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Sample records for neural circuit model

  1. 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.

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

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    Sigurdsson, T

    2016-05-03

    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.

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

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    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.

  4. An integrated modelling framework for neural circuits with multiple neuromodulators

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    Vemana, Vinith

    2017-01-01

    Neuromodulators are endogenous neurochemicals that regulate biophysical and biochemical processes, which control brain function and behaviour, and are often the targets of neuropharmacological drugs. Neuromodulator effects are generally complex partly owing to the involvement of broad innervation, co-release of neuromodulators, complex intra- and extrasynaptic mechanism, existence of multiple receptor subtypes and high interconnectivity within the brain. In this work, we propose an efficient yet sufficiently realistic computational neural modelling framework to study some of these complex behaviours. Specifically, we propose a novel dynamical neural circuit model that integrates the effective neuromodulator-induced currents based on various experimental data (e.g. electrophysiology, neuropharmacology and voltammetry). The model can incorporate multiple interacting brain regions, including neuromodulator sources, simulate efficiently and easily extendable to large-scale brain models, e.g. for neuroimaging purposes. As an example, we model a network of mutually interacting neural populations in the lateral hypothalamus, dorsal raphe nucleus and locus coeruleus, which are major sources of neuromodulator orexin/hypocretin, serotonin and norepinephrine/noradrenaline, respectively, and which play significant roles in regulating many physiological functions. We demonstrate that such a model can provide predictions of systemic drug effects of the popular antidepressants (e.g. reuptake inhibitors), neuromodulator antagonists or their combinations. Finally, we developed user-friendly graphical user interface software for model simulation and visualization for both fundamental sciences and pharmacological studies. PMID:28100828

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

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    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.

  6. 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.

  7. 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.

  8. 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.

  9. Analog electronic neural network circuits

    Energy Technology Data Exchange (ETDEWEB)

    Graf, H.P.; Jackel, L.D. (AT and T Bell Labs., Holmdel, NJ (USA))

    1989-07-01

    The large interconnectivity and moderate precision required in neural network models present new opportunities for analog computing. This paper discusses analog circuits for a variety of problems such as pattern matching, optimization, and learning. Most of the circuits build so far are relatively small, exploratory designs. The most mature circuits are those for template matching. Chips performing this function are now being applied to pattern recognition problems.

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

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    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.

  11. Selective Manipulation of Neural Circuits.

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    Park, Hong Geun; Carmel, Jason B

    2016-04-01

    Unraveling the complex network of neural circuits that form the nervous system demands tools that can manipulate specific circuits. The recent evolution of genetic tools to target neural circuits allows an unprecedented precision in elucidating their function. Here we describe two general approaches for achieving circuit specificity. The first uses the genetic identity of a cell, such as a transcription factor unique to a circuit, to drive expression of a molecule that can manipulate cell function. The second uses the spatial connectivity of a circuit to achieve specificity: one genetic element is introduced at the origin of a circuit and the other at its termination. When the two genetic elements combine within a neuron, they can alter its function. These two general approaches can be combined to allow manipulation of neurons with a specific genetic identity by introducing a regulatory gene into the origin or termination of the circuit. We consider the advantages and disadvantages of both these general approaches with regard to specificity and efficacy of the manipulations. We also review the genetic techniques that allow gain- and loss-of-function within specific neural circuits. These approaches introduce light-sensitive channels (optogenetic) or drug sensitive channels (chemogenetic) into neurons that form specific circuits. We compare these tools with others developed for circuit-specific manipulation and describe the advantages of each. Finally, we discuss how these tools might be applied for identification of the neural circuits that mediate behavior and for repair of neural connections.

  12. Biologically based neural circuit modelling for the study of fear learning and extinction

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    Nair, Satish S.; Paré, Denis; Vicentic, Aleksandra

    2016-11-01

    The neuronal systems that promote protective defensive behaviours have been studied extensively using Pavlovian conditioning. In this paradigm, an initially neutral-conditioned stimulus is paired with an aversive unconditioned stimulus leading the subjects to display behavioural signs of fear. Decades of research into the neural bases of this simple behavioural paradigm uncovered that the amygdala, a complex structure comprised of several interconnected nuclei, is an essential part of the neural circuits required for the acquisition, consolidation and expression of fear memory. However, emerging evidence from the confluence of electrophysiological, tract tracing, imaging, molecular, optogenetic and chemogenetic methodologies, reveals that fear learning is mediated by multiple connections between several amygdala nuclei and their distributed targets, dynamical changes in plasticity in local circuit elements as well as neuromodulatory mechanisms that promote synaptic plasticity. To uncover these complex relations and analyse multi-modal data sets acquired from these studies, we argue that biologically realistic computational modelling, in conjunction with experiments, offers an opportunity to advance our understanding of the neural circuit mechanisms of fear learning and to address how their dysfunction may lead to maladaptive fear responses in mental disorders.

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

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    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.

  14. Neural Circuits on a Chip

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    Md. Fayad Hasan

    2016-09-01

    Full Text Available Neural circuits are responsible for the brain’s ability to process and store information. Reductionist approaches to understanding the brain include isolation of individual neurons for detailed characterization. When maintained in vitro for several days or weeks, dissociated neurons self-assemble into randomly connected networks that produce synchronized activity and are capable of learning. This review focuses on efforts to control neuronal connectivity in vitro and construct living neural circuits of increasing complexity and precision. Microfabrication-based methods have been developed to guide network self-assembly, accomplishing control over in vitro circuit size and connectivity. The ability to control neural connectivity and synchronized activity led to the implementation of logic functions using living neurons. Techniques to construct and control three-dimensional circuits have also been established. Advances in multiple electrode arrays as well as genetically encoded, optical activity sensors and transducers enabled highly specific interfaces to circuits composed of thousands of neurons. Further advances in on-chip neural circuits may lead to better understanding of the brain.

  15. A point-process response model for spike trains from single neurons in neural circuits under optogenetic stimulation.

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    Luo, X; Gee, S; Sohal, V; Small, D

    2016-02-10

    Optogenetics is a new tool to study neuronal circuits that have been genetically modified to allow stimulation by flashes of light. We study recordings from single neurons within neural circuits under optogenetic stimulation. The data from these experiments present a statistical challenge of modeling a high-frequency point process (neuronal spikes) while the input is another high-frequency point process (light flashes). We further develop a generalized linear model approach to model the relationships between two point processes, employing additive point-process response functions. The resulting model, point-process responses for optogenetics (PRO), provides explicit nonlinear transformations to link the input point process with the output one. Such response functions may provide important and interpretable scientific insights into the properties of the biophysical process that governs neural spiking in response to optogenetic stimulation. We validate and compare the PRO model using a real dataset and simulations, and our model yields a superior area-under-the-curve value as high as 93% for predicting every future spike. For our experiment on the recurrent layer V circuit in the prefrontal cortex, the PRO model provides evidence that neurons integrate their inputs in a sophisticated manner. Another use of the model is that it enables understanding how neural circuits are altered under various disease conditions and/or experimental conditions by comparing the PRO parameters. Copyright © 2015 John Wiley & Sons, Ltd.

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

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    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

  17. Dynamical systems, attractors, and neural circuits.

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    Miller, Paul

    2016-01-01

    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.

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

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    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.

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

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    Sorooshyari, Siamak; Huerta, Ramón; de Lecea, Luis

    2015-01-01

    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 do 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. PMID:25767461

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

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    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.

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

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    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. Neural circuit mechanisms of posttraumatic epilepsy

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    Robert F Hunt

    2013-06-01

    Full Text Available Traumatic brain injury (TBI greatly increases the risk for a number of mental health problems and is one of the most common causes of medically intractable epilepsy in humans. Several models of TBI have been developed to investigate the relationship between trauma, seizures, and epilepsy-related changes in neural circuit function. These studies have shown that the brain initiates immediate neuronal and glial responses following an injury, usually leading to significant cell loss in areas of the injured brain. Over time, long-term changes in the organization of neural circuits, particularly in neocortex and hippocampus, lead to an imbalance between excitatory and inhibitory neurotransmission and increased risk for spontaneous seizures. These include alterations to inhibitory interneurons and formation of new, excessive recurrent excitatory synaptic connectivity. Here, we review in vivo models of TBI as well as key cellular mechanisms of synaptic reorganization associated with posttraumatic epilepsy. The potential role of inflammation and increased blood brain barrier permeability in the pathophysiology of posttraumatic epilepsy is also discussed. A better understanding of mechanisms that promote the generation of epileptic activity versus those that promote compensatory brain repair and functional recovery should aid development of successful new therapies for posttraumatic epilepsy.

  3. VLSI circuits implementing computational models of neocortical circuits.

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    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.

  4. Semaphorin signaling in vertebrate neural circuit assembly

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    Yutaka eYoshida

    2012-06-01

    Full Text Available Neural circuit formation requires the coordination of many complex developmental processes. First, neurons project axons over long distances to find their final targets and then establish appropriate connectivity essential for the formation of neuronal circuitry. Growth cones, the leading edges of axons, navigate by interacting with a variety of attractive and repulsive axon guidance cues along their trajectories and at final target regions. In addition to guidance of axons, neuronal polarization, neuronal migration and dendrite development must be precisely regulated during development to establish proper neural circuitry. Semaphorins consist of a large protein family, which includes secreted and cell surface proteins, and they play important roles in many steps of neural circuit formation. The major semaphorin receptors are plexins and neuropilins, however other receptors and co-receptors also mediate signaling by semaphorins. Upon semaphorin binding to their receptors, downstream signaling molecules transduce this event within cells to mediate further events, including alteration of microtubule and actin cytoskeletal dynamics. Here, I review recent studies on semaphorin signaling in vertebrate neural circuit assembly, with the goal of highlighting how this diverse family of cues and receptors imparts exquisite specificity to neural complex connectivity.

  5. A neural circuit for angular velocity computation

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    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.

  6. Document analysis with neural net circuits

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    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.

  7. Contextual behavior and neural circuits

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    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

  8. Contextual behavior and neural circuits

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    Lee, Inah; Lee, Choong-Hee

    2013-01-01

    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 chooses an item or inhibits 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 contextual behavior

  9. Neural circuits for peristaltic wave propagation in crawling Drosophila larvae: analysis and modeling.

    Science.gov (United States)

    Gjorgjieva, Julijana; Berni, Jimena; Evers, Jan Felix; Eglen, Stephen J

    2013-01-01

    Drosophila larvae crawl by peristaltic waves of muscle contractions, which propagate along the animal body and involve the simultaneous contraction of the left and right side of each segment. Coordinated propagation of contraction does not require sensory input, suggesting that movement is generated by a central pattern generator (CPG). We characterized crawling behavior of newly hatched Drosophila larvae by quantifying timing and duration of segmental boundary contractions. We developed a CPG network model that recapitulates these patterns based on segmentally repeated units of excitatory and inhibitory (EI) neuronal populations coupled with immediate neighboring segments. A single network with symmetric coupling between neighboring segments succeeded in generating both forward and backward propagation of activity. The CPG network was robust to changes in amplitude and variability of connectivity strength. Introducing sensory feedback via "stretch-sensitive" neurons improved wave propagation properties such as speed of propagation and segmental contraction duration as observed experimentally. Sensory feedback also restored propagating activity patterns when an inappropriately tuned CPG network failed to generate waves. Finally, in a two-sided CPG model we demonstrated that two types of connectivity could synchronize the activity of two independent networks: connections from excitatory neurons on one side to excitatory contralateral neurons (E to E), and connections from inhibitory neurons on one side to excitatory contralateral neurons (I to E). To our knowledge, such I to E connectivity has not yet been found in any experimental system; however, it provides the most robust mechanism to synchronize activity between contralateral CPGs in our model. Our model provides a general framework for studying the conditions under which a single locally coupled network generates bilaterally synchronized and longitudinally propagating waves in either direction.

  10. Neural Circuits for Peristaltic Wave Propagation in Crawling Drosophila Larvae: Analysis and Modeling

    Directory of Open Access Journals (Sweden)

    Julijana eGjorgjieva

    2013-04-01

    Full Text Available Drosophila larvae crawl by peristaltic waves of muscle contractions, which propagate along the animal body and involve the simultaneous contraction of the left and right side of each segment. Coordinated propagation of contraction does not require sensory input, suggesting that movement is generated by a central pattern generator (CPG. We characterized crawling behavior of newly hatched Drosophila larvae by quantifying timing and duration of segmental boundary contractions. We developed a CPG network model that recapitulates these patterns based on segmentally repeated units of excitatory and inhibitory neuronal populations coupled with immediate neighboring segments. A single network with symmetric coupling between neighboring segments succeeded in generating both forward and backward propagation of activity. The CPG network was robust to changes in amplitude and variability of connectivity strength. Introducing sensory feedback via `stretch-sensitive' neurons improved wave propagation properties such as speed of propagation and segmental contraction duration as observed experimentally. Sensory feedback also restored propagating activity patterns when an inappropriately tuned CPG network failed to generate waves. Finally, in a two-sided CPG model we demonstrated that two types of connectivity could synchronize the activity of two independent networks: connections from excitatory neurons on one side to excitatory contralateral neurons (E to E, and connections from inhibitory neurons on one side to excitatory contralateral neurons (I to E. To our knowledge, such I to E connectivity has not yet been found in any experimental system; however, it provides the most robust mechanism to synchronize activity between contralateral CPGs in our model. Our model provides a general framework for studying the conditions under which a single locally coupled network generates bilaterally synchronized and longitudinally propagating waves in either direction.

  11. 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

  12. 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.

  13. Neural - glial circuits : Can Interneurons stop seizures

    Science.gov (United States)

    Nadkarni, Suhita; Jung, Peter

    2004-03-01

    Recent progress in neurobiology suggests that astrocytes - through calcium excitability - are active partners to the neurons by integrating their activity and, in turn, regulating synaptic transmission. In a similar fashion neurons and interneurons are the 'Yin and Yang' of the hippocampus. The dichotomy of excitation and inhibition between pyramidal neurons and interneurons plays a crucial role in the function of the neuronal circuit.We consider a model of a pyramidal cell in contact with one synaptic astrocytes. It has been shown that such a circuit - triggered by transient stimulation - can exhibit sustained oscillations ("seizures") for strong coupling. The question we are considering is, under what conditions synaptic inhibition can stop these seizures?

  14. 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.

  15. 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

  16. Integrating Neural Circuits Controlling Female Sexual Behavior

    Science.gov (United States)

    Micevych, Paul E.; Meisel, Robert L.

    2017-01-01

    The hypothalamus is most often associated with innate behaviors such as is hunger, thirst and sex. While the expression of these behaviors important for survival of the individual or the species is nested within the hypothalamus, the desire (i.e., motivation) for them is centered within the mesolimbic reward circuitry. In this review, we will use female sexual behavior as a model to examine the interaction of these circuits. We will examine the evidence for a hypothalamic circuit that regulates consummatory aspects of reproductive behavior, i.e., lordosis behavior, a measure of sexual receptivity that involves estradiol membrane-initiated signaling in the arcuate nucleus (ARH), activating β-endorphin projections to the medial preoptic nucleus (MPN), which in turn modulate ventromedial hypothalamic nucleus (VMH) activity—the common output from the hypothalamus. Estradiol modulates not only a series of neuropeptides, transmitters and receptors but induces dendritic spines that are for estrogenic induction of lordosis behavior. Simultaneously, in the nucleus accumbens of the mesolimbic system, the mating experience produces long term changes in dopamine signaling and structure. Sexual experience sensitizes the response of nucleus accumbens neurons to dopamine signaling through the induction of a long lasting early immediate gene. While estrogen alone increases spines in the ARH, sexual experience increases dendritic spine density in the nucleus accumbens. These two circuits appear to converge onto the medial preoptic area where there is a reciprocal influence of motivational circuits on consummatory behavior and vice versa. While it has not been formally demonstrated in the human, such circuitry is generally highly conserved and thus, understanding the anatomy, neurochemistry and physiology can provide useful insight into the motivation for sexual behavior and other innate behaviors in humans. PMID:28642689

  17. 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.

  18. Neuronify: An Educational Simulator for Neural Circuits

    Science.gov (United States)

    Hafreager, Anders; Malthe-Sørenssen, Anders; Fyhn, Marianne

    2017-01-01

    Abstract Educational software (apps) can improve science education by providing an interactive way of learning about complicated topics that are hard to explain with text and static illustrations. However, few educational apps are available for simulation of neural networks. Here, we describe an educational app, Neuronify, allowing the user to easily create and explore neural networks in a plug-and-play simulation environment. The user can pick network elements with adjustable parameters from a menu, i.e., synaptically connected neurons modelled as integrate-and-fire neurons and various stimulators (current sources, spike generators, visual, and touch) and recording devices (voltmeter, spike detector, and loudspeaker). We aim to provide a low entry point to simulation-based neuroscience by allowing students with no programming experience to create and simulate neural networks. To facilitate the use of Neuronify in teaching, a set of premade common network motifs is provided, performing functions such as input summation, gain control by inhibition, and detection of direction of stimulus movement. Neuronify is developed in C++ and QML using the cross-platform application framework Qt and runs on smart phones (Android, iOS) and tablet computers as well personal computers (Windows, Mac, Linux). PMID:28321440

  19. Neuronify: An Educational Simulator for Neural Circuits.

    Science.gov (United States)

    Dragly, Svenn-Arne; Hobbi Mobarhan, Milad; Våvang Solbrå, Andreas; Tennøe, Simen; Hafreager, Anders; Malthe-Sørenssen, Anders; Fyhn, Marianne; Hafting, Torkel; Einevoll, Gaute T

    2017-01-01

    Educational software (apps) can improve science education by providing an interactive way of learning about complicated topics that are hard to explain with text and static illustrations. However, few educational apps are available for simulation of neural networks. Here, we describe an educational app, Neuronify, allowing the user to easily create and explore neural networks in a plug-and-play simulation environment. The user can pick network elements with adjustable parameters from a menu, i.e., synaptically connected neurons modelled as integrate-and-fire neurons and various stimulators (current sources, spike generators, visual, and touch) and recording devices (voltmeter, spike detector, and loudspeaker). We aim to provide a low entry point to simulation-based neuroscience by allowing students with no programming experience to create and simulate neural networks. To facilitate the use of Neuronify in teaching, a set of premade common network motifs is provided, performing functions such as input summation, gain control by inhibition, and detection of direction of stimulus movement. Neuronify is developed in C++ and QML using the cross-platform application framework Qt and runs on smart phones (Android, iOS) and tablet computers as well personal computers (Windows, Mac, Linux).

  20. 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.

  1. Application of Extension Neural Network Type-1 to Fault Diagnosis of Electronic Circuits

    Directory of Open Access Journals (Sweden)

    Meng-Hui Wang

    2012-01-01

    Full Text Available The values of electronic components are always deviated, but the functions of the modern circuits are more and more precise, which makes the automatic fault diagnosis of analog circuits very complex and difficult. This paper presents an extension-neural-network-type-1-(ENN-1- based method for fault diagnosis of analog circuits. This proposed method combines the extension theory and neural networks to create a novel neural network. Using the matter-element models of fault types and a correlation function, can be calculated the correlation degree between the tested pattern and every fault type; then, the cause of the circuit malfunction can be directly diagnosed by the analysis of the correlation degree. The experimental results show that the proposed method has a high diagnostic accuracy and is more fault tolerant than the multilayer neural network (MNN and the k-means based methods.

  2. 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

  3. Classes of feedforward neural networks and their circuit complexity

    NARCIS (Netherlands)

    Shawe-Taylor, John S.; Anthony, Martin H.G.; Kern, Walter

    1992-01-01

    This paper aims to place neural networks in the context of boolean circuit complexity. We define appropriate classes of feedforward neural networks with specified fan-in, accuracy of computation and depth and using techniques of communication complexity proceed to show that the classes fit into a

  4. 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

  5. Improved estimation and interpretation of correlations in neural circuits.

    Directory of Open Access Journals (Sweden)

    Dimitri Yatsenko

    2015-03-01

    Full Text Available 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

  6. 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.

  7. Analog VLSI neural network integrated circuits

    Science.gov (United States)

    Kub, F. J.; Moon, K. K.; Just, E. A.

    1991-01-01

    Two analog very large scale integration (VLSI) vector matrix multiplier integrated circuit chips were designed, fabricated, and partially tested. They can perform both vector-matrix and matrix-matrix multiplication operations at high speeds. The 32 by 32 vector-matrix multiplier chip and the 128 by 64 vector-matrix multiplier chip were designed to perform 300 million and 3 billion multiplications per second, respectively. An additional circuit that has been developed is a continuous-time adaptive learning circuit. The performance achieved thus far for this circuit is an adaptivity of 28 dB at 300 KHz and 11 dB at 15 MHz. This circuit has demonstrated greater than two orders of magnitude higher frequency of operation than any previous adaptive learning circuit.

  8. 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.

  9. Extinction of drug seeking: Neural circuits and approaches to augmentation.

    Science.gov (United States)

    McNally, Gavan P

    2014-01-01

    Extinction training can reduce drug seeking behavior. This article reviews the neural circuits that contribute to extinction and approaches to enhancing the efficacy of extinction. Extinction of drug seeking depends on cortical-striatal-hypothalamic and cortical-hypothalamic-thalamic pathways. These pathways interface, in the hypothalamus and thalamus respectively, with the neural circuits controlling reinstatement of drug seeking. The actions of these pathways at lateral hypothalamic orexin neurons, and of perifornical/dorsomedial hypothalamic derived opioid peptides at kappa opioid receptors in the paraventricular thalamus, are important for inhibiting drug seeking. Despite effectively reducing or inhibiting drug seeking in the short term, extinguished drug seeking is prone to relapse. Three different strategies to augment extinction learning or retrieval are reviewed: pharmacological augmentation, retrieval - extinction training, and provision of extinction memory retrieval cues. These strategies have been used in animal models and with human drug users to enhance extinction or cue exposure treatments. They hold promise as novel strategies to promote abstinence from drug seeking. This article is part of a Special Issue entitled 'NIDA 40th Anniversary Issue'.

  10. A neural circuit architecture for angular integration in Drosophila.

    Science.gov (United States)

    Green, Jonathan; Adachi, Atsuko; Shah, Kunal K; Hirokawa, Jonathan D; Magani, Pablo S; Maimon, Gaby

    2017-06-01

    Many animals keep track of their angular heading over time while navigating through their environment. However, a neural-circuit architecture for computing heading has not been experimentally defined in any species. Here we describe a set of clockwise- and anticlockwise-shifting neurons in the Drosophila central complex whose wiring and physiology provide a means to rotate an angular heading estimate based on the fly's angular velocity. We show that each class of shifting neurons exists in two subtypes, with spatiotemporal activity profiles that suggest different roles for each subtype at the start and end of tethered-walking turns. Shifting neurons are required for the heading system to properly track the fly's heading in the dark, and stimulation of these neurons induces predictable shifts in the heading signal. The central features of this biological circuit are analogous to those of computational models proposed for head-direction cells in rodents and may shed light on how neural systems, in general, perform integration.

  11. 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.

  12. Railway Track Circuit Fault Diagnosis Using Recurrent Neural Networks.

    Science.gov (United States)

    de Bruin, Tim; Verbert, Kim; Babuska, Robert

    2017-03-01

    Timely detection and identification of faults in railway track circuits are crucial for the safety and availability of railway networks. In this paper, the use of the long-short-term memory (LSTM) recurrent neural network is proposed to accomplish these tasks based on the commonly available measurement signals. By considering the signals from multiple track circuits in a geographic area, faults are diagnosed from their spatial and temporal dependences. A generative model is used to show that the LSTM network can learn these dependences directly from the data. The network correctly classifies 99.7% of the test input sequences, with no false positive fault detections. In addition, the t-Distributed Stochastic Neighbor Embedding (t-SNE) method is used to examine the resulting network, further showing that it has learned the relevant dependences in the data. Finally, we compare our LSTM network with a convolutional network trained on the same task. From this comparison, we conclude that the LSTM network architecture is better suited for the railway track circuit fault detection and identification tasks than the convolutional network.

  13. Social Status-Dependent Shift in Neural Circuit Activation Affects Decision Making.

    Science.gov (United States)

    Miller, Thomas H; Clements, Katie; Ahn, Sungwoo; Park, Choongseok; Hye Ji, Eoon; Issa, Fadi A

    2017-02-22

    In a social group, animals make behavioral decisions that fit their social ranks. These behavioral choices are dependent on the various social cues experienced during social interactions. In vertebrates, little is known of how social status affects the underlying neural mechanisms regulating decision-making circuits that drive competing behaviors. Here, we demonstrate that social status in zebrafish (Danio rerio) influences behavioral decisions by shifting the balance in neural circuit activation between two competing networks (escape and swim). We show that socially dominant animals enhance activation of the swim circuit. Conversely, social subordinates display a decreased activation of the swim circuit, but an enhanced activation of the escape circuit. In an effort to understand how social status mediates these effects, we constructed a neurocomputational model of the escape and swim circuits. The model replicates our findings and suggests that social status-related shift in circuit dynamics could be mediated by changes in the relative excitability of the escape and swim networks. Together, our results reveal that changes in the excitabilities of the Mauthner command neuron for escape and the inhibitory interneurons that regulate swimming provide a cellular mechanism for the nervous system to adapt to changes in social conditions by permitting the animal to select a socially appropriate behavioral response.SIGNIFICANCE STATEMENT Understanding how social factors influence nervous system function is of great importance. Using zebrafish as a model system, we demonstrate how social experience affects decision making to enable animals to produce socially appropriate behavior. Based on experimental evidence and computational modeling, we show that behavioral decisions reflect the interplay between competing neural circuits whose activation thresholds shift in accordance with social status. We demonstrate this through analysis of the behavior and neural circuit

  14. 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

  15. A feedback neural circuit for calibrating aversive memory strength.

    Science.gov (United States)

    Ozawa, Takaaki; Ycu, Edgar A; Kumar, Ashwani; Yeh, Li-Feng; Ahmed, Touqeer; Koivumaa, Jenny; Johansen, Joshua P

    2017-01-01

    Aversive experiences powerfully regulate memory formation, and memory strength is proportional to the intensity of these experiences. Inhibition of the neural circuits that convey aversive signals when they are predicted by other sensory stimuli is hypothesized to set associative memory strength. However, the neural circuit mechanisms that produce this predictive inhibition to regulate memory formation are unknown. Here we show that predictive sensory cues recruit a descending feedback circuit from the central amygdala that activates a specific population of midbrain periaqueductal gray pain-modulatory neurons to control aversive memory strength. Optogenetic inhibition of this pathway disinhibited predicted aversive responses in lateral amygdala neurons, which store fear memories, resulting in the resetting of fear learning levels. These results reveal a control mechanism for calibrating learning signals to adaptively regulate the strength of behavioral learning. Dysregulation of this circuit could contribute to psychiatric disorders associated with heightened fear responsiveness.

  16. Distinct Neural Circuits Subserve Interpersonal and Non-interpersonal Emotions

    OpenAIRE

    Landa, Alla; Wang, Zhishun; Russell, James A.; Posner, Jonathan; Duan, Yunsuo; Kangarlu, Alayar; Huo, Yuankai; Fallon, Brian A.; Peterson, Bradley S.

    2013-01-01

    Emotions elicited by interpersonal versus non-interpersonal experiences have different effects on neurobiological functioning in both animals and humans. However, the extent to which the brain circuits underlying interpersonal and non-interpersonal emotions are distinct still remains unclear. The goal of our study was to assess whether different neural circuits are implicated in the processing of arousal and valence of interpersonal versus non-interpersonal emotions. During functional magneti...

  17. 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 suitable learning algorithm -- a continuous-time version of a temporal differential Hebbian learning algorithm for pulsed neural systems with non-linear synapses -- as well as circuits for the electronic implementation. Measurements from an experimental CMOS chip are presented. Finally, we use our test...

  18. Reassessing the HAROLD model: is the hemispheric asymmetry reduction in older adults a special case of compensatory-related utilisation of neural circuits?

