specific to the ART or generalizable, we also examined correlates of the Balloon Analogue Risk Taking (BART task in the same sample of 23 healthy adults. Exploratory analyses were conducted to examine the relationship between neural activation, performance, impulsivity and self-reported risk-taking. While activation in a valuation network was associated with reward tracking during the ART but not the BART, increased fronto-cingulate activation was seen during risky choice trials in the BART as compared to the ART. Thus, neural activation during risky decision-making trials differed between the two tasks, and this observation was likely driven by differences in task parameters, namely the absence vs. presence of ambiguity and/or stationary vs. increasing probability of loss on the ART and BART, respectively. Exploratory association analyses suggest that sensitivity of neural response to the magnitude of potential reward during the ART was associated with a suboptimal performance strategy, higher scores on a scale of dysfunctional impulsivity and a greater likelihood of engaging in risky behaviors, while this pattern was not seen for the BART. Our results suggest that the ART is decomposable and associated with distinct patterns of neural activation; this represents a preliminary step towards characterizing a behavioral measure of risky decision-making that may support a better understanding of naturalistic risk-taking.

  1. A Constraint Satisfaction Neural Network and Heuristic Combined Approach for Concurrent Activities Scheduling

    Institute of Scientific and Technical Information of China (English)

    YAN JiHong(闫纪红); WU Cheng(吴澄)

    2003-01-01

    Scheduling activities in concurrent product development process is of great sig-nificance to shorten development lead time and minimize the cost. Moreover, it can eliminate theunnecessary redesign periods and guarantee that serial activities can be executed as concurrently aspossible. This paper presents a constraint satisfaction neural network and heuristic combined ap-proach for concurrent activities scheduling. In the combined approach, the neural network is usedto obtain a feasible starting time of all the activities based on sequence constraints, the heuris-tic algorithm is used to obtain a feasible solution of the scheduling problem based on resourceconstraints. The feasible scheduling solution is obtained by a gradient optimization function. Sim-ulations have shown that the proposed combined approach is efficient and feasible with respect toconcurrent activities scheduling.

  2. Active Diverse Learning Neural Network Ensemble Approach for Power Transformer Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Yu Xu

    2010-10-01

    Full Text Available An ensemble learning algorithm was proposed in this paper by analyzing the error function of neural network ensembles, by which, individual neural networks were actively guided to learn diversity. By decomposing the ensemble error function, error correlation terms were included in the learning criterion function of individual networks. And all the individual networks in the ensemble were leaded to learn diversity through cooperative training. The method was applied in Dissolved Gas Analysis based fault diagnosis of power transformer. Experiment results show that, the algorithm has higher accuracy than IEC method and BP network. In addition, the performance is more stable than conventional ensemble method, i.e., Bagging and Boosting.

  3. Global robust dissipativity of interval recurrent neural networks with time-varying delay and discontinuous activations

    Science.gov (United States)

    Duan, Lian; Huang, Lihong; Guo, Zhenyuan

    2016-07-01

    In this paper, the problems of robust dissipativity and robust exponential dissipativity are discussed for a class of recurrent neural networks with time-varying delay and discontinuous activations. We extend an invariance principle for the study of the dissipativity problem of delay systems to the discontinuous case. Based on the developed theory, some novel criteria for checking the global robust dissipativity and global robust exponential dissipativity of the addressed neural network model are established by constructing appropriate Lyapunov functionals and employing the theory of Filippov systems and matrix inequality techniques. The effectiveness of the theoretical results is shown by two examples with numerical simulations.

  4. Enhanced Food Anticipatory Activity Associated with Enhanced Activation of Extrahypothalamic Neural Pathways in Serotonin2C Receptor Null Mutant Mice

    OpenAIRE

    Mistlberger, Ralph; Hsu, Jennifer; Yu, Lisa; Bowman, Melody; Tecott, Laurence; Sullivan, Elinor

    2010-01-01

    The ability to entrain circadian rhythms to food availability is important for survival. Food-entrained circadian rhythms are characterized by increased locomotor activity in anticipation of food availability (food anticipatory activity). However, the molecular components and neural circuitry underlying the regulation of food anticipatory activity remain unclear. Here we show that serotonin2C receptor (5-HT2CR) null mutant mice subjected to a daytime restricted feeding schedule exhibit enhanc...

  5. Enhanced Food Anticipatory Activity Associated with Enhanced Activation of Extrahypothalamic Neural Pathways in Serotonin2C Receptor Null Mutant Mice

    OpenAIRE

    Hsu, Jennifer L.; Lisa Yu; Elinor Sullivan; Melodi Bowman; Mistlberger, Ralph E.; Tecott, Laurence H.

    2010-01-01

    The ability to entrain circadian rhythms to food availability is important for survival. Food-entrained circadian rhythms are characterized by increased locomotor activity in anticipation of food availability (food anticipatory activity). However, the molecular components and neural circuitry underlying the regulation of food anticipatory activity remain unclear. Here we show that serotonin(2C) receptor (5-HT2CR) null mutant mice subjected to a daytime restricted feeding schedule exhibit enha...

  6. RBF neural network and active circles based algorithm for contours extraction

    Institute of Scientific and Technical Information of China (English)

    Zhou Zhiheng; Zeng Delu; Xie Shengli

    2007-01-01

    For the contours extraction from the images, active contour model and self-organizing map based approach are popular nowadays. But they are still confronted with the problems that the optimization of energy function will trap in local minimums and the contour evolutions greatly depend on the initial contour selection. Addressing to these problems, a contours extraction algorithm based on RBF neural network is proposed here. A series of circles with adaptive radius and center is firstly used to search image feature points that are scattered enough. After the feature points are clustered, a group of radial basis functions are constructed. Using the pixels' intensities and gradients as the input vector, the final object contour can be obtained by the predicting ability of the neural network. The RBF neural network based algorithm is tested on three kinds of images, such as changing topology, complicated background, and blurring or noisy boundary. Simulation results show that the proposed algorithm performs contours extraction greatly.

  7. Neural networkbased semi-active control strategy for structural vibration mitigation with magnetorheological damper

    DEFF Research Database (Denmark)

    Bhowmik, Subrata

    2011-01-01

    This paper presents a neural network based semi-active control method for a rotary type magnetorheological (MR) damper. The characteristics of the MR damper are described by the classic Bouc-Wen model, and the performance of the proposed control method is evaluated in terms of a base exited shear......-displacement trajectories. The proposed neural network controller is therefore trained based on data derived from these desired forcedisplacement curves, where the optimal relation between friction force level and response amplitude is determined explicitly by simply maximizing the damping ratio of the targeted vibration...... to determine the damper current based on the derived optimal damper force. For that reason an inverse MR damper model is also designed based on the neural network identification of the particular rotary MR damper. The performance of the proposed controller is compared to that of an optimal pure viscous damper...

  8. Neural network based semi-active control strategy for structural vibration mitigation with magnetorheological damper

    DEFF Research Database (Denmark)

    Bhowmik, Subrata

    2011-01-01

    This paper presents a neural network based semi-active control method for a rotary type magnetorheological (MR) damper. The characteristics of the MR damper are described by the classic Bouc-Wen model, and the performance of the proposed control method is evaluated in terms of a base exited shear......-displacement trajectories. The proposed neural network controller is therefore trained based on data derived from these desired forcedisplacement curves, where the optimal relation between friction force level and response amplitude is determined explicitly by simply maximizing the damping ratio of the targeted vibration...... to determine the damper current based on the derived optimal damper force. For that reason an inverse MR damper model is also designed based on the neural network identification of the particular rotary MR damper. The performance of the proposed controller is compared to that of an optimal pure viscous damper...

  9. Wide-field optical mapping of neural activity and brain haemodynamics: considerations and novel approaches

    Science.gov (United States)

    Ma, Ying; Shaik, Mohammed A.; Kozberg, Mariel G.; Thibodeaux, David N.; Zhao, Hanzhi T.; Yu, Hang

    2016-01-01

    Although modern techniques such as two-photon microscopy can now provide cellular-level three-dimensional imaging of the intact living brain, the speed and fields of view of these techniques remain limited. Conversely, two-dimensional wide-field optical mapping (WFOM), a simpler technique that uses a camera to observe large areas of the exposed cortex under visible light, can detect changes in both neural activity and haemodynamics at very high speeds. Although WFOM may not provide single-neuron or capillary-level resolution, it is an attractive and accessible approach to imaging large areas of the brain in awake, behaving mammals at speeds fast enough to observe widespread neural firing events, as well as their dynamic coupling to haemodynamics. Although such wide-field optical imaging techniques have a long history, the advent of genetically encoded fluorophores that can report neural activity with high sensitivity, as well as modern technologies such as light emitting diodes and sensitive and high-speed digital cameras have driven renewed interest in WFOM. To facilitate the wider adoption and standardization of WFOM approaches for neuroscience and neurovascular coupling research, we provide here an overview of the basic principles of WFOM, considerations for implementation of wide-field fluorescence imaging of neural activity, spectroscopic analysis and interpretation of results. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’. PMID:27574312

  10. 基于“心理痛苦”理论的眶额皮质介导抑郁症自杀机制%Neural Circuits of Orbitofrontal Cortex Involved in Suicidal Attempts Among Major Depression Patients

    Institute of Scientific and Technical Information of China (English)

    李欢欢; 谢蔚臻; 李永娜

    2015-01-01

    For a suicidal person, suicide seems to be the only means of escaping the torment of psychological pain. Pain avoidance, representing the wish to escape psychological pain, may be a primary predictor of subsequent suicide. Given that the Orbitofrontal cortex dysfunction may trigger overreaction to a social signal of disapproval (painful feelings) and induced high level motivation for escaping from punishment (pain avoidance), event-related functional MRI was used to measure orbitofrontal cortex and its neural circuit activity in response to stimuli representing negative/positive emotional incentives among patients with a history of suicidal attempts (vs.no suicidal history) in furture work. Our work will extend interactive modes of motivation and cognitive control circuits underying higher levels of psychological pain and ultimately may provide a empiral evidence for the neural correlates of pain avoidance which trigger suicidal behavior.%近年来,自杀的心理痛苦三因素模型(包括痛苦唤醒、痛苦体验和痛苦逃避)在临床抑郁症患者群体得到验证。痛苦逃避是该模型的核心成分。行为学研究证据显示,痛苦逃避维度得分对抑郁症患者自杀意念和自杀行为的预测力远高于Beck抑郁问卷得分和痛苦体验得分。由于眶额皮质是介导情绪反应和控制复杂行为的关键界面,主要参与负性情绪引发回避惩罚(痛苦)的动机控制,与动机、决策和行为监控密切相关。眶额皮质及其与前额叶、皮层下结构(扣带前回、杏仁核和下丘脑等)的异常激活模式可能是痛苦体验引发高逃避动机、进而产生自杀行为(风险决策)的重要神经基础。本文在综述以往研究的基础上,提出通过改编和发展情感激励延迟和金钱激励延迟的认知任务,对痛苦体验和痛苦逃避动机阶段进行时间上的分离,建立眶额皮质介导抑郁症自杀的脑功能病理模型的研究思路。

  11. Social power and approach-related neural activity

    NARCIS (Netherlands)

    M.A.S. Boksem (Maarten); R. Smolders (Ruud); D. de Cremer (David)

    2009-01-01

    textabstractIt has been argued that power activates a general tendency to approach whereas powerlessness activates a tendency to inhibit. The assumption is that elevated power involves reward-rich environments, freedom and, as a consequence, triggers an approach-related motivational orientation and

  12. A New Training Method for Feedforward Neural Networks Based on Geometric Contraction Property of Activation Functions

    OpenAIRE

    Birtea, Petre; Cernazanu-Glavan, Cosmin; Sisu, Alexandru

    2016-01-01

    We propose a new training method for a feedforward neural network having the activation functions with the geometric contraction property. The method consists of constructing a new functional that is less nonlinear in comparison with the classical functional by removing the nonlinearity of the activation functions from the output layer. We validate this new method by a series of experiments that show an improved learning speed and also a better classification error.

  13. Imaging the neural circuitry and chemical control of aggressive motivation

    Directory of Open Access Journals (Sweden)

    Blanchard D Caroline

    2008-11-01

    Full Text Available Abstract Background With the advent of functional magnetic resonance imaging (fMRI in awake animals it is possible to resolve patterns of neuronal activity across the entire brain with high spatial and temporal resolution. Synchronized changes in neuronal activity across multiple brain areas can be viewed as functional neuroanatomical circuits coordinating the thoughts, memories and emotions for particular behaviors. To this end, fMRI in conscious rats combined with 3D computational analysis was used to identifying the putative distributed neural circuit involved in aggressive motivation and how this circuit is affected by drugs that block aggressive behavior. Results To trigger aggressive motivation, male rats were presented with their female cage mate plus a novel male intruder in the bore of the magnet during image acquisition. As expected, brain areas previously identified as critical in the organization and expression of aggressive behavior were activated, e.g., lateral hypothalamus, medial basal amygdala. Unexpected was the intense activation of the forebrain cortex and anterior thalamic nuclei. Oral administration of a selective vasopressin V1a receptor antagonist SRX251 or the selective serotonin reuptake inhibitor fluoxetine, drugs that block aggressive behavior, both caused a general suppression of the distributed neural circuit involved in aggressive motivation. However, the effect of SRX251, but not fluoxetine, was specific to aggression as brain activation in response to a novel sexually receptive female was unaffected. Conclusion The putative neural circuit of aggressive motivation identified with fMRI includes neural substrates contributing to emotional expression (i.e. cortical and medial amygdala, BNST, lateral hypothalamus, emotional experience (i.e. hippocampus, forebrain cortex, anterior cingulate, retrosplenial cortex and the anterior thalamic nuclei that bridge the motor and cognitive components of aggressive responding

  14. The neural processing of taste

    Directory of Open Access Journals (Sweden)

    Katz Donald B

    2007-09-01

    Full Text Available Abstract Although there have been many recent advances in the field of gustatory neurobiology, our knowledge of how the nervous system is organized to process information about taste is still far from complete. Many studies on this topic have focused on understanding how gustatory neural circuits are spatially organized to represent information about taste quality (e.g., "sweet", "salty", "bitter", etc.. Arguments pertaining to this issue have largely centered on whether taste is carried by dedicated neural channels or a pattern of activity across a neural population. But there is now mounting evidence that the timing of neural events may also importantly contribute to the representation of taste. In this review, we attempt to summarize recent findings in the field that pertain to these issues. Both space and time are variables likely related to the mechanism of the gustatory neural code: information about taste appears to reside in spatial and temporal patterns of activation in gustatory neurons. What is more, the organization of the taste network in the brain would suggest that the parameters of space and time extend to the neural processing of gustatory information on a much grander scale.

  15. Constructive feedforward neural networks using hermite polynomial activation functions.

    Science.gov (United States)

    Ma, Liying; Khorasani, K

    2005-07-01

    In this paper, a constructive one-hidden-layer network is introduced where each hidden unit employs a polynomial function for its activation function that is different from other units. Specifically, both a structure level as well as a function level adaptation methodologies are utilized in constructing the network. The functional level adaptation scheme ensures that the "growing" or constructive network has different activation functions for each neuron such that the network may be able to capture the underlying input-output map more effectively. The activation functions considered consist of orthonormal Hermite polynomials. It is shown through extensive simulations that the proposed network yields improved performance when compared to networks having identical sigmoidal activation functions.

  16. Implementation Method of Circuit Evolution Based on Artificial Neural Network Model%基于类神经网络模型的电路演化实现方法

    Institute of Scientific and Technical Information of China (English)

    崔新风; 娄建安; 褚杰; 原亮; 丁国良

    2011-01-01

    为解决目前数字型演化硬件研究中存在的电路编码困难问题,提出一个可用矩阵形式描述组合电路的类神经网络门级电路模型,讨论在此模型上进行电路编码的具体方法.根据编码矩阵特点,对标准遗传算法进行改进,设计遗传操作算子、适应度评估方法等.通过无刷直流电动机电子换相电路的成功演化实例,验证了采用矩阵编码和改进遗传算法实现数字电路演化的可行性.%For the purpose of solving the encoding problem harassed the digital Evolvable Hardware(EHW) researchers, a gate-level circuit model which is based on the similarities between combinatorial circuit and neural network is proposed, on which the matrix encoding scheme of combinatorial circuit is discussed. An improved genetic algorithm is used to evolve the encoding matrix, genetic operators and fitness evaluation method are designed according to the characteristics of circuit encoding. The implementation of the commutation circuit of brushless direct current motor proves the feasibility of the implementation method of digital EHW by the using of matrix encoding scheme and the improved genetic algorithm.

  17. Endogenous testosterone levels are associated with neural activity in men with schizophrenia during facial emotion processing.

    Science.gov (United States)

    Ji, Ellen; Weickert, Cynthia Shannon; Lenroot, Rhoshel; Catts, Stanley V; Vercammen, Ans; White, Christopher; Gur, Raquel E; Weickert, Thomas W

    2015-06-01

    Growing evidence suggests that testosterone may play a role in the pathophysiology of schizophrenia given that testosterone has been linked to cognition and negative symptoms in schizophrenia. Here, we determine the extent to which serum testosterone levels are related to neural activity in affective processing circuitry in men with schizophrenia. Functional magnetic resonance imaging was used to measure blood-oxygen-level-dependent signal changes as 32 healthy controls and 26 people with schizophrenia performed a facial emotion identification task. Whole brain analyses were performed to determine regions of differential activity between groups during processing of angry versus non-threatening faces. A follow-up ROI analysis using a regression model in a subset of 16 healthy men and 16 men with schizophrenia was used to determine the extent to which serum testosterone levels were related to neural activity. Healthy controls displayed significantly greater activation than people with schizophrenia in the left inferior frontal gyrus (IFG). There was no significant difference in circulating testosterone levels between healthy men and men with schizophrenia. Regression analyses between activation in the IFG and circulating testosterone levels revealed a significant positive correlation in men with schizophrenia (r=.63, p=.01) and no significant relationship in healthy men. This study provides the first evidence that circulating serum testosterone levels are related to IFG activation during emotion face processing in men with schizophrenia but not in healthy men, which suggests that testosterone levels modulate neural processes relevant to facial emotion processing that may interfere with social functioning in men with schizophrenia.

  18. A canonical circuit for generating phase-amplitude coupling.

    Science.gov (United States)

    Onslow, Angela C E; Jones, Matthew W; Bogacz, Rafal

    2014-01-01

    'Phase amplitude coupling' (PAC) in oscillatory neural activity describes a phenomenon whereby the amplitude of higher frequency activity is modulated by the phase of lower frequency activity. Such coupled oscillatory activity--also referred to as 'cross-frequency coupling' or 'nested rhythms'--has been shown to occur in a number of brain regions and at behaviorally relevant time points during cognitive tasks; this suggests functional relevance, but the circuit mechanisms of PAC generation remain unclear. In this paper we present a model of a canonical circuit for generating PAC activity, showing how interconnected excitatory and inhibitory neural populations can be periodically shifted in to and out of oscillatory firing patterns by afferent drive, hence generating higher frequency oscillations phase-locked to a lower frequency, oscillating input signal. Since many brain regions contain mutually connected excitatory-inhibitory populations receiving oscillatory input, the simplicity of the mechanism generating PAC in such networks may explain the ubiquity of PAC across diverse neural systems and behaviors. Analytic treatment of this circuit as a nonlinear dynamical system demonstrates how connection strengths and inputs to the populations can be varied in order to change the extent and nature of PAC activity, importantly which phase of the lower frequency rhythm the higher frequency activity is locked to. Consequently, this model can inform attempts to associate distinct types of PAC with different network topologies and physiologies in real data. PMID:25136855

  19. Integrated Circuits for Analog Signal Processing

    CERN Document Server

    2013-01-01

      This book presents theory, design methods and novel applications for integrated circuits for analog signal processing.  The discussion covers a wide variety of active devices, active elements and amplifiers, working in voltage mode, current mode and mixed mode.  This includes voltage operational amplifiers, current operational amplifiers, operational transconductance amplifiers, operational transresistance amplifiers, current conveyors, current differencing transconductance amplifiers, etc.  Design methods and challenges posed by nanometer technology are discussed and applications described, including signal amplification, filtering, data acquisition systems such as neural recording, sensor conditioning such as biomedical implants, actuator conditioning, noise generators, oscillators, mixers, etc.   Presents analysis and synthesis methods to generate all circuit topologies from which the designer can select the best one for the desired application; Includes design guidelines for active devices/elements...