    Science.gov (United States)

    Berlingeri, Manuela; Danelli, Laura; Bottini, Gabriella; Sberna, Maurizio; Paulesu, Eraldo

    2013-02-01

    The HAROLD (hemispheric asymmetry reduction in older adults) model, proposed by Cabeza in 2002, suggests that age-related neurofunctional changes are characterised by a significant reduction in the functional hemispheric lateralisation in the prefrontal cortex (PFC). The supporting evidence, however, has been derived from qualitative explorations of the data rather than from explicit statistical assessments of functional lateralisation. In contrast, the CRUNCH (compensation-related utilisation of neural circuits hypothesis) model posits that elderly subjects recruit additional brain regions that do not necessarily belong to the contralateral hemisphere as much as they rely on additional strategies to solve cognitive problems. To better assess the validity and generalisability of the HAROLD model, we analysed the fMRI patterns of twenty-four young subjects (age range: 18-30 years) and twenty-four healthy elderly subjects (age range: 50-80 years) collected during the performance of two linguistic/semantic tasks (a picture-naming task and a sentence judgment task) and two episodic long-term memory (eLTM) recognition tasks for the same materials. The functional hemispheric lateralisation in each group and the ensuing between-group differences were quantitatively assessed using statistical lateralisation maps (SLMs). The number of clusters showing a genuine HAROLD effect was proportional to the level of task demand. In addition, when quantitatively significant, these effects were not restricted to the PFC. We conclude that, in its original version, the HAROLD model captures only some of the age-related brain patterns observed in graceful ageing. The results observed in our study are compatible with the more general CRUNCH model, suggesting that the former patterns can be considered a special manifestation of age-related compensatory processes.

  19. Modeling cortical circuits.

    Energy Technology Data Exchange (ETDEWEB)

    Rohrer, Brandon Robinson; Rothganger, Fredrick H.; Verzi, Stephen J.; Xavier, Patrick Gordon

    2010-09-01

    The neocortex is perhaps the highest region of the human brain, where audio and visual perception takes place along with many important cognitive functions. An important research goal is to describe the mechanisms implemented by the neocortex. There is an apparent regularity in the structure of the neocortex [Brodmann 1909, Mountcastle 1957] which may help simplify this task. The work reported here addresses the problem of how to describe the putative repeated units ('cortical circuits') in a manner that is easily understood and manipulated, with the long-term goal of developing a mathematical and algorithmic description of their function. The approach is to reduce each algorithm to an enhanced perceptron-like structure and describe its computation using difference equations. We organize this algorithmic processing into larger structures based on physiological observations, and implement key modeling concepts in software which runs on parallel computing hardware.

  20. 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

  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. 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.

  3. Neural circuits as computational dynamical systems.

    Science.gov (United States)

    Sussillo, David

    2014-04-01

    Many recent studies of neurons recorded from cortex reveal complex temporal dynamics. How such dynamics embody the computations that ultimately lead to behavior remains a mystery. Approaching this issue requires developing plausible hypotheses couched in terms of neural dynamics. A tool ideally suited to aid in this question is the recurrent neural network (RNN). RNNs straddle the fields of nonlinear dynamical systems and machine learning and have recently seen great advances in both theory and application. I summarize recent theoretical and technological advances and highlight an example of how RNNs helped to explain perplexing high-dimensional neurophysiological data in the prefrontal cortex.

  4. Adaptive Neurotechnology for Making Neural Circuits Functional .

    Science.gov (United States)

    Jung, Ranu

    2008-03-01

    Two of the most important trends in recent technological developments are that technology is increasingly integrated with biological systems and that it is increasingly adaptive in its capabilities. Neuroprosthetic systems that provide lost sensorimotor function after a neural disability offer a platform to investigate this interplay between biological and engineered systems. Adaptive neurotechnology (hardware and software) could be designed to be biomimetic, guided by the physical and programmatic constraints observed in biological systems, and allow for real-time learning, stability, and error correction. An example will present biomimetic neural-network hardware that can be interfaced with the isolated spinal cord of a lower vertebrate to allow phase-locked real-time neural control. Another will present adaptive neural network control algorithms for functional electrical stimulation of the peripheral nervous system to provide desired movements of paralyzed limbs in rodents or people. Ultimately, the frontier lies in being able to utilize the adaptive neurotechnology to promote neuroplasticity in the living system on a long-time scale under co-adaptive conditions.

  5. Genetic control of active neural circuits

    Directory of Open Access Journals (Sweden)

    Leon Reijmers

    2009-12-01

    Full Text Available The use of molecular tools to study the neurobiology of complex behaviors has been hampered by an inability to target the desired changes to relevant groups of neurons. Specific memories and specific sensory representations are sparsely encoded by a small fraction of neurons embedded in a sea of morphologically and functionally similar cells. In this review we discuss genetics techniques that are being developed to address this difficulty. In several studies the use of promoter elements that are responsive to neural activity have been used to drive long lasting genetic alterations into neural ensembles that are activated by natural environmental stimuli. This approach has been used to examine neural activity patterns during learning and retrieval of a memory, to examine the regulation of receptor trafficking following learning and to functionally manipulate a specific memory trace. We suggest that these techniques will provide a general approach to experimentally investigate the link between patterns of environmentally activated neural firing and cognitive processes such as perception and memory.

  6. Unraveling the central proopiomelanocortin neural circuits

    Directory of Open Access Journals (Sweden)

    Aaron J. Mercer

    2013-02-01

    Full Text Available Central proopiomelanocortin (POMC neurons form a potent anorexigenic network, but our understanding of the integration of this hypothalamic circuit throughout the central nervous system (CNS remains incomplete. POMC neurons extend projections along the rostrocaudal axis of the brain, and can signal with both POMC-derived peptides and fast amino acid neurotransmitters. Although recent experimental advances in circuit-level manipulation have been applied to POMC neurons, many pivotal questions still remain: How and where do POMC neurons integrate metabolic information? Under what conditions do POMC neurons release bioactive molecules throughout the CNS? Are GABA and glutamate or neuropeptides released from POMC neurons more crucial for modulating feeding and metabolism? Resolving the exact stoichiometry of signals evoked from POMC neurons under different metabolic conditions therefore remains an ongoing endeavor. In this review, we analyze the anatomical atlas of this network juxtaposed to the physiological signaling of POMC neurons both in vitro and in vivo. We also consider novel genetic tools to further characterize the function of the POMC circuit in vivo. Our goal is to synthesize a global view of the POMC network, and to highlight gaps that require further research to expand our knowledge on how these neurons modulate energy balance.

  7. 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.

  8. Phylogenetic plasticity in the evolution of molluscan neural circuits.

    Science.gov (United States)

    Katz, Paul S

    2016-12-01

    Recent research on molluscan nervous systems provides a unique perspective on the evolution of neural circuits. Molluscs evolved large, encephalized nervous systems independently from other phyla. Homologous body-patterning genes were re-specified in molluscs to create a plethora of body plans and nervous system organizations. Octopuses, having the largest brains of any invertebrate, independently evolved a learning circuit similar in organization and function to the mushroom body of insects and the hippocampus of mammals. In gastropods, homologous neurons have been re-specified for different functions. Even species exhibiting similar, possibly homologous behavior have fundamental differences in the connectivity of the neurons underlying that behavior. Thus, molluscan nervous systems provide clear examples of re-purposing of homologous genes and neurons for neural circuits. Copyright © 2016 Elsevier Ltd. All rights reserved.

  9. Hox genes: choreographers in neural development, architects of circuit organization.

    Science.gov (United States)

    Philippidou, Polyxeni; Dasen, Jeremy S

    2013-10-02

    The neural circuits governing vital behaviors, such as respiration and locomotion, are comprised of discrete neuronal populations residing within the brainstem and spinal cord. Work over the past decade has provided a fairly comprehensive understanding of the developmental pathways that determine the identity of major neuronal classes within the neural tube. However, the steps through which neurons acquire the subtype diversities necessary for their incorporation into a particular circuit are still poorly defined. Studies on the specification of motor neurons indicate that the large family of Hox transcription factors has a key role in generating the subtypes required for selective muscle innervation. There is also emerging evidence that Hox genes function in multiple neuronal classes to shape synaptic specificity during development, suggesting a broader role in circuit assembly. This Review highlights the functions and mechanisms of Hox gene networks and their multifaceted roles during neuronal specification and connectivity.

  10. Implementing neural architectures using analog VLSI circuits

    Science.gov (United States)

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

    1989-05-01

    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 that of biological systems, and perform computations useful for artificial sensory systems.

  11. Circuit-breakers: optical technologies for probing neural signals and systems.

    Science.gov (United States)

    Zhang, Feng; Aravanis, Alexander M; Adamantidis, Antoine; de Lecea, Luis; Deisseroth, Karl

    2007-08-01

    Neuropsychiatric disorders, which arise from a combination of genetic, epigenetic and environmental influences, epitomize the challenges faced in understanding the mammalian brain. Elucidation and treatment of these diseases will benefit from understanding how specific brain cell types are interconnected and signal in neural circuits. Newly developed neuroengineering tools based on two microbial opsins, channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR), enable the investigation of neural circuit function with cell-type-specific, temporally accurate and reversible neuromodulation. These tools could lead to the development of precise neuromodulation technologies for animal models of disease and clinical neuropsychiatry.

  12. 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.

  13. The neural circuits for arithmetic principles.

    Science.gov (United States)

    Liu, Jie; Zhang, Han; Chen, Chuansheng; Chen, Hui; Cui, Jiaxin; Zhou, Xinlin

    2017-02-15

    Arithmetic principles are the regularities underlying arithmetic computation. Little is known about how the brain supports the processing of arithmetic principles. The current fMRI study examined neural activation and functional connectivity during the processing of verbalized arithmetic principles, as compared to numerical computation and general language processing. As expected, arithmetic principles elicited stronger activation in bilateral horizontal intraparietal sulcus and right supramarginal gyrus than did language processing, and stronger activation in left middle temporal lobe and left orbital part of inferior frontal gyrus than did computation. In contrast, computation elicited greater activation in bilateral horizontal intraparietal sulcus (extending to posterior superior parietal lobule) than did either arithmetic principles or language processing. Functional connectivity analysis with the psychophysiological interaction approach (PPI) showed that left temporal-parietal (MTG-HIPS) connectivity was stronger during the processing of arithmetic principle and language than during computation, whereas parietal-occipital connectivities were stronger during computation than during the processing of arithmetic principles and language. Additionally, the left fronto-parietal (orbital IFG-HIPS) connectivity was stronger during the processing of arithmetic principles than during computation. The results suggest that verbalized arithmetic principles engage a neural network that overlaps but is distinct from the networks for computation and language processing.

  14. Developmental metaplasticity in neural circuit codes of firing and structure.

    Science.gov (United States)

    Baram, Yoram

    2017-01-01

    Firing-rate dynamics have been hypothesized to mediate inter-neural information transfer in the brain. While the Hebbian paradigm, relating learning and memory to firing activity, has put synaptic efficacy variation at the center of cortical plasticity, we suggest that the external expression of plasticity by changes in the firing-rate dynamics represents a more general notion of plasticity. Hypothesizing that time constants of plasticity and firing dynamics increase with age, and employing the filtering property of the neuron, we obtain the elementary code of global attractors associated with the firing-rate dynamics in each developmental stage. We define a neural circuit connectivity code as an indivisible set of circuit structures generated by membrane and synapse activation and silencing. Synchronous firing patterns under parameter uniformity, and asynchronous circuit firing are shown to be driven, respectively, by membrane and synapse silencing and reactivation, and maintained by the neuronal filtering property. Analytic, graphical and simulation representation of the discrete iteration maps and of the global attractor codes of neural firing rate are found to be consistent with previous empirical neurobiological findings, which have lacked, however, a specific correspondence between firing modes, time constants, circuit connectivity and cortical developmental stages.

  15. Nitrosative Stress-Induced Disruption of Baroreflex Neural Circuits in a Rat Model of Hepatic Encephalopathy: A DTI Study

    Science.gov (United States)

    Tsai, Ching-Yi; Su, Chia-Hao; Chan, Julie Y. H.; Chan, Samuel H. H.

    2017-01-01

    The onset of hepatic encephalopathy (HE) in liver failure is associated with high mortality; the underlying mechanism is undecided. Here we report that in an acute liver failure model employing intraperitoneal administration of thioacetamide in Sprague-Dawley rats, diffusion weighted imaging revealed a progressive reduction in apparent diffusion coefficient in the brain stem. Diffusion tensor imaging further showed that the connectivity between nucleus tractus solitarii (NTS), the terminal site of baroreceptor afferents in brain stem and rostral ventrolateral medulla (RVLM), the origin of sympathetic innervation of blood vessels, was progressively disrupted until its disappearance, coincidental with the irreversible cessation of baroreflex-mediated sympathetic vasomotor tone signifying clinically the occurrence of brain death. In addition, superoxide, nitric oxide, peroxynitrite and ammonia levels in the NTS or RVLM were elevated, alongside swelling of astroctytes. A scavenger of peroxynitrite, but not an antioxidant, delivered intracisternally reversed all these events. We conclude that nitrosative stress because of augmented peroxynitrite related to accumulation of ammonia and swelling of astrocytes in the NTS or RVLM, leading to cytotoxic edema in the brain stem and severance of the NTS-RVLM connectivity, underpins the defunct baroreflex-mediated sympathetic vasomotor tone that accounts for the high mortality associated with HE. PMID:28079146

  16. Genetic dissection of GABAergic neural circuits in mouse neocortex

    Directory of Open Access Journals (Sweden)

    Hiroki eTaniguchi

    2014-01-01

    Full Text Available Diverse and flexible cortical functions rely on the ability of neural circuits to perform multiple types of neuronal computations. GABAergic inhibitory interneurons significantly contribute to this task by regulating the balance of activity, synaptic integration, spiking, synchrony, and oscillation in a neural ensemble. GABAergic interneruons display a high degree of cellular diversity in morphology, physiology, connectivity, and gene expression. A considerable number of subtypes of GABAergic interneurons diversify modes of cortical inhibition, enabling various types of information processing in the cortex. Thus, comprehensively understanding fate specification, circuit assembly and physiological function of GABAergic interneurons is a key to elucidate the principles of cortical wiring and function. Recent advances in genetically encoded molecular tools have made a breakthrough to systematically study cortical circuitry at the molecular, cellular, circuit, and whole animal levels. However, the biggest obstacle to fully applying the power of these to analysis of GABAergic circuits was that there were no efficient and reliable methods to express them in subtypes of GABAergic interneurons. Here, I first summarize cortical interneuron diversity and current understanding of mechanisms, by which distinct classes of GABAergic interneurons are generated. I then review recent development in genetically encoded molecular tools for neural circuit research, and genetic targeting of GABAergic interneuron subtypes, particulary focusing on our recent effort to develop and characterize Cre/CreER knockin lines. Finally, I highlight recent success in genetic targeting of chandelier cells (ChCs, the most unique and distinct GABAergic interneuron subtype, and discuss what kind of questions need to be addressed to understand development and function of cortical inhibitory circuits.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. Breathtaking Songs: Coordinating the Neural Circuits for Breathing and Singing.

    Science.gov (United States)

    Schmidt, Marc F; Goller, Franz

    2016-11-01

    The vocal behavior of birds is remarkable for its diversity, and songs can feature elaborate characteristics such as long duration, rapid temporal pattern, and broad frequency range. The respiratory system plays a central role in generating the complex song patterns that must be integrated with its life-sustaining functions. Here, we explore how precise coordination between the neural circuits for breathing and singing is fundamental to production of these remarkable behaviors. ©2016 Int. Union Physiol. Sci./Am. Physiol. Soc.

  2. 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.

  3. Neural Circuits Trained with Standard Reinforcement Learning Can Accumulate Probabilistic Information during Decision Making.

    Science.gov (United States)

    Kurzawa, Nils; Summerfield, Christopher; Bogacz, Rafal

    2017-02-01

    Much experimental evidence suggests that during decision making, neural circuits accumulate evidence supporting alternative options. A computational model well describing this accumulation for choices between two options assumes that the brain integrates the log ratios of the likelihoods of the sensory inputs given the two options. Several models have been proposed for how neural circuits can learn these log-likelihood ratios from experience, but all of these models introduced novel and specially dedicated synaptic plasticity rules. Here we show that for a certain wide class of tasks, the log-likelihood ratios are approximately linearly proportional to the expected rewards for selecting actions. Therefore, a simple model based on standard reinforcement learning rules is able to estimate the log-likelihood ratios from experience and on each trial accumulate the log-likelihood ratios associated with presented stimuli while selecting an action. The simulations of the model replicate experimental data on both behavior and neural activity in tasks requiring accumulation of probabilistic cues. Our results suggest that there is no need for the brain to support dedicated plasticity rules, as the standard mechanisms proposed to describe reinforcement learning can enable the neural circuits to perform efficient probabilistic inference.

  4. 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...

  5. 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.

  6. Fermionic models with superconducting circuits

    Energy Technology Data Exchange (ETDEWEB)

    Las Heras, Urtzi; Garcia-Alvarez, Laura; Mezzacapo, Antonio; Lamata, Lucas [University of the Basque Country UPV/EHU, Department of Physical Chemistry, Bilbao (Spain); Solano, Enrique [University of the Basque Country UPV/EHU, Department of Physical Chemistry, Bilbao (Spain); IKERBASQUE, Basque Foundation for Science, Bilbao (Spain)

    2015-12-01

    We propose a method for the efficient quantum simulation of fermionic systems with superconducting circuits. It consists in the suitable use of Jordan-Wigner mapping, Trotter decomposition, and multiqubit gates, be with the use of a quantum bus or direct capacitive couplings. We apply our method to the paradigmatic cases of 1D and 2D Fermi-Hubbard models, involving couplings with nearest and next-nearest neighbours. Furthermore, we propose an optimal architecture for this model and discuss the benchmarking of the simulations in realistic circuit quantum electrodynamics setups. (orig.)

  7. Olfactory systems and neural circuits that modulate predator odor fear.

    Science.gov (United States)

    Takahashi, Lorey K

    2014-01-01

    When prey animals detect the odor of a predator a constellation of fear-related autonomic, endocrine, and behavioral responses rapidly occur to facilitate survival. How olfactory sensory systems process predator odor and channel that information to specific brain circuits is a fundamental issue that is not clearly understood. However, research in the last 15 years has begun to identify some of the essential features of the sensory detection systems and brain structures that underlie predator odor fear. For instance, the main (MOS) and accessory olfactory systems (AOS) detect predator odors and different types of predator odors are sensed by specific receptors located in either the MOS or AOS. However, complex predator chemosignals may be processed by both the MOS and AOS, which complicate our understanding of the specific neural circuits connected directly and indirectly from the MOS and AOS to activate the physiological and behavioral components of unconditioned and conditioned fear. Studies indicate that brain structures including the dorsal periaqueductal gray (DPAG), paraventricular nucleus (PVN) of the hypothalamus, and the medial amygdala (MeA) appear to be broadly involved in predator odor induced autonomic activity and hypothalamic-pituitary-adrenal (HPA) stress hormone secretion. The MeA also plays a key role in predator odor unconditioned fear behavior and retrieval of contextual fear memory associated with prior predator odor experiences. Other neural structures including the bed nucleus of the stria terminalis and the ventral hippocampus (VHC) appear prominently involved in predator odor fear behavior. The basolateral amygdala (BLA), medial hypothalamic nuclei, and medial prefrontal cortex (mPFC) are also activated by some but not all predator odors. Future research that characterizes how distinct predator odors are uniquely processed in olfactory systems and neural circuits will provide significant insights into the differences of how diverse predator

  8. Generating three-qubit quantum circuits with neural networks

    Science.gov (United States)

    Swaddle, Michael; Noakes, Lyle; Smallbone, Harry; Salter, Liam; Wang, Jingbo

    2017-10-01

    A new method for compiling quantum algorithms is proposed and tested for a three qubit system. The proposed method is to decompose a unitary matrix U, into a product of simpler Uj via a neural network. These Uj can then be decomposed into product of known quantum gates. Key to the effectiveness of this approach is the restriction of the set of training data generated to paths which approximate minimal normal subRiemannian geodesics, as this removes unnecessary redundancy and ensures the products are unique. The two neural networks are shown to work effectively, each individually returning low loss values on validation data after relatively short training periods. The two networks are able to return coefficients that are sufficiently close to the true coefficient values to validate this method as an approach for generating quantum circuits. There is scope for more work in scaling this approach for larger quantum systems.

  9. 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

  10. 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

    2009-08-01

    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- mum 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.

  11. 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.

  12. 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.

  13. Developing a Domain Model for Relay Circuits

    DEFF Research Database (Denmark)

    Haxthausen, Anne Elisabeth

    2009-01-01

    the statics as well as the dynamics of relay circuits, i.e. how a relay circuit can be composed legally from electrical components as well as how the components may change state over time. Finally the circuit model is transformed into an executable model, and we show how a concrete circuit can be defined......In this paper we stepwise develop a domain model for relay circuits as used in railway control systems. First we provide an abstract, property-oriented model of networks consisting of components that can be glued together with connectors. This model is strongly inspired by a network model...... for railways madeby Bjørner et.al., however our model is more general: the components can be of any kind and can later be refined to e.g. railway components or circuit components. Then we show how the abstract network model can be refined into an explicit model for relay circuits. The circuit model describes...

  14. Model reduction for circuit simulation

    CERN Document Server

    Hinze, Michael; Maten, E Jan W Ter

    2011-01-01

    Simulation based on mathematical models plays a major role in computer aided design of integrated circuits (ICs). Decreasing structure sizes, increasing packing densities and driving frequencies require the use of refined mathematical models, and to take into account secondary, parasitic effects. This leads to very high dimensional problems which nowadays require simulation times too large for the short time-to-market demands in industry. Modern Model Order Reduction (MOR) techniques present a way out of this dilemma in providing surrogate models which keep the main characteristics of the devi

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  20. Acute Stress Influences Neural Circuits of Reward Processing

    Directory of Open Access Journals (Sweden)

    Anthony John Porcelli

    2012-11-01

    Full Text Available People often make decisions under aversive conditions such as acute stress. Yet, less is known about the process in which acute stress can influence decision-making. A growing body of research has established that reward-related information associated with the outcomes of decisions exerts a powerful influence over the choices people make and that an extensive network of brain regions, prominently featuring the striatum, is involved in the processing of this reward-related information. Thus, an important step in research on the nature of acute stress’ influence over decision-making is to examine how it may modulate responses to rewards and punishments within reward-processing neural circuitry. In the current experiment, we employed a simple reward processing paradigm – where participants received monetary rewards and punishments – known to evoke robust striatal responses. Immediately prior to performing each of two task runs, participants were exposed to acute stress (i.e., cold pressor or a no stress control procedure in a between-subjects fashion. No stress group participants exhibited a pattern of activity within the dorsal striatum and orbitofrontal cortex consistent with past research on outcome processing – specifically, differential responses for monetary rewards over punishments. In contrast, acute stress group participants’ dorsal striatum and orbitofrontal cortex demonstrated decreased sensitivity to monetary outcomes and a lack of differential activity. These findings provide insight into how neural circuits may process rewards and punishments associated with simple decisions under acutely stressful conditions.

  1. 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.

  2. SIMPEL: Circuit model for photonic spike processing laser neurons

    CERN Document Server

    Shastri, Bhavin J; Tait, Alexander N; Wu, Ben; Prucnal, Paul R

    2014-01-01

    We propose an equivalent circuit model for photonic spike processing laser neurons with an embedded saturable absorber---a simulation model for photonic excitable lasers (SIMPEL). We show that by mapping the laser neuron rate equations into a circuit model, SPICE analysis can be used as an efficient and accurate engine for numerical calculations, capable of generalization to a variety of different laser neuron types found in literature. The development of this model parallels the Hodgkin--Huxley model of neuron biophysics, a circuit framework which brought efficiency, modularity, and generalizability to the study of neural dynamics. We employ the model to study various signal-processing effects such as excitability with excitatory and inhibitory pulses, binary all-or-nothing response, and bistable dynamics.

  3. 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.

  4. Clustered Protocadherins Are Required for Building Functional Neural Circuits

    Directory of Open Access Journals (Sweden)

    Takeshi Yagi

    2017-04-01

    Full Text Available Neuronal identity is generated by the cell-surface expression of clustered protocadherin (Pcdh isoforms. In mice, 58 isoforms from three gene clusters, Pcdhα, Pcdhβ, and Pcdhγ, are differentially expressed in neurons. Since cis-heteromeric Pcdh oligomers on the cell surface interact homophilically with that in other neurons in trans, it has been thought that the Pcdh isoform repertoire determines the binding specificity of synapses. We previously described the cooperative functions of isoforms from all three Pcdh gene clusters in neuronal survival and synapse formation in the spinal cord. However, the neuronal loss and the following neonatal lethality prevented an analysis of the postnatal development and characteristics of the clustered-Pcdh-null (Δαβγ neural circuits. Here, we used two methods, one to generate the chimeric mice that have transplanted Δαβγ neurons into mouse embryos, and the other to generate double mutant mice harboring null alleles of both the Pcdh gene and the proapoptotic gene Bax to prevent neuronal loss. First, our results showed that the surviving chimeric mice that had a high contribution of Δαβγ cells exhibited paralysis and died in the postnatal period. An analysis of neuronal survival in postnatally developing brain regions of chimeric mice clarified that many Δαβγ neurons in the forebrain were spared from apoptosis, unlike those in the reticular formation of the brainstem. Second, in Δαβγ/Bax null double mutants, the central pattern generator (CPG for locomotion failed to create a left-right alternating pattern even in the absence of neurodegeneraton. Third, calcium imaging of cultured hippocampal neurons showed that the network activity of Δαβγ neurons tended to be more synchronized and lost the variability in the number of simultaneously active neurons observed in the control network. Lastly, a comparative analysis for trans-homophilic interactions of the exogenously introduced single

  5. 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.

  6. Models wagging the dog: are circuits constructed with disparate parameters?

    Science.gov (United States)

    Nowotny, Thomas; Szücs, Attila; Levi, Rafael; Selverston, Allen I

    2007-08-01

    In a recent article, Prinz, Bucher, and Marder (2004) addressed the fundamental question of whether neural systems are built with a fixed blueprint of tightly controlled parameters or in a way in which properties can vary largely from one individual to another, using a database modeling approach. Here, we examine the main conclusion that neural circuits indeed are built with largely varying parameters in the light of our own experimental and modeling observations. We critically discuss the experimental and theoretical evidence, including the general adequacy of database approaches for questions of this kind, and come to the conclusion that the last word for this fundamental question has not yet been spoken.

  7. Nonsmooth Modeling and Simulation for Switched Circuits

    CERN Document Server

    Acary, Vincent; Brogliato, Bernard

    2011-01-01

    "Nonsmooth Modeling and Simulation for Switched Circuits" concerns the modeling and the numerical simulation of switched circuits with the nonsmooth dynamical systems (NSDS) approach, using piecewise-linear and multivalued models of electronic devices like diodes, transistors, switches. Numerous examples (ranging from introductory academic circuits to various types of power converters) are analyzed and many simulation results obtained with the INRIA open-source SICONOS software package are presented. Comparisons with SPICE and hybrid methods demonstrate the power of the NSDS approach

  8. Towards Confirming Neural Circuit Inference from Population Calcium Imaging. NIPS Workshop on Connectivity Inference in Neuroimaging

    OpenAIRE

    NeuroData; Mishchenko, Y.; AM, Packer; TA, Machado; Yuste, R.; Paninski, L

    2015-01-01

    Vogelstein JT, Mishchenko Y, Packer AM, Machado TA, Yuste R, Paninski L. Towards Confirming Neural Circuit Inference from Population Calcium Imaging. NIPS Workshop on Connectivity Inference in Neuroimaging, 2009

  9. 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

  10. Relating functional connectivity in V1 neural circuits and 3D natural scenes using Boltzmann machines

    Science.gov (United States)

    Zhang, Yimeng; Li, Xiong; Samonds, Jason M.

    2015-01-01

    Bayesian theory has provided a compelling conceptualization for perceptual inference in the brain. Central to Bayesian inference is the notion of statistical priors. To understand the neural mechanisms of Bayesian inference, we need to understand the neural representation of statistical regularities in the natural environment. In this paper, we investigated empirically how statistical regularities in natural 3D scenes are represented in the functional connectivity of disparity-tuned neurons in the primary visual cortex of primates. We applied a Boltzmann machine model to learn from 3D natural scenes, and found that the units in the model exhibited cooperative and competitive interactions, forming a “disparity association field”, analogous to the contour association field. The cooperative and competitive interactions in the disparity association field are consistent with constraints of computational models for stereo matching. In addition, we simulated neurophysiological experiments on the model, and found the results to be consistent with neurophysiological data in terms of the functional connectivity measurements between disparity-tuned neurons in the macaque primary visual cortex. These findings demonstrate that there is a relationship between the functional connectivity observed in the visual cortex and the statistics of natural scenes. They also suggest that the Boltzmann machine can be a viable model for conceptualizing computations in the visual cortex and, as such, can be used to predict neural circuits in the visual cortex from natural scene statistics. PMID:26712581

  11. Mathematical modelling of fractional order circuits

    CERN Document Server

    Moreles, Miguel Angel

    2016-01-01

    In this work a classical derivation of fractional order circuits models is presented. Generalized constitutive equations in terms of fractional Riemann-Liouville derivatives are introduced in the Maxwell's equations. Next the Kirchhoff voltage law is applied in a RCL circuit configuration. A fractional differential equation model is obtained with Caputo derivatives. Thus standard initial conditions apply.

  12. Sex differences in behavioral decision-making and the modulation of shared neural circuits

    Directory of Open Access Journals (Sweden)

    Mowrey William R

    2012-03-01

    Full Text Available Abstract Animals prioritize behaviors according to their physiological needs and reproductive goals, selecting a single behavioral strategy from a repertoire of possible responses to any given stimulus. Biological sex influences this decision-making process in significant ways, differentiating the responses animals choose when faced with stimuli ranging from food to conspecifics. We review here recent work in invertebrate models, including C. elegans, Drosophila, and a variety of insects, mollusks and crustaceans, that has begun to offer intriguing insights into the neural mechanisms underlying the sexual modulation of behavioral decision-making. These findings show that an animal's sex can modulate neural function in surprisingly diverse ways, much like internal physiological variables such as hunger or thirst. In the context of homeostatic behaviors such as feeding, an animal's sex and nutritional status may converge on a common physiological mechanism, the functional modulation of shared sensory circuitry, to influence decision-making. Similarly, considerable evidence suggests that decisions on whether to mate or fight with conspecifics are also mediated through sex-specific neuromodulatory control of nominally shared neural circuits. This work offers a new perspective on how sex differences in behavior emerge, in which the regulated function of shared neural circuitry plays a crucial role. Emerging evidence from vertebrates indicates that this paradigm is likely to extend to more complex nervous systems as well. As men and women differ in their susceptibility to a variety of neuropsychiatric disorders affecting shared behaviors, these findings may ultimately have important implications for human health.

  13. Demonstration of a neural circuit critical for imprinting behavior in chicks.

    Science.gov (United States)

    Nakamori, Tomoharu; Sato, Katsushige; Atoji, Yasuro; Kanamatsu, Tomoyuki; Tanaka, Kohichi; Ohki-Hamazaki, Hiroko

    2010-03-24

    Imprinting behavior in birds is elicited by visual and/or auditory cues. It has been demonstrated previously that visual cues are recognized and processed in the visual Wulst (VW), and imprinting memory is stored in the intermediate medial mesopallium (IMM) of the telencephalon. Alteration of neural responses in these two regions according to imprinting has been reported, yet direct evidence of the neural circuit linking these two regions is lacking. Thus, it remains unclear how memory is formed and expressed in this circuit. Here, we present anatomical as well as physiological evidence of the neural circuit connecting the VW and IMM and show that imprinting training during the critical period strengthens and refines this circuit. A functional connection established by imprint training resulted in an imprinting behavior. After the closure of the critical period, training could not activate this circuit nor induce the imprinting behavior. Glutamatergic neurons in the ventroposterior region of the VW, the core region of the hyperpallium densocellulare (HDCo), sent their axons to the periventricular part of the HD, just dorsal and afferent to the IMM. We found that the HDCo is important in imprinting behavior. The refinement and/or enhancement of this neural circuit are attributed to increased activity of HDCo cells, and the activity depended on NR2B-containing NMDA receptors. These findings show a neural connection in the telencephalon in Aves and demonstrate that NR2B function is indispensable for the plasticity of HDCo cells, which are key mediators of imprinting.

  14. Model Order Reduction for Electronic Circuits:

    DEFF Research Database (Denmark)

    Hjorth, Poul G.; Shontz, Suzanne

    Electronic circuits are ubiquitous; they are used in numerous industries including: the semiconductor, communication, robotics, auto, and music industries (among many others). As products become more and more complicated, their electronic circuits also grow in size and complexity. This increased ...... in the semiconductor industry. Circuit simulation proceeds by using Maxwell’s equations to create a mathematical model of the circuit. The boundary element method is then used to discretize the equations, and the variational form of the equations are then solved on the graph network....

  15. A figure of merit for neural electrical stimulation circuits.

    Science.gov (United States)

    Kolbl, Florian; Demosthenous, Andreas

    2015-01-01

    Electrical stimulators are widely used in neuro-prostheses. Many different implementations exist. However, no quantitative ranking criterion is available to allow meaningful comparison of the various stimulation circuits and systems to aid the designer. This paper presents a novel Figure of Merit (FOM) dedicated to stimulation circuits and systems. The proposed optimization performance metric takes into account tissue safety conditions and energy efficiency which can be evaluated by measurement. The FOM is used to rank several stimulator circuits and systems.

  16. 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

  17. 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.

  18. Advanced models of neural networks nonlinear dynamics and stochasticity in biological neurons

    CERN Document Server

    Rigatos, Gerasimos G

    2015-01-01

    This book provides a complete study on neural structures exhibiting nonlinear and stochastic dynamics, elaborating on neural dynamics by introducing advanced models of neural networks. It overviews the main findings in the modelling of neural dynamics in terms of electrical circuits and examines their stability properties with the use of dynamical systems theory. It is suitable for researchers and postgraduate students engaged with neural networks and dynamical systems theory.

  19. Circuit modeling for electromagnetic compatibility

    CERN Document Server

    Darney, Ian B

    2013-01-01

    Very simply, electromagnetic interference (EMI) costs money, reduces profits, and generally wreaks havoc for circuit designers in all industries. This book shows how the analytic tools of circuit theory can be used to simulate the coupling of interference into, and out of, any signal link in the system being reviewed. The technique is simple, systematic and accurate. It enables the design of any equipment to be tailored to meet EMC requirements. Every electronic system consists of a number of functional modules interconnected by signal links and power supply lines. Electromagnetic interference

  20. 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.

  1. Generalized circuit model for coupled plasmonic systems

    CERN Document Server

    Benz, Felix; Tserkezis, Christos; Chikkaraddy, Rohit; Sigle, Daniel O; Pukenas, Laurynas; Evans, Stephen D; Aizpurua, Javier; Baumberg, Jeremy J

    2015-01-01

    We develop an analytic circuit model for coupled plasmonic dimers separated by small gaps that provides a complete account of the optical resonance wavelength. Using a suitable equivalent circuit, it shows how partially conducting links can be treated and provides quantitative agreement with both experiment and full electromagnetic simulations. The model highlights how in the conducting regime, the kinetic inductance of the linkers set the spectral blue-shifts of the coupled plasmon.

  2. In Search of the Neural Circuits of Intrinsic Motivation

    Science.gov (United States)

    Kaplan, Frederic; Oudeyer, Pierre-Yves

    2007-01-01

    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. PMID:18982131

  3. 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.

  4. Gray Code ADC Based on an Analog Neural Circuit

    Directory of Open Access Journals (Sweden)

    L. Michaeli

    1995-04-01

    Full Text Available In this paper a new neural ADC design is presented, which is based on the idea to replace all functional components needed in the ADC block scheme by a simple connection of neurons. Transformation of ADC functional scheme into an analog neural structure and its computer simulation is one of the main results of this paper. Furthermore, a discrete component prototype of the proposed A/D converter is discussed and experimental results are also given.

  5. Devices and circuits for nanoelectronic implementation of artificial neural networks

    Science.gov (United States)

    Turel, Ozgur

    Biological neural networks perform complicated information processing tasks at speeds better than conventional computers based on conventional algorithms. This has inspired researchers to look into the way these networks function, and propose artificial networks that mimic their behavior. Unfortunately, most artificial neural networks, either software or hardware, do not provide either the speed or the complexity of a human brain. Nanoelectronics, with high density and low power dissipation that it provides, may be used in developing more efficient artificial neural networks. This work consists of two major contributions in this direction. First is the proposal of the CMOL concept, hybrid CMOS-molecular hardware [1-8]. CMOL may circumvent most of the problems in posed by molecular devices, such as low yield, vet provide high active device density, ˜1012/cm 2. The second contribution is CrossNets, artificial neural networks that are based on CMOL. We showed that CrossNets, with their fault tolerance, exceptional speed (˜ 4 to 6 orders of magnitude faster than biological neural networks) can perform any task any artificial neural network can perform. Moreover, there is a hope that if their integration scale is increased to that of human cerebral cortex (˜ 1010 neurons and ˜ 1014 synapses), they may be capable of performing more advanced tasks.

  6. 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.

  7. Changes in the Spinal Neural Circuits are Dependent on the Movement Speed of the Visuomotor Task.

    Science.gov (United States)

    Kubota, Shinji; Hirano, Masato; Koizume, Yoshiki; Tanabe, Shigeo; Funase, Kozo

    2015-01-01

    Previous studies have shown that spinal neural circuits are modulated by motor skill training. However, the effects of task movement speed on changes in spinal neural circuits have not been clarified. The aim of this research was to investigate whether spinal neural circuits were affected by task movement speed. Thirty-eight healthy subjects participated in this study. In experiment 1, the effects of task movement speed on the spinal neural circuits were examined. Eighteen subjects performed a visuomotor task involving ankle muscle slow (nine subjects) or fast (nine subjects) movement speed. Another nine subjects performed a non-visuomotor task (controls) in fast movement speed. The motor task training lasted for 20 min. The amounts of D1 inhibition and reciprocal Ia inhibition were measured using H-relfex condition-test paradigm and recorded before, and at 5, 15, and 30 min after the training session. In experiment 2, using transcranial magnetic stimulation (TMS), the effects of corticospinal descending inputs on the presynaptic inhibitory pathway were examined before and after performing either a visuomotor (eight subjects) or a control task (eight subjects). All measurements were taken under resting conditions. The amount of D1 inhibition increased after the visuomotor task irrespective of movement speed (P circuits, and that task movement speed is one of the critical factors for inducing plastic changes in reciprocal Ia inhibition.

  8. Implantable neurotechnologies: bidirectional neural interfaces--applications and VLSI circuit implementations.

    Science.gov (United States)

    Greenwald, Elliot; Masters, Matthew R; Thakor, Nitish V

    2016-01-01

    A bidirectional neural interface is a device that transfers information into and out of the nervous system. This class of devices has potential to improve treatment and therapy in several patient populations. Progress in very large-scale integration has advanced the design of complex integrated circuits. System-on-chip devices are capable of recording neural electrical activity and altering natural activity with electrical stimulation. Often, these devices include wireless powering and telemetry functions. This review presents the state of the art of bidirectional circuits as applied to neuroprosthetic, neurorepair, and neurotherapeutic systems.

  9. 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

  10. Equivalent Circuit Modeling of Hysteresis Motors

    Energy Technology Data Exchange (ETDEWEB)

    Nitao, J J; Scharlemann, E T; Kirkendall, B A

    2009-08-31

    We performed a literature review and found that many equivalent circuit models of hysteresis motors in use today are incorrect. The model by Miyairi and Kataoka (1965) is the correct one. We extended the model by transforming it to quadrature coordinates, amenable to circuit or digital simulation. 'Hunting' is an oscillatory phenomenon often observed in hysteresis motors. While several works have attempted to model the phenomenon with some partial success, we present a new complete model that predicts hunting from first principles.

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

    OpenAIRE

    Robinson, Jacob T.; Jorgolli, Marsela; Park, Hongkun

    2013-01-01

    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 t...

  12. Modeling digital switching circuits with linear algebra

    CERN Document Server

    Thornton, Mitchell A

    2014-01-01

    Modeling Digital Switching Circuits with Linear Algebra describes an approach for modeling digital information and circuitry that is an alternative to Boolean algebra. While the Boolean algebraic model has been wildly successful and is responsible for many advances in modern information technology, the approach described in this book offers new insight and different ways of solving problems. Modeling the bit as a vector instead of a scalar value in the set {0, 1} allows digital circuits to be characterized with transfer functions in the form of a linear transformation matrix. The use of transf

  13. Sensory processing by neural circuits in Caenorhabditis elegans.

    Science.gov (United States)

    Whittaker, Allyson J; Sternberg, Paul W

    2004-08-01

    The anatomical and developmental constancy of Caenorhabditis elegans belies the complexity of its numerically small nervous system. Indeed, there is an increased appreciation of C. elegans as an organism to study systems level questions. Many recent studies focus on the circuits that control locomotion, egg-laying, and male mating behaviors and their modulation by multiple sensory stimuli.

  14. 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.

  15. Classical Ising model test for quantum circuits

    Science.gov (United States)

    Geraci, Joseph; Lidar, Daniel A.

    2010-07-01

    We exploit a recently constructed mapping between quantum circuits and graphs in order to prove that circuits corresponding to certain planar graphs can be efficiently simulated classically. The proof uses an expression for the Ising model partition function in terms of quadratically signed weight enumerators (QWGTs), which are polynomials that arise naturally in an expansion of quantum circuits in terms of rotations involving Pauli matrices. We combine this expression with a known efficient classical algorithm for the Ising partition function of any planar graph in the absence of an external magnetic field, and the Robertson-Seymour theorem from graph theory. We give as an example a set of quantum circuits with a small number of non-nearest-neighbor gates which admit an efficient classical simulation.

  16. Astrocyte regulation of sleep circuits: experimental and modeling perspectives

    Directory of Open Access Journals (Sweden)

    Tommaso eFellin

    2012-08-01

    Full Text Available Integrated within neural circuits, astrocytes have recently been shown to modulate brain rhythms thought to mediate sleep function. Experimental evidence suggests that local impact of astrocytes on single synapses translates into global modulation of neuronal networks and behavior. We discuss these findings in the context of current conceptual models of sleep generation and function, each of which have historically focused on neural mechanisms. We highlight the implications and the challenges introduced by these results from a conceptual and computational perspective. We further provide modeling directions on how these data might extend our knowledge of astrocytic properties and sleep function. Given our evolving understanding of how local cellular activities during sleep lead to functional outcomes for the brain, further mechanistic and theoretical understanding of astrocytic contribution to these dynamics will undoubtedly be of great basic and translational benefit.

  17. Resonant circuit model for efficient metamaterial absorber.

    Science.gov (United States)

    Sellier, Alexandre; Teperik, Tatiana V; de Lustrac, André

    2013-11-04

    The resonant absorption in a planar metamaterial is studied theoretically. We present a simple physical model describing this phenomenon in terms of equivalent resonant circuit. We discuss the role of radiative and dissipative damping of resonant mode supported by a metamaterial in the formation of absorption spectra. We show that the results of rigorous calculations of Maxwell equations can be fully retrieved with simple model describing the system in terms of equivalent resonant circuit. This simple model allows us to explain the total absorption effect observed in the system on a common physical ground by referring it to the impedance matching condition at the resonance.

  18. An improved model of Robinson equivalent circuit analytical model

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    The Robinson equivalent circuit analytical model can be used only in calculating shielding effectiveness of enclosure with the same multi-holes in one wall, but cannot be used in different multi-holes in two walls. According to the practical requirement, this article uses Konefal’s and Farhana’s characteristic impedance of apertures to improve the equivalent circuit analytical model in different multi-holes in two walls. The improved equivalent circuit analytical model is more useful than Robinson equivalent circuit analytical model. In the article, all kinds of enclosures are simulated by TLM (Transmission-Line Matrix method) to prove that this improved model is feasible in multimode.

  19. Scaling down DNA circuits with competitive neural networks.

    Science.gov (United States)

    Genot, Anthony J; Fujii, Teruo; Rondelez, Yannick

    2013-08-06

    DNA has proved to be an exquisite substrate to compute at the molecular scale. However, nonlinear computations (such as amplification, comparison or restoration of signals) remain costly in term of strands and are prone to leak. Kim et al. showed how competition for an enzymatic resource could be exploited in hybrid DNA/enzyme circuits to compute a powerful nonlinear primitive: the winner-take-all (WTA) effect. Here, we first show theoretically how the nonlinearity of the WTA effect allows the robust and compact classification of four patterns with only 16 strands and three enzymes. We then generalize this WTA effect to DNA-only circuits and demonstrate similar classification capabilities with only 23 strands.

  20. Multidisciplinary Modelling Tools for Power Electronic Circuits

    DEFF Research Database (Denmark)

    Bahman, Amir Sajjad

    This thesis presents multidisciplinary modelling techniques in a Design For Reliability (DFR) approach for power electronic circuits. With increasing penetration of renewable energy systems, the demand for reliable power conversion systems is becoming critical. Since a large part of electricity...... in reliability assessment of power modules, a three-dimensional lumped thermal network is proposed to be used for fast, accurate and detailed temperature estimation of power module in dynamic operation and different boundary conditions. Since an important issue in the reliability of power electronics...... are generic and valid to be used in circuit simulators or any programing software. These models are important building blocks for the reliable design process or performance assessment of power electronic circuits. The models can save time and cost in power electronics packaging and power converter to evaluate...

  1. Adult Neurogenesis Leads to the Functional Reconstruction of a Telencephalic Neural Circuit

    Science.gov (United States)

    Macedo-Lima, Matheus; Miller, Kimberly E.; Brenowitz, Eliot A.

    2016-01-01

    Seasonally breeding songbirds exhibit pronounced annual changes in song behavior, and in the morphology and physiology of the telencephalic neural circuit underlying production of learned song. Each breeding season, new adult-born neurons are added to the pallial nucleus HVC in response to seasonal changes in steroid hormone levels, and send long axonal projections to their target nucleus, the robust nucleus of the arcopallium (RA). We investigated the role that adult neurogenesis plays in the seasonal reconstruction of this circuit. We labeled newborn HVC neurons with BrdU, and RA-projecting HVC neurons (HVCRA) with retrograde tracer injected in RA of adult male white-crowned sparrows (Zonotrichia leucophrys gambelii) in breeding or nonbreeding conditions. We found that there were many more HVCRA neurons in breeding than nonbreeding birds. Furthermore, we observed that more newborn HVC neurons were back-filled by the tracer in breeding animals. Behaviorally, song structure degraded as the HVC-RA circuit degenerated, and recovered as the circuit regenerated, in close correlation with the number of new HVCRA neurons. These results support the hypothesis that the HVC-RA circuit degenerates in nonbreeding birds, and that newborn neurons reconstruct the circuit in breeding birds, leading to functional recovery of song behavior. SIGNIFICANCE STATEMENT We investigated the role that adult neurogenesis plays in the seasonal reconstruction of a telencephalic neural circuit that controls song behavior in white-crowned sparrows. We showed that nonbreeding birds had a 36%–49% reduction in the number of projection neurons compared with breeding birds, and the regeneration of the circuit in the breeding season is due to the integration of adult-born projection neurons. Additionally, song structure degraded as the circuit degenerated and recovered as the circuit regenerated, in close correlation with new projection neuron number. This study demonstrates that steroid hormones

  2. A neural circuit encoding sexual preference in humans.

    OpenAIRE

    Poeppl, Timm B.; Langguth, Berthold; Rupprecht, Rainer; Laird, Angela R.; Eickhoff, Simon

    2016-01-01

    Sexual preference determines mate choice for reproduction and hence guarantees conservation of species in mammals. Despite this fundamental role in human behavior, current knowledge on its target-specific neurofunctional substrate is based on lesion studies and therefore limited. We used meta-analytic remodeling of neuroimaging data from 364 human subjects with diverse sexual interests during sexual stimulation to quantify neural regions associated with sexual preference manipulations. We fou...

  3. Small Signal Circuit Model of Double Photodiodes

    Institute of Scientific and Technical Information of China (English)

    HAN Jian-zhong; Ni Guo-qiang; MAO Lu-hong

    2004-01-01

    The transmission delay of photogenerated carriers in a CMOS-process-compatible double photodiode (DPD) is analyzed by using device simulation. The DPD small signal equivalent circuit model which includes transmission delay of photogenerated carriers is given. From analysis on the frequency domain of the circuit model the device has two poles. One has the relationship with junction capacitance and the DPD's load,the other with the depth and the doping concentration of the N-well in the DPD. Different depth of the Nwell and different area of the DPDs with bandwidth were compared. The analysis results are important to design the high speed DPDs.

  4. Modeling Deterministic Chaos Using Electronic Circuits

    Directory of Open Access Journals (Sweden)

    T. Gotthans

    2011-06-01

    Full Text Available This paper brings a note on systematic circuit synthesis methods for modeling the dynamical systems given by mathematical model. Both classical synthesis and integrator based method is demonstrated via the relatively complicated real physical systems with possible chaotic solution. A variety of the different active building blocks are utilized to make the final circuits as simple as possible while preserving easily measurable voltage-mode state variables. Brief experimental verification, i.e. oscilloscope screenshots, is presented. The observed attractors have some structural stability and good relationship to their numerically integrated counterparts.

  5. Monitoring activity in neural circuits with genetically encoded indicators

    Directory of Open Access Journals (Sweden)

    Gerard Joseph Broussard

    2014-12-01

    Full Text Available Recent developments in genetically encoded indicators of neural activity (GINAs have greatly advanced the field of systems neuroscience. As they are encoded by DNA, GINAs can be targeted to genetically defined cellular populations. Combined with fluorescence microscopy, most notably multi-photon imaging, GINAs allow chronic simultaneous optical recordings from large populations of neurons or glial cells in awake, behaving mammals, particularly rodents. This large-scale recording of neural activity at multiple temporal and spatial scales has greatly advanced our understanding of the dynamics of neural circuitry underlying behavior—a critical first step toward understanding the complexities of brain function, such as sensorimotor integration and learning.Here, we summarize the recent development and applications of the major classes of GINAs. In particular, we take an in-depth look at the design of available GINA families with a particular focus on genetically encoded calcium indicators, sensors probing synaptic activity, and genetically encoded voltage indicators. Using the family of the genetically encoded calcium indicator GCaMP as an example, we review established sensor optimization pipelines. We also discuss practical considerations for end users of GINAs about experimental methods including approaches for gene delivery, imaging system requirements, and data analysis techniques. With the growing toolbox of GINAs and with new microscopy techniques pushing beyond their current limits, the age of light can finally achieve the goal of broad and dense sampling of neuronal activity across time and brain structures to obtain a dynamic picture of brain function.

  6. 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.

  7. 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

  8. 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

  9. 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.

  10. Bio-Inspired Neural Model for Learning Dynamic Models

    Science.gov (United States)

    Duong, Tuan; Duong, Vu; Suri, Ronald

    2009-01-01

    A neural-network mathematical model that, relative to prior such models, places greater emphasis on some of the temporal aspects of real neural physical processes, has been proposed as a basis for massively parallel, distributed algorithms that learn dynamic models of possibly complex external processes by means of learning rules that are local in space and time. The algorithms could be made to perform such functions as recognition and prediction of words in speech and of objects depicted in video images. The approach embodied in this model is said to be "hardware-friendly" in the following sense: The algorithms would be amenable to execution by special-purpose computers implemented as very-large-scale integrated (VLSI) circuits that would operate at relatively high speeds and low power demands.

  11. Accurate Electromagnetic Modeling Methods for Integrated Circuits

    NARCIS (Netherlands)

    Sheng, Z.

    2010-01-01

    The present development of modern integrated circuits (IC’s) is characterized by a number of critical factors that make their design and verification considerably more difficult than before. This dissertation addresses the important questions of modeling all electromagnetic behavior of features on t

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

    Science.gov (United States)

    Duval, Elizabeth R; Javanbakht, Arash; Liberzon, Israel

    2015-01-01

    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.

  13. 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.

  14. Quantum Computation Beyond the Circuit Model

    OpenAIRE

    Jordan, Stephen P.

    2008-01-01

    The quantum circuit model is the most widely used model of quantum computation. It provides both a framework for formulating quantum algorithms and an architecture for the physical construction of quantum computers. However, several other models of quantum computation exist which provide useful alternative frameworks for both discovering new quantum algorithms and devising new physical implementations of quantum computers. In this thesis, I first present necessary background material for a ge...

  15. 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.

  16. Neural-Based Models of Semiconductor Devices for SPICE Simulator

    Directory of Open Access Journals (Sweden)

    Hanene B. Hammouda

    2008-01-01

    Full Text Available The paper addresses a simple and fast new approach to implement Artificial Neural Networks (ANN models for the MOS transistor into SPICE. The proposed approach involves two steps, the modeling phase of the device by NN providing its input/output patterns, and the SPICE implementation process of the resulting model. Using the Taylor series expansion, a neural based small-signal model is derived. The reliability of our approach is validated through simulations of some circuits in DC and small-signal analyses.

  17. Refinement and Pattern Formation in Neural Circuits by the Interaction of Traveling Waves with Spike-Timing Dependent Plasticity

    Science.gov (United States)

    Bennett, James E. M.; Bair, Wyeth

    2015-01-01

    Traveling waves in the developing brain are a prominent source of highly correlated spiking activity that may instruct the refinement of neural circuits. A candidate mechanism for mediating such refinement is spike-timing dependent plasticity (STDP), which translates correlated activity patterns into changes in synaptic strength. To assess the potential of these phenomena to build useful structure in developing neural circuits, we examined the interaction of wave activity with STDP rules in simple, biologically plausible models of spiking neurons. We derive an expression for the synaptic strength dynamics showing that, by mapping the time dependence of STDP into spatial interactions, traveling waves can build periodic synaptic connectivity patterns into feedforward circuits with a broad class of experimentally observed STDP rules. The spatial scale of the connectivity patterns increases with wave speed and STDP time constants. We verify these results with simulations and demonstrate their robustness to likely sources of noise. We show how this pattern formation ability, which is analogous to solutions of reaction-diffusion systems that have been widely applied to biological pattern formation, can be harnessed to instruct the refinement of postsynaptic receptive fields. Our results hold for rich, complex wave patterns in two dimensions and over several orders of magnitude in wave speeds and STDP time constants, and they provide predictions that can be tested under existing experimental paradigms. Our model generalizes across brain areas and STDP rules, allowing broad application to the ubiquitous occurrence of traveling waves and to wave-like activity patterns induced by moving stimuli. PMID:26308406

  18. A simple structure wavelet transform circuit employing function link neural networks and SI filters

    Science.gov (United States)

    Mu, Li; Yigang, He

    2016-12-01

    Signal processing by means of analog circuits offers advantages from a power consumption viewpoint. Implementing wavelet transform (WT) using analog circuits is of great interest when low-power consumption becomes an important issue. In this article, a novel simple structure WT circuit in analog domain is presented by employing functional link neural network (FLNN) and switched-current (SI) filters. First, the wavelet base is approximated using FLNN algorithms for giving a filter transfer function that is suitable for simple structure WT circuit implementation. Next, the WT circuit is constructed with the wavelet filter bank, whose impulse response is the approximated wavelet and its dilations. The filter design that follows is based on a follow-the-leader feedback (FLF) structure with multiple output bilinear SI integrators and current mirrors as the main building blocks. SI filter is well suited for this application since the dilation constant across different scales of the transform can be precisely implemented and controlled by the clock frequency of the circuit with the same system architecture. Finally, to illustrate the design procedure, a seventh-order FLNN-approximated Gaussian wavelet is implemented as an example. Simulations have successfully verified that the designed simple structure WT circuit has low sensitivity, low-power consumption and litter effect to the imperfections.

  19. 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.

  20. 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.

  1. Improved Monosynaptic Neural Circuit Tracing Using Engineered Rabies Virus Glycoproteins

    Directory of Open Access Journals (Sweden)

    Euiseok J. Kim

    2016-04-01

    Full Text Available Monosynaptic rabies virus tracing is a unique and powerful tool used to identify neurons making direct presynaptic connections onto neurons of interest across the entire nervous system. Current methods utilize complementation of glycoprotein gene-deleted rabies of the SAD B19 strain with its glycoprotein, B19G, to mediate retrograde transsynaptic spread across a single synaptic step. In most conditions, this method labels only a fraction of input neurons and would thus benefit from improved efficiency of transsynaptic spread. Here, we report newly engineered glycoprotein variants to improve transsynaptic efficiency. Among them, oG (optimized glycoprotein is a codon-optimized version of a chimeric glycoprotein consisting of the transmembrane/cytoplasmic domain of B19G and the extracellular domain of rabies Pasteur virus strain glycoprotein. We demonstrate that oG increases the tracing efficiency for long-distance input neurons up to 20-fold compared to B19G. oG-mediated rabies tracing will therefore allow identification and study of more complete monosynaptic input neural networks.