  20. Video-based convolutional neural networks for activity recognition from robot-centric videos

    Science.gov (United States)

    Ryoo, M. S.; Matthies, Larry

    2016-05-01

    In this evaluation paper, we discuss convolutional neural network (CNN)-based approaches for human activity recognition. In particular, we investigate CNN architectures designed to capture temporal information in videos and their applications to the human activity recognition problem. There have been multiple previous works to use CNN-features for videos. These include CNNs using 3-D XYT convolutional filters, CNNs using pooling operations on top of per-frame image-based CNN descriptors, and recurrent neural networks to learn temporal changes in per-frame CNN descriptors. We experimentally compare some of these different representatives CNNs while using first-person human activity videos. We especially focus on videos from a robots viewpoint, captured during its operations and human-robot interactions.

  1. Model Integrating Fuzzy Argument with Neural Network Enhancing the Performance of Active Queue Management

    Directory of Open Access Journals (Sweden)

    Nguyen Kim Quoc

    2015-08-01

    Full Text Available The bottleneck control by active queue management mechanisms at network nodes is essential. In recent years, some researchers have used fuzzy argument to improve the active queue management mechanisms to enhance the network performance. However, the projects using the fuzzy controller depend heavily on professionals and their parameters cannot be updated according to changes in the network, so the effectiveness of this mechanism is not high. Therefore, we propose a model combining the fuzzy controller with neural network (FNN to overcome the limitations above. Results of the training of the neural networks will find the optimal parameters for the adaptive fuzzy controller well to changes of the network. This improves the operational efficiency of the active queue management mechanisms at network nodes.

  2. Dampened neural activity and abolition of epileptic-like activity in cortical slices by active ingredients of spices.

    Science.gov (United States)

    Pezzoli, Maurizio; Elhamdani, Abdeladim; Camacho, Susana; Meystre, Julie; González, Stephanie Michlig; le Coutre, Johannes; Markram, Henry

    2014-10-31

    Active ingredients of spices (AIS) modulate neural response in the peripheral nervous system, mainly through interaction with TRP channel/receptors. The present study explores how different AIS modulate neural response in layer 5 pyramidal neurons of S1 neocortex. The AIS tested are agonists of TRPV1/3, TRPM8 or TRPA1. Our results demonstrate that capsaicin, eugenol, menthol, icilin and cinnamaldehyde, but not AITC dampen the generation of APs in a voltage- and time-dependent manner. This effect was further tested for the TRPM8 ligands in the presence of a TRPM8 blocker (BCTC) and on TRPM8 KO mice. The observable effect was still present. Finally, the influence of the selected AIS was tested on in vitro gabazine-induced seizures. Results coincide with the above observations: except for cinnamaldehyde, the same AIS were able to reduce the number, duration of the AP bursts and increase the concentration of gabazine needed to elicit them. In conclusion, our data suggests that some of these AIS can modulate glutamatergic neurons in the brain through a TRP-independent pathway, regardless of whether the neurons are stimulated intracellularly or by hyperactive microcircuitry.

  3. Larger bases and mixed analog/digital neural nets

    Energy Technology Data Exchange (ETDEWEB)

    Beiu, V.

    1998-12-31

    The paper overviews results dealing with the approximation capabilities of neural networks, and bounds on the size of threshold gate circuits. Based on an explicit numerical algorithm for Kolmogorov`s superpositions the authors show that minimum size neural networks--for implementing any Boolean function--have the identity function as the activation function. Conclusions and several comments on the required precision are ending the paper.

  4. Progress Towards Biocompatible Intracortical Microelectrodes for Neural Interfacing Applications

    OpenAIRE

    Jorfi, Mehdi; Skousen, John L.; Weder, Christoph; Capadona, Jeffrey R.

    2014-01-01

    To ensure long-term consistent neural recordings, next-generation intracortical microelectrodes are being developed with an increased emphasis on reducing the neuro-inflammatory response. The increased emphasis stems from the improved understanding of the multifaceted role that inflammation may play in disrupting both biologic and abiologic components of the overall neural interface circuit. To combat neuro-inflammation and improve recording quality, the field is actively progressing from tra...

  5. Cholinergic Circuit Control of Postnatal Neurogenesis

    Science.gov (United States)

    Asrican, Brent; Paez-Gonzalez, Patricia; Erb, Joshua; Kuo, Chay T.

    2016-01-01

    New neuron addition via continued neurogenesis in the postnatal/adult mammalian brain presents a distinct form of nervous system plasticity. During embryonic development, precise temporal and spatial patterns of neurogenesis are necessary to create the nervous system architecture. Similar between embryonic and postnatal stages, neurogenic proliferation is regulated by neural stem cell (NSC)-intrinsic mechanisms layered upon cues from their local microenvironmental niche. Following developmental assembly, it remains relatively unclear what may be the key driving forces that sustain continued production of neurons in the postnatal/adult brain. Recent experimental evidence suggests that patterned activity from specific neural circuits can also directly govern postnatal/adult neurogenesis. Here, we review experimental findings that revealed cholinergic modulation, and how patterns of neuronal activity and acetylcholine release may differentially or synergistically activate downstream signaling in NSCs. Higher-order excitatory and inhibitory inputs regulating cholinergic neuron firing, and their implications in neurogenesis control are also considered.

  6. Neural control of glutamine synthetase activity in rat skeletal muscles.

    Science.gov (United States)

    Feng, B; Konagaya, M; Konagaya, Y; Thomas, J W; Banner, C; Mill, J; Max, S R

    1990-05-01

    The mechanism of glutamine synthetase induction in rat skeletal muscle after denervation or limb immobilization was investigated. Adult male rats were subjected to midthigh section of the sciatic nerve. At 1, 2, and 5 h and 1, 2, and 7 days after denervation, rats were killed and denervated, and contralateral control soleus and plantaris muscles were excised, weighted, homogenized, and assayed for glutamine synthetase. Glutamine synthetase activity increased approximately twofold 1 h after denervation in both muscles. By 7 days postdenervation enzyme activity had increased to three times the control level in plantaris muscle and to four times the control level in soleus muscle. Increased enzyme activity after nerve section was associated with increased maximum velocity with no change in apparent Michaelis constant. Immunotitration with an antiglutamine synthetase antibody suggested that denervation caused an increase in the number of glutamine synthetase molecules in muscle. However, Northern-blot analysis revealed no increase in the steady-state level of glutamine synthetase mRNA after denervation. A mixing experiment failed to yield evidence for the presence of a soluble factor involved in regulating the activity of glutamine synthetase in denervated muscle. A combination of denervation and dexamethasone injections resulted in additive increases in glutamine synthetase. Thus the mechanism underlying increased glutamine synthetase after denervation appears to be posttranscriptional and is distinct from that of the glucocorticoid-mediated glutamine synthetase induction previously described by us. PMID:1970709

  7. Concurrent multitasking : From neural activity to human cognition

    NARCIS (Netherlands)

    Nijboer, Menno

    2016-01-01

    Multitasking has become an important part of our daily lives. This delicate juggling act between several activities occurs when people drive, when they are working, and even when they should be paying attention in the classroom. While multitasking is typically considered as something to avoid, there

  8. Neural activities during affective processing in people with Alzheimer's disease

    NARCIS (Netherlands)

    Lee, Tatia M. C.; Sun, Delin; Leung, Mei-Kei; Chu, Leung-Wing; Keysers, Christian

    2013-01-01

    This study examined brain activities in people with Alzheimer's disease when viewing happy, sad, and fearful facial expressions of others. A functional magnetic resonance imaging and a voxel-based morphometry methodology together with a passive viewing of emotional faces paradigm were employed to co

  9. Quantitative meta-analysis of neural activity in posttraumatic stress disorder

    Directory of Open Access Journals (Sweden)

    Hayes Jasmeet P

    2012-05-01

    Full Text Available Abstract Background In recent years, neuroimaging techniques such as functional magnetic resonance imaging (fMRI and positron emission tomography (PET have played a significant role in elucidating the neural underpinnings of posttraumatic stress disorder (PTSD. However, a detailed understanding of the neural regions implicated in the disorder remains incomplete because of considerable variability in findings across studies. The aim of this meta-analysis was to identify consistent patterns of neural activity across neuroimaging study designs in PTSD to improve understanding of the neurocircuitry of PTSD. Methods We conducted a literature search for PET and fMRI studies of PTSD that were published before February 2011. The article search resulted in 79 functional neuroimaging PTSD studies. Data from 26 PTSD peer-reviewed neuroimaging articles reporting results from 342 adult patients and 342 adult controls were included. Peak activation coordinates from selected articles were used to generate activation likelihood estimate maps separately for symptom provocation and cognitive-emotional studies of PTSD. A separate meta-analysis examined the coupling between ventromedial prefrontal cortex and amygdala activity in patients. Results Results demonstrated that the regions most consistently hyperactivated in PTSD patients included mid- and dorsal anterior cingulate cortex, and when ROI studies were included, bilateral amygdala. By contrast, widespread hypoactivity was observed in PTSD including the ventromedial prefrontal cortex and the inferior frontal gyrus. Furthermore, decreased ventromedial prefrontal cortex activity was associated with increased amygdala activity. Conclusions These results provide evidence for a neurocircuitry model of PTSD that emphasizes alteration in neural networks important for salience detection and emotion regulation.

  10. What are the odds? The neural correlates of active choice during gambling

    Directory of Open Access Journals (Sweden)

    Bettina eStuder

    2012-04-01

    Full Text Available Gambling is a widespread recreational activity and requires pitting the values of potential wins and losses against their probability of occurrence. Neuropsychological research showed that betting behavior on laboratory gambling tasks is highly sensitive to focal lesions to the ventromedial prefrontal cortex (vmPFC and insula. In the current study, we assessed the neural basis of betting choices in healthy participants, using functional magnetic resonance imaging of the Roulette Betting Task. In half of the trials participants actively chose their bets; in the other half the computer dictated the bet size. Our results highlight the impact of volitional choice upon the neural substrates of gambling: Neural activity in a distributed network - including key structures of the reward circuitry (midbrain, striatum - was higher during active compared to computer-dictated bet selection. In line with neuropsychological data, the anterior insula and vmPFC were more activated during self-directed bet selection, and responses in these areas were differentially modulated by the odds of winning in the two choice conditions. In addition, responses in the vmPFC and ventral striatum were modulated by the bet size. Convergent with electrophysiological research in macaques, our results further implicate the inferior parietal cortex (IPC in the processing of the likelihood of potential outcomes: Neural responses in the IPC bilaterally reflected the probability of winning during bet selection. Moreover, the IPC was particularly sensitive to the odds of winning in the active choice condition, where this information was used to guide bet selection. Our results indicate a neglected role of the IPC in human decision-making under risk and help to integrate neuropsychological data of risk-taking following vmPFC and insula damage with models of choice derived from human neuroimaging and monkey electrophysiology.

  11. Implications of the Dependence of Neuronal Activity on Neural Network States for the Design of Brain-Machine Interfaces.

    Science.gov (United States)

    Panzeri, Stefano; Safaai, Houman; De Feo, Vito; Vato, Alessandro

    2016-01-01

    Brain-machine interfaces (BMIs) can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stimulating electrodes, because trial-to-trial variability of neural activity partly arises from intrinsic factors (collectively known as the network state) that include ongoing spontaneous activity and neuromodulation, and so is shared among neurons. Here we review recent progress in characterizing the state dependence of neural responses, and in particular of how neural responses depend on endogenous slow fluctuations of network excitability. We then elaborate on how this knowledge may be used to increase the amount of information that BMIs exchange with brain. Knowledge of network state can be used to fine-tune the stimulation pattern that should reliably elicit a target neural response used to encode information in the brain, and to discount part of the trial-by-trial variability of neural responses, so that they can be decoded more accurately. PMID:27147955

  12. Implications of the dependence of neuronal activity on neural network states for the design of brain-machine interfaces

    Directory of Open Access Journals (Sweden)

    Stefano ePanzeri

    2016-04-01

    Full Text Available Brain-machine interfaces (BMIs can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stimulating electrodes, because trial-to-trial variability of neural activity partly arises from intrinsic factors (collectively known as the network state that include ongoing spontaneous activity and neuromodulation, and so is shared among neurons. Here we review recent progress in characterizing the state dependence of neural responses, and in particular of how neural responses depend on endogenous slow fluctuations of network excitability. We then elaborate on how this knowledge may be used to increase the amount of information that BMIs exchange with brains. Knowledge of network state can be used to fine-tune the stimulation pattern that should reliably elicit a target neural response used to encode information in the brain, and to discount part of the trial-by-trial variability of neural responses, so that they can be decoded more accurately.

  13. Implications of the Dependence of Neuronal Activity on Neural Network States for the Design of Brain-Machine Interfaces.

    Science.gov (United States)

    Panzeri, Stefano; Safaai, Houman; De Feo, Vito; Vato, Alessandro

    2016-01-01

    Brain-machine interfaces (BMIs) can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stimulating electrodes, because trial-to-trial variability of neural activity partly arises from intrinsic factors (collectively known as the network state) that include ongoing spontaneous activity and neuromodulation, and so is shared among neurons. Here we review recent progress in characterizing the state dependence of neural responses, and in particular of how neural responses depend on endogenous slow fluctuations of network excitability. We then elaborate on how this knowledge may be used to increase the amount of information that BMIs exchange with brain. Knowledge of network state can be used to fine-tune the stimulation pattern that should reliably elicit a target neural response used to encode information in the brain, and to discount part of the trial-by-trial variability of neural responses, so that they can be decoded more accurately.

  14. Alternative Sensor System and MLP Neural Network for Vehicle Pedal Activity Estimation

    Directory of Open Access Journals (Sweden)

    Ahmed M. Wefky

    2010-04-01

    Full Text Available It is accepted that the activity of the vehicle pedals (i.e., throttle, brake, clutch reflects the driver’s behavior, which is at least partially related to the fuel consumption and vehicle pollutant emissions. This paper presents a solution to estimate the driver activity regardless of the type, model, and year of fabrication of the vehicle. The solution is based on an alternative sensor system (regime engine, vehicle speed, frontal inclination and linear acceleration that reflects the activity of the pedals in an indirect way, to estimate that activity by means of a multilayer perceptron neural network with a single hidden layer.

  15. Optimal Recognition Method of Human Activities Using Artificial Neural Networks

    Science.gov (United States)

    Oniga, Stefan; József, Sütő

    2015-12-01

    The aim of this research is an exhaustive analysis of the various factors that may influence the recognition rate of the human activity using wearable sensors data. We made a total of 1674 simulations on a publically released human activity database by a group of researcher from the University of California at Berkeley. In a previous research, we analyzed the influence of the number of sensors and their placement. In the present research we have examined the influence of the number of sensor nodes, the type of sensor node, preprocessing algorithms, type of classifier and its parameters. The final purpose is to find the optimal setup for best recognition rates with lowest hardware and software costs.

  16. Average activity of excitatory and inhibitory neural populations

    Science.gov (United States)

    Roulet, Javier; Mindlin, Gabriel B.

    2016-09-01

    We develop an extension of the Ott-Antonsen method [E. Ott and T. M. Antonsen, Chaos 18(3), 037113 (2008)] that allows obtaining the mean activity (spiking rate) of a population of excitable units. By means of the Ott-Antonsen method, equations for the dynamics of the order parameters of coupled excitatory and inhibitory populations of excitable units are obtained, and their mean activities are computed. Two different excitable systems are studied: Adler units and theta neurons. The resulting bifurcation diagrams are compared with those obtained from studying the phenomenological Wilson-Cowan model in some regions of the parameter space. Compatible behaviors, as well as higher dimensional chaotic solutions, are observed. We study numerical simulations to further validate the equations.

  17. Optimal Recognition Method of Human Activities Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Oniga Stefan

    2015-12-01

    Full Text Available The aim of this research is an exhaustive analysis of the various factors that may influence the recognition rate of the human activity using wearable sensors data. We made a total of 1674 simulations on a publically released human activity database by a group of researcher from the University of California at Berkeley. In a previous research, we analyzed the influence of the number of sensors and their placement. In the present research we have examined the influence of the number of sensor nodes, the type of sensor node, preprocessing algorithms, type of classifier and its parameters. The final purpose is to find the optimal setup for best recognition rates with lowest hardware and software costs.

  18. Environmental layout complexity affects neural activity during navigation in humans.

    Science.gov (United States)

    Slone, Edward; Burles, Ford; Iaria, Giuseppe

    2016-05-01

    Navigating large-scale surroundings is a fundamental ability. In humans, it is commonly assumed that navigational performance is affected by individual differences, such as age, sex, and cognitive strategies adopted for orientation. We recently showed that the layout of the environment itself also influences how well people are able to find their way within it, yet it remains unclear whether differences in environmental complexity are associated with changes in brain activity during navigation. We used functional magnetic resonance imaging to investigate how the brain responds to a change in environmental complexity by asking participants to perform a navigation task in two large-scale virtual environments that differed solely in interconnection density, a measure of complexity defined as the average number of directional choices at decision points. The results showed that navigation in the simpler, less interconnected environment was faster and more accurate relative to the complex environment, and such performance was associated with increased activity in a number of brain areas (i.e. precuneus, retrosplenial cortex, and hippocampus) known to be involved in mental imagery, navigation, and memory. These findings provide novel evidence that environmental complexity not only affects navigational behaviour, but also modulates activity in brain regions that are important for successful orientation and navigation.

  19. Stress and CRF gate neural activation of BDNF in the mesolimbic reward pathway.

    Science.gov (United States)

    Walsh, Jessica J; Friedman, Allyson K; Sun, Haosheng; Heller, Elizabeth A; Ku, Stacy M; Juarez, Barbara; Burnham, Veronica L; Mazei-Robison, Michelle S; Ferguson, Deveroux; Golden, Sam A; Koo, Ja Wook; Chaudhury, Dipesh; Christoffel, Daniel J; Pomeranz, Lisa; Friedman, Jeffrey M; Russo, Scott J; Nestler, Eric J; Han, Ming-Hu

    2014-01-01

    Mechanisms controlling release of brain-derived neurotrophic factor (BDNF) in the mesolimbic dopamine reward pathway remain unknown. We report that phasic optogenetic activation of this pathway increases BDNF amounts in the nucleus accumbens (NAc) of socially stressed mice but not of stress-naive mice. This stress gating of BDNF signaling is mediated by corticotrophin-releasing factor (CRF) acting in the NAc. These results unravel a stress context-detecting function of the brain's mesolimbic circuit.

  20. A Granger causality measure for point process models of ensemble neural spiking activity.

    Directory of Open Access Journals (Sweden)

    Sanggyun Kim

    2011-03-01

    Full Text Available The ability to identify directional interactions that occur among multiple neurons in the brain is crucial to an understanding of how groups of neurons cooperate in order to generate specific brain functions. However, an optimal method of assessing these interactions has not been established. Granger causality has proven to be an effective method for the analysis of the directional interactions between multiple sets of continuous-valued data, but cannot be applied to neural spike train recordings due to their discrete nature. This paper proposes a point process framework that enables Granger causality to be applied to point process data such as neural spike trains. The proposed framework uses the point process likelihood function to relate a neuron's spiking probability to possible covariates, such as its own spiking history and the concurrent activity of simultaneously recorded neurons. Granger causality is assessed based on the relative reduction of the point process likelihood of one neuron obtained excluding one of its covariates compared to the likelihood obtained using all of its covariates. The method was tested on simulated data, and then applied to neural activity recorded from the primary motor cortex (MI of a Felis catus subject. The interactions present in the simulated data were predicted with a high degree of accuracy, and when applied to the real neural data, the proposed method identified causal relationships between many of the recorded neurons. This paper proposes a novel method that successfully applies Granger causality to point process data, and has the potential to provide unique physiological insights when applied to neural spike trains.