  2. Dynamic Circuit Model for Spintronic Devices

    KAUST Repository

    Alawein, Meshal

    2017-01-09

    In this work we propose a finite-difference scheme based circuit model of a general spintronic device and benchmark it with other models proposed for spintronic switching devices. Our model is based on the four-component spin circuit theory and utilizes the widely used coupled stochastic magnetization dynamics/spin transport framework. In addition to the steady-state analysis, this work offers a transient analysis of carrier transport. By discretizing the temporal and spatial derivatives to generate a linear system of equations, we derive new and simple finite-difference conductance matrices that can, to the first order, capture both static and dynamic behaviors of a spintronic device. We also discuss an extension of the spin modified nodal analysis (SMNA) for time-dependent situations based on the proposed scheme.

  3. Inter Digital Transducer Modelling through Mason Equivalent Circuit Model

    DEFF Research Database (Denmark)

    Mishra, Dipti; Singh, Abhishek; Hussain, Dil muhammed Akbar

    2016-01-01

    by Simulation program with Integrated Circuit Emphasis (HSPICE), a well-liked electronic path simulator. The acoustic wave devices are not suitable to simulation through circuit simulator.In this paper, an electrical model of Mason’s Equivalent electricalcircuit for an inter-digital transducer (IDT......The frequency reliance of inter-digital transducer is analyzed with the help of MASON’s Equivalent circuit which is based on Smith’s Equivalent circuit which is further based on Foster’sNetwork. An inter-digital transducer has been demonstratedas a RLC network. The circuit is simulated......) is projected which is well-suitedwith a broadlycast-offuniversalresolution circuit simulator SPICE built-in out with the proficiency to simulatethenegative capacitances and inductances. The investigationis done to prove the straightforwardness of establishing the frequency and time domain physical...

  4. Contribution of visual and circadian neural circuits to memory for prolonged mating induced by rivals.

    Science.gov (United States)

    Kim, Woo Jae; Jan, Lily Yeh; Jan, Yuh Nung

    2012-06-01

    Rival exposure causes Drosophila melanogaster males to prolong mating. Longer mating duration (LMD) may enhance reproductive success, but its underlying mechanism is currently unknown. We found that LMD is context dependent and can be induced solely via visual stimuli. In addition, we found that LMD involves neural circuits that are important for visual memory, including central neurons in the ellipsoid body, but not the mushroom bodies or the fan-shaped bodies, and may rely on the rival exposure memory lasting for several hours. LMD is affected by a subset of learning and memory mutants. LMD depends on the circadian clock genes timeless and period, but not Clock or cycle, and persists in many arrhythmic conditions. Moreover, LMD critically depends on a subset of pigment dispersing factor neurons rather than the entire circadian neural circuit. Our study thus delineates parts of the molecular and cellular basis for LMD, a plastic social behavior elicited by visual cues.

  5. Current-mode subthreshold MOS circuits for analog VLSI neural systems

    Science.gov (United States)

    Andreou, Andreas G.; Boahen, Kwabena A.; Pouliquen, Philippe O.; Pavasovic, Aleksandra; Jenkins, Robert E.

    1991-03-01

    An overview of the current-mode approach for designing analog VLSI neural systems in subthreshold CMOS technology is presented. Emphasis is given to design techniques at the device level using the current-controlled current conveyor and the translinear principle. Circuits for associative memory and silicon retina systems are used as examples. The design methodology and how it relates to actual biological microcircuits are discussed.

  6. Current-mode subthreshold MOS circuits for analog VLSI neural systems.

    Science.gov (United States)

    Andreou, A G; Boahen, K A; Pouliquen, P O; Pavasovic, A; Jenkins, R E; Strohbehn, K

    1991-01-01

    An overview of the current-mode approach for designing analog VLSI neural systems in subthreshold CMOS technology is presented. Emphasis is given to design techniques at the device level using the current-controlled current conveyor and the translinear principle. Circuits for associative memory and silicon retina systems are used as examples. The design methodology and how it relates to actual biological microcircuits are discussed.

  7. 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.

    2012-01-01

    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

  8. Neural circuit components of the Drosophila OFF motion vision pathway.

    Science.gov (United States)

    Meier, Matthias; Serbe, Etienne; Maisak, Matthew S; Haag, Jürgen; Dickson, Barry J; Borst, Alexander

    2014-02-17

    Detecting the direction of visual motion is an essential task of the early visual system. The Reichardt detector has been proven to be a faithful description of the underlying computation in insects. A series of recent studies addressed the neural implementation of the Reichardt detector in Drosophila revealing the overall layout in parallel ON and OFF channels, its input neurons from the lamina (L1→ON, and L2→OFF), and the respective output neurons to the lobula plate (ON→T4, and OFF→T5). While anatomical studies showed that T4 cells receive input from L1 via Mi1 and Tm3 cells, the neurons connecting L2 to T5 cells have not been identified so far. It is, however, known that L2 contacts, among others, two neurons, called Tm2 and L4, which show a pronounced directionality in their wiring. We characterized the visual response properties of both Tm2 and L4 neurons via Ca(2+) imaging. We found that Tm2 and L4 cells respond with an increase in activity to moving OFF edges in a direction-unselective manner. To investigate their participation in motion vision, we blocked their output while recording from downstream tangential cells in the lobula plate. Silencing of Tm2 and L4 completely abolishes the response to moving OFF edges. Our results demonstrate that both cell types are essential components of the Drosophila OFF motion vision pathway, prior to the computation of directionality in the dendrites of T5 cells. Copyright © 2014 Elsevier Ltd. All rights reserved.

  9. Large scale neural circuit mapping data analysis accelerated with the graphical processing unit (GPU)

    Science.gov (United States)

    Shi, Yulin; Veidenbaum, Alexander V.; Nicolau, Alex; Xu, Xiangmin

    2014-01-01

    Background Modern neuroscience research demands computing power. Neural circuit mapping studies such as those using laser scanning photostimulation (LSPS) produce large amounts of data and require intensive computation for post-hoc processing and analysis. New Method Here we report on the design and implementation of a cost-effective desktop computer system for accelerated experimental data processing with recent GPU computing technology. A new version of Matlab software with GPU enabled functions is used to develop programs that run on Nvidia GPUs to harness their parallel computing power. Results We evaluated both the central processing unit (CPU) and GPU-enabled computational performance of our system in benchmark testing and practical applications. The experimental results show that the GPU-CPU co-processing of simulated data and actual LSPS experimental data clearly outperformed the multi-core CPU with up to a 22x speedup, depending on computational tasks. Further, we present a comparison of numerical accuracy between GPU and CPU computation to verify the precision of GPU computation. In addition, we show how GPUs can be effectively adapted to improve the performance of commercial image processing software such as Adobe Photoshop. Comparison with Existing Method(s) To our best knowledge, this is the first demonstration of GPU application in neural circuit mapping and electrophysiology-based data processing. Conclusions Together, GPU enabled computation enhances our ability to process large-scale data sets derived from neural circuit mapping studies, allowing for increased processing speeds while retaining data precision. PMID:25277633

  10. Electrophysiological Data and the Biophysical Modelling of Local Cortical Circuits

    Directory of Open Access Journals (Sweden)

    Dimitris Pinotsis

    2014-03-01

    Full Text Available This paper shows how recordings of gamma oscillations – under different experimental conditions or from different subjects – can be combined with a class of population models called neural fields and dynamic causal modeling (DCM to distinguish among alternative hypotheses regarding cortical structure and function. This approach exploits inter-subject variability and trial-specific effects associated with modulations in the peak frequency of gamma oscillations. It draws on the computational power of Bayesian model inversion, when applied to neural field models of cortical dynamics. Bayesian model comparison allows one to adjudicate among different mechanistic hypotheses about cortical excitability, synaptic kinetics and the cardinal topographic features of local cortical circuits. It also provides optimal parameter estimates that quantify neuromodulation and the spatial dispersion of axonal connections or summation of receptive fields in the visual cortex. This paper provides an overview of a family of neural field models that have been recently implemented using the DCM toolbox of the academic freeware Statistical Parametric Mapping (SPM. The SPM software is a popular platform for analyzing neuroimaging data, used by several neuroscience communities worldwide. DCM allows for a formal (Bayesian statistical analysis of cortical network connectivity, based upon realistic biophysical models of brain responses. It is this particular feature of DCM – the unique combination of generative models with optimization techniques based upon (variational Bayesian principles – that furnishes a novel way to characterize functional brain architectures. In particular, it provides answers to questions about how the brain is wired and how it responds to different experimental manipulations. For a review of the general role of neural fields in SPM the reader can consult e.g. see [1]. Neural fields have a long and illustrious history in mathematical

  11. Priming Neural Circuits to Modulate Spinal Reflex Excitability

    Science.gov (United States)

    Estes, Stephen P.; Iddings, Jennifer A.; Field-Fote, Edelle C.

    2017-01-01

    While priming is most often thought of as a strategy for modulating neural excitability to facilitate voluntary motor control, priming stimulation can also be utilized to target spinal reflex excitability. In this application, priming can be used to modulate the involuntary motor output that often follows central nervous system injury. Individuals with spinal cord injury (SCI) often experience spasticity, for which antispasmodic medications are the most common treatment. Physical therapeutic/electroceutic interventions offer an alternative treatment for spasticity, without the deleterious side effects that can accompany pharmacological interventions. While studies of physical therapeutic/electroceutic interventions have been published, a systematic comparison of these approaches has not been performed. The purpose of this study was to compare four non-pharmacological interventions to a sham-control intervention to assess their efficacy for spasticity reduction. Participants were individuals (n = 10) with chronic SCI (≥1 year) who exhibited stretch-induced quadriceps spasticity. Spasticity was quantified using the pendulum test before and at two time points after (immediate, 45 min delayed) each of four different physical therapeutic/electroceutic interventions, plus a sham-control intervention. Interventions included stretching, cyclic passive movement (CPM), transcutaneous spinal cord stimulation (tcSCS), and transcranial direct current stimulation (tDCS). The sham-control intervention consisted of a brief ramp-up and ramp-down of knee and ankle stimulation while reclined with legs extended. The order of interventions was randomized, and each was tested on a separate day with at least 48 h between sessions. Compared to the sham-control intervention, stretching, CPM, and tcSCS were associated with a significantly greater reduction in spasticity immediately after treatment. While the immediate effect was largest for stretching, the reduction persisted

  12. Imaging neuronal populations in behaving rodents: paradigms for studying neural circuits underlying behavior in the mammalian cortex.

    Science.gov (United States)

    Chen, Jerry L; Andermann, Mark L; Keck, Tara; Xu, Ning-Long; Ziv, Yaniv

    2013-11-06

    Understanding the neural correlates of behavior in the mammalian cortex requires measurements of activity in awake, behaving animals. Rodents have emerged as a powerful model for dissecting the cortical circuits underlying behavior attributable to the convergence of several methods. Genetically encoded calcium indicators combined with viral-mediated or transgenic tools enable chronic monitoring of calcium signals in neuronal populations and subcellular structures of identified cell types. Stable one- and two-photon imaging of neuronal activity in awake, behaving animals is now possible using new behavioral paradigms in head-fixed animals, or using novel miniature head-mounted microscopes in freely moving animals. This mini-symposium will highlight recent applications of these methods for studying sensorimotor integration, decision making, learning, and memory in cortical and subcortical brain areas. We will outline future prospects and challenges for identifying the neural underpinnings of task-dependent behavior using cellular imaging in rodents.

  13. Plant Growth Models Using Artificial Neural Networks

    Science.gov (United States)

    Bubenheim, David

    1997-01-01

    In this paper, we descrive our motivation and approach to devloping models and the neural network architecture. Initial use of the artificial neural network for modeling the single plant process of transpiration is presented.

  14. Generalization performance of regularized neural network models

    DEFF Research Database (Denmark)

    Larsen, Jan; Hansen, Lars Kai

    1994-01-01

    Architecture optimization is a fundamental problem of neural network modeling. The optimal architecture is defined as the one which minimizes the generalization error. This paper addresses estimation of the generalization performance of regularized, complete neural network models. Regularization...

  15. Optogenetic dissection of neural circuit underlying locomotory decision-making in Caenorhabditis Elegans

    Science.gov (United States)

    Kocabas, Askin; Guo, Zengcai; Ramanathan, Sharad

    2011-03-01

    Despite the knowledge of the physical connectivity of the entire nervous system of C.elegans, we know little about how neuronal dynamics results in decision-making. Detailed understanding of functional and dynamic relations of the neural circuitry requires spatiotemporal control of the neuronal activity. Recent discoveries of light gated ion channels have allowed temporal optical control of neural activity. However, excitation of a specific neuron from among many expressing the channel has been a challenge. By combining optogenetic tools, micro mirror array technology and fast real time image processing, we have developed a technique to activate specific single or multiple neurons in an intact crawling animal while tracking its behavior. Using this setup we traced the neural pathway controlling the gradual turning of the animal during the locomotion. We found that the activity of a specific neuronal circuit that receives inputs from sensory neurons is coordinated with head movement. This coordination allows the animal to turn left or right based on the variation of sensory stimulus during head movement. By directly modulating the activity of the neural circuit, we can force the animal to turn in a specific direction independent of sensory stimuli. Human Frontier Science Program.

  16. A multi-channel fully differential programmable integrated circuit for neural recording application

    Science.gov (United States)

    Yun, Gui; Xu, Zhang; Yuan, Wang; Ming, Liu; Weihua, Pei; Kai, Liang; Suibiao, Huang; Bin, Li; Hongda, Chen

    2013-10-01

    A multi-channel, fully differential programmable chip for neural recording application is presented. The integrated circuit incorporates eight neural recording amplifiers with tunable bandwidth and gain, eight 4th-order Bessel switch capacitor filters, an 8-to-1 analog time-division multiplexer, a fully differential successive approximation register analog-to-digital converter (SAR ADC), and a serial peripheral interface for communication. The neural recording amplifier presents a programmable gain from 53 dB to 68 dB, a tunable low cut-off frequency from 0.1 Hz to 300 Hz, and 3.77 μVrms input-referred noise over a 5 kHz bandwidth. The SAR ADC digitizes signals at maximum sampling rate of 20 kS/s per channel and achieves an ENOB of 7.4. The integrated circuit is designed and fabricated in 0.18-μm CMOS mix-signal process. We successfully performed a multi-channel in-vivo recording experiment from a rat cortex using the neural recording chip.

  17. Amigo Adhesion Protein Regulates Development of Neural Circuits in Zebrafish Brain*

    Science.gov (United States)

    Zhao, Xiang; Kuja-Panula, Juha; Sundvik, Maria; Chen, Yu-Chia; Aho, Vilma; Peltola, Marjaana A.; Porkka-Heiskanen, Tarja; Panula, Pertti; Rauvala, Heikki

    2014-01-01

    The Amigo protein family consists of three transmembrane proteins characterized by six leucine-rich repeat domains and one immunoglobulin-like domain in their extracellular moieties. Previous in vitro studies have suggested a role as homophilic adhesion molecules in brain neurons, but the in vivo functions remain unknown. Here we have cloned all three zebrafish amigos and show that amigo1 is the predominant family member expressed during nervous system development in zebrafish. Knockdown of amigo1 expression using morpholino oligonucleotides impairs the formation of fasciculated tracts in early fiber scaffolds of brain. A similar defect in fiber tract development is caused by mRNA-mediated expression of the Amigo1 ectodomain that inhibits adhesion mediated by the full-length protein. Analysis of differentiated neural circuits reveals defects in the catecholaminergic system. At the behavioral level, the disturbed formation of neural circuitry is reflected in enhanced locomotor activity and in the inability of the larvae to perform normal escape responses. We suggest that Amigo1 is essential for the development of neural circuits of zebrafish, where its mechanism involves homophilic interactions within the developing fiber tracts and regulation of the Kv2.1 potassium channel to form functional neural circuitry that controls locomotion. PMID:24904058

  18. Neural network modeling of emotion

    Science.gov (United States)

    Levine, Daniel S.

    2007-03-01

    This article reviews the history and development of computational neural network modeling of cognitive and behavioral processes that involve emotion. The exposition starts with models of classical conditioning dating from the early 1970s. Then it proceeds toward models of interactions between emotion and attention. Then models of emotional influences on decision making are reviewed, including some speculative (not and not yet simulated) models of the evolution of decision rules. Through the late 1980s, the neural networks developed to model emotional processes were mainly embodiments of significant functional principles motivated by psychological data. In the last two decades, network models of these processes have become much more detailed in their incorporation of known physiological properties of specific brain regions, while preserving many of the psychological principles from the earlier models. Most network models of emotional processes so far have dealt with positive and negative emotion in general, rather than specific emotions such as fear, joy, sadness, and anger. But a later section of this article reviews a few models relevant to specific emotions: one family of models of auditory fear conditioning in rats, and one model of induced pleasure enhancing creativity in humans. Then models of emotional disorders are reviewed. The article concludes with philosophical statements about the essential contributions of emotion to intelligent behavior and the importance of quantitative theories and models to the interdisciplinary enterprise of understanding the interactions of emotion, cognition, and behavior.

  19. System partitioning on MCM using a new neural network model

    Institute of Scientific and Technical Information of China (English)

    胡卫明; 徐俊华; 严晓浪; 何志钧

    1999-01-01

    A new self-organizing neural network model is presented, which can get rid of some fatal defects facing the Kohonen self-organizing neural network, known as the slow training speed, difficulty in designing neighboring zone, and disability to deal with area constraints directly. Based on the new neural network, a new approach for performance-driven system partitioning on MCM is presented. In the algorithm, the total routing cost between the chips and the circle time are both minimized, while satisfying area and timing constraints. The neural network has a reasonable structure and its training speed is high. The algorithm is able to deal with the large scale circuit partitioning, and has total optimization effect. The algorithm is programmed with Visual C + + language, and experimental result shows that it is an effective method.

  20. An automatic synthesis method of compact models of integrated circuit devices based on equivalent circuits

    Science.gov (United States)

    Abramov, I. I.

    2006-05-01

    An automatic synthesis method of equivalent circuits of integrated circuit devices is described in the paper. This method is based on a physical approach to construction of finite-difference approximation to basic equations of semiconductor device physics. It allows to synthesize compact equivalent circuits of different devices automatically as alternative to, for example, sufficiently formal BSIM2 and BSIM3 models used in circuit simulation programs of SPICE type. The method is one of possible variants of general methodology for automatic synthesis of compact equivalent circuits of almost arbitrary devices and circuit-type structures of micro- and nanoelecronics [1]. The method is easily extended in the case of necessity to account thermal effects in integrated circuits. It was shown that its application would be especially perspective for analysis of integrated circuit fragments as a whole and for identification of significant collective physical effects, including parasitic effects in VLSI and ULSI. In the paper the examples illustrating possibilities of the method for automatic synthesis of compact equivalent circuits of some of semiconductor devices and integrated circuit devices are considered. Special attention is given to examples of integrated circuit devices for coarse grids of spatial discretization (less than 10 nodes).

  1. Advances in two photon scanning and scanless microscopy technologies for functional neural circuit imaging.

    Science.gov (United States)

    Schultz, Simon R; Copeland, Caroline S; Foust, Amanda J; Quicke, Peter; Schuck, Renaud

    2017-01-01

    Recent years have seen substantial developments in technology for imaging neural circuits, raising the prospect of large scale imaging studies of neural populations involved in information processing, with the potential to lead to step changes in our understanding of brain function and dysfunction. In this article we will review some key recent advances: improved fluorophores for single cell resolution functional neuroimaging using a two photon microscope; improved approaches to the problem of scanning active circuits; and the prospect of scanless microscopes which overcome some of the bandwidth limitations of current imaging techniques. These advances in technology for experimental neuroscience have in themselves led to technical challenges, such as the need for the development of novel signal processing and data analysis tools in order to make the most of the new experimental tools. We review recent work in some active topics, such as region of interest segmentation algorithms capable of demixing overlapping signals, and new highly accurate algorithms for calcium transient detection. These advances motivate the development of new data analysis tools capable of dealing with spatial or spatiotemporal patterns of neural activity, that scale well with pattern size.

  2. Dynamic changes in neural circuit topology following mild mechanical injury in vitro.

    Science.gov (United States)

    Patel, Tapan P; Ventre, Scott C; Meaney, David F

    2012-01-01

    Despite its enormous incidence, mild traumatic brain injury is not well understood. One aspect that needs more definition is how the mechanical energy during injury affects neural circuit function. Recent developments in cellular imaging probes provide an opportunity to assess the dynamic state of neural networks with single-cell resolution. In this article, we developed imaging methods to assess the state of dissociated cortical networks exposed to mild injury. We estimated the imaging conditions needed to achieve accurate measures of network properties, and applied these methodologies to evaluate if mild mechanical injury to cortical neurons produces graded changes to either spontaneous network activity or altered network topology. We found that modest injury produced a transient increase in calcium activity that dissipated within 1 h after injury. Alternatively, moderate mechanical injury produced immediate disruption in network synchrony, loss in excitatory tone, and increased modular topology. A calcium-activated neutral protease (calpain) was a key intermediary in these changes; blocking calpain activation restored the network nearly completely to its pre-injury state. Together, these findings show a more complex change in neural circuit behavior than previously reported for mild mechanical injury, and highlight at least one important early mechanism responsible for these changes.

  3. Dual random circuit breaker network model with equivalent thermal circuit network

    Science.gov (United States)

    Kim, Kwanyong; Yoon, Seong Jun; Choi, Woo Young

    2014-02-01

    A SPICE-based dual random circuit breaker (RCB) network model with an equivalent thermal circuit network has been proposed in order to emulate resistance switching (RS) of unipolar resistive random access memory (RRAM). The dual RCB network model consists of the electrical RCB network model for the forming and set operations and the equivalent thermal circuit network model for the reset operation. In addition, the proposed model can explain the effects of heat dissipation on the memory and threshold RS with the variation in electrode thickness.

  4. AgRP Neural Circuits Mediate Adaptive Behaviors in the Starved State

    Science.gov (United States)

    Padilla, Stephanie L.; Qiu, Jian; Soden, Marta E.; Sanz, Elisenda; Nestor, Casey C; Barker, Forrest D.; Quintana, Albert; Zweifel, Larry S.; Rønnekleiv, Oline K.; Kelly, Martin J.; Palmiter, Richard D.

    2016-01-01

    In the face of starvation animals will engage in high-risk behaviors that would normally be considered maladaptive. Starving rodents for example will forage in areas that are more susceptible to predators and will also modulate aggressive behavior within a territory of limited or depleted nutrients. The neural basis of these adaptive behaviors likely involves circuits that link innate feeding, aggression, and fear. Hypothalamic AgRP neurons are critically important for driving feeding and project axons to brain regions implicated in aggression and fear. Using circuit-mapping techniques, we define a disynaptic network originating from a subset of AgRP neurons that project to the medial nucleus of the amygdala and then to the principle bed nucleus of the stria terminalis, which plays a role in suppressing territorial aggression and reducing contextual fear. We propose that AgRP neurons serve as a master switch capable of coordinating behavioral decisions relative to internal state and environmental cues. PMID:27019015

  5. 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.

  6. Equivalent circuit models for ac impedance data analysis

    Science.gov (United States)

    Danford, M. D.

    1990-01-01

    A least-squares fitting routine has been developed for the analysis of ac impedance data. It has been determined that the checking of the derived equations for a particular circuit with a commercially available electronics circuit program is essential. As a result of the investigation described, three equivalent circuit models were selected for use in the analysis of ac impedance data.

  7. 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.

  8. Cell biology in neuroscience: Architects in neural circuit design: glia control neuron numbers and connectivity.

    Science.gov (United States)

    Corty, Megan M; Freeman, Marc R

    2013-11-11

    Glia serve many important functions in the mature nervous system. In addition, these diverse cells have emerged as essential participants in nearly all aspects of neural development. Improved techniques to study neurons in the absence of glia, and to visualize and manipulate glia in vivo, have greatly expanded our knowledge of glial biology and neuron-glia interactions during development. Exciting studies in the last decade have begun to identify the cellular and molecular mechanisms by which glia exert control over neuronal circuit formation. Recent findings illustrate the importance of glial cells in shaping the nervous system by controlling the number and connectivity of neurons.

  9. 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.

  10. An investigation of the neural circuits underlying reaching and reach-to-grasp movements: from planning to execution.

    Directory of Open Access Journals (Sweden)

    Chiara eBegliomini

    2014-09-01

    Full Text Available Experimental evidence suggests the existence of a sophisticated brain circuit specifically dedicated to reach-to-grasp planning and execution, both in human and non human primates (Castiello, 2005. Studies accomplished by means of neuroimaging techniques suggest the hypothesis of a dichotomy between a reach-to-grasp circuit, involving the intraparietal area (AIP, the dorsal and ventral premotor cortices (PMd and PMv - Castiello and Begliomini, 2008; Filimon, 2010 and a reaching circuit involving the medial intraparietal area (mIP and the Superior Parieto-Occipital Cortex (SPOC (Culham et al., 2006. However, the time course characterizing the involvement of these regions during the planning and execution of these two types of movements has yet to be delineated. A functional magnetic resonance imaging (fMRI study has been conducted, including reach-to grasp and reaching only movements, performed towards either a small or a large stimulus, and Finite Impulse Response model (FIR - Henson, 2003 was adopted to monitor activation patterns from stimulus onset for a time window of 10 seconds duration. Data analysis focused on brain regions belonging either to the reaching or to the grasping network, as suggested by Castiello & Begliomini (2008.Results suggest that reaching and grasping movements planning and execution might share a common brain network, providing further confirmation to the idea that the neural underpinnings of reaching and grasping may overlap in both spatial and temporal terms (Verhagen et al., 2013.

  11. Model Order Reduction for Electronic Circuits:

    DEFF Research Database (Denmark)

    Hjorth, Poul G.; Shontz, Suzanne

    Electronic circuits are ubiquitous; they are used in numerous industries including: the semiconductor, communication, robotics, auto, and music industries (among many others). As products become more and more complicated, their electronic circuits also grow in size and complexity. This increased...... the need for circuit simulators to evaluate potential designs before fabrication, as integrated circuit prototypes are expensive to build, and troubleshooting is difficult. In this report, we focus on the simulation of printed circuit boards (PCB’s) and interconnects both of which are of great importance...

  12. The Neural Representation of 3-Dimensional Objects in Rodent Memory Circuits

    Science.gov (United States)

    Burke, Sara N.; Barnes, Carol A.

    2014-01-01

    Three-dimensional objects are common stimuli that rodents and other animals encounter in the natural world that contribute to the associations that are the hallmark of explicit memory. Thus, the use of 3-dimensional objects for investigating the circuits that support associative and episodic memories has a long history. In rodents, the neural representation of these types of stimuli is a polymodal process and lesion data suggest that the perirhinal cortex, an area of the medial temporal lobe that receives afferent input from all sensory modalities, is particularly important for integrating sensory information across modalities to support object recognition. Not surprisingly, recent data from in vivo electrophysiological recordings have shown that principal cells within the perirhinal cortex are activated at locations of an environment that contain 3-dimensional objects. Interestingly, it appears that neural activity patterns related to object stimuli are ubiquitous across memory circuits and have now been observed in many medial temporal lobe structures as well as in the anterior cingulate cortex. This review summarizes behavioral and neurophysiological data that examine the representation of 3-dimensional objects across brain regions that are involved in memory. PMID:25205370

  13. Does the capsaicin-sensitive local neural circuit constitutively regulate vagally evoked esophageal striated muscle contraction in rats?

    Science.gov (United States)

    Shima, Takeshi; Shiina, Takahiko; Naitou, Kiyotada; Nakamori, Hiroyuki; Sano, Yuuki; Shimizu, Yasutake

    2016-03-01

    To determine whether a capsaicin-sensitive local neural circuit constitutively modulates vagal neuromuscular transmission in the esophageal striated muscle or whether the neural circuit operates in a stimulus-dependent manner, we compared the motility of esophageal preparations isolated from intact rats with those in which capsaicin-sensitive neurons had been destroyed. Electrical stimulation of the vagus nerve trunk evoked contractile responses in the esophagus isolated from a capsaicin-treated rat in a manner similar to those in the esophagus from a control rat. No obvious differences were observed in the inhibitory effects of D-tubocurarine on intact and capsaicin-treated rat esophageal motility. Destruction of the capsaicin-sensitive neurons did not significantly affect latency, time to peak and duration of a vagally evoked twitch-like contraction. These findings indicate that the capsaicin-sensitive neural circuit does not operate constitutively but rather is activated in response to an applied stimulus.

  14. 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.