  1. Primary and secondary rewards differentially modulate neural activity dynamics during working memory.

    Directory of Open Access Journals (Sweden)

    Stefanie M Beck

    Full Text Available BACKGROUND: Cognitive control and working memory processes have been found to be influenced by changes in motivational state. Nevertheless, the impact of different motivational variables on behavior and brain activity remains unclear. METHODOLOGY/PRINCIPAL FINDINGS: The current study examined the impact of incentive category by varying on a within-subjects basis whether performance during a working memory task was reinforced with either secondary (monetary or primary (liquid rewards. The temporal dynamics of motivation-cognition interactions were investigated by employing an experimental design that enabled isolation of sustained and transient effects. Performance was dramatically and equivalently enhanced in each incentive condition, whereas neural activity dynamics differed between incentive categories. The monetary reward condition was associated with a tonic activation increase in primarily right-lateralized cognitive control regions including anterior prefrontal cortex (PFC, dorsolateral PFC, and parietal cortex. In the liquid condition, the identical regions instead showed a shift in transient activation from a reactive control pattern (primary probe-based activation during no-incentive trials to proactive control (primary cue-based activation during rewarded trials. Additionally, liquid-specific tonic activation increases were found in subcortical regions (amygdala, dorsal striatum, nucleus accumbens, indicating an anatomical double dissociation in the locus of sustained activation. CONCLUSIONS/SIGNIFICANCE: These different activation patterns suggest that primary and secondary rewards may produce similar behavioral changes through distinct neural mechanisms of reinforcement. Further, our results provide new evidence for the flexibility of cognitive control, in terms of the temporal dynamics of activation.

  2. Differences in neural activity when processing emotional arousal and valence in autism spectrum disorders.

    Science.gov (United States)

    Tseng, Angela; Wang, Zhishun; Huo, Yuankai; Goh, Suzanne; Russell, James A; Peterson, Bradley S

    2016-02-01

    Individuals with autism spectrum disorders (ASD) often have difficulty recognizing and interpreting facial expressions of emotion, which may impair their ability to navigate and communicate successfully in their social, interpersonal environments. Characterizing specific differences between individuals with ASD and their typically developing (TD) counterparts in the neural activity subserving their experience of emotional faces may provide distinct targets for ASD interventions. Thus we used functional magnetic resonance imaging (fMRI) and a parametric experimental design to identify brain regions in which neural activity correlated with ratings of arousal and valence for a broad range of emotional faces. Participants (51 ASD, 84 TD) were group-matched by age, sex, IQ, race, and socioeconomic status. Using task-related change in blood-oxygen-level-dependent (BOLD) fMRI signal as a measure, and covarying for age, sex, FSIQ, and ADOS scores, we detected significant differences across diagnostic groups in the neural activity subserving the dimension of arousal but not valence. BOLD-signal in TD participants correlated inversely with ratings of arousal in regions associated primarily with attentional functions, whereas BOLD-signal in ASD participants correlated positively with arousal ratings in regions commonly associated with impulse control and default-mode activity. Only minor differences were detected between groups in the BOLD signal correlates of valence ratings. Our findings provide unique insight into the emotional experiences of individuals with ASD. Although behavioral responses to face-stimuli were comparable across diagnostic groups, the corresponding neural activity for our ASD and TD groups differed dramatically. The near absence of group differences for valence correlates and the presence of strong group differences for arousal correlates suggest that individuals with ASD are not atypical in all aspects of emotion-processing. Studying these similarities

  3. Relation of obesity to neural activation in response to food commercials.

    Science.gov (United States)

    Gearhardt, Ashley N; Yokum, Sonja; Stice, Eric; Harris, Jennifer L; Brownell, Kelly D

    2014-07-01

    Adolescents view thousands of food commercials annually, but the neural response to food advertising and its association with obesity is largely unknown. This study is the first to examine how neural response to food commercials differs from other stimuli (e.g. non-food commercials and television show) and to explore how this response may differ by weight status. The blood oxygen level-dependent functional magnetic resonance imaging activation was measured in 30 adolescents ranging from lean to obese in response to food and non-food commercials imbedded in a television show. Adolescents exhibited greater activation in regions implicated in visual processing (e.g. occipital gyrus), attention (e.g. parietal lobes), cognition (e.g. temporal gyrus and posterior cerebellar lobe), movement (e.g. anterior cerebellar cortex), somatosensory response (e.g. postcentral gyrus) and reward [e.g. orbitofrontal cortex and anterior cingulate cortex (ACC)] during food commercials. Obese participants exhibited less activation during food relative to non-food commercials in neural regions implicated in visual processing (e.g. cuneus), attention (e.g. posterior cerebellar lobe), reward (e.g. ventromedial prefrontal cortex and ACC) and salience detection (e.g. precuneus). Obese participants did exhibit greater activation in a region implicated in semantic control (e.g. medial temporal gyrus). These findings may inform current policy debates regarding the impact of food advertising to minors. PMID:23576811

  4. Model for a flexible motor memory based on a self-active recurrent neural network.

    Science.gov (United States)

    Boström, Kim Joris; Wagner, Heiko; Prieske, Markus; de Lussanet, Marc

    2013-10-01

    Using recent recurrent network architecture based on the reservoir computing approach, we propose and numerically simulate a model that is focused on the aspects of a flexible motor memory for the storage of elementary movement patterns into the synaptic weights of a neural network, so that the patterns can be retrieved at any time by simple static commands. The resulting motor memory is flexible in that it is capable to continuously modulate the stored patterns. The modulation consists in an approximately linear inter- and extrapolation, generating a large space of possible movements that have not been learned before. A recurrent network of thousand neurons is trained in a manner that corresponds to a realistic exercising scenario, with experimentally measured muscular activations and with kinetic data representing proprioceptive feedback. The network is "self-active" in that it maintains recurrent flow of activation even in the absence of input, a feature that resembles the "resting-state activity" found in the human and animal brain. The model involves the concept of "neural outsourcing" which amounts to the permanent shifting of computational load from higher to lower-level neural structures, which might help to explain why humans are able to execute learned skills in a fluent and flexible manner without the need for attention to the details of the movement.

  5. A nonlinear neural fir filter with an adaptive activation function

    Directory of Open Access Journals (Sweden)

    Lee Su Goh

    2003-01-01

    Full Text Available An adaptive amplitude normalized nonlinear gradient descent (AANNGD algorithm for the class of nonlinear finite impulse response (FIR adaptive filters (dynamical perception is introduced. This is achieved by making the amplitude of the nonlinear activation function gradient adaptive. The proposed learning algorithm is suitable for processing of nonlinear and nonstationary signals with a large dynamical range, and removes the unwanted effect of saturation nonlinearities. For rigor, sensitivity analysis is performed and the improved performance of the AANNGD algorithm over the standard LMS, NGD, NNGD, the fully adaptive NNGD (FANNGD and the sign algorithm is verified by simulations on nonlinear and nonstationary inputs with large dynamics.

  6. Spontaneous neural activity during human slow wave sleep

    OpenAIRE

    Dang-Vu, Thien Thanh; Schabus, Manuel; Desseilles, Martin; Albouy, Geneviève; Boly, Mélanie; Darsaud, Annabelle; Gais, Steffen; Rauchs, Géraldine; Sterpenich, Virginie; Vandewalle, Gilles; Carrier, Julie; Moonen, Gustave; Balteau, Evelyne; Degueldre, Christian; Luxen, André

    2008-01-01

    Slow wave sleep (SWS) is associated with spontaneous brain oscillations that are thought to participate in sleep homeostasis and to support the processing of information related to the experiences of the previous awake period. At the cellular level, during SWS, a slow oscillation (140 μV) and delta waves (75–140 μV) during SWS in 14 non-sleep-deprived normal human volunteers. Significant increases in activity were associated with these waves in several cortical areas, including the inferior f...

  7. Category-based induction from similarity of neural activation.

    Science.gov (United States)

    Weber, Matthew J; Osherson, Daniel

    2014-03-01

    The idea that similarity might be an engine of inductive inference dates back at least as far as David Hume. However, Hume's thesis is difficult to test without begging the question, since judgments of similarity may be infected by inferential processes. We present a one-parameter model of category-based induction that generates predictions about arbitrary statements of conditional probability over a predicate and a set of items. The prediction is based on the unconditional probabilities and similarities that characterize that predicate and those items. To test Hume's thesis, we collected brain activation from various regions of the ventral visual stream during a categorization task that did not invite comparison of categories. We then calculated the similarity of those activation patterns using a simple measure of vectorwise similarity and supplied those similarities to the model. The model's outputs correlated well with subjects' judgments of conditional probability. Our results represent a promising first step toward confirming Hume's thesis; similarity, assessed without reference to induction, may well drive inductive inference.

  8. Category-based induction from similarity of neural activation.

    Science.gov (United States)

    Weber, Matthew J; Osherson, Daniel

    2014-03-01

    The idea that similarity might be an engine of inductive inference dates back at least as far as David Hume. However, Hume's thesis is difficult to test without begging the question, since judgments of similarity may be infected by inferential processes. We present a one-parameter model of category-based induction that generates predictions about arbitrary statements of conditional probability over a predicate and a set of items. The prediction is based on the unconditional probabilities and similarities that characterize that predicate and those items. To test Hume's thesis, we collected brain activation from various regions of the ventral visual stream during a categorization task that did not invite comparison of categories. We then calculated the similarity of those activation patterns using a simple measure of vectorwise similarity and supplied those similarities to the model. The model's outputs correlated well with subjects' judgments of conditional probability. Our results represent a promising first step toward confirming Hume's thesis; similarity, assessed without reference to induction, may well drive inductive inference. PMID:24254747

  9. Deep neural nets as a method for quantitative structure-activity relationships.

    Science.gov (United States)

    Ma, Junshui; Sheridan, Robert P; Liaw, Andy; Dahl, George E; Svetnik, Vladimir

    2015-02-23

    Neural networks were widely used for quantitative structure-activity relationships (QSAR) in the 1990s. Because of various practical issues (e.g., slow on large problems, difficult to train, prone to overfitting, etc.), they were superseded by more robust methods like support vector machine (SVM) and random forest (RF), which arose in the early 2000s. The last 10 years has witnessed a revival of neural networks in the machine learning community thanks to new methods for preventing overfitting, more efficient training algorithms, and advancements in computer hardware. In particular, deep neural nets (DNNs), i.e. neural nets with more than one hidden layer, have found great successes in many applications, such as computer vision and natural language processing. Here we show that DNNs can routinely make better prospective predictions than RF on a set of large diverse QSAR data sets that are taken from Merck's drug discovery effort. The number of adjustable parameters needed for DNNs is fairly large, but our results show that it is not necessary to optimize them for individual data sets, and a single set of recommended parameters can achieve better performance than RF for most of the data sets we studied. The usefulness of the parameters is demonstrated on additional data sets not used in the calibration. Although training DNNs is still computationally intensive, using graphical processing units (GPUs) can make this issue manageable. PMID:25635324

  10. Data Fusion Using Different Activation Functions in Artificial Neural Networks for Vehicular Navigation

    Directory of Open Access Journals (Sweden)

    MALLESWARAN M,

    2010-12-01

    Full Text Available Global positioning System (GPS and Inertial Navigation System (INS data can be integrated together to provide a reliable navigation. GPS/INS data integration provides reliable navigation solutions by overcoming each of their shortcomings, including signal blockage for GPS and increase in position errors with time for INS. This paper aims to provide GPS/INS data integration utilizing Artificial Neural Network (ANN architecture. This architecture is based on Feed Forward Neural Networks, which generally includes Radial Basis Function (RBF neural network and Back Propagation neural network (BPN. These are systematic methods for training multi-layer artificial networks. The BPN-ANN and RBF-ANN modules are trained to predict the INS position error and provide accurate positioning of the moving vehicle. This paper also compares performance of theGPS/INS data integration system by using different activation function like Bipolar Sigmoidal Function (BPSF, Binary Sigmoidal Function (BISF, Hyperbolic Tangential Function (HTF and Gaussian Function (GF in BPN-ANN and using Gaussian function in RBF-ANN.

  11. Mutual information and self-control of a fully-connected low-activity neural network

    Science.gov (United States)

    Bollé, D.; Carreta, D. Dominguez

    2000-11-01

    A self-control mechanism for the dynamics of a three-state fully connected neural network is studied through the introduction of a time-dependent threshold. The self-adapting threshold is a function of both the neural and the pattern activity in the network. The time evolution of the order parameters is obtained on the basis of a recently developed dynamical recursive scheme. In the limit of low activity the mutual information is shown to be the relevant parameter in order to determine the retrieval quality. Due to self-control an improvement of this mutual information content as well as an increase of the storage capacity and an enlargement of the basins of attraction are found. These results are compared with numerical simulations.

  12. Cognitive-affective neural plasticity following active-controlled mindfulness intervention

    DEFF Research Database (Denmark)

    Allen, Micah Galen

    Mindfulness meditation is a set of attention-based, regulatory and self-inquiry training regimes. Although the impact of mindfulness meditation training (MT) on self-regulation is well established, the neural mechanisms supporting such plasticity are poorly understood. MT is thought to act through...... for cognitive and treatment effects with an active control group. We measured behavioral metacognition and whole-brain Blood Oxygenation Level Dependent (BOLD) signals using functional MRI during an affective Stroop task before and after intervention in healthy human subjects. Although both groups improved...... prefrontal cortex (mPFC), and right anterior insula during negative valence processing. Our findings highlight the importance of active control in MT research, indicate unique neural mechanisms for progressive stages of mindfulness training, and suggest that optimal application of MT may differ depending...

  13. QSAR study on estrogenic activity of structurally diverse compounds using generalized regression neural network

    Institute of Scientific and Technical Information of China (English)

    JI Li; WANG XiaoDong; LUO Si; QIN Liang; YANG XvShu; LIU ShuShen; WANG LianSheng

    2008-01-01

    Computer-based quantitative structure-activity relationship (QSAR) model has been becoming a powerful tool in understanding the structural requirements for chemicals to bind the estrogen receptor (ER), designing drugs for human estrogen replacement therapy, and identifying potential estrogenic endocrine disruptors, in this study, a simple yet powerful neural network technique, generalized regression neural network (GRNN) was used to develop a QSAR model based on 131 structurally diverse estrogens (training set). Only nine descriptors calculated solely from the molecular structures of compounds selected by objective and subjective feature selections were used as inputs of the GRNN model. The predictive power of the built model was found to be comparable to that of the more traditional techniques but requiring significantly easy implementation and a shorter computation-time. The obtained result indicates that the proposed GRNN model is robust and satisfactory, and can provide a feasible and practical tool for the rapid screening of the estrogenic activity of organic compounds.

  14. Single-cell transcriptome analyses reveal signals to activate dormant neural stem cells.

    Science.gov (United States)

    Luo, Yuping; Coskun, Volkan; Liang, Aibing; Yu, Juehua; Cheng, Liming; Ge, Weihong; Shi, Zhanping; Zhang, Kunshan; Li, Chun; Cui, Yaru; Lin, Haijun; Luo, Dandan; Wang, Junbang; Lin, Connie; Dai, Zachary; Zhu, Hongwen; Zhang, Jun; Liu, Jie; Liu, Hailiang; deVellis, Jean; Horvath, Steve; Sun, Yi Eve; Li, Siguang

    2015-05-21

    The scarcity of tissue-specific stem cells and the complexity of their surrounding environment have made molecular characterization of these cells particularly challenging. Through single-cell transcriptome and weighted gene co-expression network analysis (WGCNA), we uncovered molecular properties of CD133(+)/GFAP(-) ependymal (E) cells in the adult mouse forebrain neurogenic zone. Surprisingly, prominent hub genes of the gene network unique to ependymal CD133(+)/GFAP(-) quiescent cells were enriched for immune-responsive genes, as well as genes encoding receptors for angiogenic factors. Administration of vascular endothelial growth factor (VEGF) activated CD133(+) ependymal neural stem cells (NSCs), lining not only the lateral but also the fourth ventricles and, together with basic fibroblast growth factor (bFGF), elicited subsequent neural lineage differentiation and migration. This study revealed the existence of dormant ependymal NSCs throughout the ventricular surface of the CNS, as well as signals abundant after injury for their activation. PMID:26000486

  15. Individual differences in sensitivity to reward and punishment and neural activity during reward and avoidance learning.

    Science.gov (United States)

    Kim, Sang Hee; Yoon, HeungSik; Kim, Hackjin; Hamann, Stephan

    2015-09-01

    In this functional neuroimaging study, we investigated neural activations during the process of learning to gain monetary rewards and to avoid monetary loss, and how these activations are modulated by individual differences in reward and punishment sensitivity. Healthy young volunteers performed a reinforcement learning task where they chose one of two fractal stimuli associated with monetary gain (reward trials) or avoidance of monetary loss (avoidance trials). Trait sensitivity to reward and punishment was assessed using the behavioral inhibition/activation scales (BIS/BAS). Functional neuroimaging results showed activation of the striatum during the anticipation and reception periods of reward trials. During avoidance trials, activation of the dorsal striatum and prefrontal regions was found. As expected, individual differences in reward sensitivity were positively associated with activation in the left and right ventral striatum during reward reception. Individual differences in sensitivity to punishment were negatively associated with activation in the left dorsal striatum during avoidance anticipation and also with activation in the right lateral orbitofrontal cortex during receiving monetary loss. These results suggest that learning to attain reward and learning to avoid loss are dependent on separable sets of neural regions whose activity is modulated by trait sensitivity to reward or punishment.

  16. Abnormal Task Modulation of Oscillatory Neural Activity in Schizophrenia

    Directory of Open Access Journals (Sweden)

    Elisa C Dias

    2013-08-01

    Full Text Available Schizophrenia patients have deficits in cognitive function that are a core feature of the disorder. AX-CPT is commonly used to study cognition in schizophrenia, and patients have characteristic pattern of behavioral and ERP response. In AX-CPT subjects respond when a flashed cue A is followed by a target X, ignoring other letter combinations. Patients show reduced hit rate to go trials, and increased false alarms to sequences that require inhibition of a prepotent response. EEG recordings show reduced sensory (P1/N1, as well as later cognitive components (N2, P3, CNV. Behavioral deficits correlate most strongly with sensory dysfunction. Oscillatory analyses provide critical information regarding sensory/cognitive processing over and above standard ERP analyses. Recent analyses of induced oscillatory activity in single trials during AX-CPT in healthy volunteers showed characteristic response patterns in theta, alpha and beta frequencies tied to specific sensory and cognitive processes. Alpha and beta modulated during the trials and beta modulation over the frontal cortex correlated with reaction time. In this study, EEG data was obtained from 18 schizophrenia patients and 13 controls during AX-CPT performance, and single trial decomposition of the signal yielded power in the target wavelengths.Significant task-related event-related desynchronization (ERD was observed in both alpha and beta frequency bands over parieto-occipital cortex related to sensory encoding of the cue. This modulation was reduced in patients for beta, but not for alpha. In addition, significant beta ERD was observed over motor cortex, related to motor preparation for the response, and was also reduced in patients. These findings demonstrate impaired dynamic modulation of beta frequency rhythms in schizophrenia, and suggest that failures of oscillatory activity may underlie impaired sensory information processing in schizophrenia that in turn contributes to cognitive deficits.