  15. Circuit Model of Plasmon-Enhanced Fluorescence

    Directory of Open Access Journals (Sweden)

    Constantin Simovski

    2015-05-01

    Full Text Available Hybridized decaying oscillations in a nanosystem of two coupled elements—a quantum emitter and a plasmonic nanoantenna—are considered as a classical effect. The circuit model of the nanosystem extends beyond the assumption of inductive or elastic coupling and implies the near-field dipole-dipole interaction. Its results fit those of the previously developed classical model of Rabi splitting, however going much farther. Using this model, we show that the hybridized oscillations depending on the relationships between design parameters of the nanosystem correspond to several characteristic regimes of spontaneous emission. These regimes were previously revealed in the literature and explained involving semiclassical theory. Our original classical model is much simpler: it results in a closed-form solution for the emission spectra. It allows fast prediction of the regime for different distances and locations of the emitter with respect to the nanoantenna (of a given geometry if the dipole moment of the emitter optical transition and its field coupling constant are known.

  16. Initial conditions in the neural field model

    CERN Document Server

    Valdes-Hernandez, Pedro A

    2016-01-01

    In spite of the large amount of existing neural models in the literature, there is a lack of a systematic review of the possible effect of choosing different initial conditions on the dynamic evolution of neural systems. In this short review we intend to give insights into this topic by discussing some published examples. First, we briefly introduce the different ingredients of a neural dynamical model. Secondly, we introduce some concepts used to describe the dynamic behavior of neural models, namely phase space and its portraits, time series, spectra, multistability and bifurcations. We end with an analysis of the irreversibility of processes and its implications on the functioning of normal and pathological brains.

  17. Estimation of Effectivty Connectivity via Data-Driven Neural Modeling

    Directory of Open Access Journals (Sweden)

    Dean Robert Freestone

    2014-11-01

    Full Text Available This research introduces a new method for functional brain imaging via a process of model inversion. By estimating parameters of a computational model, we are able to track effective connectivity and mean membrane potential dynamics that cannot be directly measured using electrophysiological measurements alone. The ability to track the hidden aspects of neurophysiology will have a profound impact on the way we understand and treat epilepsy. For example, under the assumption the model captures the key features of the cortical circuits of interest, the framework will provide insights into seizure initiation and termination on a patient-specific basis. It will enable investigation into the effect a particular drug has on specific neural populations and connectivity structures using minimally invasive measurements. The method is based on approximating brain networks using an interconnected neural population model. The neural population model is based on a neural mass model that describes the functional activity of the brain, capturing the mesoscopic biophysics and anatomical structure. The model is made subject-specific by estimating the strength of intra-cortical connections within a region and inter-cortical connections between regions using a novel Kalman filtering method. We demonstrate through simulation how the framework can be used the track the mechanisms involved in seizure initiation and termination.

  18. 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.…

  19. Short-Circuit Modeling of a Wind Power Plant: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Muljadi, E.; Gevorgian, V.

    2011-03-01

    This paper investigates the short-circuit behavior of a WPP for different types of wind turbines. The short-circuit behavior will be presented. Both the simplified models and detailed models are used in the simulations and both symmetrical faults and unsymmetrical faults are discussed.

  20. Four Order Electrostatic Discharge Circuit Model and its Simulation

    Directory of Open Access Journals (Sweden)

    Xiaodong Wang

    2012-12-01

    Full Text Available According to the international electrotechnical commission issued IEC61000-4-2 test standard, through the electrostatic discharge current waveform characteristics analysis and numerical experiment method, and construct a new ESD current expression. Using Laplasse transform, established the ESD system mathematical model. According to the mathematical model, construction of passive four order ESD system circuit model and active four order ESD system circuit model, and simulation. The simulation results meet the IEC61000-4-2 standard, and verify the consistency of the ESD current expression, the mathematical model and the circuit model.

  1. A neural network model of attention-modulated neurodynamics.

    Science.gov (United States)

    Gu, Yuqiao; Liljenström, Hans

    2007-12-01

    Visual attention appears to modulate cortical neurodynamics and synchronization through various cholinergic mechanisms. In order to study these mechanisms, we have developed a neural network model of visual cortex area V4, based on psychophysical, anatomical and physiological data. With this model, we want to link selective visual information processing to neural circuits within V4, bottom-up sensory input pathways, top-down attention input pathways, and to cholinergic modulation from the prefrontal lobe. We investigate cellular and network mechanisms underlying some recent analytical results from visual attention experimental data. Our model can reproduce the experimental findings that attention to a stimulus causes increased gamma-frequency synchronization in the superficial layers. Computer simulations and STA power analysis also demonstrate different effects of the different cholinergic attention modulation action mechanisms.

  2. Model GC1312S Multifunction Integrated Optical Circuit Devices

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Model GC1312S multifunction integrated optical circuit device (MIOC) used in inertial-grade interferometric fiber optics gyroscopes (IFOGs) is fabricated by annealing and proton exchange process (APE). The unique feature of the device is the incorporation of the beat detection circuit besides all the features the conventional single Y-branch multifunction integrated optical circuit devices have. The device structure, operation principle and typical characteristics, etc., are briefly presented in this paper.

  3. Microbiota-generated metabolites promote metabolic benefits via gut-brain neural circuits.

    Science.gov (United States)

    De Vadder, Filipe; Kovatcheva-Datchary, Petia; Goncalves, Daisy; Vinera, Jennifer; Zitoun, Carine; Duchampt, Adeline; Bäckhed, Fredrik; Mithieux, Gilles

    2014-01-16

    Soluble dietary fibers promote metabolic benefits on body weight and glucose control, but underlying mechanisms are poorly understood. Recent evidence indicates that intestinal gluconeogenesis (IGN) has beneficial effects on glucose and energy homeostasis. Here, we show that the short-chain fatty acids (SCFAs) propionate and butyrate, which are generated by fermentation of soluble fiber by the gut microbiota, activate IGN via complementary mechanisms. Butyrate activates IGN gene expression through a cAMP-dependent mechanism, while propionate, itself a substrate of IGN, activates IGN gene expression via a gut-brain neural circuit involving the fatty acid receptor FFAR3. The metabolic benefits on body weight and glucose control induced by SCFAs or dietary fiber in normal mice are absent in mice deficient for IGN, despite similar modifications in gut microbiota composition. Thus, the regulation of IGN is necessary for the metabolic benefits associated with SCFAs and soluble fiber.

  4. Combining BMI stimulation and mathematical modeling for acute stroke recovery and neural repair

    Directory of Open Access Journals (Sweden)

    Sara L Gonzalez Andino

    2011-07-01

    Full Text Available Rehabilitation is a neural plasticity-exploiting approach that forces undamaged neural circuits to undertake the functionality of other circuits damaged by stroke. It aims to partial restoration of the neural functions by circuit remodeling rather than by the regeneration of damaged circuits. The core hypothesis of the present paper is that - in stroke - Brain Machine Interfaces can be designed to target neural repair instead of rehabilitation. To support this hypothesis we first review existing evidence on the role of endogenous or externally applied electric fields on all processes involved in CNS repair. We then describe our own results to illustrate the neuroprotective and neuroregenerative effects of BMI- electrical stimulation on sensory deprivation-related degenerative processes of the CNS. Finally, we discuss three of the crucial issues involved in the design of neural repair-oriented BMIs: when to stimulate, where to stimulate and - the particularly important but unsolved issue of - how to stimulate. We argue that optimal parameters for the electrical stimulation can be determined from studying and modeling the dynamics of the electric fields that naturally emerge at the central and peripheral nervous system during spontaneous healing in both, experimental animals and human patients. We conclude that a closed-loop BMI that defines the optimal stimulation parameters from a priori developed experimental models of the dynamics of spontaneous repair and the on-line monitoring of neural activity might place BMIs as an alternative or complement to stem-cell transplantation or pharmacological approaches, intensively pursued nowadays.

  5. Modelling Microwave Devices Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Andrius Katkevičius

    2012-04-01

    Full Text Available Artificial neural networks (ANN have recently gained attention as fast and flexible equipment for modelling and designing microwave devices. The paper reviews the opportunities to use them for undertaking the tasks on the analysis and synthesis. The article focuses on what tasks might be solved using neural networks, what challenges might rise when using artificial neural networks for carrying out tasks on microwave devices and discusses problem-solving techniques for microwave devices with intermittent characteristics.Article in Lithuanian

  6. Optical dissection of neural circuits responsible for Drosophila larval locomotion with halorhodopsin.

    Directory of Open Access Journals (Sweden)

    Kengo Inada

    Full Text Available Halorhodopsin (NpHR, a light-driven microbial chloride pump, enables silencing of neuronal function with superb temporal and spatial resolution. Here, we generated a transgenic line of Drosophila that drives expression of NpHR under control of the Gal4/UAS system. Then, we used it to dissect the functional properties of neural circuits that regulate larval peristalsis, a continuous wave of muscular contraction from posterior to anterior segments. We first demonstrate the effectiveness of NpHR by showing that global and continuous NpHR-mediated optical inhibition of motor neurons or sensory feedback neurons induce the same behavioral responses in crawling larvae to those elicited when the function of these neurons are inhibited by Shibire(ts, namely complete paralyses or slowed locomotion, respectively. We then applied transient and/or focused light stimuli to inhibit the activity of motor neurons in a more temporally and spatially restricted manner and studied the effects of the optical inhibition on peristalsis. When a brief light stimulus (1-10 sec was applied to a crawling larva, the wave of muscular contraction stopped transiently but resumed from the halted position when the light was turned off. Similarly, when a focused light stimulus was applied to inhibit motor neurons in one or a few segments which were about to be activated in a dissected larva undergoing fictive locomotion, the propagation of muscular constriction paused during the light stimulus but resumed from the halted position when the inhibition (>5 sec was removed. These results suggest that (1 Firing of motor neurons at the forefront of the wave is required for the wave to proceed to more anterior segments, and (2 The information about the phase of the wave, namely which segment is active at a given time, can be memorized in the neural circuits for several seconds.

  7. A Neural Circuit for Acoustic Navigation combining Heterosynaptic and Non-synaptic Plasticity that learns Stable Trajectories

    DEFF Research Database (Denmark)

    Shaikh, Danish; Manoonpong, Poramate

    2017-01-01

    controllers be resolved in a manner that generates consistent and stable robot trajectories? We propose a neural circuit that minimises this conflict by learning sensorimotor mappings as neuronal transfer functions between the perceived sound direction and wheel velocities of a simulated non-holonomic mobile...

  8. An improved equivalent circuit model of radial mode piezoelectric transformer.

    Science.gov (United States)

    Huang, Yihua; Huang, Wei

    2011-05-01

    In this paper, both the equivalent circuit models of the radial mode and the coupled thickness vibration mode of the radial mode piezoelectric transformer are deduced, and then with the Y-parameter matrix method and the dual-port network theory, an improved equivalent circuit model for the multilayer radial mode piezoelectric transformer is established. A radial mode transformer sample is tested to verify the equivalent circuit model. The experimental results show that the model proposed in this paper is more precise than the typical model.

  9. 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.

  10. Estimating neural background input with controlled and fast perturbations: A bandwidth comparison between inhibitory opsins and neural circuits

    Directory of Open Access Journals (Sweden)

    David Eriksson

    2016-08-01

    Full Text Available To test the importance of a certain cell type or brain area it is common to make a lack of function experiment in which the neuronal population of interest is inhibited. Here we review physiological and methodological constraints for making controlled perturbations using the corticothalamic circuit as an example. The brain with its many types of cells and rich interconnectivity offers many paths through which a perturbation can spread within a short time. To understand the side effects of the perturbation one should record from those paths. We find that ephaptic effects, gap-junctions, and fast chemical synapses are so fast that they can react to the perturbation during the few milliseconds it takes for an opsin to change the membrane potential. The slow chemical synapses, astrocytes, extracellular ions and vascular signals, will continue to give their physiological input for around 20 milliseconds before they also react to the perturbation. Although we show that some pathways can react within milliseconds the strength/speed reported in this review should be seen as an upper bound since we have omitted how polysynaptic signals are attenuated. Thus the number of additional recordings that has to be made to control for the perturbation side effects is expected to be fewer than proposed here. To summarize, the reviewed literature not only suggests that it is possible to make controlled lack of function experiments, but, it also suggests that such a lack of function experiment can be used to measure the context of local neural computations.

  11. A Neural Model of Mind Wandering.

    Science.gov (United States)

    Mittner, Matthias; Hawkins, Guy E; Boekel, Wouter; Forstmann, Birte U

    2016-08-01

    The role of the default-mode network (DMN) in the emergence of mind wandering and task-unrelated thought has been studied extensively. In parallel work, mind wandering has been associated with neuromodulation via the locus coeruleus (LC) norepinephrine (LC-NE) system. Here we propose a neural model that links the two systems in an integrative framework. The model attempts to explain how dynamic changes in brain systems give rise to the subjective experience of mind wandering. The model implies a neural and conceptual distinction between an off-focus state and an active mind-wandering state and provides a potential neural grounding for well-known cognitive theories of mind wandering. Finally, the proposed neural model of mind wandering generates precise, testable predictions at neural and behavioral levels.

  12. Lyapunov exponents from CHUA's circuit time series using artificial neural networks

    Science.gov (United States)

    Gonzalez, J. Jesus; Espinosa, Ismael E.; Fuentes, Alberto M.

    1995-01-01

    In this paper we present the general problem of identifying if a nonlinear dynamic system has a chaotic behavior. If the answer is positive the system will be sensitive to small perturbations in the initial conditions which will imply that there is a chaotic attractor in its state space. A particular problem would be that of identifying a chaotic oscillator. We present an example of three well known different chaotic oscillators where we have knowledge of the equations that govern the dynamical systems and from there we can obtain the corresponding time series. In a similar example we assume that we only know the time series and, finally, in another example we have to take measurements in the Chua's circuit to obtain sample points of the time series. With the knowledge about the time series the phase plane portraits are plotted and from them, by visual inspection, it is concluded whether or not the system is chaotic. This method has the problem of uncertainty and subjectivity and for that reason a different approach is needed. A quantitative approach is the computation of the Lyapunov exponents. We describe several methods for obtaining them and apply a little known method of artificial neural networks to the different examples mentioned above. We end the paper discussing the importance of the Lyapunov exponents in the interpretation of the dynamic behavior of biological neurons and biological neural networks.

  13. Research Domain Criteria: cognitive systems, neural circuits, and dimensions of behavior.

    Science.gov (United States)

    Morris, Sarah E; Cuthbert, Bruce N

    2012-03-01

    Current diagnostic systems for mental disorders were established before the tools of neuroscience were available, and although they have improved the reliability of psychiatric classification, progress toward the discovery of disease etiologies and novel approaches to treatment and prevention may benefit from alternative conceptualizations of mental disorders. The Research Domain Criteria (RDoC) initiative is the centerpiece of NIMH's effort to achieve its strategic goal of developing new methods to classify mental disorders for research purposes. The RDoC matrix provides a research framework that encourages investigators to reorient their research perspective by taking a dimensional approach to the study of the genetic, neural, and behavioral features of mental disorders, RDoCs integrative approach includes cognition along with social processes, arousal/regulatory systems, and negative and positive valence systems as the major domains, because these neurobehavioral systems have all evolved to serve the motivational and adaptive needs of the organism. With its focus on neural circuits informed by the growing evidence of the neurodevelopmental nature of many disorders and its capacity to capture the patterns of co-occurrence of behaviors and symptoms, the RDoC approach holds promise to advance our understanding of the nature of mental disorders.

  14. Analgesic Neural Circuits Are Activated by Electroacupuncture at Two Sets of Acupoints

    Directory of Open Access Journals (Sweden)

    Man-Li Hu

    2016-01-01

    Full Text Available To investigate analgesic neural circuits activated by electroacupuncture (EA at different sets of acupoints in the brain, goats were stimulated by EA at set of Baihui-Santai acupoints or set of Housanli acupoints for 30 min. The pain threshold was measured using the potassium iontophoresis method. The levels of c-Fos were determined with Streptavidin-Biotin Complex immunohistochemistry. The results showed pain threshold induced by EA at set of Baihui-Santai acupoints was 44.74%±4.56% higher than that by EA at set of Housanli acupoints (32.64%±5.04%. Compared with blank control, EA at two sets of acupoints increased c-Fos expression in the medial septal nucleus (MSN, the arcuate nucleus (ARC, the nucleus amygdala basalis (AB, the lateral habenula nucleus (HL, the ventrolateral periaqueductal grey (vlPAG, the locus coeruleus (LC, the nucleus raphe magnus (NRM, the pituitary gland, and spinal cord dorsal horn (SDH. Compared with EA at set of Housanli points, EA at set of Baihui-Santai points induced increased c-Fos expression in AB but decrease in MSN, the paraventricular nucleus of the hypothalamus, HL, and SDH. It suggests that ARC-PAG-NRM/LC-SDH and the hypothalamus-pituitary may be the common activated neural pathways taking part in EA-induced analgesia at the two sets of acupoints.

  15. RF Circuit linearity optimization using a general weak nonlinearity model

    NARCIS (Netherlands)

    Cheng, W.; Oude Alink, M.S.; Annema, Anne J.; Croon, Jeroen A.; Nauta, Bram

    2012-01-01

    This paper focuses on optimizing the linearity in known RF circuits, by exploring the circuit design space that is usually available in today’s deep submicron CMOS technologies. Instead of using brute force numerical optimizers we apply a generalized weak nonlinearity model that only involves AC

  16. HCMT models of optical microring-resonator circuits

    NARCIS (Netherlands)

    Lohmeyer, Manfred

    2010-01-01

    Circuits of dielectric integrated optical microring resonators are addressed through a two-dimensional hybrid analytical/numerical coupled mode theory (HCMT) model. Analytical modes of all straight and curved cores form templates for the optical fields of the entire circuits. Our variational techniq

  17. High Resolution PV Power Modeling for Distribution Circuit Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Norris, B. L.; Dise, J. H.

    2013-09-01

    NREL has contracted with Clean Power Research to provide 1-minute simulation datasets of PV systems located at three high penetration distribution feeders in the service territory of Southern California Edison (SCE): Porterville, Palmdale, and Fontana, California. The resulting PV simulations will be used to separately model the electrical circuits to determine the impacts of PV on circuit operations.

  18. Model of Pulsed Electrical Discharge Machining (EDM using RL Circuit

    Directory of Open Access Journals (Sweden)

    Ade Erawan Bin Minhat

    2014-10-01

    Full Text Available This article presents a model of pulsed Electrical Discharge Machining (EDM using RL circuit. There are several mathematical models have been successfully developed based on the initial, ignition and discharge phase of current and voltage gap. According to these models, the circuit schematic of transistor pulse power generator has been designed using electrical model in Matlab Simulink software to identify the profile of voltage and current during machining process. Then, the simulation results are compared with the experimental results.

  19. Circuit Modeling of a MEMS Varactor Including Dielectric Charging Dynamics

    Science.gov (United States)

    Giounanlis, P.; Andrade-Miceli, D.; Gorreta, S.; Pons-Nin, J.; Dominguez-Pumar, M.; Blokhina, E.

    2016-10-01

    Electrical models for MEMS varactors including the effect of dielectric charging dynamics are not available in commercial circuit simulators. In this paper a circuit model using lumped ideal elements available in the Cadence libraries and a basic Verilog-A model, has been implemented. The model has been used to simulate the dielectric charging in function of time and its effects over the MEMS capacitance value.

  20. Emerging Carbon Nanotube Electronic Circuits, Modeling, and Performance

    OpenAIRE

    Yao Xu; Ashok Srivastava; Sharma, Ashwani K.

    2010-01-01

    Current transport and dynamic models of carbon nanotube field-effect transistors are presented. A model of single-walled carbon nanotube as interconnect is also presented and extended in modeling of single-walled carbon nanotube bundles. These models are applied in studying the performances of circuits such as the complementary carbon nanotube inverter pair and carbon nanotube as interconnect. Cadence/Spectre simulations show that carbon nanotube field-effect transistor circuits can operate a...

  1. A computational model for epidural electrical stimulation of spinal sensorimotor circuits.

    Science.gov (United States)

    Capogrosso, Marco; Wenger, Nikolaus; Raspopovic, Stanisa; Musienko, Pavel; Beauparlant, Janine; Bassi Luciani, Lorenzo; Courtine, Grégoire; Micera, Silvestro

    2013-12-04

    Epidural electrical stimulation (EES) of lumbosacral segments can restore a range of movements after spinal cord injury. However, the mechanisms and neural structures through which EES facilitates movement execution remain unclear. Here, we designed a computational model and performed in vivo experiments to investigate the type of fibers, neurons, and circuits recruited in response to EES. We first developed a realistic finite element computer model of rat lumbosacral segments to identify the currents generated by EES. To evaluate the impact of these currents on sensorimotor circuits, we coupled this model with an anatomically realistic axon-cable model of motoneurons, interneurons, and myelinated afferent fibers for antagonistic ankle muscles. Comparisons between computer simulations and experiments revealed the ability of the model to predict EES-evoked motor responses over multiple intensities and locations. Analysis of the recruited neural structures revealed the lack of direct influence of EES on motoneurons and interneurons. Simulations and pharmacological experiments demonstrated that EES engages spinal circuits trans-synaptically through the recruitment of myelinated afferent fibers. The model also predicted the capacity of spatially distinct EES to modulate side-specific limb movements and, to a lesser extent, extension versus flexion. These predictions were confirmed during standing and walking enabled by EES in spinal rats. These combined results provide a mechanistic framework for the design of spinal neuroprosthetic systems to improve standing and walking after neurological disorders.

  2. Symbolic Model Checking for Sequential Circuit Verification

    Science.gov (United States)

    1993-07-15

    umI4A8807, and in paut by the Semiconductor Research Corporation under Contract 92cW~-294. The fourti author was supported by an AT&T Bell Laboamtories Ph.D...found late in the design phase of a digital circuit are a major cause of unexpected delays in realising the circuit in hardware. As a result, interest in...block diagram of the stack. It consists of an array of d cells, each cell consisting of a control part, a data part and a completion tree. The data

  3. Electric circuit model for strained-layer epitaxy

    Science.gov (United States)

    Kujofsa, Tedi; Ayers, John E.

    2016-11-01

    For the design and analysis of a strained-layer semiconductor device structure, the equilibrium strain profile may be determined numerically by energy minimization but this method is computationally intense and non-intuitive. Here we present an electric circuit model approach for the equilibrium analysis of an epitaxial stack, in which each sublayer may be represented by an analogous configuration involving a current source, a resistor, a voltage source, and an ideal diode. The resulting node voltages in the analogous electric circuit correspond to the equilibrium strains in the original epitaxial structure. This new approach enables analysis using widely accessible circuit simulators, and an intuitive understanding of electric circuits may be translated to the relaxation of strained-layer structures. In this paper, we describe the mathematical foundation of the electrical circuit model and demonstrate its application to epitaxial layers of Si1-x Ge x grown on a Si (001) substrate.

  4. Comparison of NASCAP modelling results with lumped circuit analysis

    Science.gov (United States)

    Stang, D. B.; Purvis, C. K.

    1980-01-01

    Engineering design tools that can be used to predict the development of absolute and differential potentials by realistic spacecraft under geomagnetic substorm conditions are described. Two types of analyses are in use: (1) the NASCAP code, which computes quasistatic charging of geometrically complex objects with multiple surface materials in three dimensions; (2) lumped element equivalent circuit models that are used for analyses of particular spacecraft. The equivalent circuit models require very little computation time, however, they cannot account for effects, such as the formation of potential barriers, that are inherently multidimensional. Steady state potentials of structure and insulation are compared with those resulting from the equivalent circuit model.

  5. Information Flow through a Model of the C. elegans Klinotaxis Circuit.

    Directory of Open Access Journals (Sweden)

    Eduardo J Izquierdo

    Full Text Available Understanding how information about external stimuli is transformed into behavior is one of the central goals of neuroscience. Here we characterize the information flow through a complete sensorimotor circuit: from stimulus, to sensory neurons, to interneurons, to motor neurons, to muscles, to motion. Specifically, we apply a recently developed framework for quantifying information flow to a previously published ensemble of models of salt klinotaxis in the nematode worm Caenorhabditis elegans. Despite large variations in the neural parameters of individual circuits, we found that the overall information flow architecture circuit is remarkably consistent across the ensemble. This suggests structural connectivity is not necessarily predictive of effective connectivity. It also suggests information flow analysis captures general principles of operation for the klinotaxis circuit. In addition, information flow analysis reveals several key principles underlying how the models operate: (1 Interneuron class AIY is responsible for integrating information about positive and negative changes in concentration, and exhibits a strong left/right information asymmetry. (2 Gap junctions play a crucial role in the transfer of information responsible for the information symmetry observed in interneuron class AIZ. (3 Neck motor neuron class SMB implements an information gating mechanism that underlies the circuit's state-dependent response. (4 The neck carries more information about small changes in concentration than about large ones, and more information about positive changes in concentration than about negative ones. Thus, not all directions of movement are equally informative for the worm. Each of these findings corresponds to hypotheses that could potentially be tested in the worm. Knowing the results of these experiments would greatly refine our understanding of the neural circuit underlying klinotaxis.

  6. 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.

  7. Neural compensation, muscle load distribution and muscle function in control of biped models

    Science.gov (United States)

    Bavarian, B.

    Three aspects of the neuromuscular control of muscle actuators in biped movements were studied: neural compensation, muscle load distribution, and muscle function. A block diagram of a neural control circuit model of the control nervous system is presented. Based on this block diagram a circuit comprised of a dynamic compensator, an inverse plant, and pre-programmed reference trajectory generators is proposed for control of a general n-link biped model. This circuit is used to study the postural stability and point-to-point voluntary movement of a two-link planar biped with two pairs of muscle models. The muscle load distribution, relevant to functional electrical stimulation of paraplegic patients for restoration of limited motor function, is considered. A quantitative analysis of the local controllability of a two-link planar biped model incorporating six major muscles of the lower extremities is presented. A model of the muscle for the lower extremities is presented.

  8. Neural network models of protein domain evolution

    OpenAIRE

    Sylvia Nagl

    2000-01-01

    Protein domains are complex adaptive systems, and here a novel procedure is presented that models the evolution of new functional sites within stable domain folds using neural networks. Neural networks, which were originally developed in cognitive science for the modeling of brain functions, can provide a fruitful methodology for the study of complex systems in general. Ethical implications of developing complex systems models of biomolecules are discussed, with particular reference to molecu...

  9. Circuit modeling and performance analysis of photoconductive antenna

    Science.gov (United States)

    Prajapati, Jitendra; Bharadwaj, Mrinmoy; Chatterjee, Amitabh; Bhattacharjee, Ratnajit

    2017-07-01

    In recent years, several experimental and simulation studies have been reported on the terahertz (THz) generation using a photoconductive antenna (PCA). The major problem with PCA is its low overall efficiency, which depends on several parameters related to a semiconductor material, an antenna geometry, and characteristics of the laser beam. To analyze the effect of different parameters on PCA efficiency, accurate circuit modeling, using physics undergoing in the device, is necessary. Although a few equivalent circuit models have been proposed in the literature, these models do not adequately capture the semiconductor physics in PCA. This paper presents an equivalent electrical circuit model of PCA incorporating basic semiconductor device physics. The proposed equivalent circuit model is validated using Sentaurus TCAD device level modeling tool as well as with the experimental results available in the literature. The results obtained from the proposed circuit model are in close agreement with the TCAD results as well as available experimental results. The proposed circuit model is expected to contribute towards future research efforts aimed at optimization of the performance of the PCA system.

  10. Note on homological modeling of the electric circuits

    OpenAIRE

    2014-01-01

    Based on a simple example, it is explained how the homological analysis may be applied for modeling of the electric circuits. The homological branch, mesh and nodal analyses are presented. Geometrical interpretations are given.

  11. Differential regulation of polarized synaptic vesicle trafficking and synapse stability in neural circuit rewiring in Caenorhabditis elegans.

    Directory of Open Access Journals (Sweden)

    Naina Kurup

    2017-06-01

    Full Text Available Neural circuits are dynamic, with activity-dependent changes in synapse density and connectivity peaking during different phases of animal development. In C. elegans, young larvae form mature motor circuits through a dramatic switch in GABAergic neuron connectivity, by concomitant elimination of existing synapses and formation of new synapses that are maintained throughout adulthood. We have previously shown that an increase in microtubule dynamics during motor circuit rewiring facilitates new synapse formation. Here, we further investigate cellular control of circuit rewiring through the analysis of mutants obtained in a forward genetic screen. Using live imaging, we characterize novel mutations that alter cargo binding in the dynein motor complex and enhance anterograde synaptic vesicle movement during remodeling, providing in vivo evidence for the tug-of-war between kinesin and dynein in fast axonal transport. We also find that a casein kinase homolog, TTBK-3, inhibits stabilization of nascent synapses in their new locations, a previously unexplored facet of structural plasticity of synapses. Our study delineates temporally distinct signaling pathways that are required for effective neural circuit refinement.

  12. Navigating Monogamy: Nonapeptide Sensitivity in a Memory Neural Circuit May Shape Social Behavior and Mating Decisions.

    Science.gov (United States)

    Ophir, Alexander G

    2017-01-01

    The role of memory in mating systems is often neglected despite the fact that most mating systems are defined in part by how animals use space. Monogamy, for example, is usually characterized by affiliative (e.g., pairbonding) and defensive (e.g., mate guarding) behaviors, but a high degree of spatial overlap in home range use is the easiest defining feature of monogamous animals in the wild. The nonapeptides vasopressin and oxytocin have been the focus of much attention for their importance in modulating social behavior, however this work has largely overshadowed their roles in learning and memory. To date, the understanding of memory systems and mechanisms governing social behavior have progressed relatively independently. Bridging these two areas will provide a deeper appreciation for understanding behavior, and in particular the mechanisms that mediate reproductive decision-making. Here, I argue that the ability to mate effectively as monogamous individuals is linked to the ability to track conspecifics in space. I discuss the connectivity across some well-known social and spatial memory nuclei, and propose that the nonapeptide receptors within these structures form a putative "socio-spatial memory neural circuit." This purported circuit may function to integrate social and spatial information to shape mating decisions in a context-dependent fashion. The lateral septum and/or the nucleus accumbens, and neuromodulation therein, may act as an intermediary to relate socio-spatial information with social behavior. Identifying mechanisms responsible for relating information about the social world with mechanisms mediating mating tactics is crucial to fully appreciate the suite of factors driving reproductive decisions and social decision-making.

  13. 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

  14. NEURAL CORRELATES FOR APATHY: FRONTAL - PREFRONTAL AND PARIETAL CORTICAL - SUBCORTICAL CIRCUITS

    Directory of Open Access Journals (Sweden)

    Rita Moretti

    2016-12-01

    Full Text Available Apathy is an uncertain nosographical entity, which includes reduced motivation, abulia, decreased empathy, and lack of emotional invovlement; it is an important and heavy-burden clinical condition which strongly impacts in every day life events, affects the common daily living abilities, reduced the inner goal directed behavior, and gives the heaviest burden on caregivers. Is a quite common comorbidity of many neurological disease, However, there is no definite consensus on the role of apathy in clinical practice, no definite data on anatomical circuits involved in its development, and no definite instrument to detect it at bedside. As a general observation, the occurrence of apathy is connected to damage of prefrontal cortex (PFC and basal ganglia; emotional affective apathy may be related to the orbitomedial PFC and ventral striatum; cognitive apathy may be associated with dysfunction of lateral PFC and dorsal caudate nuclei; deficit of autoactivation may be due to bilateral lesions of the internal portion of globus pallidus, bilateral paramedian thalamic lesions, or the dorsomedial portion of PFC. On the other hand, apathy severity has been connected to neurofibrillary tangles density in the anterior cingulate gyrus and to grey matter atrophy in the anterior cingulate (ACC and in the left medial frontal cortex, confirmed by functional imaging studies. These neural networks are linked to projects, judjing and planning, execution and selection common actions, and through the basolateral amygdala and nucleus accumbens projects to the frontostriatal and to the dorsolateral prefrontal cortex. Therefore, an alteration of these circuitry caused a lack of insight, a reduction of decision-making strategies and a reduced speedness in action decsion, major resposnible for apathy. Emergent role concerns also the parietal cortex, with its direct action motivation control.We will discuss the importance of these circuits in different pathologies

  15. Circuit oriented electromagnetic modeling using the PEEC techniques

    CERN Document Server

    Ruehli, Albert; Jiang, Lijun

    2017-01-01

    This book provides intuitive solutions to electromagnetic problems by using the Partial Eelement Eequivalent Ccircuit (PEEC) method. This book begins with an introduction to circuit analysis techniques, laws, and frequency and time domain analyses. The authors also treat Maxwell's equations, capacitance computations, and inductance computations through the lens of the PEEC method. Next, readers learn to build PEEC models in various forms: equivalent circuit models, non orthogonal PEEC models, skin-effect models, PEEC models for dielectrics, incident and radiate field models, and scattering PEEC models. The book concludes by considering issues like such as stability and passivity, and includes five appendices some with formulas for partial elements.

  16. Structural basis for cholinergic regulation of neural circuits in the mouse olfactory bulb.

    Science.gov (United States)

    Hamamoto, Masakazu; Kiyokage, Emi; Sohn, Jaerin; Hioki, Hiroyuki; Harada, Tamotsu; Toida, Kazunori

    2017-02-15

    Odor information is regulated by olfactory inputs, bulbar interneurons, and centrifugal inputs in the olfactory bulb (OB). Cholinergic neurons projecting from the nucleus of the horizontal limb of the diagonal band of Broca and the magnocellular preoptic nucleus are one of the primary centrifugal inputs to the OB. In this study, we focused on cholinergic regulation of the OB and analyzed neural morphology with a particular emphasis on the projection pathways of cholinergic neurons. Single-cell imaging of a specific neuron within dense fibers is critical to evaluate the structure and function of the neural circuits. We labeled cholinergic neurons by infection with virus vector and then reconstructed them three-dimensionally. We also examined the ultramicrostructure of synapses by electron microscopy tomography. To further clarify the function of cholinergic neurons, we performed confocal laser scanning microscopy to investigate whether other neurotransmitters are present within cholinergic axons in the OB. Our results showed the first visualization of complete cholinergic neurons, including axons projecting to the OB, and also revealed frequent axonal branching within the OB where it innervated multiple glomeruli in different areas. Furthermore, electron tomography demonstrated that cholinergic axons formed asymmetrical synapses with a morphological variety of thicknesses of the postsynaptic density. Although we have not yet detected the presence of other neurotransmitters, the range of synaptic morphology suggests multiple modes of transmission. The present study elucidates the ways that cholinergic neurons could contribute to the elaborate mechanisms involved in olfactory processing in the OB. J. Comp. Neurol. 525:574-591, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  17. Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

    Science.gov (United States)

    Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

    1997-01-01

    One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

  18. The Derived Equivalent Circuit Model for Magnetized Anisotropic Graphene

    CERN Document Server

    Cao, Ying S; Ruehli, Albert E

    2015-01-01

    Due to the static magnetic field, the conductivity for graphene becomes a dispersive and anisotropic tensor, which complicates most modeling methodologies. In this paper, a novel equivalent circuit model is proposed for graphene with the magnetostatic bias based on the electric field integral equation (EFIE). To characterize the anisotropic property of the biased graphene, the resistive part of the unit circuit is replaced by a resistor in series with current control voltage sources (CCVSs). The CCVSs account for the off-diagonal parts of the surface conductivity tensor for the magnetized graphene. Furthermore, the definitions of the absorption cross section and the scattering cross section are revisited to make them feasible for derived circuit analysis. This proposed method is benchmarked with several numerical examples. This paper also provides a new equivalent circuit model to deal with dispersive and anisotropic materials.

  19. Rules and mechanisms for efficient two-stage learning in neural circuits

    Science.gov (United States)

    Teşileanu, Tiberiu; Ölveczky, Bence; Balasubramanian, Vijay

    2017-01-01

    Trial-and-error learning requires evaluating variable actions and reinforcing successful variants. In songbirds, vocal exploration is induced by LMAN, the output of a basal ganglia-related circuit that also contributes a corrective bias to the vocal output. This bias is gradually consolidated in RA, a motor cortex analogue downstream of LMAN. We develop a new model of such two-stage learning. Using stochastic gradient descent, we derive how the activity in ‘tutor’ circuits (e.g., LMAN) should match plasticity mechanisms in ‘student’ circuits (e.g., RA) to achieve efficient learning. We further describe a reinforcement learning framework through which the tutor can build its teaching signal. We show that mismatches between the tutor signal and the plasticity mechanism can impair 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. DOI: http://dx.doi.org/10.7554/eLife.20944.001 PMID:28374674

  20. 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.

  1. Hybrid Spintronic-CMOS Spiking Neural Network with On-Chip Learning: Devices, Circuits, and Systems

    Science.gov (United States)

    Sengupta, Abhronil; Banerjee, Aparajita; Roy, Kaushik

    2016-12-01

    Over the past decade, spiking neural networks (SNNs) have emerged as one of the popular architectures to emulate the brain. In SNNs, information is temporally encoded and communication between neurons is accomplished by means of spikes. In such networks, spike-timing-dependent plasticity mechanisms require the online programing of synapses based on the temporal information of spikes transmitted by spiking neurons. In this work, we propose a spintronic synapse with decoupled spike-transmission and programing-current paths. The spintronic synapse consists of a ferromagnet-heavy-metal heterostructure where the programing current through the heavy metal generates spin-orbit torque to modulate the device conductance. Low programing energy and fast programing times demonstrate the efficacy of the proposed device as a nanoelectronic synapse. We perform a simulation study based on an experimentally benchmarked device-simulation framework to demonstrate the interfacing of such spintronic synapses with CMOS neurons and learning circuits operating in the transistor subthreshold region to form a network of spiking neurons that can be utilized for pattern-recognition problems.

  2. 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.

  3. Information Flow through a Model of the C. elegans Klinotaxis Circuit

    Science.gov (United States)

    Izquierdo, Eduardo J.; Williams, Paul L.; Beer, Randall D.

    2015-01-01

    Understanding how information about external stimuli is transformed into behavior is one of the central goals of neuroscience. Here we characterize the information flow through a complete sensorimotor circuit: from stimulus, to sensory neurons, to interneurons, to motor neurons, to muscles, to motion. Specifically, we apply a recently developed framework for quantifying information flow to a previously published ensemble of models of salt klinotaxis in the nematode worm Caenorhabditis elegans. Despite large variations in the neural parameters of individual circuits, we found that the overall information flow architecture circuit is remarkably consistent across the ensemble. This suggests structural connectivity is not necessarily predictive of effective connectivity. It also suggests information flow analysis captures general principles of operation for the klinotaxis circuit. In addition, information flow analysis reveals several key principles underlying how the models operate: (1) Interneuron class AIY is responsible for integrating information about positive and negative changes in concentration, and exhibits a strong left/right information asymmetry. (2) Gap junctions play a crucial role in the transfer of information responsible for the information symmetry observed in interneuron class AIZ. (3) Neck motor neuron class SMB implements an information gating mechanism that underlies the circuit’s state-dependent response. (4) The neck carries more information about small changes in concentration than about large ones, and more information about positive changes in concentration than about negative ones. Thus, not all directions of movement are equally informative for the worm. Each of these findings corresponds to hypotheses that could potentially be tested in the worm. Knowing the results of these experiments would greatly refine our understanding of the neural circuit underlying klinotaxis. PMID:26465883

  4. Systems level circuit model of C. elegans undulatory locomotion: mathematical modeling and molecular genetics.

    Science.gov (United States)

    Karbowski, Jan; Schindelman, Gary; Cronin, Christopher J; Seah, Adeline; Sternberg, Paul W

    2008-06-01

    To establish the relationship between locomotory behavior and dynamics of neural circuits in the nematode C. elegans we combined molecular and theoretical approaches. In particular, we quantitatively analyzed the motion of C. elegans with defective synaptic GABA and acetylcholine transmission, defective muscle calcium signaling, and defective muscles and cuticle structures, and compared the data with our systems level circuit model. The major experimental findings are: (1) anterior-to-posterior gradients of body bending flex for almost all strains both for forward and backward motion, and for neuronal mutants, also analogous weak gradients of undulatory frequency, (2) existence of some form of neuromuscular (stretch receptor) feedback, (3) invariance of neuromuscular wavelength, (4) biphasic dependence of frequency on synaptic signaling, and (5) decrease of frequency with increase of the muscle time constant. Based on (1) we hypothesize that the Central Pattern Generator (CPG) is located in the head both for forward and backward motion. Points (1) and (2) are the starting assumptions for our theoretical model, whose dynamical patterns are qualitatively insensitive to the details of the CPG design if stretch receptor feedback is sufficiently strong and slow. The model reveals that stretch receptor coupling in the body wall is critical for generation of the neuromuscular wave. Our model agrees with our behavioral data (3), (4), and (5), and with other pertinent published data, e.g., that frequency is an increasing function of muscle gap-junction coupling.

  5. Resilience of the quantum Rabi model in circuit QED

    Science.gov (United States)

    E Manucharyan, Vladimir; Baksic, Alexandre; Ciuti, Cristiano

    2017-07-01

    In circuit quantum electrodynamics (circuit QED), an artificial ‘circuit atom’ can couple to a quantized microwave radiation much stronger than its real atomic counterpart. The celebrated quantum Rabi model describes the simplest interaction of a two-level system with a single-mode boson field. When the coupling is large enough, the bare multilevel structure of a realistic circuit atom cannot be ignored even if the circuit is strongly anharmonic. We explored this situation theoretically for flux (fluxonium) and charge (Cooper pair box) type multi-level circuits tuned to their respective flux/charge degeneracy points. We identified which spectral features of the quantum Rabi model survive and which are renormalized for large coupling. Despite significant renormalization of the low-energy spectrum in the fluxonium case, the key quantum Rabi feature—nearly-degenerate vacuum consisting of an atomic state entangled with a multi-photon field—appears in both types of circuits when the coupling is sufficiently large. Like in the quantum Rabi model, for very large couplings the entanglement spectrum is dominated by only two, nearly equal eigenvalues, in spite of the fact that a large number of bare atomic states are actually involved in the atom-resonator ground state. We interpret the emergence of the two-fold degeneracy of the vacuum of both circuits as an environmental suppression of flux/charge tunneling due to their dressing by virtual low-/high-impedance photons in the resonator. For flux tunneling, the dressing is nothing else than the shunting of a Josephson atom with a large capacitance of the resonator. Suppression of charge tunneling is a manifestation of the dynamical Coulomb blockade of transport in tunnel junctions connected to resistive leads.

  6. Development and verification of printed circuit board toroidal transformer model

    DEFF Research Database (Denmark)

    Pejtersen, Jens; Mønster, Jakob Døllner; Knott, Arnold

    2013-01-01

    by comparing calculated parameters with 3D finite element simulations and experimental measurement results. The developed transformer model shows good agreement with the simulated and measured results. The model can be used to predict the parameters of printed circuit board toroidal transformer configurations......An analytical model of an air core printed circuit board embedded toroidal transformer configuration is presented. The transformer has been developed for galvanic isolation of very high frequency switch-mode dc-dc power converter applications. The theoretical model is developed and verified...

  7. 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。

  8. Inter Digital Transducer Modelling through Mason Equivalent Circuit Model

    DEFF Research Database (Denmark)

    Mishra, Dipti; Singh, Abhishek; Hussain, Dil muhammed Akbar

    2016-01-01

    The frequency reliance of inter-digital transducer is analyzed with the help of MASON’s Equivalent circuit which is based on Smith’s Equivalent circuit which is further based on Foster’sNetwork. An inter-digital transducer has been demonstratedas a RLC network. The circuit is simulated by Simulat...

  9. Inter digital transducer modelling through Mason equivalent circuit model

    DEFF Research Database (Denmark)

    Mishra, Dipti; Singh, Abhishek; Hussain, Dil muhammed Akbar

    2016-01-01

    The frequency reliance of inter-digital transducer is analyzed with the help of MASON's Equivalent circuit which is based on Smith's Equivalent circuit which is further based on Foster's Network. An inter-digital transducer has been demonstrated as a RLC network. The circuit is simulated by Simul...

  10. Genetic control of encoding strategy in a food-sensing neural circuit

    Science.gov (United States)

    Diana, Giovanni; Patel, Dhaval S; Entchev, Eugeni V; Zhan, Mei; Lu, Hang; Ch'ng, QueeLim

    2017-01-01

    Neuroendocrine circuits encode environmental information via changes in gene expression and other biochemical activities to regulate physiological responses. Previously, we showed that daf-7 TGFβ and tph-1 tryptophan hydroxylase expression in specific neurons encode food abundance to modulate lifespan in Caenorhabditis elegans, and uncovered cross- and self-regulation among these genes (Entchev et al., 2015). Here, we now extend these findings by showing that these interactions between daf-7 and tph-1 regulate redundancy and synergy among neurons in food encoding through coordinated control of circuit-level signal and noise properties. Our analysis further shows that daf-7 and tph-1 contribute to most of the food-responsiveness in the modulation of lifespan. We applied a computational model to capture the general coding features of this system. This model agrees with our previous genetic analysis and highlights the consequences of redundancy and synergy during information transmission, suggesting a rationale for the regulation of these information processing features. DOI: http://dx.doi.org/10.7554/eLife.24040.001 PMID:28166866

  11. Modelling of boiler heating surfaces and evaporator circuits

    DEFF Research Database (Denmark)

    Sørensen, K.; Condra, T.; Houbak, Niels

    2002-01-01

    the circulation in the evaporator circuit. The models have been developed as Differential-Algebraic-Equations (DAE) and MATLAB has been applied for the integration of the models. In general MATLAB has proved to be very stable for the relatively stiff equation systems. Experimental verification is planned......Dynamic models for simulating boiler performance have been developed. Models for the flue gas side and for the evaporator circuit have been developed for the purpose of determining material temperatures and heat transfer from the flue gas side to the water-/steam side in order to simulate...... at a full scale plant equipped with instrumentation to verify heat transfer and circulation in the evaporator circuit....

  12. Modelling of Boiler Heating Surfaces and Evaporator Circuits

    DEFF Research Database (Denmark)

    Sørensen, Kim; Condra, Thomas Joseph; Houbak, Niels

    2002-01-01

    the circulation in the evaporator circuit. The models have been developed as Differential-Algebraic-Equation systems (DAE) and MATLAB has been applied for the integration of the models. In general MATLAB has proved to be very stable for these relatively stiff equation systems. Experimental verication is planned......Dynamic models for simulating boiler performance have been developed. Models for the ue gas side and for the evaporator circuit have been developed for the purpose of determining material temperatures and heat transfer from the ue gas side to the water-/steam side in order to simulate...... at a full scale plant equipped with instrumentation to verify heat transfer and circulation in the evaporator circuit....

  13. 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.

  14. Modulatory Effects of Modafinil on Neural Circuits Regulating Emotion and Cognition

    Science.gov (United States)

    Rasetti, Roberta; Mattay, Venkata S; Stankevich, Beth; Skjei, Kelsey; Blasi, Giuseppe; Sambataro, Fabio; Arrillaga-Romany, Isabel C; Goldberg, Terry E; Callicott, Joseph H; Apud, José A; Weinberger, Daniel R

    2010-01-01

    Modafinil differs from other arousal-enhancing agents in chemical structure, neurochemical profile, and behavioral effects. Most functional neuroimaging studies to date examined the effect of modafinil only on information processing underlying executive cognition, but cognitive enhancers in general have been shown to have pronounced effects on emotional behavior, too. We examined the effect of modafinil on neural circuits underlying affective processing and cognitive functions. Healthy volunteers were enrolled in this double-blinded placebo-controlled trial (100 mg/day for 7 days). They underwent BOLD fMRI while performing an emotion information-processing task that activates the amygdala and two prefrontally dependent cognitive tasks—a working memory (WM) task and a variable attentional control (VAC) task. A clinical assessment that included measurement of blood pressure, heart rate, the Hamilton anxiety scale, and the profile of mood state (POMS) questionnaire was also performed on each test day. BOLD fMRI revealed significantly decreased amygdala reactivity to fearful stimuli on modafinil compared with the placebo condition. During executive cognition tasks, a WM task and a VAC task, modafinil reduced BOLD signal in the prefrontal cortex and anterior cingulate. Although not statistically significant, there were trends for reduced anxiety, for decreased fatigue-inertia and increased vigor-activity, as well as decreased anger-hostility on modafinil. Modafinil in low doses has a unique physiologic profile compared with stimulant drugs: it enhances the efficiency of prefrontal cortical cognitive information processing, while dampening reactivity to threatening stimuli in the amygdala, a brain region implicated in anxiety. PMID:20555311

  15. A quantum-implementable neural network model

    Science.gov (United States)

    Chen, Jialin; Wang, Lingli; Charbon, Edoardo

    2017-10-01

    A quantum-implementable neural network, namely quantum probability neural network (QPNN) model, is proposed in this paper. QPNN can use quantum parallelism to trace all possible network states to improve the result. Due to its unique quantum nature, this model is robust to several quantum noises under certain conditions, which can be efficiently implemented by the qubus quantum computer. Another advantage is that QPNN can be used as memory to retrieve the most relevant data and even to generate new data. The MATLAB experimental results of Iris data classification and MNIST handwriting recognition show that much less neuron resources are required in QPNN to obtain a good result than the classical feedforward neural network. The proposed QPNN model indicates that quantum effects are useful for real-life classification tasks.

  16. Neural network models of categorical perception.

    Science.gov (United States)

    Damper, R I; Harnad, S R

    2000-05-01

    Studies of the categorical perception (CP) of sensory continua have a long and rich history in psychophysics. In 1977, Macmillan, Kaplan, and Creelman introduced the use of signal detection theory to CP studies. Anderson and colleagues simultaneously proposed the first neural model for CP, yet this line of research has been less well explored. In this paper, we assess the ability of neural-network models of CP to predict the psychophysical performance of real observers with speech sounds and artificial/novel stimuli. We show that a variety of neural mechanisms are capable of generating the characteristics of CP. Hence, CP may not be a special model of perception but an emergent property of any sufficiently powerful general learning system.

  17. Weather forecasting based on hybrid neural model

    Science.gov (United States)

    Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

    2017-02-01

    Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

  18. Equivalent Circuit Modeling of a Rotary Piezoelectric Motor

    DEFF Research Database (Denmark)

    El, Ghouti N.; Helbo, Jan

    2000-01-01

    In this paper, an enhanced equivalent circuit model of a rotary traveling wave piezoelectric ultrasonic motor "shinsei type USR60" is derived. The modeling is performed on the basis of an empirical approach combined with the electrical network method and some simplification assumptions about...

  19. Modeling of Nonlinear Marine Cooling Systems with Closed Circuit Flow

    DEFF Research Database (Denmark)

    Hansen, Michael; Stoustrup, Jakob; Bendtsen, Jan Dimon

    2011-01-01

    of container ships. The purpose of the model is to describe the important dynamics of the system, such as nonlinearities, transport delays and closed circuit flow dynamics to enable the model to be used for control design and simulation. The control challenge is related to the highly non-standard type of step...

  20. Modeling brain resonance phenomena using a neural mass model.

    Directory of Open Access Journals (Sweden)

    Andreas Spiegler

    2011-12-01

    Full Text Available Stimulation with rhythmic light flicker (photic driving plays an important role in the diagnosis of schizophrenia, mood disorder, migraine, and epilepsy. In particular, the adjustment of spontaneous brain rhythms to the stimulus frequency (entrainment is used to assess the functional flexibility of the brain. We aim to gain deeper understanding of the mechanisms underlying this technique and to predict the effects of stimulus frequency and intensity. For this purpose, a modified Jansen and Rit neural mass model (NMM of a cortical circuit is used. This mean field model has been designed to strike a balance between mathematical simplicity and biological plausibility. We reproduced the entrainment phenomenon observed in EEG during a photic driving experiment. More generally, we demonstrate that such a single area model can already yield very complex dynamics, including chaos, for biologically plausible parameter ranges. We chart the entire parameter space by means of characteristic Lyapunov spectra and Kaplan-Yorke dimension as well as time series and power spectra. Rhythmic and chaotic brain states were found virtually next to each other, such that small parameter changes can give rise to switching from one to another. Strikingly, this characteristic pattern of unpredictability generated by the model was matched to the experimental data with reasonable accuracy. These findings confirm that the NMM is a useful model of brain dynamics during photic driving. In this context, it can be used to study the mechanisms of, for example, perception and epileptic seizure generation. In particular, it enabled us to make predictions regarding the stimulus amplitude in further experiments for improving the entrainment effect.

  1. Theory and Circuit Model for Lossy Coaxial Transmission Line

    Energy Technology Data Exchange (ETDEWEB)

    Genoni, T. C.; Anderson, C. N.; Clark, R. E.; Gansz-Torres, J.; Rose, D. V.; Welch, Dale Robert

    2017-04-01

    The theory of signal propagation in lossy coaxial transmission lines is revisited and new approximate analytic formulas for the line impedance and attenuation are derived. The accuracy of these formulas from DC to 100 GHz is demonstrated by comparison to numerical solutions of the exact field equations. Based on this analysis, a new circuit model is described which accurately reproduces the line response over the entire frequency range. Circuit model calculations are in excellent agreement with the numerical and analytic results, and with finite-difference-time-domain simulations which resolve the skindepths of the conducting walls.

  2. Neural Network based Modeling and Simulation of Transformer Inrush Current

    Directory of Open Access Journals (Sweden)

    Puneet Kumar Singh

    2012-05-01

    Full Text Available Inrush current is a very important phenomenon which occurs during energization of transformer at no load due to temporary over fluxing. It depends on several factors like magnetization curve, resistant and inductance of primary winding, supply frequency, switching angle of circuit breaker etc. Magnetizing characteristics of core represents nonlinearity which requires improved nonlinearity solving technique to know the practical behavior of inrush current. Since several techniques still working on modeling of transformer inrush current but neural network ensures exact modeling with experimental data. Therefore, the objective of this study was to develop an Artificial Neural Network (ANN model based on data of switching angle and remanent flux for predicting peak of inrush current. Back Propagation with Levenberg-Marquardt (LM algorithm was used to train the ANN architecture and same was tested for the various data sets. This research work demonstrates that the developed ANN model exhibits good performance in prediction of inrush current’s peak with an average of percentage error of -0.00168 and for modeling of inrush current with an average of percentage error of -0.52913.

  3. Analysis of bilinear noise models in circuits and devices

    Science.gov (United States)

    Willsky, A. S.; Marcus, S. I.

    1976-01-01

    There are a number of applications in which linear noise models are inappropriate. In the paper, the use of bilinear noise models in circuits and devices is considered. Several physical problems are studied in this framework. These include circuits involving varying parameters (such as variable resistance circuits constructed using field-effect transistors), the effect of switching jitter on sampled data system performance and communication systems involving voltage-controlled oscillators and phase-lock loops. In addition, several types of analytical techniques for stochastic bilinear systems are considered. Specifically, the moment equations of Brockett for bilinear systems driven by white noise are discussed, and closed-form expressions for certain bilinear systems (those that evolve an Abelian or solvable Lie groups) driven by white or colored noise are derived. In addition, an approximate statistical technique involving the use of harmonic expansions is described.

  4. A perturbation-based model for rectifier circuits

    Directory of Open Access Journals (Sweden)

    Vipin B. Vats

    2006-01-01

    Full Text Available A perturbation-theoretic analysis of rectifier circuits is presented. The governing differential equation of the half-wave rectifier with capacitor filter is analyzed by expanding the output voltage as a Taylor series with respect to an artificially introduced parameter in the nonlinearity of the diode characteristic as is done in quantum theory. The perturbation parameter introduced in the analysis is independent of the circuit components as compared to the method presented by multiple scales. The various terms appearing in the perturbation series are then modeled in the form of an equivalent circuit. This model is subsequently used in the analysis of full-wave rectifier. Matlab simulation results are included which confirm the validity of the theoretical formulations. Perturbation analysis acts a helpful tool in analyzing time-varying systems and chaotic systems.

  5. Behavioral modeling of rf VCO circuit with MEMS LC resonator

    Science.gov (United States)

    Mohamed, Amal; Elsimary, Hamed; Ismail, Mohammed

    2001-04-01

    In this work, a behavioral Modeling of RF VCO circuit which has a tank designed by Microelectromechanical system (MEMS) technology is presented emphasizing robust design that can obtain the parametric variable of the suspended spiral inductor and the MEMS tunable capacitor to high performance and reliable design of the VCO circuit. The MEMS spiral inductor has a low phase noise effect on the VCO output, and the MEMS tunable capacitance has very high quality factor with enabling 20% change of oscillation frequency. The designed monolithic RF VCO circuit and the high-Q MEMS inductor and tunable capacitor are modeled using specter-s simulator in the CADENCE design framework and (Verilog-A) behavioral simulator. Complete monolithic fabrication of RF VCO with high-Q MEMS devices using standard CMOS process (MOSIS, AMI 1.2 micrometer).