  17. Distinct neural activity associated with focused-attention meditation and loving-kindness meditation.

    Directory of Open Access Journals (Sweden)

    Tatia M C Lee

    Full Text Available This study examined the dissociable neural effects of ānāpānasati (focused-attention meditation, FAM and mettā (loving-kindness meditation, LKM on BOLD signals during cognitive (continuous performance test, CPT and affective (emotion-processing task, EPT, in which participants viewed affective pictures processing. Twenty-two male Chinese expert meditators (11 FAM experts, 11 LKM experts and 22 male Chinese novice meditators (11 FAM novices, 11 LKM novices had their brain activity monitored by a 3T MRI scanner while performing the cognitive and affective tasks in both meditation and baseline states. We examined the interaction between state (meditation vs. baseline and expertise (expert vs. novice separately during LKM and FAM, using a conjunction approach to reveal common regions sensitive to the expert meditative state. Additionally, exclusive masking techniques revealed distinct interactions between state and group during LKM and FAM. Specifically, we demonstrated that the practice of FAM was associated with expertise-related behavioral improvements and neural activation differences in attention task performance. However, the effect of state LKM meditation did not carry over to attention task performance. On the other hand, both FAM and LKM practice appeared to affect the neural responses to affective pictures. For viewing sad faces, the regions activated for FAM practitioners were consistent with attention-related processing; whereas responses of LKM experts to sad pictures were more in line with differentiating emotional contagion from compassion/emotional regulation processes. Our findings provide the first report of distinct neural activity associated with forms of meditation during sustained attention and emotion processing.

  18. Distinct neural activity associated with focused-attention meditation and loving-kindness meditation.

    Science.gov (United States)

    Lee, Tatia M C; Leung, Mei-Kei; Hou, Wai-Kai; Tang, Joey C Y; Yin, Jing; So, Kwok-Fai; Lee, Chack-Fan; Chan, Chetwyn C H

    2012-01-01

    This study examined the dissociable neural effects of ānāpānasati (focused-attention meditation, FAM) and mettā (loving-kindness meditation, LKM) on BOLD signals during cognitive (continuous performance test, CPT) and affective (emotion-processing task, EPT, in which participants viewed affective pictures) processing. Twenty-two male Chinese expert meditators (11 FAM experts, 11 LKM experts) and 22 male Chinese novice meditators (11 FAM novices, 11 LKM novices) had their brain activity monitored by a 3T MRI scanner while performing the cognitive and affective tasks in both meditation and baseline states. We examined the interaction between state (meditation vs. baseline) and expertise (expert vs. novice) separately during LKM and FAM, using a conjunction approach to reveal common regions sensitive to the expert meditative state. Additionally, exclusive masking techniques revealed distinct interactions between state and group during LKM and FAM. Specifically, we demonstrated that the practice of FAM was associated with expertise-related behavioral improvements and neural activation differences in attention task performance. However, the effect of state LKM meditation did not carry over to attention task performance. On the other hand, both FAM and LKM practice appeared to affect the neural responses to affective pictures. For viewing sad faces, the regions activated for FAM practitioners were consistent with attention-related processing; whereas responses of LKM experts to sad pictures were more in line with differentiating emotional contagion from compassion/emotional regulation processes. Our findings provide the first report of distinct neural activity associated with forms of meditation during sustained attention and emotion processing.

  19. Social Exclusion in Middle Childhood: Rejection Events, Slow-wave Neural Activity and Ostracism Distress

    OpenAIRE

    Crowley, Michael J.; Wu, Jia; MOLFESE, PETER J.; Mayes, Linda C.

    2010-01-01

    This study examined neural activity with event-related potentials (ERPs) in middle childhood during a computer-simulated ball-toss game, Cyberball. Experiencing fair play initially, children were ultimately excluded by the other players. We focused specifically on “not my turn” events within fair play and rejection events within social exclusion. Dense-array ERPs revealed that rejection events are perceived rapidly. Condition differences (“not my turn” vs. rejection) were evident in a posteri...

  20. Human facial neural activities and gesture recognition for machine-interfacing applications

    Directory of Open Access Journals (Sweden)

    Hamedi M

    2011-12-01

    Full Text Available M Hamedi1, Sh-Hussain Salleh2, TS Tan2, K Ismail2, J Ali3, C Dee-Uam4, C Pavaganun4, PP Yupapin51Faculty of Biomedical and Health Science Engineering, Department of Biomedical Instrumentation and Signal Processing, University of Technology Malaysia, Skudai, 2Centre for Biomedical Engineering Transportation Research Alliance, 3Institute of Advanced Photonics Science, Nanotechnology Research Alliance, University of Technology Malaysia (UTM, Johor Bahru, Malaysia; 4College of Innovative Management, Valaya Alongkorn Rajabhat University, Pathum Thani, 5Nanoscale Science and Engineering Research Alliance (N'SERA, Advanced Research Center for Photonics, Faculty of Science, King Mongkut's Institute of Technology Ladkrabang, Bangkok, ThailandAbstract: The authors present a new method of recognizing different human facial gestures through their neural activities and muscle movements, which can be used in machine-interfacing applications. Human–machine interface (HMI technology utilizes human neural activities as input controllers for the machine. Recently, much work has been done on the specific application of facial electromyography (EMG-based HMI, which have used limited and fixed numbers of facial gestures. In this work, a multipurpose interface is suggested that can support 2–11 control commands that can be applied to various HMI systems. The significance of this work is finding the most accurate facial gestures for any application with a maximum of eleven control commands. Eleven facial gesture EMGs are recorded from ten volunteers. Detected EMGs are passed through a band-pass filter and root mean square features are extracted. Various combinations of gestures with a different number of gestures in each group are made from the existing facial gestures. Finally, all combinations are trained and classified by a Fuzzy c-means classifier. In conclusion, combinations with the highest recognition accuracy in each group are chosen. An average accuracy

  1. Brain-machine interface circuits and systems

    CERN Document Server

    Zjajo, Amir

    2016-01-01

    This book provides a complete overview of significant design challenges in respect to circuit miniaturization and power reduction of the neural recording system, along with circuit topologies, architecture trends, and (post-silicon) circuit optimization algorithms. The introduced novel circuits for signal conditioning, quantization, and classification, as well as system configurations focus on optimized power-per-area performance, from the spatial resolution (i.e. number of channels), feasible wireless data bandwidth and information quality to the delivered power of implantable system.

  2. Global exponential stability of the periodic solution of a delayed neural network with discontinuous activations

    Energy Technology Data Exchange (ETDEWEB)

    Papini, Duccio [Dipartimento di Ingegneria dell' Informazione, Universita degli Studi di Siena, via Roma 56, 53100 Siena (Italy)]. E-mail: papini@dii.unisi.it; Taddei, Valentina [Dipartimento di Ingegneria dell' Informazione, Universita degli Studi di Siena, via Roma 56, 53100 Siena (Italy)]. E-mail: taddei@dii.unisi.it

    2005-08-01

    We study the stability of a delayed Hopfield neural network with periodic coefficients and inputs and an arbitrary and constant delay. We consider non-decreasing activation functions which may also have jump discontinuities in order to model the ideal situation where the gain of the neuron amplifiers is very high and tends to infinity. In particular, we drop the assumption of Lipschitz continuity on the activation functions, which is usually required in most of the papers. Under suitable assumptions on the interconnection matrices, we prove that the delayed neural network has a unique periodic solution which is globally exponentially stable independently of the size of the delay. The assumptions we exploit concern the theory of M-matrices and are easy to check. Due to the possible discontinuities of the activation functions, the convergence of the output of the neural network is also studied by a suitable notion of limit. The existence, uniqueness and continuability of the solution of suitable initial value problems are proved.

  3. Dynamics of modularity of neural activity in the brain during development

    Science.gov (United States)

    Deem, Michael; Chen, Man

    2014-03-01

    Theory suggests that more modular systems can have better response functions at short times. This theory suggests that greater cognitive performance may be achieved for more modular neural activity, and that modularity of neural activity may, therefore, likely increase with development in children. We study the relationship between age and modularity of brain neural activity in developing children. The value of modularity calculated from fMRI data is observed to increase during childhood development and peak in young adulthood. We interpret these results as evidence of selection for plasticity in the cognitive function of the human brain. We present a model to illustrate how modularity can provide greater cognitive performance at short times and enhance fast, low-level, automatic cognitive processes. Conversely, high-level, effortful, conscious cognitive processes may not benefit from modularity. We use quasispecies theory to predict how the average modularity evolves with age, given a fitness function extracted from the model. We suggest further experiments exploring the effect of modularity on cognitive performance and suggest that modularity may be a potential biomarker for injury, rehabilitation, or disease.

  4. Recent Advances in Neural Recording Microsystems

    Directory of Open Access Journals (Sweden)

    Benoit Gosselin

    2011-04-01

    Full Text Available The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field.

  5. ACUTE EFFECTS OF THREE DIFFERENT CIRCUIT WEIGHT TRAINING PROTOCOLS ON BLOOD LACTATE, HEART RATE, AND RATING OF PERCEIVED EXERTION IN RECREATIONALLY ACTIVE WOMEN

    Directory of Open Access Journals (Sweden)

    Brook L. Skidmore

    2012-12-01

    Full Text Available Interval and circuit weight training are popular training methods for maximizing time-efficiency, and are purported to deliver greater physiological benefits faster than traditional training methods. Adding interval training into a circuit weight-training workout may further enhance the benefits of circuit weight training by placing increased demands upon the cardiovascular system. Our purpose was to compare acute effects of three circuit weight training protocols 1 traditional circuit weight training, 2 aerobic circuit weight training, and 3 combined circuit weight-interval training on blood lactate (BLA, heart rate (HR, and ratings of perceived exertion (RPE. Eleven recreationally active women completed 7 exercise sessions. Session 1 included measurements of height, weight, estimated VO2max, and 13 repetition maximum (RM testing of the weight exercises. Sessions 2-4 were held on non-consecutive days for familiarization with traditional circuit weight training (TRAD, aerobic circuit weight training (ACWT, and combined circuit weight-interval training (CWIT protocols. In sessions 5-7, TRAD, ACWT, and CWIT were performed in a randomized order > 72 hr apart for measures of BLA, HR, and RPE at pre-exercise and following each of three mini-circuit weight training stations. Repeated-measures ANOVAs yielded significant interactions (p < 0.05 in BLA, HR, and RPE. Combined circuit weight- interval training (CWIT produced higher BLA (7.31 ± 0.37 vs. TRAD: 3.99 ± 0.26, ACWT: 4.54 ± 0.31 mmol.L-1, HR (83.51 ± 1.18 vs. TRAD: 70.42 ± 1.67, ACWT: 74.13 ± 1.43 beats.min-1 and RPE (8.14 ± 0.41 vs. TRAD: 5.06 ± 0.43, ACWT: 6.15 ± 0.42 at all measures. Aerobic circuit weight training (ACWT elicited greater RPE than traditional circuit weight training (TRAD at all measures. Including combined circuit weight-interval training (CWIT workouts into exercise programming may enhance fitness benefits and maximize time-efficiency more so than traditional circuit

  6. Chaotic memristive circuit: equivalent circuit realization and dynamical analysis

    Institute of Scientific and Technical Information of China (English)

    Bao Bo-Cheng; Xu Jian-Ping; Zhou Guo-Hua; Ma Zheng-Hua; Zou Ling

    2011-01-01

    In this paper,a practical equivalent circuit of an active flux-controlled memristor characterized by smooth piecewise-quadratic nonlinearity is designed and an experimental chaotic memristive circuit is implemented.The chaotic memristive circuit has an equilibrium set and its stability is dependent on the initial state of the memristor.The initial state-dependent and the circuit parameter-dependent dynamics of the chaotic memristive circuit are investigated via phase portraits,bifurcation diagrams and Lyapunov exponents.Both experimental and simulation results validate the proposed equivalent circuit realization of the active flux-controlled memristor.

  7. Noise influence on spike activation in a Hindmarsh–Rose small-world neural network

    Science.gov (United States)

    Zhe, Sun; Micheletto, Ruggero

    2016-07-01

    We studied the role of noise in neural networks, especially focusing on its relation to the propagation of spike activity in a small sized system. We set up a source of information using a single neuron that is constantly spiking. This element called initiator x o feeds spikes to the rest of the network that is initially quiescent and subsequently reacts with vigorous spiking after a transitional period of time. We found that noise quickly suppresses the initiator’s influence and favors spontaneous spike activity and, using a decibel representation of noise intensity, we established a linear relationship between noise amplitude and the interval from the initiator’s first spike and the rest of the network activation. We studied the same process with networks of different sizes (number of neurons) and found that the initiator x o has a measurable influence on small networks, but as the network grows in size, spontaneous spiking emerges disrupting its effects on networks of more than about N = 100 neurons. This suggests that the mechanism of internal noise generation allows information transmission within a small neural neighborhood, but decays for bigger network domains. We also analyzed the Fourier spectrum of the whole network membrane potential and verified that noise provokes the reduction of main θ and α peaks before transitioning into chaotic spiking. However, network size does not reproduce a similar phenomena; instead we recorded a reduction in peaks’ amplitude, a better sharpness and definition of Fourier peaks, but not the evident degeneration to chaos observed with increasing external noise. This work aims to contribute to the understanding of the fundamental mechanisms of propagation of spontaneous spiking in neural networks and gives a quantitative assessment of how noise can be used to control and modulate this phenomenon in Hindmarsh‑Rose (H‑R) neural networks.

  8. Rejuvenation of MPTP-induced human neural precursor cell senescence by activating autophagy

    Energy Technology Data Exchange (ETDEWEB)

    Zhu, Liang [East Hospital, Tongji University School of Medicine, Shanghai (China); Dong, Chuanming [East Hospital, Tongji University School of Medicine, Shanghai (China); Department of Anatomy and Neurobiology, The Jiangsu Key Laboratory of Neuroregeneration, Nantong University, Nantong (China); Sun, Chenxi; Ma, Rongjie; Yang, Danjing [East Hospital, Tongji University School of Medicine, Shanghai (China); Zhu, Hongwen, E-mail: hongwen_zhu@hotmail.com [Tianjin Hospital, Tianjin Academy of Integrative Medicine, Tianjin (China); Xu, Jun, E-mail: xunymc2000@yahoo.com [East Hospital, Tongji University School of Medicine, Shanghai (China)

    2015-08-21

    Aging of neural stem cell, which can affect brain homeostasis, may be caused by many cellular mechanisms. Autophagy dysfunction was found in aged and neurodegenerative brains. However, little is known about the relationship between autophagy and human neural stem cell (hNSC) aging. The present study used 1-methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine (MPTP) to treat neural precursor cells (NPCs) derived from human embryonic stem cell (hESC) line H9 and investigate related molecular mechanisms involved in this process. MPTP-treated NPCs were found to undergo premature senescence [determined by increased senescence-associated-β-galactosidase (SA-β-gal) activity, elevated intracellular reactive oxygen species level, and decreased proliferation] and were associated with impaired autophagy. Additionally, the cellular senescence phenotypes were manifested at the molecular level by a significant increase in p21 and p53 expression, a decrease in SOD2 expression, and a decrease in expression of some key autophagy-related genes such as Atg5, Atg7, Atg12, and Beclin 1. Furthermore, we found that the senescence-like phenotype of MPTP-treated hNPCs was rejuvenated through treatment with a well-known autophagy enhancer rapamycin, which was blocked by suppression of essential autophagy gene Beclin 1. Taken together, these findings reveal the critical role of autophagy in the process of hNSC aging, and this process can be reversed by activating autophagy. - Highlights: • We successfully establish hESC-derived neural precursor cells. • MPTP treatment induced senescence-like state in hESC-derived NPCs. • MPTP treatment induced impaired autophagy of hESC-derived NPCs. • MPTP-induced hESC-derived NPC senescence was rejuvenated by activating autophagy.

  9. Rejuvenation of MPTP-induced human neural precursor cell senescence by activating autophagy

    International Nuclear Information System (INIS)

    Aging of neural stem cell, which can affect brain homeostasis, may be caused by many cellular mechanisms. Autophagy dysfunction was found in aged and neurodegenerative brains. However, little is known about the relationship between autophagy and human neural stem cell (hNSC) aging. The present study used 1-methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine (MPTP) to treat neural precursor cells (NPCs) derived from human embryonic stem cell (hESC) line H9 and investigate related molecular mechanisms involved in this process. MPTP-treated NPCs were found to undergo premature senescence [determined by increased senescence-associated-β-galactosidase (SA-β-gal) activity, elevated intracellular reactive oxygen species level, and decreased proliferation] and were associated with impaired autophagy. Additionally, the cellular senescence phenotypes were manifested at the molecular level by a significant increase in p21 and p53 expression, a decrease in SOD2 expression, and a decrease in expression of some key autophagy-related genes such as Atg5, Atg7, Atg12, and Beclin 1. Furthermore, we found that the senescence-like phenotype of MPTP-treated hNPCs was rejuvenated through treatment with a well-known autophagy enhancer rapamycin, which was blocked by suppression of essential autophagy gene Beclin 1. Taken together, these findings reveal the critical role of autophagy in the process of hNSC aging, and this process can be reversed by activating autophagy. - Highlights: • We successfully establish hESC-derived neural precursor cells. • MPTP treatment induced senescence-like state in hESC-derived NPCs. • MPTP treatment induced impaired autophagy of hESC-derived NPCs. • MPTP-induced hESC-derived NPC senescence was rejuvenated by activating autophagy

  10. Activation of endogenous neural stem cells in experimental intracerebral hemorrhagic rat brains

    Institute of Scientific and Technical Information of China (English)

    唐涛; 黎杏群; 武衡; 罗杰坤; 张花先; 罗团连

    2004-01-01

    Background Many researchers suggest that adult mammalian central nervous system (CNS) is incapable of completing self-repair or regeneration. And there are accumulating lines of evidence which suggest that endogenous neural stem cells (NSCs) are activated in many pathological conditions, including stroke in the past decades, which might partly account for rehabilitation afterwards. In this study, we investigated whether there was endogenous neural stem cell activation in intracerebral hemorrhagic (ICH) rat brains.Methods After ICH induction by stereotactical injection of collagenase type Ⅶ into globus pallidus, 5-Bromo-2 Deoxyuridine (BrdU) was administered intraperitoneally to label newborn cells. Immunohistochemical method was used to detect Nestin, a marker for neural stem cells, and BrdU.Results Nestin-positive or BrdU-Labeled cells were predominantly located at 2 sites: basal ganglion around hemotoma, ependyma and nearby subventricular zone (SVZ). No positive cells for the 2 markers were found in the 2 sites of normal control group and sham group, as well as in non-leisoned parenchyma, both hippocampi and olfactory bulbs in the 4 groups. Nestin+ cells presented 4 types of morphology, and BrdU+ nucleus were polymorphologic. Postive cell counting around hemotoma showed that at day 2, Nestin+ cells were seen around hemotoma in model group , the number of which increased at day 4, day 7(P<0.01), peaked at day 14(P<0.05), and reduced significantly by day 28(P<0.01).Conclusion Endogenous neural stem cells were activated in experimental intracerebral hemorrhagic rat brains.

  11. GaAs Optoelectronic Integrated-Circuit Neurons

    Science.gov (United States)

    Lin, Steven H.; Kim, Jae H.; Psaltis, Demetri

    1992-01-01

    Monolithic GaAs optoelectronic integrated circuits developed for use as artificial neurons. Neural-network computer contains planar arrays of optoelectronic neurons, and variable synaptic connections between neurons effected by diffraction of light from volume hologram in photorefractive material. Basic principles of neural-network computers explained more fully in "Optoelectronic Integrated Circuits For Neural Networks" (NPO-17652). In present circuits, devices replaced by metal/semiconductor field effect transistors (MESFET's), which consume less power.

  12. Orphan nuclear receptor TLX activates Wnt/β-catenin signalling to stimulate neural stem cell proliferation and self-renewal

    OpenAIRE

    Qu, Qiuhao; Sun, Guoqiang; Li, Wenwu; Yang, Su; Ye, Peng; Zhao, Chunnian; Yu, Ruth T.; Gage, Fred H; Evans, Ronald M; Shi, Yanhong

    2009-01-01

    The nuclear receptor TLX (also known as NR2E1) is essential for adult neural stem cell self-renewal; however, the molecular mechanisms involved remain elusive. Here we show that TLX activates the canonical Wnt/β-catenin pathway in adult mouse neural stem cells. Furthermore, we demonstrate that Wnt/β-catenin signalling is important in the proliferation and self-renewal of adult neural stem cells in the presence of epidermal growth factor and fibroblast growth factor. Wnt7a and active β-catenin...

  13. Refractory Neuron Circuits

    OpenAIRE

    Sarpeshkar, Rahul; Watts, Lloyd; Mead, Carver

    1992-01-01

    Neural networks typically use an abstraction of the behaviour of a biological neuron, in which the continuously varying mean firing rate of the neuron is presumed to carry information about the neuron's time-varying state of excitation. However, the detailed timing of action potentials is known to be important in many biological systems. To build electronic models of such systems, one must have well-characterized neuron circuits that capture the essential behaviour of real neur...

  14. A flexible organic active matrix circuit fabricated using novel organic thin film transistors and organic light-emitting diodes

    KAUST Repository

    Gutiérrez-Heredia, Gerardo

    2010-10-04

    We present an active matrix circuit fabricated on plastic (polyethylene naphthalene, PEN) and glass substrates using organic thin film transistors and organic capacitors to control organic light-emitting diodes (OLEDs). The basic circuit is fabricated using two pentacene-based transistors and a capacitor using a novel aluminum oxide/parylene stack (Al2O3/ parylene) as the dielectric for both the transistor and the capacitor. We report that our circuit can deliver up to 15 μA to each OLED pixel. To achieve 200 cd m-2 of brightness a 10 μA current is needed; therefore, our approach can initially deliver 1.5× the required current to drive a single pixel. In contrast to parylene-only devices, the Al2O 3/parylene stack does not fail after stressing at a field of 1.7 MV cm-1 for >10 000 s, whereas \\'parylene only\\' devices show breakdown at approximately 1000 s. Details of the integration scheme are presented. © 2010 IOP Publishing Ltd.

  15. A flexible organic active matrix circuit fabricated using novel organic thin film transistors and organic light-emitting diodes

    Science.gov (United States)

    Gutiérrez-Heredia, G.; González, L. A.; Alshareef, H. N.; Gnade, B. E.; Quevedo-López, M.

    2010-11-01

    We present an active matrix circuit fabricated on plastic (polyethylene naphthalene, PEN) and glass substrates using organic thin film transistors and organic capacitors to control organic light-emitting diodes (OLEDs). The basic circuit is fabricated using two pentacene-based transistors and a capacitor using a novel aluminum oxide/parylene stack (Al2O3/parylene) as the dielectric for both the transistor and the capacitor. We report that our circuit can deliver up to 15 µA to each OLED pixel. To achieve 200 cd m-2 of brightness a 10 µA current is needed; therefore, our approach can initially deliver 1.5× the required current to drive a single pixel. In contrast to parylene-only devices, the Al2O3/parylene stack does not fail after stressing at a field of 1.7 MV cm-1 for >10 000 s, whereas 'parylene only' devices show breakdown at approximately 1000 s. Details of the integration scheme are presented.

  16. Perceptual Salience and Reward Both Influence Feedback-Related Neural Activity Arising from Choice.

    Science.gov (United States)

    Lou, Bin; Hsu, Wha-Yin; Sajda, Paul

    2015-09-23

    expected reward. Here, we use electroencephelography to identify trial-by-trial neural activity of perceived stimulus salience, showing that this activity can be combined with the value of choice options to form a representation of expected reward. Our results provide insight into the neural processing governing the interaction between salience and value and the formation of subjective expected reward and prediction error. This work is potentially important for identifying neural markers of abnormal sensory/value processing, as is seen in some cases of psychiatric illnesses. PMID:26400937

  17. Adaptive neural networks control for camera stabilization with active suspension system

    Directory of Open Access Journals (Sweden)

    Feng Zhao

    2015-08-01

    Full Text Available The camera always suffers from image instability on the moving vehicle due to unintentional vibrations caused by road roughness. This article presents an adaptive neural network approach mixed with linear quadratic regulator control for a quarter-car active suspension system to stabilize the image captured area of the camera. An active suspension system provides extra force through the actuator which allows it to suppress vertical vibration of sprung mass. First, to deal with the road disturbance and the system uncertainties, radial basis function neural network is proposed to construct the map between the state error and the compensation component, which can correct the optimal state-feedback control law. The weights matrix of radial basis function neural network is adaptively tuned online. Then, the closed-loop stability and asymptotic convergence performance is guaranteed by Lyapunov analysis. Finally, the simulation results demonstrate that the proposed controller effectively suppresses the vibration of the camera and enhances the stabilization of the entire camera, where different excitations are considered to validate the system performance.

  18. Neural regions that underlie reinforcement learning are also active for social expectancy violations.

    Science.gov (United States)

    Harris, Lasana T; Fiske, Susan T

    2010-01-01

    Prediction error, the difference between an expected and an actual outcome, serves as a learning signal that interacts with reward and punishment value to direct future behavior during reinforcement learning. We hypothesized that similar learning and valuation signals may underlie social expectancy violations. Here, we explore the neural correlates of social expectancy violation signals along the universal person-perception dimensions trait warmth and competence. In this context, social learning may result from expectancy violations that occur when a target is inconsistent with an a priori schema. Expectancy violation may activate neural regions normally implicated in prediction error and valuation during appetitive and aversive conditioning. Using fMRI, we first gave perceivers high warmth or competence behavioral information that led to dispositional or situational attributions for the behavior. Participants then saw pictures of people responsible for the behavior; they represented social groups either inconsistent (rated low on either warmth or competence) or consistent (rated high on either warmth or competence) with the behavior information. Warmth and competence expectancy violations activate striatal regions that represent evaluative and prediction error signals. Social cognition regions underlie consistent expectations. These findings suggest that regions underlying reinforcement learning may work in concert with social cognition regions in warmth and competence social expectancy. This study illustrates the neural overlap between neuroeconomics and social neuroscience.

  19. Active vibration control of flexible cantilever plates using piezoelectric materials and artificial neural networks

    Science.gov (United States)

    Abdeljaber, Osama; Avci, Onur; Inman, Daniel J.

    2016-02-01

    The study presented in this paper introduces a new intelligent methodology to mitigate the vibration response of flexible cantilever plates. The use of the piezoelectric sensor/actuator pairs for active control of plates is discussed. An intelligent neural network based controller is designed to control the optimal voltage applied on the piezoelectric patches. The control technique utilizes a neurocontroller along with a Kalman Filter to compute the appropriate actuator command. The neurocontroller is trained based on an algorithm that incorporates a set of emulator neural networks which are also trained to predict the future response of the cantilever plate. Then, the neurocontroller is evaluated by comparing the uncontrolled and controlled responses under several types of dynamic excitations. It is observed that the neurocontroller reduced the vibration response of the flexible cantilever plate significantly; the results demonstrated the success and robustness of the neurocontroller independent of the type and distribution of the excitation force.

  20. The BDNF Val66Met Polymorphism Influences Reading Ability and Patterns of Neural Activation in Children.

    Science.gov (United States)

    Jasińska, Kaja K; Molfese, Peter J; Kornilov, Sergey A; Mencl, W Einar; Frost, Stephen J; Lee, Maria; Pugh, Kenneth R; Grigorenko, Elena L; Landi, Nicole

    2016-01-01

    Understanding how genes impact the brain's functional activation for learning and cognition during development remains limited. We asked whether a common genetic variant in the BDNF gene (the Val66Met polymorphism) modulates neural activation in the young brain during a critical period for the emergence and maturation of the neural circuitry for reading. In animal models, the bdnf variation has been shown to be associated with the structure and function of the developing brain and in humans it has been associated with multiple aspects of cognition, particularly memory, which are relevant for the development of skilled reading. Yet, little is known about the impact of the Val66Met polymorphism on functional brain activation in development, either in animal models or in humans. Here, we examined whether the BDNF Val66Met polymorphism (dbSNP rs6265) is associated with children's (age 6-10) neural activation patterns during a reading task (n = 81) using functional magnetic resonance imaging (fMRI), genotyping, and standardized behavioral assessments of cognitive and reading development. Children homozygous for the Val allele at the SNP rs6265 of the BDNF gene outperformed Met allele carriers on reading comprehension and phonological memory, tasks that have a strong memory component. Consistent with these behavioral findings, Met allele carriers showed greater activation in reading-related brain regions including the fusiform gyrus, the left inferior frontal gyrus and left superior temporal gyrus as well as greater activation in the hippocampus during a word and pseudoword reading task. Increased engagement of memory and spoken language regions for Met allele carriers relative to Val/Val homozygotes during reading suggests that Met carriers have to exert greater effort required to retrieve phonological codes. PMID:27551971

  1. Reconstruction of Neural Activity from EEG Data Using Dynamic Spatiotemporal Constraints.

    Science.gov (United States)

    Giraldo-Suarez, E; Martinez-Vargas, J D; Castellanos-Dominguez, G

    2016-11-01

    We present a novel iterative regularized algorithm (IRA) for neural activity reconstruction that explicitly includes spatiotemporal constraints, performing a trade-off between space and time resolutions. For improving the spatial accuracy provided by electroencephalography (EEG) signals, we explore a basis set that describes the smooth, localized areas of potentially active brain regions. In turn, we enhance the time resolution by adding the Markovian assumption for brain activity estimation at each time period. Moreover, to deal with applications that have either distributed or localized neural activity, the spatiotemporal constraints are expressed through [Formula: see text] and [Formula: see text] norms, respectively. For the purpose of validation, we estimate the neural reconstruction performance in time and space separately. Experimental testing is carried out on artificial data, simulating stationary and non-stationary EEG signals. Also, validation is accomplished on two real-world databases, one holding Evoked Potentials and another with EEG data of focal epilepsy. Moreover, responses of functional magnetic resonance imaging for the former EEG data have been measured in advance, allowing to contrast our findings. Obtained results show that the [Formula: see text]-based IRA produces a spatial resolution that is comparable to the one achieved by some widely used sparse-based estimators of brain activity. At the same time, the [Formula: see text]-based IRA outperforms other similar smooth solutions, providing a spatial resolution that is lower than the sparse [Formula: see text]-based solution. As a result, the proposed IRA is a promising method for improving the accuracy of brain activity reconstruction. PMID:27354190

  2. Circuits and filters handbook

    CERN Document Server

    Chen, Wai-Kai

    2003-01-01

    A bestseller in its first edition, The Circuits and Filters Handbook has been thoroughly updated to provide the most current, most comprehensive information available in both the classical and emerging fields of circuits and filters, both analog and digital. This edition contains 29 new chapters, with significant additions in the areas of computer-aided design, circuit simulation, VLSI circuits, design automation, and active and digital filters. It will undoubtedly take its place as the engineer's first choice in looking for solutions to problems encountered in the design, analysis, and behavi

  3. Subliminal versus supraliminal stimuli activate neural responses in anterior cingulate cortex, fusiform gyrus and insula: a meta-analysis of fMRI studies

    OpenAIRE

    Meneguzzo, Paolo; Tsakiris, Manos; Schioth, Helgi B.; Dan J Stein; Brooks, Samantha J.

    2014-01-01

    Background Non-conscious neural activation may underlie various psychological functions in health and disorder. However, the neural substrates of non-conscious processing have not been entirely elucidated. Examining the differential effects of arousing stimuli that are consciously, versus unconsciously perceived will improve our knowledge of neural circuitry involved in non-conscious perception. Here we conduct preliminary analyses of neural activation in studies that have used both sublimina...

  4. Using convolutional neural networks for human activity classification on micro-Doppler radar spectrograms

    Science.gov (United States)

    Jordan, Tyler S.

    2016-05-01

    This paper presents the findings of using convolutional neural networks (CNNs) to classify human activity from micro-Doppler features. An emphasis on activities involving potential security threats such as holding a gun are explored. An automotive 24 GHz radar on chip was used to collect the data and a CNN (normally applied to image classification) was trained on the resulting spectrograms. The CNN achieves an error rate of 1.65 % on classifying running vs. walking, 17.3 % error on armed walking vs. unarmed walking, and 22 % on classifying six different actions.

  5. Functional and structural specific roles of activity-driven BDNF within circuits formed by single spiny stellate neurons of the barrel cortex

    Directory of Open Access Journals (Sweden)

    Qian-Quan eSun

    2014-11-01

    Full Text Available Brain derived neurotrophic factor (BDNF plays key roles in several neurodevelopmental disorders and actions of pharmacological treatments. However it is uncealr how specific BDNF’s effects are on diffeerent circuit components. Current studies have largely focused on the role of BDNF in modification of synaptic development. The precise roles of BDNF in the refinement of a functional circuit in vivo remain unclear. Val66Met polymorphism of BDNF may be associated with increased risk for cognitive impairments and is mediated at least in part by activity-dependent trafficking and/or secretion of BDNF. Using mutant mice that lacked activity-driven BDNF expression (bdnf-KIV, we previously reported that experience regulation of the cortical GABAergic network is mediated by activity-driven BDNF expression. Here, we demonstrate that activity-driven BDNF’s effects on circuits formed by the layer IV spiny stellate cells are highly specific. Structurally, dendritic but not axonal morphology was altered in the mutant. Physiologically, GABAergic but not glutamatergic synapses were severely affected. The effects on GABA transmission occurs via presynaptic alteration of calcium-dependent release probability. These results suggest that neuronal activity through activity-driven BDNF expression, can selectively regulate specific features of layer IV circuits in vivo. We postulate that the role of activity-dependent BDNF is to modulate the computational ability of circuits that relate to the gain control (i.e. feed-forward inhibition; whereas the basic wiring of circuits relevant to the sensory pathway is spared. Gain control modulation within cortical circuits has broad impact on cognitive processing and brain state-transitions. Cognitive behavior and mode is determined by brain states, thus the studying of circuit alteration by endogenous BDNF provides insights into the cellular and molecular mechanisms of diseases mediated by BDNF.

  6. 基于有源广义忆阻的无感混沌电路研究∗%Inductorless chaotic circuit based on active generalized memristors

    Institute of Scientific and Technical Information of China (English)

    2015-01-01

    Equivalently implementing a generalized memristor by using common components and then making a nonlinear circuit with a reliable property, are conducive to experimentally exhibit the nonlinear phenomena of the memristive chaotic circuit and show practical applications in generating chaotic signals. Firstly, based on a memristive diode bridge circuit, a new first-order actively generalized memristor emulator is constructed with no grounded restriction and ease to realize. The mathematical model of the emulator is established and its fingerprints are analyzed by the pinched hysteresis loops with different sinusoidal voltage stimuli. The results verified by experimental measurements indicate that the emulator uses only one operational amplifier and nine elementary electronic circuit elements and is an active voltage-controlled generalized memristor. Secondly, by parallelly connecting the proposed emulator to a capacitor and then linearly coupling with an RC bridge oscillator, a memristor based chaotic circuit without any inductance element is constructed. The dynamical model of the inductorless memristive chaotic circuit is established and the phase portraits of the chaotic attractor with typical circuit parameters are obtained numerically. The dissipativity, equilibrium points, and stabilities are derived, which indicate that in the phase space of the inductorless memristive chaotic circuit there exists a dissipative area where are distributed two unstable nonzero saddle-foci and a non-dissipative area containing an unstable origin saddle point. Furthermore, by utilizing the bifurcation diagram, Lyapunov exponent spectra, and phase portraits, the dynamical behaviors of the inductorless memristive chaotic circuit are investigated. Results show that with the evolution of the parameter value of the coupling resistor, the complex nonlinear phenomena of the coexisting bifurcation modes and coexisting attractors under two different initial conditions of the state variables

  7. A Voltage Mode Memristor Bridge Synaptic Circuit with Memristor Emulators

    Directory of Open Access Journals (Sweden)

    Leon Chua

    2012-03-01

    Full Text Available A memristor bridge neural circuit which is able to perform signed synaptic weighting was proposed in our previous study, where the synaptic operation was verified via software simulation of the mathematical model of the HP memristor. This study is an extension of the previous work advancing toward the circuit implementation where the architecture of the memristor bridge synapse is built with memristor emulator circuits. In addition, a simple neural network which performs both synaptic weighting and summation is built by combining memristor emulators-based synapses and differential amplifier circuits. The feasibility of the memristor bridge neural circuit is verified via SPICE simulations.

  8. Thinking about the thoughts of others; temporal and spatial neural activation during false belief reasoning.

    Science.gov (United States)

    Mossad, Sarah I; AuCoin-Power, Michelle; Urbain, Charline; Smith, Mary Lou; Pang, Elizabeth W; Taylor, Margot J

    2016-07-01

    Theory of Mind (ToM) is the ability to understand the perspectives, mental states and beliefs of others in order to anticipate their behaviour and is therefore crucial to social interactions. Although fMRI has been widely used to establish the neural networks implicated in ToM, little is known about the timing of ToM-related brain activity. We used magnetoencephalography (MEG) to measure the neural processes underlying ToM, as MEG provides very accurate timing and excellent spatial localization of brain processes. We recorded MEG activity during a false belief task, a reliable measure of ToM, in twenty young adults (10 females). MEG data were recorded in a 151 sensor CTF system (MISL, Coquitlam, BC) and data were co-registered to each participant's MRI (Siemens 3T) for source reconstruction. We found stronger right temporoparietal junction (rTPJ) activations in the false belief condition from 150ms to 225ms, in the right precuneus from 275ms to 375ms, in the right inferior frontal gyrus from 200ms to 300ms and the superior frontal gyrus from 300ms to 400ms. Our findings extend the literature by demonstrating the timing and duration of neural activity in the main regions involved in the "mentalizing" network, showing that activations related to false belief in adults are predominantly right lateralized and onset around 100ms. The sensitivity of MEG will allow us to determine spatial and temporal differences in the brain processes in ToM in younger populations or those who demonstrate deficits in this ability. PMID:27039146

  9. The visual perception of natural motion: abnormal task-related neural activity in DYT1 dystonia.

    Science.gov (United States)

    Sako, Wataru; Fujita, Koji; Vo, An; Rucker, Janet C; Rizzo, John-Ross; Niethammer, Martin; Carbon, Maren; Bressman, Susan B; Uluğ, Aziz M; Eidelberg, David

    2015-12-01

    Although primary dystonia is defined by its characteristic motor manifestations, non-motor signs and symptoms have increasingly been recognized in this disorder. Recent neuroimaging studies have related the motor features of primary dystonia to connectivity changes in cerebello-thalamo-cortical pathways. It is not known, however, whether the non-motor manifestations of the disorder are associated with similar circuit abnormalities. To explore this possibility, we used functional magnetic resonance imaging to study primary dystonia and healthy volunteer subjects while they performed a motion perception task in which elliptical target trajectories were visually tracked on a computer screen. Prior functional magnetic resonance imaging studies of healthy subjects performing this task have revealed selective activation of motor regions during the perception of 'natural' versus 'unnatural' motion (defined respectively as trajectories with kinematic properties that either comply with or violate the two-thirds power law of motion). Several regions with significant connectivity changes in primary dystonia were situated in proximity to normal motion perception pathways, suggesting that abnormalities of these circuits may also be present in this disorder. To determine whether activation responses to natural versus unnatural motion in primary dystonia differ from normal, we used functional magnetic resonance imaging to study 10 DYT1 dystonia and 10 healthy control subjects at rest and during the perception of 'natural' and 'unnatural' motion. Both groups exhibited significant activation changes across perceptual conditions in the cerebellum, pons, and subthalamic nucleus. The two groups differed, however, in their responses to 'natural' versus 'unnatural' motion in these regions. In healthy subjects, regional activation was greater during the perception of natural (versus unnatural) motion (P perception of unnatural (versus natural) motion (P perception is disrupted in DYT1

  10. Passive and active RF-microwave circuits course and exercises with solutions

    CERN Document Server

    Jarry, Pierre

    2015-01-01

    Microwave and radiofrequency (RF) circuits play an important role in communication systems. Due to the proliferation of radar, satellite, and mobile wireless systems, there is a need for design methods that can satisfy the ever increasing demand for accuracy, reliability, and fast development times. This book explores the principal elements for receiving and emitting signals between Earth stations, satellites, and RF (mobile phones) in four parts; the theory and realization of couplers, computation and realization of microwave and RF filters, amplifiers and microwave and RF oscillators. Pas

  11. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

    Directory of Open Access Journals (Sweden)

    Francisco Javier Ordóñez

    2016-01-01

    Full Text Available Human activity recognition (HAR tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i is suitable for multimodal wearable sensors; (ii can perform sensor fusion naturally; (iii does not require expert knowledge in designing features; and (iv explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation.