  6. Efficient Modeling for Short Channel MOS Circuit Simulation.

    Science.gov (United States)

    1982-08-01

    of Conpube Science and Engineering Key words and phrases: MOS Trasistor Modeling. Numerical Optimization. None Parameter Estimation. sacunrI... current - voltage characteristics of MOS transistors. Although capacitances and their model parameters have been omitted for simplicity, there is no...constructing a circuit model of the MOS field-effect transistor. The model is nothing more than a set of equations which predicts the device’s current -voltage

  7. Photodiode Circuit Macro-model for SPICE Simulation

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    An accurate photodiode circuit macro-model is proposed for SPICE simulation. The definition and implementation of the macro-model is based on carrier stationary continuity equation. In this macro-model, the photodiode is a device of three pins, one for light intensity input and the other two for photocurrent output, which represent the relationship between photocurrent and incident light. The validity of the proposed macro-model is demonstrated with its PSPICE simulation result compared with reported experimental data.

  8. Boolean network model of the Pseudomonas aeruginosa quorum sensing circuits.

    Science.gov (United States)

    Dallidis, Stylianos E; Karafyllidis, Ioannis G

    2014-09-01

    To coordinate their behavior and virulence and to synchronize attacks against their hosts, bacteria communicate by continuously producing signaling molecules (called autoinducers) and continuously monitoring the concentration of these molecules. This communication is controlled by biological circuits called quorum sensing (QS) circuits. Recently QS circuits and have been recognized as an alternative target for controlling bacterial virulence and infections without the use of antibiotics. Pseudomonas aeruginosa is a Gram-negative bacterium that infects insects, plants, animals and humans and can cause acute infections. This bacterium has three interconnected QS circuits that form a very complex and versatile QS system, the operation of which is still under investigation. Here we use Boolean networks to model the complete QS system of Pseudomonas aeruginosa and we simulate and analyze its operation in both synchronous and asynchronous modes. The state space of the QS system is constructed and it turned out to be very large, hierarchical, modular and scale-free. Furthermore, we developed a simulation tool that can simulate gene knock-outs and study their effect on the regulons controlled by the three QS circuits. The model and tools we developed will give to life scientists a deeper insight to this complex QS system.

  9. 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.

  10. Axonal Activity in vivo: Technical considerations and implications for the exploration of neural circuits in freely moving animals

    Directory of Open Access Journals (Sweden)

    Jeremy Michael Barry

    2015-05-01

    Full Text Available While extracellular somatic action potentials from freely moving rats have been well characterized, axonal activity has not. We have recently reported extracellular tetrode recordings of short duration waveforms (SDW with an average peak-trough duration less than 172 µs. These waveforms have significantly shorter duration than somatic action potentials and tend to be triphasic. The present review discusses further data that suggests SDWs are representative of axonal activity, how this characterization allows for more accurate classification of somatic activity and could serve as a means of exploring signal integration in neural circuits. The review also discusses how axons may function as more than neural cables and the implications this may have for axonal information processing. While the technical challenges necessary for the exploration of axonal processes in functional neural circuits during behavior are impressive, preliminary evidence suggests that the in vivo study of axons is attainable. The resulting theoretical implications for systems level function make refinement of this approach a necessary goal toward developing a more complete understanding of the processes underlying learning, memory and attention as well as the pathological states underlying mental illness and epilepsy.

  11. Psychometric Measurement Models and Artificial Neural Networks

    Science.gov (United States)

    Sese, Albert; Palmer, Alfonso L.; Montano, Juan J.

    2004-01-01

    The study of measurement models in psychometrics by means of dimensionality reduction techniques such as Principal Components Analysis (PCA) is a very common practice. In recent times, an upsurge of interest in the study of artificial neural networks apt to computing a principal component extraction has been observed. Despite this interest, the…

  12. Neural modeling of prefrontal executive function

    Energy Technology Data Exchange (ETDEWEB)

    Levine, D.S. [Univ. of Texas, Arlington, TX (United States)

    1996-12-31

    Brain executive function is based in a distributed system whereby prefrontal cortex is interconnected with other cortical. and subcortical loci. Executive function is divided roughly into three interacting parts: affective guidance of responses; linkage among working memory representations; and forming complex behavioral schemata. Neural network models of each of these parts are reviewed and fit into a preliminary theoretical framework.

  13. Modelling a river catchment using an electrical circuit analogue

    Directory of Open Access Journals (Sweden)

    C. G. Collier

    1998-01-01

    Full Text Available An electrical circuit analogue of a river catchment is described from which is derived an hydrological model of river flow called the River Electrical Water Analogue Research and Development (REWARD model. The model is based upon an analytic solution to the equation governing the flow of electricity in an inductance-capacitance-resistance (LCR circuit. An interpretation of L, C and R in terms of catchment parameters and physical processes is proposed, and tested for the River Irwell catchment in northwest England. Hydrograph characteristics evaluated using the model are compared with observed hydrographs, confirming that the modelling approach does provide a reliable framework within which to investigate the impact of variations in model input data.

  14. Fuzzy Neural Model for Flatness Pattern Recognition

    Institute of Scientific and Technical Information of China (English)

    JIA Chun-yu; SHAN Xiu-ying; LIU Hong-min; NIU Zhao-ping

    2008-01-01

    For the problems occurring in a least square method model,a fuzzy model,and a neural network model for flatness pattern recognition,a fuzzy neural network model for flatness pattern recognition with only three-input and three-output signals was proposed with Legendre orthodoxy polynomial as basic pattern,based on fuzzy logic expert experiential knowledge and genetic-BP hybrid optimization algorithm.The model not only had definite physical meanings in its inner nodes,but also had strong self-adaptability,anti-interference ability,high recognition precision,and high velocity,thereby meeting the demand of high-precision flatness control for cold strip mill and providing a convenient,practical,and novel method for flatness pattern recognition.

  15. Implementation of recurrent artificial neural networks for nonlinear dynamic modeling in biomedical applications.

    Science.gov (United States)

    Stošovic, Miona V Andrejevic; Litovski, Vanco B

    2013-11-01

    Simulation is indispensable during the design of many biomedical prostheses that are based on fundamental electrical and electronic actions. However, simulation necessitates the use of adequate models. The main difficulties related to the modeling of such devices are their nonlinearity and dynamic behavior. Here we report the application of recurrent artificial neural networks for modeling of a nonlinear, two-terminal circuit equivalent to a specific implantable hearing device. The method is general in the sense that any nonlinear dynamic two-terminal device or circuit may be modeled in the same way. The model generated was successfully used for simulation and optimization of a driver (operational amplifier)-transducer ensemble. This confirms our claim that in addition to the proper design and optimization of the hearing actuator, optimization in the electronic domain, at the electronic driver circuit-to-actuator interface, should take place in order to achieve best performance of the complete hearing aid.

  16. Using Hydraulic Network Models to Teach Electric Circuit Principles

    Science.gov (United States)

    Jones, Irvin; EERC (Engineering Education Research Center) Collaboration

    2013-11-01

    Unlike other engineering disciplines, teaching electric circuit principles is difficult for some students because there isn't a visual context to rely on. So concepts such as electric potential, current, resistance, capacitance, and inductance have little meaning outside of their definition and the derived mathematical relationships. As a work in progress, we are developing a tool to support teaching, learning, and research of electric circuits. The tool will allow the user to design, build, and operate electric circuits in the form of hydraulic networks. We believe that this system will promote greater learning of electric circuit principles by visually realizing the conceptual and abstract concepts of electric circuits. Furthermore, as a teaching and learning tool, the hydraulic network system can be used to teach and improve comprehension of electrical principles in K through 12 classrooms and in cross-disciplinary environments such as Bioengineering, Mechanical Engineering, Industrial Engineering, and Aeronautical Engineering. As a research tool, the hydraulic network can model and simulate micro/nano bio-electro-chemical systems. Organization within the Swanson School of Engineering at the University of Pittsburgh.

  17. Fuse Modeling for Reliability Study of Power Electronics Circuits

    DEFF Research Database (Denmark)

    Bahman, Amir Sajjad; Iannuzzo, Francesco; Blaabjerg, Frede

    2017-01-01

    This paper describes a comprehensive modeling approach on reliability of fuses used in power electronic circuits. When fuses are subjected to current pulses, cyclic temperature stress is introduced to the fuse element and will wear out the component. Furthermore, the fuse may be used in a large...

  18. Fuse Modeling for Reliability Study of Power Electronics Circuits

    DEFF Research Database (Denmark)

    Bahman, Amir Sajjad; Iannuzzo, Francesco; Blaabjerg, Frede

    2017-01-01

    This paper describes a comprehensive modeling approach on reliability of fuses used in power electronic circuits. When fuses are subjected to current pulses, cyclic temperature stress is introduced to the fuse element and will wear out the component. Furthermore, the fuse may be used in a large...

  19. Advanced Breakdown Modeling for Solid-State Circuit Design

    NARCIS (Netherlands)

    Milovanović, V.

    2010-01-01

    Modeling of the effects occurring outside the usual region of application of semiconductor devices is becoming more important with increasing demands set upon electronic systems for simultaneous speed and output power. Analog integrated circuit designers are forced to enter regimes of transistor ope

  20. A new wide range Euclidean distance circuit for neural network hardware implementations.

    Science.gov (United States)

    Gopalan, A; Titus, A H

    2003-01-01

    In this paper, we describe an analog very large-scale integration (VLSI) implementation of a wide range Euclidean distance computation circuit - the key element of many synapse circuits. This circuit is essentially a wide-range absolute value circuit that is designed to be as small as possible (80 /spl times/ 76 /spl mu/m) in order to achieve maximum synapse density while maintaining a wide range of operation (0.5 to 4.5 V) and low power consumption (less than 200 /spl mu/W). The circuit has been fabricated in 1.5-/spl mu/m technology through MOSIS. We present simulated and experimental results of the circuit, and compare these results. Ultimately, this circuit is intended for use as part of a high-density hardware implementation of a self-organizing map (SOM). We describe how this circuit can be used as part of the SOM and how the SOM is going to be used as part of a larger bio-inspired vision system based on the octopus visual system.

  1. Novel Parametric Circuit Modeling for Li-Ion Batteries

    Directory of Open Access Journals (Sweden)

    Ximing Cheng

    2016-07-01

    Full Text Available Because of their simplicity and dynamic response, current pulse series are often used to extract parameters for equivalent electrical circuit modeling of Li-ion batteries. These models are then applied for performance simulation, state estimation, and thermal analysis in electric vehicles. However, these methods have two problems: The assumption of linear dependence of the matrix columns and negative parameters estimated from discrete-time equations and least-squares methods. In this paper, continuous-time equations are exploited to construct a linearly independent data matrix and parameterize the circuit model by the combination of non-negative least squares and genetic algorithm, which constrains the model parameters to be positive. Trigonometric functions are then developed to fit the parameter curves. The developed model parameterization methodology was applied and assessed by a standard driving cycle.

  2. The Relation between Finger Gnosis and Mathematical Ability: Why Redeployment of Neural Circuits Best Explains the Finding

    Directory of Open Access Journals (Sweden)

    Marcie ePenner-Wilger

    2013-12-01

    Full Text Available This paper elaborates a novel hypothesis regarding the observed predictive relation between finger gnosis and mathematical ability. In brief, we suggest that these two cognitive phenomena have overlapping neural substrates, as the result of the re-use (redeployment of part of the finger gnosis circuit for the purpose of representing numbers. We offer some background on the relation and current explanations for it; an outline of our alternate hypothesis; some evidence supporting redeployment over current views; and a plan for further research.

  3. Functional model of biological neural networks.

    Science.gov (United States)

    Lo, James Ting-Ho

    2010-12-01

    A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.

  4. Analogue Behavioral Modeling of Switched-Current Building Block Circuits

    Institute of Scientific and Technical Information of China (English)

    ZENG Xuan; WANG Wei; SHI Jianlei; TANG Pushan; D.ZHOU

    2001-01-01

    This paper proposes a behavioral modeling technique for the second-generation switched-current building block circuits. The proposed models are capable of capturing the non-ideal behavior of switched-current circuits, which includes the charge injection effects and device mismatch effects. As a result, system performance degradations due to the building block imperfections can be detected at the early design stage by fast behavioral simulations. To evaluate the accuracy of the proposed models, we developed a time-domain behavioral simulator. Experimental results have shown that compared with SPICE, the behavioral modeling error is less than 2.15%, while behavioral simulation speed up is 4 orders in time-domain.

  5. Temporal-pattern learning in neural models

    CERN Document Server

    Genís, Carme Torras

    1985-01-01

    While the ability of animals to learn rhythms is an unquestionable fact, the underlying neurophysiological mechanisms are still no more than conjectures. This monograph explores the requirements of such mechanisms, reviews those previously proposed and postulates a new one based on a direct electric coding of stimulation frequencies. Experi­ mental support for the option taken is provided both at the single neuron and neural network levels. More specifically, the material presented divides naturally into four parts: a description of the experimental and theoretical framework where this work becomes meaningful (Chapter 2), a detailed specifica­ tion of the pacemaker neuron model proposed together with its valida­ tion through simulation (Chapter 3), an analytic study of the behavior of this model when submitted to rhythmic stimulation (Chapter 4) and a description of the neural network model proposed for learning, together with an analysis of the simulation results obtained when varying seve­ ral factors r...

  6. Lumped element modelling of superconducting circuits with SPICE

    CERN Document Server

    Baveco, Maurice Antoine

    2015-01-01

    In this project research is carried out aimed at benchmarking a general-purpose circuit simulation software tool (”SPICE”). The project lasted for 8 weeks, from 29 June 2015 until 21 August 2015 at Performance Evaluation section at CERN. The goal was to apply it on a model of superconducting magnets, namely the main dipole circuit (RB circuit) of the the LHC (Large Hadron Collider), developed by members of the section. Then the strengths and the flaws of the tool were investigated. Transient effects were the main simulation focus point. In the first stage a simplified RB circuit was modelled in SPICE based on subcircuits. The first results were promising but still not with a perfect agreement. After implementing more detailed subcircuits there is an improvement and promising agreement achieved between SPICE and the results of the paper (PSpice) [2]. In general there are more strengths than drawbacks of simulating with SPICE. For example, it should have a shorter simulation time than PSpice for the same mo...

  7. Computational modeling of stuttering caused by impairments in a basal ganglia thalamo-cortical circuit involved in syllable selection and initiation

    Science.gov (United States)

    Civier, Oren; Bullock, Daniel; Max, Ludo; Guenther, Frank H.

    2013-01-01

    A typical white-matter integrity and elevated dopamine levels have been reported for individuals who stutter. We investigated how such abnormalities may lead to speech dysfluencies due to their effects on a syllable-sequencing circuit that consists of basal ganglia (BG), thalamus, and left ventral premotor cortex (vPMC). “Neurally impaired” versions of the neurocomputational speech production model GODIVA were utilized to test two hypotheses: (1) that white-matter abnormalities disturb the circuit via corticostriatal projections carrying copies of executed motor commands, and (2) that dopaminergic abnormalities disturb the circuit via the striatum. Simulation results support both hypotheses: in both scenarios, the neural abnormalities delay readout of the next syllable’s motor program, leading to dysfluency. The results also account for brain imaging findings during dysfluent speech. It is concluded that each of the two abnormality types can cause stuttering moments, probably by affecting the same BG-thalamus-vPMC circuit. PMID:23872286

  8. Combining logistic regression and neural networks to create predictive models.

    OpenAIRE

    Spackman, K. A.

    1992-01-01

    Neural networks are being used widely in medicine and other areas to create predictive models from data. The statistical method that most closely parallels neural networks is logistic regression. This paper outlines some ways in which neural networks and logistic regression are similar, shows how a small modification of logistic regression can be used in the training of neural network models, and illustrates the use of this modification for variable selection and predictive model building wit...

  9. Optimizing neural network models: motivation and case studies

    OpenAIRE

    Harp, S A; T. Samad

    2012-01-01

    Practical successes have been achieved  with neural network models in a variety of domains, including energy-related industry. The large, complex design space presented by neural networks is only minimally explored in current practice. The satisfactory results that nevertheless have been obtained testify that neural networks are a robust modeling technology; at the same time, however, the lack of a systematic design approach implies that the best neural network models generally  rem...

  10. A Delay Model of Multiple-Valued Logic Circuits Consisting of Min, Max, and Literal Operations

    Science.gov (United States)

    Takagi, Noboru

    Delay models for binary logic circuits have been proposed and clarified their mathematical properties. Kleene's ternary logic is one of the simplest delay models to express transient behavior of binary logic circuits. Goto first applied Kleene's ternary logic to hazard detection of binary logic circuits in 1948. Besides Kleene's ternary logic, there are many delay models of binary logic circuits, Lewis's 5-valued logic etc. On the other hand, multiple-valued logic circuits recently play an important role for realizing digital circuits. This is because, for example, they can reduce the size of a chip dramatically. Though multiple-valued logic circuits become more important, there are few discussions on delay models of multiple-valued logic circuits. Then, in this paper, we introduce a delay model of multiple-valued logic circuits, which are constructed by Min, Max, and Literal operations. We then show some of the mathematical properties of our delay model.

  11. Development of circuit model for arcing on solar panels

    Energy Technology Data Exchange (ETDEWEB)

    Mehta, Bhoomi K; Deshpande, S P; Mukherjee, S; Gupta, S B; Ranjan, M; Rane, R; Vaghela, N; Acharya, V [FCIPT, Institute for Plasma Research, Bhat, Gandhinagar 382428 (India); Sudhakar, M; Sankaran, M; Suresh, E P, E-mail: bhoomi@ipr.res.i [ISRO Satellite Centre (ISAC), Bangalore 560017 (India)

    2010-02-01

    The increased requirements of payload capacity of the satellites have resulted in much higher power requirements of the satellites. In order to minimize the energy loss during power transmission due to cable loss, use of high voltage solar panels becomes necessary. When a satellite encounters space plasma it floats negatively with respect to the surrounding space plasma environment. At high voltage, charging and discharging on solar panels causes the power system breakdown. Once a solar panel surface is charged and potential difference between surface insulator and conductor exceeds certain value, electrostatic discharge (ESD) may occur. This ESD may trigger a secondary arc that can destroy the solar panel circuit. ESD is also called as primary or minor arc and secondary is called major arc. The energy of minor arc is supplied by the charge stored in the coverglass of solar array and is a pulse of typically several 100 ns to several 100 {mu}s duration. The damage caused by minor arc is less compared to major arcs, but it is observed that the minor arc is cause of major arc. Therefore it is important to develop an understanding of minor arc and mitigation techniques. In this paper we present a linear circuit analysis for minor arcs on solar panels. To study arcing event, a ground experimental facility to simulate space plasma environment has been developed at Facilitation Centre for Industrial Plasma Technologies (Institute for Plasma Research) in collaboration with Indian Space Research Organization's ISRO Satellite Technology Centre (ISAC). A linear circuit model has been developed to explain the experimental results by representing the coverglass, solar cell interconnect and wiring by an LCR circuit and the primary arc by an equivalent LR circuit. The aim of the circuit analysis is to predict the arc current which flows through the arc plasma. It is established from the model that the current depends on various parameters like potential difference between

  12. Empirical generalization assessment of neural network models

    DEFF Research Database (Denmark)

    Larsen, Jan; Hansen, Lars Kai

    1995-01-01

    competing models. Since all models are trained on the same data, a key issue is to take this dependency into account. The optimal split of the data set of size N into a cross-validation set of size Nγ and a training set of size N(1-γ) is discussed. Asymptotically (large data sees), γopt→1......This paper addresses the assessment of generalization performance of neural network models by use of empirical techniques. We suggest to use the cross-validation scheme combined with a resampling technique to obtain an estimate of the generalization performance distribution of a specific model...

  13. [Building artificial neural networks model on portable NIR integrity wheat component measuring apparatus].

    Science.gov (United States)

    Ji, Hai-yan; Wen, Ming; Hao, Bin

    2006-01-01

    The quantitative analysis model of protein in integrity wheat was built by three layers back propagation artificial neural networks for portable near infrared (NIR) integrity wheat component measuring apparatus. The structure diagram of integrity wheat component measuring apparatus, light route structure of apparatus and the spectrum of integrity wheat were given in the present paper. The theory of artificial neural network was briefly introduced and the results of quantitative analysis model of protein were given. For calibration set and prediction set, the correlation coefficient was 0.90 and 0.96 respectively; the relative standard deviation is 3.77% and 4.46% respectively. Because of the influence of light route structure, electrical circuit, and integrity sample forms on the measuring apparatus, some nonlinearity exists between the spectral parameters and chemical values. The results of artificial neural networks nonlinear model were superior to linear model.

  14. Is there anybody out there? Neural circuits of threat detection in vertebrates.

    Science.gov (United States)

    Pereira, Ana G; Moita, Marta A

    2016-12-01

    Avoiding or escaping a predator is arguably one of the most important functions of a prey's brain, hence of most animals' brains. Studies on fear conditioning have greatly advanced our understanding of the circuits that regulate learned defensive behaviours. However, animals possess a multitude of threat detection mechanisms, from hardwired circuits that ensure innate responses to predator cues, to the use of social information. Surprisingly, only more recently have these circuits captured the attention of a wider range of researchers working on different species and behavioural paradigms. These have shed new light into the mechanisms of threat detection revealing conservation of the kinds of cues animals use and of its underlying detection circuits across vertebrates. As most of these studies focus on single cues, we argue for the need to study multisensory integration, a process that we believe is determinant for the prey's defence responses.

  15. Artificial Neural Network Model for Predicting Compressive

    Directory of Open Access Journals (Sweden)

    Salim T. Yousif

    2013-05-01

    Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

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

    Science.gov (United States)

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

    2015-09-09

    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

  17. Studies of the neural mechanisms of deep brain stimulation in rodent models of Parkinson's disease.

    Science.gov (United States)

    Chang, Jing-Yu; Shi, Li-Hong; Luo, Fei; Zhang, Wang-Ming; Woodward, Donald J

    2008-01-01

    Several rodent models of deep brain stimulation (DBS) have been developed in recent years. Electrophysiological and neurochemical studies have been performed to examine the mechanisms underlying the effects of DBS. In vitro studies have provided deep insights into the role of ion channels in response to brain stimulation. In vivo studies reveal neural responses in the context of intact neural circuits. Most importantly, recording of neural responses to behaviorally effective DBS in freely moving animals provides a direct means for examining how DBS modulates the basal ganglia thalamocortical circuits and thereby improves motor function. DBS can modulate firing rate, normalize irregular burst firing patterns and reduce low frequency oscillations associated with the Parkinsonian state. Our current efforts are focused on elucidating the mechanisms by which DBS effects on neural circuitry improve motor performance. New behavioral models and improved recording techniques will aide researchers conducting future DBS studies in a variety of behavioral modalities and enable new treatment strategies to be explored, such as closed-loop stimulations based on real time computation of ensemble neural activity.

  18. Ising model for neural data

    DEFF Research Database (Denmark)

    Roudi, Yasser; Tyrcha, Joanna; Hertz, John

    2009-01-01

    extract the optimal couplings for subsets of size up to $200$ neurons, essentially exactly, using Boltzmann learning. We then study the quality of several approximate methods for finding the couplings by comparing their results with those found from Boltzmann learning. Two of these methods -- inversion......(dansk abstrakt findes ikke) We study pairwise Ising models for describing the statistics of multi-neuron spike trains, using data from a simulated cortical network. We explore efficient ways of finding the optimal couplings in these models and examine their statistical properties. To do this, we...... of the Thouless-Anderson-Palmer equations and an approximation proposed by Sessak and Monasson -- are remarkably accurate. Using these approximations for larger subsets of neurons, we find that extracting couplings using data from a subset smaller than the full network tends systematically to overestimate...

  19. Biophysical Models of Neural Computation: Max and Tuning Circuits

    Science.gov (United States)

    2007-04-20

    observation that strong excitation in form of an excitatory postsynaptic potential ( EPSP ) is generally followed by an inhibitory postsynaptic...cortex, the x units correspond to thalamic cells while the p, y and z units correspond to cortical cells. The inhibitory interneurons p synapse onto y...units of the same channel as well as the other channel. The operating regime of these interneurons will be the determining factor for which computation

  20. Neuronal mechanisms and circuits underlying repetitive behaviors in mouse models of autism spectrum disorder.

    Science.gov (United States)

    Kim, Hyopil; Lim, Chae-Seok; Kaang, Bong-Kiun

    2016-01-20

    Autism spectrum disorder (ASD) refers to a broad spectrum of neurodevelopmental disorders characterized by three central behavioral symptoms: impaired social interaction, impaired social communication, and restricted and repetitive behaviors. However, the symptoms are heterogeneous among patients and a number of ASD mouse models have been generated containing mutations that mimic the mutations found in human patients with ASD. Each mouse model was found to display a unique set of repetitive behaviors. In this review, we summarize the repetitive behaviors of the ASD mouse models and variations found in their neural mechanisms including molecular and electrophysiological features. We also propose potential neuronal mechanisms underlying these repetitive behaviors, focusing on the role of the cortico-basal ganglia-thalamic circuits and brain regions associated with both social and repetitive behaviors. Further understanding of molecular and circuitry mechanisms of the repetitive behaviors associated with ASD is necessary to aid the development of effective treatments for these disorders.

  1. Analysis and Evaluation of Statistical Models for Integrated Circuits Design

    Directory of Open Access Journals (Sweden)

    Sáenz-Noval J.J.

    2011-10-01

    Full Text Available Statistical models for integrated circuits (IC allow us to estimate the percentage of acceptable devices in the batch before fabrication. Actually, Pelgrom is the statistical model most accepted in the industry; however it was derived from a micrometer technology, which does not guarantee reliability in nanometric manufacturing processes. This work considers three of the most relevant statistical models in the industry and evaluates their limitations and advantages in analog design, so that the designer has a better criterion to make a choice. Moreover, it shows how several statistical models can be used for each one of the stages and design purposes.

  2. Arbitrary modeling of TSVs for 3D integrated circuits

    CERN Document Server

    Salah, Khaled; El-Rouby, Alaa

    2014-01-01

    This book presents a wide-band and technology independent, SPICE-compatible RLC model for through-silicon vias (TSVs) in 3D integrated circuits. This model accounts for a variety of effects, including skin effect, depletion capacitance and nearby contact effects. Readers will benefit from in-depth coverage of concepts and technology such as 3D integration, Macro modeling, dimensional analysis and compact modeling, as well as closed form equations for the through silicon via parasitics. Concepts covered are demonstrated by using TSVs in applications such as a spiral inductor?and inductive-based

  3. A Hardware-Implementation-Friendly Pulse-Coupled Neural Network Algorithm for Analog Image-Feature-Generation Circuits

    Science.gov (United States)

    Chen, Jun; Shibata, Tadashi

    2007-04-01

    Pulse-coupled neural networks (PCNNs) are biologically inspired algorithms that have been shown to be highly effective for image feature generation. However, conventional PCNNs are software-oriented algorithms that are too complicated to implement as very-large-scale integration (VLSI) hardware. To employ PCNNs in image-feature-generation VLSIs, a hardware-implementation-friendly PCNN is proposed here. By introducing the concepts of exponentially decaying output and a one-branch dendritic tree, the new PCNN eliminates the large number of convolution operators and floating-point multipliers in conventional PCNNs without compromising its performance at image feature generation. As an analog VLSI implementation of the new PCNN, an image-feature-generation circuit is proposed. By employing floating-gate metal-oxide-semiconductor (MOS) technology, the circuit achieves a full voltage-mode implementation of the PCNN in a compact structure. Inheriting the merits of the PCNN, the circuit is capable of generating rotation-independent and translation-independent features for input patterns, which has been verified by SPICE simulation.

  4. Cerebellar Neural Circuits Involving Executive Control Network Predict Response to Group Cognitive Behavior Therapy in Social Anxiety Disorder.