  12. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

    Science.gov (United States)

    Ordóñez, Francisco Javier; Roggen, Daniel

    2016-01-01

    Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation. PMID:26797612

  13. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition.

    Science.gov (United States)

    Ordóñez, Francisco Javier; Roggen, Daniel

    2016-01-18

    Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters' influence on performance to provide insights about their optimisation.

  14. Human activities recognition by head movement using partial recurrent neural network

    Science.gov (United States)

    Tan, Henry C. C.; Jia, Kui; De Silva, Liyanage C.

    2003-06-01

    Traditionally, human activities recognition has been achieved mainly by the statistical pattern recognition methods or the Hidden Markov Model (HMM). In this paper, we propose a novel use of the connectionist approach for the recognition of ten simple human activities: walking, sitting down, getting up, squatting down and standing up, in both lateral and frontal views, in an office environment. By means of tracking the head movement of the subjects over consecutive frames from a database of different color image sequences, and incorporating the Elman model of the partial recurrent neural network (RNN) that learns the sequential patterns of relative change of the head location in the images, the proposed system is able to robustly classify all the ten activities performed by unseen subjects from both sexes, of different race and physique, with a recognition rate as high as 92.5%. This demonstrates the potential of employing partial RNN to recognize complex activities in the increasingly popular human-activities-based applications.

  15. Mild blast events alter anxiety, memory, and neural activity patterns in the anterior cingulate cortex.

    Directory of Open Access Journals (Sweden)

    Kun Xie

    Full Text Available There is a general interest in understanding of whether and how exposure to emotionally traumatizing events can alter memory function and anxiety behaviors. Here we have developed a novel laboratory-version of mild blast exposure comprised of high decibel bomb explosion sound coupled with strong air blast to mice. This model allows us to isolate the effects of emotionally fearful components from those of traumatic brain injury or bodily injury typical associated with bomb blasts. We demonstrate that this mild blast exposure is capable of impairing object recognition memory, increasing anxiety in elevated O-maze test, and resulting contextual generalization. Our in vivo neural ensemble recording reveal that such mild blast exposures produced diverse firing changes in the anterior cingulate cortex, a region processing emotional memory and inhibitory control. Moreover, we show that these real-time neural ensemble patterns underwent post-event reverberations, indicating rapid consolidation of those fearful experiences. Identification of blast-induced neural activity changes in the frontal brain may allow us to better understand how mild blast experiences result in abnormal changes in memory functions and excessive fear generalization related to post-traumatic stress disorder.

  16. Architecture and development of olivocerebellar circuit topography

    Directory of Open Access Journals (Sweden)

    Stacey L Reeber

    2013-01-01

    Full Text Available The cerebellum has a simple tri-laminar structure that is comprised of relatively few cell types. Yet, its internal micro-circuitry is anatomically, biochemically, and functionally complex. The most striking feature of cerebellar circuit complexity is its compartmentalized topography. Each cell type within the cerebellar cortex is organized into an exquisite map; molecular expression patterns, dendrite projections, and axon terminal fields divide the medial-lateral axis of the cerebellum into topographic sagittal zones. Here, we discuss the mechanisms that establish zones and highlight how gene expression and neural activity contribute to cerebellar pattern formation. We focus on the olivocerebellar system because its developmental mechanisms are becoming clear, its topographic termination patterns are very precise, and its contribution to zonal function is debated. This review deconstructs the architecture and development of the olivocerebellar pathway to provide an update on how brain circuit maps form and function.

  17. Musical molecules: the molecular junction as an active component in audio distortion circuits.

    Science.gov (United States)

    Bergren, Adam Johan; Zeer-Wanklyn, Lucas; Semple, Mitchell; Pekas, Nikola; Szeto, Bryan; McCreery, Richard L

    2016-03-01

    Molecular junctions that have a non-linear current-voltage characteristic consistent with quantum mechanical tunneling are demonstrated as analog audio clipping elements in overdrive circuits widely used in electronic music, particularly with electric guitars. The performance of large-area molecular junctions fabricated at the wafer level is compared to currently standard semiconductor diode clippers, showing a difference in the sound character. The harmonic distributions resulting from the use of traditional and molecular clipping elements are reported and discussed, and differences in performance are noted that result from the underlying physics that controls the electronic properties of each clipping component. In addition, the ability to tune the sound using the molecular junction is demonstrated. Finally, the hybrid circuit is compared to an overdriven tube amplifier, which has been the standard reference electric guitar clipped tone for over 60 years. In order to investigate the feasibility of manufacturing molecular junctions for use in commercial applications, devices are fabricated using a low-density format at the wafer level, where 38 dies per wafer, each containing two molecular junctions, are made with exceptional non-shorted yield (99.4%, representing 718 out of 722 tested devices) without requiring clean room facilities. PMID:26871885

  18. Musical molecules: the molecular junction as an active component in audio distortion circuits

    Science.gov (United States)

    Bergren, Adam Johan; Zeer-Wanklyn, Lucas; Semple, Mitchell; Pekas, Nikola; Szeto, Bryan; McCreery, Richard L.

    2016-03-01

    Molecular junctions that have a non-linear current-voltage characteristic consistent with quantum mechanical tunneling are demonstrated as analog audio clipping elements in overdrive circuits widely used in electronic music, particularly with electric guitars. The performance of large-area molecular junctions fabricated at the wafer level is compared to currently standard semiconductor diode clippers, showing a difference in the sound character. The harmonic distributions resulting from the use of traditional and molecular clipping elements are reported and discussed, and differences in performance are noted that result from the underlying physics that controls the electronic properties of each clipping component. In addition, the ability to tune the sound using the molecular junction is demonstrated. Finally, the hybrid circuit is compared to an overdriven tube amplifier, which has been the standard reference electric guitar clipped tone for over 60 years. In order to investigate the feasibility of manufacturing molecular junctions for use in commercial applications, devices are fabricated using a low-density format at the wafer level, where 38 dies per wafer, each containing two molecular junctions, are made with exceptional non-shorted yield (99.4%, representing 718 out of 722 tested devices) without requiring clean room facilities.

  19. Musical molecules: the molecular junction as an active component in audio distortion circuits

    International Nuclear Information System (INIS)

    Molecular junctions that have a non-linear current–voltage characteristic consistent with quantum mechanical tunneling are demonstrated as analog audio clipping elements in overdrive circuits widely used in electronic music, particularly with electric guitars. The performance of large-area molecular junctions fabricated at the wafer level is compared to currently standard semiconductor diode clippers, showing a difference in the sound character. The harmonic distributions resulting from the use of traditional and molecular clipping elements are reported and discussed, and differences in performance are noted that result from the underlying physics that controls the electronic properties of each clipping component. In addition, the ability to tune the sound using the molecular junction is demonstrated. Finally, the hybrid circuit is compared to an overdriven tube amplifier, which has been the standard reference electric guitar clipped tone for over 60 years. In order to investigate the feasibility of manufacturing molecular junctions for use in commercial applications, devices are fabricated using a low-density format at the wafer level, where 38 dies per wafer, each containing two molecular junctions, are made with exceptional non-shorted yield (99.4%, representing 718 out of 722 tested devices) without requiring clean room facilities. (paper)

  20. Neural activation in speech production and reading aloud in native and non-native languages.

    Science.gov (United States)

    Berken, Jonathan A; Gracco, Vincent L; Chen, Jen-Kai; Soles, Jennika; Watkins, Kate E; Baum, Shari; Callahan, Megan; Klein, Denise

    2015-05-15

    We used fMRI to investigate neural activation in reading aloud in bilinguals differing in age of acquisition. Three groups were compared: French-English bilinguals who acquired two languages from birth (simultaneous), French-English bilinguals who learned their L2 after the age of 5 years (sequential), and English-speaking monolinguals. While the bilingual groups contrasted in age of acquisition, they were matched for language proficiency, although sequential bilinguals produced speech with a less native-like accent in their L2 than in their L1. Simultaneous bilinguals activated similar brain regions to an equivalent degree when reading in their two languages. In contrast, sequential bilinguals more strongly activated areas related to speech-motor control and orthographic to phonological mapping, the left inferior frontal gyrus, left premotor cortex, and left fusiform gyrus, when reading aloud in L2 compared to L1. In addition, the activity in these regions showed a significant positive correlation with age of acquisition. The results provide evidence for the engagement of overlapping neural substrates for processing two languages when acquired in native context from birth. However, it appears that the maturation of certain brain regions for both speech production and phonological encoding is limited by a sensitive period for L2 acquisition regardless of language proficiency.

  1. Right hemisphere neural activations in the recall of waking fantasies and of dreams.

    Science.gov (United States)

    Benedetti, Francesco; Poletti, Sara; Radaelli, Daniele; Ranieri, Rebecca; Genduso, Valeria; Cavallotti, Simone; Castelnovo, Anna; Smeraldi, Enrico; Scarone, Silvio; D'Agostino, Armando

    2015-10-01

    The story-like organization of dreams is characterized by a pervasive bizarreness of events and actions that resembles psychotic thought, and largely exceeds that observed in normal waking fantasies. Little is known about the neural correlates of the confabulatory narrative construction of dreams. In this study, dreams, fantasies elicited by ambiguous pictorial stimuli, and non-imaginative first- and third-person narratives from healthy participants were recorded, and were then studied for brain blood oxygen level-dependent functional magnetic resonance imaging on a 3.0-Tesla scanner while listening to their own narrative reports and attempting a retrieval of the corresponding experience. In respect to non-bizarre reports of daytime activities, the script-driven recall of dreams and fantasies differentially activated a right hemisphere network including areas in the inferior frontal gyrus, and superior and middle temporal gyrus. Neural responses were significantly greater for fantasies than for dreams in all regions, and inversely proportional to the degree of bizarreness observed in narrative reports. The inferior frontal gyrus, superior and middle temporal gyrus have been implicated in the semantic activation, integration and selection needed to build a coherent story representation and to resolve semantic ambiguities; in deductive and inferential reasoning; in self- and other-perspective taking, theory of mind, moral and autobiographical reasoning. Their degree of activation could parallel the level of logical robustness or inconsistency experienced when integrating information and mental representations in the process of building fantasy and dream narratives.

  2. Emergence of spatially heterogeneous burst suppression in a neural field model of electrocortical activity

    Directory of Open Access Journals (Sweden)

    Ingo eBojak

    2015-02-01

    Full Text Available Burst suppression in the electroencephalogram (EEG is a well described phenomenon that occurs during deep anaesthesia, as well as in a variety of congenital and acquired brain insults. Classically it is thought of as spatially synchronous, quasi-periodic bursts of high amplitude EEG separated by low amplitude activity. However, its characterisation as a ``global brain state'' has been challenged by recent results obtained with intracranial electrocortigraphy. Not only does it appear that burst suppression activity is highly asynchronous across cortex, but also that it may occur in isolated regions of circumscribed spatial extent. Here we outline a realistic neural field model for burst suppression by adding a slow process of synaptic resource depletion and recovery, which is able to reproduce qualitatively the empirically observed features during general anaesthesia at the whole cortex level. Simulations reveal heterogeneous bursting over the model cortex and complex spatiotemporal dynamics during simulated anaesthetic action, and provide forward predictions of neuroimaging signals for subsequent empirical comparisons and more detailed characterisation.Because burst suppression corresponds to a dynamical end-point of brain activity, theoretically accounting for its spatiotemporal emergence will vitally contribute to efforts aimed at clarifying whether a common physiological trajectory is induced by the actions of general anaesthetic agents. We have taken a first step in this direction by showing that a neural field model can qualitatively match recent experimental data that indicate spatial differentiation of burst suppression activity across cortex.

  3. Right hemisphere neural activations in the recall of waking fantasies and of dreams.

    Science.gov (United States)

    Benedetti, Francesco; Poletti, Sara; Radaelli, Daniele; Ranieri, Rebecca; Genduso, Valeria; Cavallotti, Simone; Castelnovo, Anna; Smeraldi, Enrico; Scarone, Silvio; D'Agostino, Armando

    2015-10-01

    The story-like organization of dreams is characterized by a pervasive bizarreness of events and actions that resembles psychotic thought, and largely exceeds that observed in normal waking fantasies. Little is known about the neural correlates of the confabulatory narrative construction of dreams. In this study, dreams, fantasies elicited by ambiguous pictorial stimuli, and non-imaginative first- and third-person narratives from healthy participants were recorded, and were then studied for brain blood oxygen level-dependent functional magnetic resonance imaging on a 3.0-Tesla scanner while listening to their own narrative reports and attempting a retrieval of the corresponding experience. In respect to non-bizarre reports of daytime activities, the script-driven recall of dreams and fantasies differentially activated a right hemisphere network including areas in the inferior frontal gyrus, and superior and middle temporal gyrus. Neural responses were significantly greater for fantasies than for dreams in all regions, and inversely proportional to the degree of bizarreness observed in narrative reports. The inferior frontal gyrus, superior and middle temporal gyrus have been implicated in the semantic activation, integration and selection needed to build a coherent story representation and to resolve semantic ambiguities; in deductive and inferential reasoning; in self- and other-perspective taking, theory of mind, moral and autobiographical reasoning. Their degree of activation could parallel the level of logical robustness or inconsistency experienced when integrating information and mental representations in the process of building fantasy and dream narratives. PMID:25871325

  4. Overlapping patterns of neural activity for different forms of novelty in fMRI

    Directory of Open Access Journals (Sweden)

    Colin Shaun Hawco

    2014-09-01

    Full Text Available When stimuli are presented multiple times, the neural response to repeated stimuli is reduced relative to novel stimuli (repetition suppression. Responses to different types of novelty were examined. Stimulus novelty was examined by contrasting first vs. second presentation of triads of objects during memory encoding. Semantic novelty was contrasted by comparing unrelated (semantically novel triads of objects to triads in which all three objects were related (e.g. all objects were tools. In recognition, associative novelty was examined by contrasting rearranged triads (previously seen objects in a new association with intact triads. Activity was observed in posterior regions (occipital and fusiform, with the largest extent of activity for stimulus novelty and smallest for associational novelty. Frontal activity was also observed in stimulus and semantic novelty. Additional analysis indicated that the hemodynamic response in voxels identified in the stimulus and semantic novelty contrasts was modulated by reaction time on a trial-by-trial basis. That is, the duration of the hemodynamic response was driven by reaction time. This was not the case for associative novelty. The high level of overlap across different forms of novelty suggests a similar mechanism for reduced neural activity, which may be related to reduced visual processing time. This is consistent with a facilitation model of repetition suppression, which posits a reduced peak and duration of neuronal firing for repeated stimuli.

  5. An Active Stereo Vision System Based on Neural Pathways of Human Binocular Motor System

    Institute of Scientific and Technical Information of China (English)

    Yu-zhang Gu; Makoto Sato; Xiao-lin Zhang

    2007-01-01

    An active stereo vision system based on a model of neural pathways of human binocular motor system is proposed. With this model, it is guaranteed that the two cameras of the active stereo vision system can keep their lines of sight fixed on the same target object during smooth pursuit. This feature is very important for active stereo vision systems, since not only 3D reconstruction needs the two cameras have an overlapping field of vision, but also it can facilitate the 3D reconstruction algorithm. To evaluate the effectiveness of the proposed method, some software simulations are done to demonstrate the same target tracking characteristic in a virtual environment apt to mistracking easily. Here, mistracking means two eyes track two different objects separately. Then the proposed method is implemented in our active stereo vision system to perform real tracking task in a laboratory scene where several persons walk self-determining. Before the proposed model is implemented in the system, mistracking occurred frequently. After it is enabled, mistracking never occurred. The result shows that the vision system based on neural pathways of human binocular motor system can reliably avoid mistracking.

  6. Analytically tractable studies of traveling waves of activity in integrate-and-fire neural networks

    Science.gov (United States)

    Zhang, Jie; Osan, Remus

    2016-05-01

    In contrast to other large-scale network models for propagation of electrical activity in neural tissue that have no analytical solutions for their dynamics, we show that for a specific class of integrate and fire neural networks the acceleration depends quadratically on the instantaneous speed of the activity propagation. We use this property to analytically compute the network spike dynamics and to highlight the emergence of a natural time scale for the evolution of the traveling waves. These results allow us to examine other applications of this model such as the effect that a nonconductive gap of tissue has on further activity propagation. Furthermore we show that activity propagation also depends on local conditions for other more general connectivity functions, by converting the evolution equations for network dynamics into a low-dimensional system of ordinary differential equations. This approach greatly enhances our intuition into the mechanisms of the traveling waves evolution and significantly reduces the simulation time for this class of models.

  7. Bioimpedance Harmonic Analysis as a Diagnostic Tool to Assess Regional Circulation and Neural Activity

    Science.gov (United States)

    Mudraya, I. S.; Revenko, S. V.; Khodyreva, L. A.; Markosyan, T. G.; Dudareva, A. A.; Ibragimov, A. R.; Romich, V. V.; Kirpatovsky, V. I.

    2013-04-01

    The novel technique based on harmonic analysis of bioimpedance microvariations with original hard- and software complex incorporating a high-resolution impedance converter was used to assess the neural activity and circulation in human urinary bladder and penis in patients with pelvic pain, erectile dysfunction, and overactive bladder. The therapeutic effects of shock wave therapy and Botulinum toxin detrusor injections were evaluated quantitatively according to the spectral peaks at low 0.1 Hz frequency (M for Mayer wave), respiratory (R) and cardiac (C) rhythms with their harmonics. Enhanced baseline regional neural activity identified according to M and R peaks was found to be presumably sympathetic in pelvic pain patients, and parasympathetic - in patients with overactive bladder. Total pulsatile activity and pulsatile resonances found in the bladder as well as in the penile spectrum characterised regional circulation and vascular tone. The abnormal spectral parameters characteristic of the patients with genitourinary diseases shifted to the norm in the cases of efficient therapy. Bioimpedance harmonic analysis seems to be a potent tool to assess regional peculiarities of circulatory and autonomic nervous activity in the course of patient treatment.

  8. Bioimpedance Harmonic Analysis as a Diagnostic Tool to Assess Regional Circulation and Neural Activity

    International Nuclear Information System (INIS)

    The novel technique based on harmonic analysis of bioimpedance microvariations with original hard- and software complex incorporating a high-resolution impedance converter was used to assess the neural activity and circulation in human urinary bladder and penis in patients with pelvic pain, erectile dysfunction, and overactive bladder. The therapeutic effects of shock wave therapy and Botulinum toxin detrusor injections were evaluated quantitatively according to the spectral peaks at low 0.1 Hz frequency (M for Mayer wave), respiratory (R) and cardiac (C) rhythms with their harmonics. Enhanced baseline regional neural activity identified according to M and R peaks was found to be presumably sympathetic in pelvic pain patients, and parasympathetic – in patients with overactive bladder. Total pulsatile activity and pulsatile resonances found in the bladder as well as in the penile spectrum characterised regional circulation and vascular tone. The abnormal spectral parameters characteristic of the patients with genitourinary diseases shifted to the norm in the cases of efficient therapy. Bioimpedance harmonic analysis seems to be a potent tool to assess regional peculiarities of circulatory and autonomic nervous activity in the course of patient treatment.

  9. The habenulo-raphe serotonergic circuit encodes an aversive expectation value essential for adaptive active avoidance of danger.

    Science.gov (United States)

    Amo, Ryunosuke; Fredes, Felipe; Kinoshita, Masae; Aoki, Ryo; Aizawa, Hidenori; Agetsuma, Masakazu; Aoki, Tazu; Shiraki, Toshiyuki; Kakinuma, Hisaya; Matsuda, Masaru; Yamazaki, Masako; Takahoko, Mikako; Tsuboi, Takashi; Higashijima, Shin-ichi; Miyasaka, Nobuhiko; Koide, Tetsuya; Yabuki, Yoichi; Yoshihara, Yoshihiro; Fukai, Tomoki; Okamoto, Hitoshi

    2014-12-01

    Anticipation of danger at first elicits panic in animals, but later it helps them to avoid the real threat adaptively. In zebrafish, as fish experience more and more danger, neurons in the ventral habenula (vHb) showed tonic increase in the activity to the presented cue and activated serotonergic neurons in the median raphe (MR). This neuronal activity could represent the expectation of a dangerous outcome and be used for comparison with a real outcome when the fish is learning how to escape from a dangerous to a safer environment. Indeed, inhibiting synaptic transmission from vHb to MR impaired adaptive avoidance learning, while panic behavior induced by classical fear conditioning remained intact. Furthermore, artificially triggering this negative outcome expectation signal by optogenetic stimulation of vHb neurons evoked place avoidance behavior. Thus, vHb-MR circuit is essential for representing the level of expected danger and behavioral programming to adaptively avoid potential hazard.

  10. Forecast and restoration of geomagnetic activity indices by using the software-computational neural network complex

    Science.gov (United States)

    Barkhatov, Nikolay; Revunov, Sergey

    2010-05-01

    It is known that currently used indices of geomagnetic activity to some extent reflect the physical processes occurring in the interaction of the perturbed solar wind with Earth's magnetosphere. Therefore, they are connected to each other and with the parameters of near-Earth space. The establishment of such nonlinear connections is interest. For such purposes when the physical problem is complex or has many parameters the technology of artificial neural networks is applied. Such approach for development of the automated forecast and restoration method of geomagnetic activity indices with the establishment of creative software-computational neural network complex is used. Each neural network experiments were carried out at this complex aims to search for a specific nonlinear relation between the analyzed indices and parameters. At the core of the algorithm work program a complex scheme of the functioning of artificial neural networks (ANN) of different types is contained: back propagation Elman network, feed forward network, fuzzy logic network and Kohonen layer classification network. Tools of the main window of the complex (the application) the settings used by neural networks allow you to change: the number of hidden layers, the number of neurons in the layer, the input and target data, the number of cycles of training. Process and the quality of training the ANN is a dynamic plot of changing training error. Plot of comparison of network response with the test sequence is result of the network training. The last-trained neural network with established nonlinear connection for repeated numerical experiments can be run. At the same time additional training is not executed and the previously trained network as a filter input parameters get through and output parameters with the test event are compared. At statement of the large number of different experiments provided the ability to run the program in a "batch" mode is stipulated. For this purpose the user a

  11. Sex differences in neural activation to facial expressions denoting contempt and disgust.

    Directory of Open Access Journals (Sweden)

    André Aleman

    Full Text Available The facial expression of contempt has been regarded to communicate feelings of moral superiority. Contempt is an emotion that is closely related to disgust, but in contrast to disgust, contempt is inherently interpersonal and hierarchical. The aim of this study was twofold. First, to investigate the hypothesis of preferential amygdala responses to contempt expressions versus disgust. Second, to investigate whether, at a neural level, men would respond stronger to biological signals of interpersonal superiority (e.g., contempt than women. We performed an experiment using functional magnetic resonance imaging (fMRI, in which participants watched facial expressions of contempt and disgust in addition to neutral expressions. The faces were presented as distractors in an oddball task in which participants had to react to one target face. Facial expressions of contempt and disgust activated a network of brain regions, including prefrontal areas (superior, middle and medial prefrontal gyrus, anterior cingulate, insula, amygdala, parietal cortex, fusiform gyrus, occipital cortex, putamen and thalamus. Contemptuous faces did not elicit stronger amygdala activation than did disgusted expressions. To limit the number of statistical comparisons, we confined our analyses of sex differences to the frontal and temporal lobes. Men displayed stronger brain activation than women to facial expressions of contempt in the medial frontal gyrus, inferior frontal gyrus, and superior temporal gyrus. Conversely, women showed stronger neural responses than men to facial expressions of disgust. In addition, the effect of stimulus sex differed for men versus women. Specifically, women showed stronger responses to male contemptuous faces (as compared to female expressions, in the insula and middle frontal gyrus. Contempt has been conceptualized as signaling perceived moral violations of social hierarchy, whereas disgust would signal violations of physical purity. Thus, our

  12. Social status alters defeat-induced neural activation in Syrian hamsters.

    Science.gov (United States)

    Morrison, K E; Curry, D W; Cooper, M A

    2012-05-17

    Although exposure to social stress leads to increased depression-like and anxiety-like behavior, some individuals are more vulnerable than others to these stress-induced changes in behavior. Prior social experience is one factor that can modulate how individuals respond to stressful events. In this study, we investigated whether experience-dependent resistance to the behavioral consequences of social defeat was associated with a specific pattern of neural activation. We paired weight-matched male Syrian hamsters in daily aggressive encounters for 2 weeks, during which they formed a stable dominance relationship. We also included control animals that were exposed to an empty cage each day for 2 weeks. Twenty-four hours after the final pairing or empty cage exposure, half of the subjects were socially defeated in 3, 5-min encounters, whereas the others were not socially defeated. Twenty-four hours after social defeat, animals were tested for conditioned defeat in a 5-min social interaction test with a non-aggressive intruder. We collected brains after social defeat and processed the tissue for c-Fos immunoreactivity. We found that dominants were more likely than subordinates to counter-attack the resident aggressor during social defeat, and they showed less submissive and defensive behavior at conditioned defeat testing compared with subordinates. Also, social status was associated with distinct patterns of defeat-induced neural activation in select brain regions, including the amygdala, prefrontal cortex, hypothalamus, and lateral septum. Our results indicate that social status is an important form of prior experience that predicts both initial coping style and the degree of resistance to social defeat. Further, the differences in defeat-induced neural activation suggest possible brain regions that may control resistance to conditioned defeat in dominant individuals.

  13. Cognitive emotion regulation in children: Reappraisal of emotional faces modulates neural source activity in a frontoparietal network

    Directory of Open Access Journals (Sweden)

    Ida Wessing

    2015-06-01

    Full Text Available Emotion regulation has an important role in child development and psychopathology. Reappraisal as cognitive regulation technique can be used effectively by children. Moreover, an ERP component known to reflect emotional processing called late positive potential (LPP can be modulated by children using reappraisal and this modulation is also related to children's emotional adjustment. The present study seeks to elucidate the neural generators of such LPP effects. To this end, children aged 8–14 years reappraised emotional faces, while neural activity in an LPP time window was estimated using magnetoencephalography-based source localization. Additionally, neural activity was correlated with two indexes of emotional adjustment and age. Reappraisal reduced activity in the left dorsolateral prefrontal cortex during down-regulation and enhanced activity in the right parietal cortex during up-regulation. Activity in the visual cortex decreased with increasing age, more adaptive emotion regulation and less anxiety. Results demonstrate that reappraisal changed activity within a frontoparietal network in children. Decreasing activity in the visual cortex with increasing age is suggested to reflect neural maturation. A similar decrease with adaptive emotion regulation and less anxiety implies that better emotional adjustment may be associated with an advance in neural maturation.

  14. Integration of Optical Manipulation and Electrophysiological Tools to Modulate and Record Activity in Neural Networks

    Science.gov (United States)

    Difato, F.; Schibalsky, L.; Benfenati, F.; Blau, A.

    2011-07-01

    We present an optical system that combines IR (1064 nm) holographic optical tweezers with a sub-nanosecond-pulsed UV (355 nm) laser microdissector for the optical manipulation of single neurons and entire networks both on transparent and non-transparent substrates in vitro. The phase-modulated laser beam can illuminate the sample concurrently or independently from above or below assuring compatibility with different types of microelectrode array and patch-clamp electrophysiology. By combining electrophysiological and optical tools, neural activity in response to localized stimuli or injury can be studied and quantified at sub-cellular, cellular, and network level.

  15. Plasmodium berghei ANKA: erythropoietin activates neural stem cells in an experimental cerebral malaria model

    DEFF Research Database (Denmark)

    Core, Andrew; Hempel, Casper; Kurtzhals, Jørgen A L;

    2011-01-01

    Cerebral malaria (CM) causes substantial mortality and neurological sequelae in survivors, and no neuroprotective regimens are currently available for this condition. Erythropoietin (EPO) reduces neuropathology and improves survival in murine CM. Using the Plasmodium berghei model of CM, we...... investigated if EPO's neuroprotective effects include activation of endogenous neural stem cells (NSC). By using immunohistochemical markers of different NSC maturation stages, we show that EPO increased the number of nestin(+) cells in the dentate gyrus and in the sub-ventricular zone of the lateral...

  16. Common features of neural activity during singing and sleep periods in a basal ganglia nucleus critical for vocal learning in a juvenile songbird.

    Directory of Open Access Journals (Sweden)

    Shin Yanagihara

    Full Text Available Reactivations of waking experiences during sleep have been considered fundamental neural processes for memory consolidation. In songbirds, evidence suggests the importance of sleep-related neuronal activity in song system motor pathway nuclei for both juvenile vocal learning and maintenance of adult song. Like those in singing motor nuclei, neurons in the basal ganglia nucleus Area X, part of the basal ganglia-thalamocortical circuit essential for vocal plasticity, exhibit singing-related activity. It is unclear, however, whether Area X neurons show any distinctive spiking activity during sleep similar to that during singing. Here we demonstrate that, during sleep, Area X pallidal neurons exhibit phasic spiking activity, which shares some firing properties with activity during singing. Shorter interspike intervals that almost exclusively occurred during singing in awake periods were also observed during sleep. The level of firing variability was consistently higher during singing and sleep than during awake non-singing states. Moreover, deceleration of firing rate, which is considered to be an important firing property for transmitting signals from Area X to the thalamic nucleus DLM, was observed mainly during sleep as well as during singing. These results suggest that songbird basal ganglia circuitry may be involved in the off-line processing potentially critical for vocal learning during sensorimotor learning phase.

  17. A role of phase-resetting in coordinating large scale neural oscillations during attention and goal-directed behavior

    Directory of Open Access Journals (Sweden)

    Benjamin eVoloh

    2016-03-01

    Full Text Available Short periods of oscillatory activation are ubiquitous signatures of neural circuits. A broad range of studies documents not only their circuit origins, but also a fundamental role for oscillatory activity in coordinating information transfer during goal directed behavior. Recent studies suggest that resetting the phase of ongoing oscillatory activity to endogenous or exogenous cues facilitates coordinated information transfer within circuits and between distributed brain areas. Here, we review evidence that pinpoints phase resetting as a critical marker of dynamic state changes of functional networks. Phase resets (1 set a neural context in terms of narrow band frequencies that uniquely characterizes the activated circuits, (2 impose coherent low frequency phases to which high frequency activations can synchronize, identifiable as cross-frequency correlations across large anatomical distances, (3 are critical for neural coding models that depend on phase, increasing the informational content of neural representations, and (4 likely originate from the dynamics of canonical E-I circuits that are anatomically ubiquitous. These multiple signatures of phase resets are directly linked to enhanced information transfer and behavioral success. We survey how phase resets re-organize oscillations in diverse task contexts, including sensory perception, attentional stimulus selection, cross-modal integration, Pavlovian conditioning, and spatial navigation. The evidence we consider suggests that phase-resets can drive changes in neural excitability, ensemble organization, functional networks, and ultimately, overt behavior.

  18. A Role of Phase-Resetting in Coordinating Large Scale Neural Networks During Attention and Goal-Directed Behavior.

    Science.gov (United States)

    Voloh, Benjamin; Womelsdorf, Thilo

    2016-01-01

    Short periods of oscillatory activation are ubiquitous signatures of neural circuits. A broad range of studies documents not only their circuit origins, but also a fundamental role for oscillatory activity in coordinating information transfer during goal directed behavior. Recent studies suggest that resetting the phase of ongoing oscillatory activity to endogenous or exogenous cues facilitates coordinated information transfer within circuits and between distributed brain areas. Here, we review evidence that pinpoints phase resetting as a critical marker of dynamic state changes of functional networks. Phase resets: (1) set a "neural context" in terms of narrow band frequencies that uniquely characterizes the activated circuits; (2) impose coherent low frequency phases to which high frequency activations can synchronize, identifiable as cross-frequency correlations across large anatomical distances; (3) are critical for neural coding models that depend on phase, increasing the informational content of neural representations; and (4) likely originate from the dynamics of canonical E-I circuits that are anatomically ubiquitous. These multiple signatures of phase resets are directly linked to enhanced information transfer and behavioral success. We survey how phase resets re-organize oscillations in diverse task contexts, including sensory perception, attentional stimulus selection, cross-modal integration, Pavlovian conditioning, and spatial navigation. The evidence we consider suggests that phase-resets can drive changes in neural excitability, ensemble organization, functional networks, and ultimately, overt behavior. PMID:27013986

  19. The Mind Grows Circuits

    CERN Document Server

    Panigrahy, Rina

    2012-01-01

    There is a vast supply of prior art that study models for mental processes. Some studies in psychology and philosophy approach it from an inner perspective in terms of experiences and percepts. Others such as neurobiology or connectionist-machines approach it externally by viewing the mind as complex circuit of neurons where each neuron is a primitive binary circuit. In this paper, we also model the mind as a place where a circuit grows, starting as a collection of primitive components at birth and then builds up incrementally in a bottom up fashion. A new node is formed by a simple composition of prior nodes when we undergo a repeated experience that can be described by that composition. Unlike neural networks, however, these circuits take "concepts" or "percepts" as inputs and outputs. Thus the growing circuits can be likened to a growing collection of lambda expressions that are built on top of one another in an attempt to compress the sensory input as a heuristic to bound its Kolmogorov Complexity.

  20. Artificial neural network and multiple regression model for nickel(II) adsorption on powdered activated carbons.

    Science.gov (United States)

    Hema, M; Srinivasan, K

    2011-07-01

    Nickel removal efficiency of powered activated carbons of coconut oilcake, neem oilcake and commercial carbon was investigated by using artificial neural network. The effective parameters for the removal of nickel (%R) by adsorption process, which included the pH, contact time (T), distinctiveness of activated carbon (Cn), amount of activated carbon (Cw) and initial concentration of nickel (Co) were investigated. Levenberg-Marquardt (LM) Back-propagation algorithm is used to train the network. The network topology was optimized by varying number of hidden layer and number of neurons in hidden layer. The model was developed in terms of training; validation and testing of experimental data, the test subsets that each of them contains 60%, 20% and 20% of total experimental data, respectively. Multiple regression equation was developed for nickel adsorption system and the output was compared with both simulated and experimental outputs. Standard deviation (SD) with respect to experimental output was quite higher in the case of regression model when compared with ANN model. The obtained experimental data best fitted with the artificial neural network. PMID:23029923

  1. Neural Activities Underlying the Feedback Express Salience Prediction Errors for Appetitive and Aversive Stimuli

    Science.gov (United States)

    Gu, Yan; Hu, Xueping; Pan, Weigang; Yang, Chun; Wang, Lijun; Li, Yiyuan; Chen, Antao

    2016-01-01

    Feedback information is essential for us to adapt appropriately to the environment. The feedback-related negativity (FRN), a frontocentral negative deflection after the delivery of feedback, has been found to be larger for outcomes that are worse than expected, and it reflects a reward prediction error derived from the midbrain dopaminergic projections to the anterior cingulate cortex (ACC), as stated in reinforcement learning theory. In contrast, the prediction of response-outcome (PRO) model claims that the neural activity in the mediofrontal cortex (mPFC), especially the ACC, is sensitive to the violation of expectancy, irrespective of the valence of feedback. Additionally, increasing evidence has demonstrated significant activities in the striatum, anterior insula and occipital lobe for unexpected outcomes independently of their valence. Thus, the neural mechanism of the feedback remains under dispute. Here, we investigated the feedback with monetary reward and electrical pain shock in one task via functional magnetic resonance imaging. The results revealed significant prediction-error-related activities in the bilateral fusiform gyrus, right middle frontal gyrus and left cingulate gyrus for both money and pain. This implies that some regions underlying the feedback may signal a salience prediction error rather than a reward prediction error. PMID:27694920

  2. Shaping prestimulus neural activity with auditory rhythmic stimulation improves the temporal allocation of attention

    Science.gov (United States)

    Pincham, Hannah L.; Cristoforetti, Giulia; Facoetti, Andrea; Szűcs, Dénes

    2016-01-01

    Human attention fluctuates across time, and even when stimuli have identical physical characteristics and the task demands are the same, relevant information is sometimes consciously perceived and at other times not. A typical example of this phenomenon is the attentional blink, where participants show a robust deficit in reporting the second of two targets (T2) in a rapid serial visual presentation (RSVP) stream. Previous electroencephalographical (EEG) studies showed that neural correlates of correct T2 report are not limited to the RSVP period, but extend before visual stimulation begins. In particular, reduced oscillatory neural activity in the alpha band (8-12 Hz) before the onset of the RSVP has been linked to lower T2 accuracy. We therefore examined whether auditory rhythmic stimuli presented at a rate of 10 Hz (within the alpha band) could increase oscillatory alpha-band activity and improve T2 performance in the attentional blink time window. Behaviourally, the auditory rhythmic stimulation worked to enhance T2 accuracy. This enhanced perception was associated with increases in the posterior T2-evoked N2 component of the event-related potentials and this effect was observed selectively at lag 3. Frontal and posterior oscillatory alpha-band activity was also enhanced during auditory stimulation in the pre-RSVP period and positively correlated with T2 accuracy. These findings suggest that ongoing fluctuations can be shaped by sensorial events to improve the allocation of attention in time. PMID:26986506

  3. A logical molecular circuit for programmable and autonomous regulation of protein activity using DNA aptamer-protein interactions

    OpenAIRE

    Han, Da; Zhu, Zhi; Wu, Cuichen; Peng, Lu; Zhou, Leiji; Gulbakan, Basri; Zhu, Guizhi; Williams, Kathryn R.; Tan, Weihong

    2012-01-01

    Researchers increasingly envision an important role for artificial biochemical circuits in biological engineering, much like electrical circuits in electrical engineering. Similar to electrical circuits, which control electromechanical devices, biochemical circuits could be utilized as a type of servomechanism to control nanodevices in vitro, monitor chemical reactions in situ, or regulate gene expressions in vivo.1 As a consequence of their relative robustness and potential applicability for...

  4. Spine pruning drives antipsychotic-sensitive locomotion via circuit control of striatal dopamine.

    Science.gov (United States)

    Kim, Il Hwan; Rossi, Mark A; Aryal, Dipendra K; Racz, Bence; Kim, Namsoo; Uezu, Akiyoshi; Wang, Fan; Wetsel, William C; Weinberg, Richard J; Yin, Henry; Soderling, Scott H

    2015-06-01

    Psychiatric and neurodevelopmental disorders may arise from anomalies in long-range neuronal connectivity downstream of pathologies in dendritic spines. However, the mechanisms that may link spine pathology to circuit abnormalities relevant to atypical behavior remain unknown. Using a mouse model to conditionally disrupt a critical regulator of the dendritic spine cytoskeleton, the actin-related protein 2/3 complex (Arp2/3), we report here a molecular mechanism that unexpectedly reveals the inter-relationship of progressive spine pruning, elevated frontal cortical excitation of pyramidal neurons and striatal hyperdopaminergia in a cortical-to-midbrain circuit abnormality. The main symptomatic manifestations of this circuit abnormality are psychomotor agitation and stereotypical behaviors, which are relieved by antipsychotics. Moreover, this antipsychotic-responsive locomotion can be mimicked in wild-type mice by optogenetic activation of this circuit. Collectively these results reveal molecular and neural-circuit mechanisms, illustrating how diverse pathologies may converge to drive behaviors relevant to psychiatric disorders.

  5. Differences in neural activation for object-directed grasping in chimpanzees and humans.

    Science.gov (United States)

    Hecht, Erin E; Murphy, Lauren E; Gutman, David A; Votaw, John R; Schuster, David M; Preuss, Todd M; Orban, Guy A; Stout, Dietrich; Parr, Lisa A

    2013-08-28

    The human faculty for object-mediated action, including tool use and imitation, exceeds that of even our closest primate relatives and is a key foundation of human cognitive and cultural uniqueness. In humans and macaques, observing object-directed grasping actions activates a network of frontal, parietal, and occipitotemporal brain regions, but differences in human and macaque activation suggest that this system has been a focus of selection in the primate lineage. To study the evolution of this system, we performed functional neuroimaging in humans' closest living relatives, chimpanzees. We compare activations during performance of an object-directed manual grasping action, observation of the same action, and observation of a mimed version of the action that consisted of only movements without results. Performance and observation of the same action activated a distributed frontoparietal network similar to that reported in macaques and humans. Like humans and unlike macaques, these regions were also activated by observing movements without results. However, in a direct chimpanzee/human comparison, we also identified unique aspects of human neural responses to observed grasping. Chimpanzee activation showed a prefrontal bias, including significantly more activity in ventrolateral prefrontal cortex, whereas human activation was more evenly distributed across more posterior regions, including significantly more activation in ventral premotor cortex, inferior parietal cortex, and inferotemporal cortex. This indicates a more "bottom-up" representation of observed action in the human brain and suggests that the evolution of tool use, social learning, and cumulative culture may have involved modifications of frontoparietal interactions. PMID:23986247

  6. The relation of ongoing brain activity, evoked neural responses, and cognition

    Directory of Open Access Journals (Sweden)

    Sepideh Sadaghiani

    2010-06-01

    Full Text Available Ongoing brain activity has been observed since the earliest neurophysiological recordings and is found over a wide range of temporal and spatial scales. It is characterized by remarkably large spontaneous modulations. Here, we review evidence for the functional role of these ongoing activity fluctuations and argue that they constitute an essential property of the neural architecture underlying cognition. The role of spontaneous activity fluctuations is probably best understood when considering both their spatiotemporal structure and their functional impact on cognition. We first briefly argue against a ‘segregationist’ view on ongoing activity, both in time and space, countering this view with an emphasis on integration within a hierarchical spatiotemporal organization of intrinsic activity. We then highlight the flexibility and context-sensitivity of intrinsic functional connectivity that suggest its involvement in functionally relevant information processing. This role in information processing is pursued by reviewing how ongoing brain activity interacts with afferent and efferent information exchange of the brain with its environment. We focus on the relationship between the variability of ongoing and evoked brain activity, and review recent reports that tie ongoing brain activity fluctuations to variability in human perception and behavior. Finally, these observations are discussed within the framework of the free-energy principle which – applied to human brain function - provides a theoretical account for a non-random, coordinated interaction of ongoing and evoked activity in perception and behaviour.

  7. Trait self-esteem and neural activities related to self-evaluation and social feedback.

    Science.gov (United States)

    Yang, Juan; Xu, Xiaofan; Chen, Yu; Shi, Zhenhao; Han, Shihui

    2016-01-01

    Self-esteem has been associated with neural responses to self-reflection and attitude toward social feedback but in different brain regions. The distinct associations might arise from different tasks or task-related attitudes in the previous studies. The current study aimed to clarify these by investigating the association between self-esteem and neural responses to evaluation of one's own personality traits and of others' opinion about one's own personality traits. We scanned 25 college students using functional MRI during evaluation of oneself or evaluation of social feedback. Trait self-esteem was measured using the Rosenberg self-esteem scale after scanning. Whole-brain regression analyses revealed that trait self-esteem was associated with the bilateral orbitofrontal activity during evaluation of one's own positive traits but with activities in the medial prefrontal cortex, posterior cingulate, and occipital cortices during evaluation of positive social feedback. Our findings suggest that trait self-esteem modulates the degree of both affective processes in the orbitofrontal cortex during self-reflection and cognitive processes in the medial prefrontal cortex during evaluation of social feedback.

  8. Trait self-esteem and neural activities related to self-evaluation and social feedback.

    Science.gov (United States)

    Yang, Juan; Xu, Xiaofan; Chen, Yu; Shi, Zhenhao; Han, Shihui

    2016-01-01

    Self-esteem has been associated with neural responses to self-reflection and attitude toward social feedback but in different brain regions. The distinct associations might arise from different tasks or task-related attitudes in the previous studies. The current study aimed to clarify these by investigating the association between self-esteem and neural responses to evaluation of one's own personality traits and of others' opinion about one's own personality traits. We scanned 25 college students using functional MRI during evaluation of oneself or evaluation of social feedback. Trait self-esteem was measured using the Rosenberg self-esteem scale after scanning. Whole-brain regression analyses revealed that trait self-esteem was associated with the bilateral orbitofrontal activity during evaluation of one's own positive traits but with activities in the medial prefrontal cortex, posterior cingulate, and occipital cortices during evaluation of positive social feedback. Our findings suggest that trait self-esteem modulates the degree of both affective processes in the orbitofrontal cortex during self-reflection and cognitive processes in the medial prefrontal cortex during evaluation of social feedback. PMID:26842975

  9. Patterns of neural and behavioral activity in freely-moving Navanax inermis (Mollusca; Opisthobranchia).

    Science.gov (United States)

    Leonard, J L

    1992-01-01

    As part of an ongoing neuroethological study of complex behavior in the opisthobranch mollusc, Navanax inermis, I have extended the available gross anatomical descriptions and used cuff electrodes to obtain chronic recordings from whole nerves or connectives. The major anatomical findings concern a) finer branches of the pedal nerves, particularly P3C P4 and P5; b) the distribution of nerves from the abdominal and subintestinal ganglia; and c) a possible neurohaemal area of the supraintestinal ganglion. With cuff electrodes it has been possible to get good quality recordings (often with spikes in the mv range) during the full repertoire of sexual, predatory and cannibalistic behaviors. The high degree of cryptic neural activity and the fact that in Navanax behaviors are not mutually exclusive, make it difficult to identify one-to-one correspondences between behaviors and neural patterns, However, there is an apparent correlation between the activity of a very large unit(s) on P5 and an exploratory behavior, the Face-Down head posture when it is directed at the substrate rather than prey, or a conspecific. PMID:1299122

  10. Artificial neural network prediction of the psychometric activities of phenylalkylamines using DFT-calculated molecular descriptors

    Directory of Open Access Journals (Sweden)

    MINA HAGHDADI

    2010-10-01

    Full Text Available In the present work, a quantitative structure–activity relationship (QSAR method was used to predict the psychometric activity values (as mescaline unit, log MU of 48 phenylalkylamine derivatives from their density functional theory (DFT calculated molecular descriptors and an artificial neural network (ANN. In the first step, the molecular descriptors were obtained by DFT calculation at the 6-311G level of theory. Then the stepwise multiple linear regression method was employed to screen the descriptor spaces. In the next step, an artificial neural network and multiple linear regressions (MLR models were developed to construct nonlinear and linear QSAR models, respectively. The standard errors in the prediction of log MU by the MLR model were 0.398, 0.443 and 0.427 for training, internal and external test sets, respectively, while these values for the ANN model were 0.132, 0.197 and 0.202, respectively. The obtained results show the applicability of QSAR approaches by using ANN techniques in prediction of log MU of phenylalkylamine derivatives from their DFT-calculated molecular descriptors.

  11. Neural activation during imitation with or without performance feedback: An fMRI study.

    Science.gov (United States)

    Zhang, Kaihua; Wang, Hui; Dong, Guangheng; Wang, Mengxing; Zhang, Jilei; Zhang, Hui; Meng, Weixia; Du, Xiaoxia

    2016-08-26

    In our daily lives, we often receive performance feedback (PF) during imitative learning, and we adjust our behaviors accordingly to improve performance. However, little is known regarding the neural mechanisms underlying this learning process. We hypothesized that appropriate PF would enhance neural activation or recruit additional brain areas during subsequent action imitation. Pictures of 20 different finger gestures without any social meaning were shown to participants from the first-person perspective. Imitation with or without PF was investigated by functional magnetic resonance imaging in 30 healthy subjects. The PF was given by a real person or by a computer. PF from a real person induced hyperactivation of the parietal lobe (precuneus and cuneus), cingulate cortex (posterior and anterior), temporal lobe (superior and transverse temporal gyri), and cerebellum (posterior and anterior lobes) during subsequent imitation. The positive PF and negative PF from a real person, induced the activation of more brain areas during the following imitation. The hyperactivation of the cerebellum, posterior cingulate cortex, precuneus, and cuneus suggests that the subjects exhibited enhanced motor control and visual attention during imitation after PF. Additionally, random PF from a computer had a small effect on the next imitation. We suggest that positive and accurate PF may be helpful for imitation learning. PMID:27422729

  12. Altered Brain Activities Associated with Neural Repetition Effects in Mild Cognitive Impairment Patients.

    Science.gov (United States)

    Yu, Jing; Li, Rui; Jiang, Yang; Broster, Lucas S; Li, Juan

    2016-05-11

    Older adults with mild cognitive impairment (MCI) manifest impaired explicit memory. However, studies on implicit memory such as repetition effects in persons with MCI have been limited. In the present study, 17 MCI patients and 16 healthy normal controls (NC) completed a modified delayed-match-to-sample task while undergoing functional magnetic resonance imaging. We aim to examine the neural basis of repetition; specifically, to elucidate whether and how repetition-related brain responses are altered in participants with MCI. When repeatedly rejecting distracters, both NC and MCI showed similar behavioral repetition effects; however, in both whole-brain and region-of-interest analyses of functional data, persons with MCI showed reduced repetition-driven suppression in the middle occipital and middle frontal gyrus. Further, individual difference analysis found that activation in the left middle occipital gyrus was positively correlated with rejecting reaction time and negatively correlated with accuracy rate, suggesting a predictor of repetition behavioral performance. These findings provide new evidence to support the view that neural mechanisms of repetition effect are altered in MCI who manifests compensatory repetition-related brain activities along with their neuropathology. PMID:27176074

  13. QSAR study on estrogenic activity of structurally diverse compounds using generalized regression neural network

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Computer-based quantitative structure-activity relationship (QSAR) model has been becoming a pow- erful tool in understanding the structural requirements for chemicals to bind the estrogen receptor (ER), designing drugs for human estrogen replacement therapy, and identifying potential estrogenic endo- crine disruptors. In this study, a simple yet powerful neural network technique, generalized regression neural network (GRNN) was used to develop a QSAR model based on 131 structurally diverse estro- gens (training set). Only nine descriptors calculated solely from the molecular structures of com- pounds selected by objective and subjective feature selections were used as inputs of the GRNN model. The predictive power of the built model was found to be comparable to that of the more traditional techniques but requiring significantly easy implementation and a shorter computation-time. The ob- tained result indicates that the proposed GRNN model is robust and satisfactory, and can provide a feasible and practical tool for the rapid screening of the estrogenic activity of organic compounds.

  14. Adaptive Neural-Sliding Mode Control of Active Suspension System for Camera Stabilization

    Directory of Open Access Journals (Sweden)

    Feng Zhao

    2015-01-01

    Full Text Available The camera always suffers from image instability on the moving vehicle due to the unintentional vibrations caused by road roughness. This paper presents a novel adaptive neural network based on sliding mode control strategy to stabilize the image captured area of the camera. The purpose is to suppress vertical displacement of sprung mass with the application of active suspension system. Since the active suspension system has nonlinear and time varying characteristics, adaptive neural network (ANN is proposed to make the controller robustness against systematic uncertainties, which release the model-based requirement of the sliding model control, and the weighting matrix is adjusted online according to Lyapunov function. The control system consists of two loops. The outer loop is a position controller designed with sliding mode strategy, while the PID controller in the inner loop is to track the desired force. The closed loop stability and asymptotic convergence performance can be guaranteed on the basis of the Lyapunov stability theory. Finally, the simulation results show that the employed controller effectively suppresses the vibration of the camera and enhances the stabilization of the entire camera, where different excitations are considered to validate the system performance.

  15. Determination of platinum by radiochemical neutron activation analysis in neural tissues from rats, monkeys and patients treated with cisplatin

    DEFF Research Database (Denmark)

    Rietz, B.; Krarup-Hansen, A.; Rorth, M.

    2001-01-01

    of the animals mentioned and in the neural tissues of human patients. For the determination of platinum in the tissues radiochemical neutron activation analysis has been used. The detection limit is 1 ng Pt g(-1). The platinum results indicate that platinum becomes accumulated in the dorsal root ganglia......Cisplatin is one of the most used antineoplastic drugs, essential for the treatment of germ cell tumours. Its use in medical treatment of cancer patients often causes chronic peripheral neuropathy in these patients. The distribution of cisplatin in neural tissues is, therefore, of great interest....... Rats and monkeys were used as animal models for the study of sensory changes in different neural tissues, like spinal cord (ventral and dorsal part), dorsal root ganglia and sural nerve. The study was combined with quantitative measurements of the content of platinum in the neural tissues...

  16. Habenula circuit development: past, present and future

    Directory of Open Access Journals (Sweden)

    Carlo Antonio Beretta

    2012-04-01

    Full Text Available The habenular neural circuit is attracting increasing attention from researchers in fields as diverse as neuroscience, medicine, behavior, development and evolution. Recent studies have revealed that this part of the limbic system in the dorsal diencephalon is involved in reward, addiction and other behaviors and its impairment is associated with various neurological conditions and diseases. Since the initial description of the Dorsal Diencephalic Conduction system (DDC with the habenulae in its center at the end of the 19th century, increasingly sophisticated techniques have resolved much of its anatomy and have shown that these pathways relay information from different parts of the forebrain to the tegmentum, midbrain and hindbrain. The first part of this review gives a brief historical overview on how the improving experimental approaches have allowed the stepwise uncovering of much of the architecture of the habenula circuit as we know it today. Our brain distributes tasks differentially between left and right and it has become a paradigm that this functional lateralization is a universal feature of vertebrates. Moreover, task dependent differential brain activities have been linked to anatomical differences across the left-right axis in humans. A good way to further explore this fundamental issue will be to study the functional consequences of subtle changes in neural network formation, which requires that we fully understand DDC system development. As the habenular circuit is evolutionarily highly conserved, researchers have the option to perform such difficult experiments in more experimentally amenable vertebrate systems. Indeed, research in the last decade has shown that the zebrafish is well suited for the study of DDC system development and the phenomenon of functional lateralization. We will critically discuss the advantages of the zebrafish model, available techniques and others that are needed to fully understand habenular circuit

  17. Activating Endogenous Neural Precursor Cells Using Metformin Leads to Neural Repair and Functional Recovery in a Model of Childhood Brain Injury

    Directory of Open Access Journals (Sweden)

    Parvati Dadwal

    2015-08-01

    Full Text Available The development of cell replacement strategies to repair the injured brain has gained considerable attention, with a particular interest in mobilizing endogenous neural stem and progenitor cells (known as neural precursor cells [NPCs] to promote brain repair. Recent work demonstrated metformin, a drug used to manage type II diabetes, promotes neurogenesis. We sought to determine its role in neural repair following brain injury. We find that metformin administration activates endogenous NPCs, expanding the size of the NPC pool and promoting NPC migration and differentiation in the injured neonatal brain in a hypoxia-ischemia (H/I injury model. Importantly, metformin treatment following H/I restores sensory-motor function. Lineage tracking reveals that metformin treatment following H/I causes an increase in the absolute number of subependyma-derived NPCs relative to untreated H/I controls in areas associated with sensory-motor function. Hence, activation of endogenous NPCs is a promising target for therapeutic intervention in childhood brain injury models.

  18. Unbalanced Neuronal Circuits in Addiction

    OpenAIRE

    Volkow, Nora D; Wang, Gen-Jack; Tomasi, Dardo; Baler, Ruben D.

    2013-01-01

    Through sequential waves of drug-induced neurochemical stimulation, addiction co-opts the brain's neuronal circuits that mediate reward, motivation, , to behavioral inflexibility and a severe disruption of self-control and compulsive drug intake. Brain imaging technologies have allowed neuroscientists to map out the neural landscape of addiction in the human brain and to understand how drugs modify it.

  19. Cascaded active silicon microresonator array cross-connect circuits for WDM networks-on-chip

    Science.gov (United States)

    Poon, Andrew W.; Xu, Fang; Luo, Xianshu

    2008-02-01

    We propose a design of an optical switch on a silicon chip comprising a 5 × 5 array of cascaded waveguide-crossing-coupled microring resonator-based switches for photonic networks-on-chip applications. We adopt our recently demonstrated design of multimode-interference (MMI)-based wire waveguide crossings, instead of conventional plain waveguide crossings, for the merits of low loss and low crosstalk. The microring resonator is integrated with a lateral p-i-n diode for carrier-injection-based GHz-speed on-off switching. All 25 microring resonators are assumed to be identical within a relatively wide resonance line width. The optical circuit switch can employ a single wavelength channel or multiple wavelength channels that are spaced by the microring resonator free spectral range. We analyze the potential performance of the proposed photonic network in terms of (i) light path cross-connections loss budget, and (ii) DC on-off power consumption for establishing a light path. As a proof-of-concept, our initial experiments on cascaded passive silicon MMI-crossing-coupled microring resonators demonstrate 3.6-Gbit/s non-return-to-zero data transmissions at on- and off-resonance wavelengths.

  20. GABAA receptors in visual and auditory cortex and neural activity changes during basic visual stimulation

    Directory of Open Access Journals (Sweden)

    Pengmin eQin

    2012-12-01

    Full Text Available Recent imaging studies have demonstrated that levels of resting GABA in the visual cortex predict the degree of stimulus-induced activity in the same region. These studies have used the presentation of discrete visual stimulus; the change from closed eyes to open also represents a simple visual stimulus, however, and has been shown to induce changes in local brain activity and in functional connectivity between regions. We thus aimed to investigate the role of the GABA system, specifically GABAA receptors, in the changes in brain activity between the eyes closed (EC and eyes open (EO state in order to provide detail at the receptor level to complement previous studies of GABA concentrations. We conducted an fMRI study involving two different modes of the change from EC to EO: An EO and EC block design, allowing the modelling of the haemodynamic response, followed by longer periods of EC and EO to allow the measuring of functional connectivity. The same subjects also underwent [18F]Flumazenil PET measure GABAA receptor binding potentials. It was demonstrated that the local-to-global ratio of GABAA receptor binding potential in the visual cortex predicted the degree of changes in neural activity from EC to EO. This same relationship was also shown in the auditory cortex. Furthermore, the local-to-global ratio of GABAA receptor binding potential in the visual cortex also predicts the change of functional connectivity between visual and auditory cortex from EC to EO. These findings contribute to our understanding of the role of GABAA receptors in stimulus-induced neural activity in local regions and in inter-regional functional connectivity.