    Science.gov (United States)

    MinlanYuan; Meng, Yajing; Zhang, Yan; Nie, Xiaojing; Ren, Zhengjia; Zhu, Hongru; Li, Yuchen; Lui, Su; Gong, Qiyong; Qiu, Changjian; Zhang, Wei

    2017-02-02

    Some intrinsic connectivity networks including the default mode network (DMN) and executive control network (ECN) may underlie social anxiety disorder (SAD). Although the cerebellum has been implicated in the pathophysiology of SAD and several networks relevant to higher-order cognition, it remains unknown whether cerebellar areas involved in DMN and ECN exhibit altered resting-state functional connectivity (rsFC) with cortical networks in SAD. Forty-six patients with SAD and 64 healthy controls (HC) were included and submitted to the baseline resting-state functional magnetic resonance imaging (fMRI). Seventeen SAD patients who completed post-treatment clinical assessments were included after group cognitive behavior therapy (CBT). RsFC of three cerebellar subregions in both groups was assessed respectively in a voxel-wise way, and these rsFC maps were compared by two-sample t tests between groups. Whole-brain voxel-wise regression was performed to examine whether cerebellar connectivity networks can predict response to CBT. Lower rsFC circuits of cerebellar subregions compared with HC at baseline (p circuits involving DMN and ECN are possible neuropathologic mechanisms of SAD. Stronger pretreatment cerebellar rsFC circuits involving ECN suggest potential neural markers to predict CBT response.

  5. Mathematical Modelling and Simulation of Electrical Circuits and Semiconductor Devices

    CERN Document Server

    Merten, K; Bulirsch, R

    1990-01-01

    Numerical simulation and modelling of electric circuits and semiconductor devices are of primal interest in today's high technology industries. At the Oberwolfach Conference more than forty scientists from around the world, in­ cluding applied mathematicians and electrical engineers from industry and universities, presented new results in this area of growing importance. The contributions to this conference are presented in these proceedings. They include contributions on special topics of current interest in circuit and device simulation, as well as contributions that present an overview of the field. In the semiconductor area special lectures were given on mixed finite element methods and iterative procedures for the solution of large linear systems. For three dimensional models new discretization procedures including software packages were presented. Con­ nections between semiconductor equations and the Boltzmann equation were shown as well as relations to the quantum transport equation. Other issues dis...

  6. A corticothalamic circuit model for sound identification in complex scenes.

    Directory of Open Access Journals (Sweden)

    Gonzalo H Otazu

    Full Text Available The identification of the sound sources present in the environment is essential for the survival of many animals. However, these sounds are not presented in isolation, as natural scenes consist of a superposition of sounds originating from multiple sources. The identification of a source under these circumstances is a complex computational problem that is readily solved by most animals. We present a model of the thalamocortical circuit that performs level-invariant recognition of auditory objects in complex auditory scenes. The circuit identifies the objects present from a large dictionary of possible elements and operates reliably for real sound signals with multiple concurrently active sources. The key model assumption is that the activities of some cortical neurons encode the difference between the observed signal and an internal estimate. Reanalysis of awake auditory cortex recordings revealed neurons with patterns of activity corresponding to such an error signal.

  7. Hybrid neural network models of transducers

    Science.gov (United States)

    Xie, Shilin; Zhang, Xinong; Chen, Shenglai; Zhu, Changchun

    2011-10-01

    A hybrid neural network (NN) approach is proposed and applied to modeling of transducers in the paper. The modeling procedures are also presented in detail. First, the simulated studies on the modeling of single input-single output and multi input-multi output transducers are conducted respectively by use of the developed hybrid NN scheme. Secondly, the hybrid NN modeling approach is utilized to characterize a six-axis force sensor prototype based on the measured data. The results show that the hybrid NN approach can significantly improve modeling precision in comparison with the conventional modeling method. In addition, the method is superior to NN black-box modeling because the former possesses smaller network scale, higher convergence speed, higher model precision and better generalization performance.

  8. Modeling and Experimental Demonstration of a Hopfield Network Analog-to-Digital Converter with Hybrid CMOS/Memristor Circuits.

    Science.gov (United States)

    Guo, Xinjie; Merrikh-Bayat, Farnood; Gao, Ligang; Hoskins, Brian D; Alibart, Fabien; Linares-Barranco, Bernabe; Theogarajan, Luke; Teuscher, Christof; Strukov, Dmitri B

    2015-01-01

    The purpose of this work was to demonstrate the feasibility of building recurrent artificial neural networks with hybrid complementary metal oxide semiconductor (CMOS)/memristor circuits. To do so, we modeled a Hopfield network implementing an analog-to-digital converter (ADC) with up to 8 bits of precision. Major shortcomings affecting the ADC's precision, such as the non-ideal behavior of CMOS circuitry and the specific limitations of memristors, were investigated and an effective solution was proposed, capitalizing on the in-field programmability of memristors. The theoretical work was validated experimentally by demonstrating the successful operation of a 4-bit ADC circuit implemented with discrete Pt/TiO2- x /Pt memristors and CMOS integrated circuit components.

  9. Microbial growth modelling with artificial neural networks.

    Science.gov (United States)

    Jeyamkonda, S; Jaya, D S; Holle, R A

    2001-03-20

    There is a growing interest in modelling microbial growth as an alternative to time-consuming, traditional, microbiological enumeration techniques. Several statistical models have been reported to describe the growth of different microorganisms, but there are accuracy problems. An alternate technique 'artificial neural networks' (ANN) for modelling microbial growth is explained and evaluated. Published data were used to build separate general regression neural network (GRNN) structures for modelling growth of Aeromonas hydrophila, Shigella flexneri, and Brochothrix thermosphacta. Both GRNN and published statistical model predictions were compared against the experimental data using six statistical indices. For training data sets, the GRNN predictions were far superior than the statistical model predictions, whereas the GRNN predictions were similar or slightly worse than statistical model predictions for test data sets for all the three data sets. GRNN predictions can be considered good, considering its performance for unseen data. Graphical plots, mean relative percentage residual, mean absolute relative residual, and root mean squared residual were identified as suitable indices for comparing competing models. ANN can now become a vehicle whereby predictive microbiology can be applied in food product development and food safety risk assessment.

  10. Recent topics on modeling of semiconductor processes, devices, and circuits

    CERN Document Server

    Topaloglu, Rasit Onur

    2011-01-01

    The last couple of years have been very busy for the semiconductor industry and researchers. The rapid speed of production channel length reduction has brought lithographic challenges to semiconductor modeling. These include stress optimization, transistor reliability and efficient circuit design with respect to interconnects, power and leakage at the chip level. This e-book focuses on the latest semiconductor techniques devised to address these issues. It should be a useful resource for electronic engineers and semiconductor chip designers.

  11. Modeling neural differentiation on micropatterned substrates coated with neural matrix components

    Directory of Open Access Journals (Sweden)

    Patricia eGarcía-Parra

    2012-03-01

    Full Text Available Topographical and biochemical characteristics of the substrate are critical for neuronal differentiation including axonal outgrowth and regeneration of neural circuits in vivo. Contact stimuli and signaling molecules allow neurons to develop and stabilize synaptic contacts. Here we present the development, characterization and functional validation of a new polymeric support able to induce neuronal differentiation in both PC12 cell line and adult primary skin-derived precursor cells in vitro. By combining a photolithographic technique with use of neural extracellular matrix as a substrate, a biocompatible and efficient microenvironment for neuronal differentiation was developed.

  12. 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.

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

    Science.gov (United States)

    Zhang, Minming; Hu, Shaohua; Xu, Lijuan; Wang, Qidong; Xu, Xiaojun; Wei, Erqing; Yan, Leqin; Hu, Jianbo; Wei, Ning; Zhou, Weihua; Huang, Manli; Xu, Yi

    2011-11-01

    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 (ptype of erotic stimuli during disgust of homosexual and heterosexual men.

  14. Neural Network Program Package for Prosody Modeling

    Directory of Open Access Journals (Sweden)

    J. Santarius

    2004-04-01

    Full Text Available This contribution describes the programme for one part of theautomatic Text-to-Speech (TTS synthesis. Some experiments (for example[14] documented the considerable improvement of the naturalness ofsynthetic speech, but this approach requires completing the inputfeature values by hand. This completing takes a lot of time for bigfiles. We need to improve the prosody by other approaches which useonly automatically classified features (input parameters. Theartificial neural network (ANN approach is used for the modeling ofprosody parameters. The program package contains all modules necessaryfor the text and speech signal pre-processing, neural network training,sensitivity analysis, result processing and a module for the creationof the input data protocol for Czech speech synthesizer ARTIC [1].

  15. Research on dynamic model of printed circuit board based on finite element method

    Science.gov (United States)

    Wei, Hui; Xu, Liangjun

    2017-08-01

    The vibration characteristics of printed circuit boards are related to the reliability of electronic components installed on their surface. Finite element software is a powerful tool to analyze the vibration characteristics of printed circuit boards, and the correct establishment of finite element model is very important. In this paper, the dynamic model of anisotropic printed circuit board is established by analyzing the material properties of printed circuit board. The influence of boundary condition and lumped mass on the vibration characteristics of printed circuit board is analyzed. In order to establish a more realistic printed circuit The finite element model of the plate provides the necessary basis.

  16. Modelling, simulating and optimizing boiler heating surfaces and evaporator circuits

    DEFF Research Database (Denmark)

    Sørensen, Kim; Condra, Thomas Joseph; Houbak, Niels

    2003-01-01

    developed as a Differential-Algebraic-Equation system (DAE) and MATLAB has been applied for the integration of the models. In generalMATLAB has proved to be very stable for these DAE systems. Experimental verication has been carried out at a full scale plant equipped with instrumentation to verify heat....... The dynamic model has been developed for the purpose of determining boiler material temperatures and heat transfer from the ue gas side to the water-/steam side in order to simulate the circulation in the evaporator circuit and hereby the water level uctuations in the drum. The dynamic model has been...

  17. Modelling, simulating and optimizing boiler heating surfaces and evaporator circuits

    DEFF Research Database (Denmark)

    Sørensen, K.; Condra, T.; Houbak, Niels

    2003-01-01

    developed as a Differential-Algebraic-Equation system (DAE) and MATLAB has been applied for the integration of the models. In general MATLAB has proved to be very stable for these DAE systems. Experimental verification has been carried out at a full scale plant equipped with instrumentation to verify heat....... The dynamic model has been developed for the purpose of determining boiler material temperatures and heat transfer from the flue gas side to the water-/steam side in order to simulate the circulation in the evaporator circuit and hereby the water level fluctuations in the drum. The dynamic model has been...

  18. Functional lateralization of the baso-lateral amygdala neural circuits modulating the motivated exploratory behaviour in rats: role of histamine.

    Science.gov (United States)

    Alvarez, Edgardo O; Banzan, Arturo M

    2011-03-17

    Functional laterality appears to be present in many brain functions in man and animals. The existence of paired neural circuits which act differentially to modulate a specific behavioural function seems to be an evolutionary successful strategy in animal evolution. In spite of many examples described in mammals, birds and other vertebrates and invertebrates, still its intrinsic mechanism is not completely understood. In this work the participation of the baso-lateral amygdala (BLA) on lateralized motivated exploratory behaviour and the possible influence of histamine neurons in these mechanisms were studied in rats. Different groups of animals under xylacine-ketamine anesthesia were implanted with microinjection guide cannulae into the right or left BLA. 72 h after implantation, animals were tested in hole-board cage (OVM) with a novelty object positioned in the center of the arena, as a model of exploration of a non-conflictive environment, and 24h later they were tested in the Elevated Asymmetric Plus Maze (APM) as a model of conflictive exploration. In the day of the experiment, lidocaine was applied into the left, or right BLA in order to block the electrical activity of BLA neurons. Saline in the contralateral BLA was considered control. Results showed that exploratory activity in the OVM was significantly inhibited when lidocaine was microinjected into the left BLA, and no changes were observed when lidocaine was applied into the right BLA. When histamine was microinjected into the right BLA and lidocaine into the contralateral BLA, head-dipping, rearing, and focalized exploration behaviour were significantly inhibited. In the APM, lidocaine treatment increased equally the exploration of the "single wall" and "high and low walls" arms of the labyrinth, independently if blocking of electrical activity of the BLA neurons was performed in the left or right amygdala. Histamine treatment inhibited significantly exploration of the lesser fear-inducing arms of the

  19. Lightning Modelling: From 3D to Circuit Approach

    Science.gov (United States)

    Moussa, H.; Abdi, M.; Issac, F.; Prost, D.

    2012-05-01

    The topic of this study is electromagnetic environment and electromagnetic interferences (EMI) effects, specifically the modelling of lightning indirect effects [1] on aircraft electrical systems present on deported and highly exposed equipments, such as nose landing gear (NLG) and nacelle, through a circuit approach. The main goal of the presented work, funded by a French national project: PREFACE, is to propose a simple equivalent electrical circuit to represent a geometrical structure, taking into account mutual, self inductances, and resistances, which play a fundamental role in the lightning current distribution. Then this model is intended to be coupled to a functional one, describing a power train chain composed of: a converter, a shielded power harness and a motor or a set of resistors used as a load for the converter. The novelty here, is to provide a pre-sizing qualitative approach allowing playing on integration in pre-design phases. This tool intends to offer a user-friendly way for replying rapidly to calls for tender, taking into account the lightning constraints. Two cases are analysed: first, a NLG that is composed of tubular pieces that can be easily approximated by equivalent cylindrical straight conductors. Therefore, passive R, L, M elements of the structure can be extracted through analytical engineer formulas such as those implemented in the partial element equivalent circuit (PEEC) [2] technique. Second, the same approach is intended to be applied on an electrical de-icing nacelle sub-system.

  20. Distribution of language-related Cntnap2 protein in neural circuits critical for vocal learning.

    Science.gov (United States)

    Condro, Michael C; White, Stephanie A

    2014-01-01

    Variants of the contactin associated protein-like 2 (Cntnap2) gene are risk factors for language-related disorders including autism spectrum disorder, specific language impairment, and stuttering. Songbirds are useful models for study of human speech disorders due to their shared capacity for vocal learning, which relies on similar cortico-basal ganglia circuitry and genetic factors. Here we investigate Cntnap2 protein expression in the brain of the zebra finch, a songbird species in which males, but not females, learn their courtship songs. We hypothesize that Cntnap2 has overlapping functions in vocal learning species, and expect to find protein expression in song-related areas of the zebra finch brain. We further expect that the distribution of this membrane-bound protein may not completely mirror its mRNA distribution due to the distinct subcellular localization of the two molecular species. We find that Cntnap2 protein is enriched in several song control regions relative to surrounding tissues, particularly within the adult male, but not female, robust nucleus of the arcopallium (RA), a cortical song control region analogous to human layer 5 primary motor cortex. The onset of this sexually dimorphic expression coincides with the onset of sensorimotor learning in developing males. Enrichment in male RA appears due to expression in projection neurons within the nucleus, as well as to additional expression in nerve terminals of cortical projections to RA from the lateral magnocellular nucleus of the nidopallium. Cntnap2 protein expression in zebra finch brain supports the hypothesis that this molecule affects neural connectivity critical for vocal learning across taxonomic classes. Copyright © 2013 Wiley Periodicals, Inc.

  1. Neural networks in economic modelling : An empirical study

    NARCIS (Netherlands)

    Verkooijen, W.J.H.

    1996-01-01

    This dissertation addresses the statistical aspects of neural networks and their usability for solving problems in economics and finance. Neural networks are discussed in a framework of modelling which is generally accepted in econometrics. Within this framework a neural network is regarded as a sta

  2. Neural circuit changes mediating lasting brain and behavioral response to predator stress.

    Science.gov (United States)

    Adamec, Robert E; Blundell, Jacqueline; Burton, Paul

    2005-01-01

    This paper reviews recent work which points to critical neural circuitry involved in lasting changes in anxiety like behavior following unprotected exposure of rats to cats (predator stress). Predator stress may increase anxiety like behavior in a variety of behavioral tests including: elevated plus maze, light dark box, acoustic startle, and social interaction. Studies of neural transmission in two limbic pathways, combined with path and covariance analysis relating physiology to behavior, suggest long term potentiation like changes in one or both of these pathways in the right hemisphere accounts for stress induced changes in all behaviors changed by predator stress except light dark box and social interaction. Findings will be discussed within the context of what is known about neural substrates activated by predator odor.

  3. 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%.

  4. Flood routing modelling with Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    R. Peters

    2006-01-01

    Full Text Available For the modelling of the flood routing in the lower reaches of the Freiberger Mulde river and its tributaries the one-dimensional hydrodynamic modelling system HEC-RAS has been applied. Furthermore, this model was used to generate a database to train multilayer feedforward networks. To guarantee numerical stability for the hydrodynamic modelling of some 60 km of streamcourse an adequate resolution in space requires very small calculation time steps, which are some two orders of magnitude smaller than the input data resolution. This leads to quite high computation requirements seriously restricting the application – especially when dealing with real time operations such as online flood forecasting. In order to solve this problem we tested the application of Artificial Neural Networks (ANN. First studies show the ability of adequately trained multilayer feedforward networks (MLFN to reproduce the model performance.

  5. Equivalent Circuit Modeling of a Rotary Piezoelectric Motor

    DEFF Research Database (Denmark)

    El, Ghouti N.; Helbo, Jan

    2000-01-01

    In this paper, an enhanced equivalent circuit model of a rotary traveling wave piezoelectric ultrasonic motor "shinsei type USR60" is derived. The modeling is performed on the basis of an empirical approach combined with the electrical network method and some simplification assumptions about...... the physical behavior of the real system. This paper highlights the importance of the electromechanical coupling factor, which is responsible for the electrical to mechanical energy conversion. The emphasis is put on the difference between the effective coupling factor and the modal coupling factor. The effect...

  6. Modeling Broadband Microwave Structures by Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    V. Otevrel

    2004-06-01

    Full Text Available The paper describes the exploitation of feed-forward neural networksand recurrent neural networks for replacing full-wave numerical modelsof microwave structures in complex microwave design tools. Building aneural model, attention is turned to the modeling accuracy and to theefficiency of building a model. Dealing with the accuracy, we describea method of increasing it by successive completing a training set.Neural models are mutually compared in order to highlight theiradvantages and disadvantages. As a reference model for comparisons,approximations based on standard cubic splines are used. Neural modelsare used to replace both the time-domain numeric models and thefrequency-domain ones.

  7. A Circuit Model of Real Time Human Body Hydration.

    Science.gov (United States)

    Asogwa, Clement Ogugua; Teshome, Assefa K; Collins, Stephen F; Lai, Daniel T H

    2016-06-01

    Changes in human body hydration leading to excess fluid losses or overload affects the body fluid's ability to provide the necessary support for healthy living. We propose a time-dependent circuit model of real-time human body hydration, which models the human body tissue as a signal transmission medium. The circuit model predicts the attenuation of a propagating electrical signal. Hydration rates are modeled by a time constant τ, which characterizes the individual specific metabolic function of the body part measured. We define a surrogate human body anthropometric parameter θ by the muscle-fat ratio and comparing it with the body mass index (BMI), we find theoretically, the rate of hydration varying from 1.73 dB/min, for high θ and low τ to 0.05 dB/min for low θ and high τ. We compare these theoretical values with empirical measurements and show that real-time changes in human body hydration can be observed by measuring signal attenuation. We took empirical measurements using a vector network analyzer and obtained different hydration rates for various BMI, ranging from 0.6 dB/min for 22.7 [Formula: see text] down to 0.04 dB/min for 41.2 [Formula: see text]. We conclude that the galvanic coupling circuit model can predict changes in the volume of the body fluid, which are essential in diagnosing and monitoring treatment of body fluid disorder. Individuals with high BMI would have higher time-dependent biological characteristic, lower metabolic rate, and lower rate of hydration.

  8. 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

    . 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...... are presented to verify the effectiveness of the proposed modeling and control method....

  9. Canonical Cortical Circuit Model Explains Rivalry, Intermittent Rivalry, and Rivalry Memory.

    Directory of Open Access Journals (Sweden)

    Shashaank Vattikuti

    2016-05-01

    Full Text Available It has been shown that the same canonical cortical circuit model with mutual inhibition and a fatigue process can explain perceptual rivalry and other neurophysiological responses to a range of static stimuli. However, it has been proposed that this model cannot explain responses to dynamic inputs such as found in intermittent rivalry and rivalry memory, where maintenance of a percept when the stimulus is absent is required. This challenges the universality of the basic canonical cortical circuit. Here, we show that by including an overlooked realistic small nonspecific background neural activity, the same basic model can reproduce intermittent rivalry and rivalry memory without compromising static rivalry and other cortical phenomena. The background activity induces a mutual-inhibition mechanism for short-term memory, which is robust to noise and where fine-tuning of recurrent excitation or inclusion of sub-threshold currents or synaptic facilitation is unnecessary. We prove existence conditions for the mechanism and show that it can explain experimental results from the quartet apparent motion illusion, which is a prototypical intermittent rivalry stimulus.

  10. Hierarchical Stochastic Simulation Algorithm for SBML Models of Genetic Circuits

    Directory of Open Access Journals (Sweden)

    Leandro eWatanabe

    2014-11-01

    Full Text Available This paper describes a hierarchical stochastic simulation algorithm which has been implemented within iBioSim, a tool used to model, analyze, and visualize genetic circuits. Many biological analysis tools flatten out hierarchy before simulation, but there are many disadvantages associated with this approach. First, the memory required to represent the model can quickly expand in the process. Second, the flattening process is computationally expensive. Finally, when modeling a dynamic cellular population within iBioSim, inlining the hierarchy of the model is inefficient since models must grow dynamically over time. This paper discusses a new approach to handle hierarchy on the fly to make the tool faster and more memory-efficient. This approach yields significant performance improvements as compared to the former flat analysis method.

  11. Neural Modeling and Control of Diesel Engine with Pollution Constraints

    CERN Document Server

    Ouladsine, Mustapha; Dovifaaz, Xavier; 10.1007/s10846-005-3806-y

    2009-01-01

    The paper describes a neural approach for modelling and control of a turbocharged Diesel engine. A neural model, whose structure is mainly based on some physical equations describing the engine behaviour, is built for the rotation speed and the exhaust gas opacity. The model is composed of three interconnected neural submodels, each of them constituting a nonlinear multi-input single-output error model. The structural identi?cation and the parameter estimation from data gathered on a real engine are described. The neural direct model is then used to determine a neural controller of the engine, in a specialized training scheme minimising a multivariable criterion. Simulations show the effect of the pollution constraint weighting on a trajectory tracking of the engine speed. Neural networks, which are ?exible and parsimonious nonlinear black-box models, with universal approximation capabilities, can accurately describe or control complex nonlinear systems, with little a priori theoretical knowledge. The present...

  12. Interpretation of electrochemical impedance spectroscopy (EIS) circuit model for soils

    Institute of Scientific and Technical Information of China (English)

    韩鹏举; 张亚芬; 陈幼佳; 白晓红

    2015-01-01

    Based on three different kinds of conductive paths in microstructure of soil and theory of electrochemical impedance spectroscopy (EIS), an integrated equivalent circuit model and impedance formula for soils were proposed, which contain 6 meaningful resistance and reactance parameters. Considering the conductive properties of soils and dispersion effects, mathematical equations for impedance under various circuit models were deduced and studied. The mathematical expression presents two semicircles for theoretical EIS Nyquist spectrum, in which the center of one semicircle is degraded to simply the equivalent model. Based on the measured parameters of EIS Nyquist spectrum, meaningful soil parameters can easily be determined. Additionally, EIS was used to investigate the soil properties with different water contents along with the mathematical relationships and mechanism between the physical parameters and water content. Magnitude of the impedance decreases with the increase of testing frequency and water content for Bode graphs. The proposed model would help us to better understand the soil microstructure and properties and offer more reasonable explanations for EIS spectra.

  13. 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.

  14. Deep Modeling: Circuit Characterization Using Theory Based Models in a Data Driven Framework

    Energy Technology Data Exchange (ETDEWEB)

    Bolme, David S [ORNL; Mikkilineni, Aravind K [ORNL; Rose, Derek C [ORNL; Yoginath, Srikanth B [ORNL; Holleman, Jeremy [University of Tennessee, Knoxville (UTK); Judy, Mohsen [University of Tennessee, Knoxville (UTK), Department of Electrical Engineering and Computer Science

    2017-01-01

    Analog computational circuits have been demonstrated to provide substantial improvements in power and speed relative to digital circuits, especially for applications requiring extreme parallelism but only modest precision. Deep machine learning is one such area and stands to benefit greatly from analog and mixed-signal implementations. However, even at modest precisions, offsets and non-linearity can degrade system performance. Furthermore, in all but the simplest systems, it is impossible to directly measure the intermediate outputs of all sub-circuits. The result is that circuit designers are unable to accurately evaluate the non-idealities of computational circuits in-situ and are therefore unable to fully utilize measurement results to improve future designs. In this paper we present a technique to use deep learning frameworks to model physical systems. Recently developed libraries like TensorFlow make it possible to use back propagation to learn parameters in the context of modeling circuit behavior. Offsets and scaling errors can be discovered even for sub-circuits that are deeply embedded in a computational system and not directly observable. The learned parameters can be used to refine simulation methods or to identify appropriate compensation strategies. We demonstrate the framework using a mixed-signal convolution operator as an example circuit.

  15. Graphene-based THz modulator analyzed by equivalent circuit model

    DEFF Research Database (Denmark)

    Xiao, Binggang; Chen, Jing; Xie, Zhiyi;

    2016-01-01

    A terahertz (THz) modulator based on graphene is proposed and analysed by use of equivalent transmission line of a homogeneous mediumand the local anisotropic model of the graphene conductivity. The result calculated by the equivalent circuit is consistent with that obtained byFresnel transfer...... matrices. For the modulator proposed here, when the frequency of carrier wave is 0.6 THz, the theoretical analysis indicatesthat the modulation bandwidth is 55.5 kHz and the modulation depth is 81.3% for voltage change from 0 to 50 V...

  16. Models of neural networks with fuzzy activation functions

    Science.gov (United States)

    Nguyen, A. T.; Korikov, A. M.

    2017-02-01

    This paper investigates the application of a new form of neuron activation functions that are based on the fuzzy membership functions derived from the theory of fuzzy systems. On the basis of the results regarding neuron models with fuzzy activation functions, we created the models of fuzzy-neural networks. These fuzzy-neural network models differ from conventional networks that employ the fuzzy inference systems using the methods of neural networks. While conventional fuzzy-neural networks belong to the first type, fuzzy-neural networks proposed here are defined as the second-type models. The simulation results show that the proposed second-type model can successfully solve the problem of the property prediction for time – dependent signals. Neural networks with fuzzy impulse activation functions can be widely applied in many fields of science, technology and mechanical engineering to solve the problems of classification, prediction, approximation, etc.

  17. Calcium imaging of neural circuits with extended depth-of-field light-sheet microscopy.

    Science.gov (United States)

    Quirin, Sean; Vladimirov, Nikita; Yang, Chao-Tsung; Peterka, Darcy S; Yuste, Rafael; Ahrens, Misha B

    2016-03-01

    Increasing the volumetric imaging speed of light-sheet microscopy will improve its ability to detect fast changes in neural activity. Here, a system is introduced for brain-wide imaging of neural activity in the larval zebrafish by coupling structured illumination with cubic phase extended depth-of-field (EDoF) pupil encoding. This microscope enables faster light-sheet imaging and facilitates arbitrary plane scanning-removing constraints on acquisition speed, alignment tolerances, and physical motion near the sample. The usefulness of this method is demonstrated by performing multi-plane calcium imaging in the fish brain with a 416×832×160  μm field of view at 33 Hz. The optomotor response behavior of the zebrafish is monitored at high speeds, and time-locked correlations of neuronal activity are resolved across its brain.

  18. A Fuzzy Neural Model for Face Recognition

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    In this paper, a fuzzy neural model is proposed for face recognition. Each rule in the proposed fuzzy neural model is used to estimate one cluster of pattern distribution in a form, which is different from the classical possibilitydensity function. Through self-adaptive learning and fuzzy inference, a confidence value will be assigned to a given pattern to denote the possibility of this pattern's belongingness to some certain class/subject. The architecture of the whole system takes structure of one-class-in-one-network (OCON), which has many advantages such as easy convergence, suitable for distribution application, quickretrieving, and incremental training. Novel methods are used to determine the number of fuzzy rules and initialize fuzzy subsets. The proposed approach has characteristics of quick learning/recognition speed, high recognition accuracy and robustness. The proposed approach can even recognize very low-resolution face images (e.g., 7x6) well that human cannot when the number of subjects is not very large. Experiments on ORL demonstrate the effectiveness of the proposed approachand an average error rate of 3.95% is obtained.

  19. Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity

    OpenAIRE

    Wiebke ePotjans; Abigail Morrison; Markus Diesmann

    2010-01-01

    A major puzzle in the field of computational neuroscience is how to relate system-level learning in higher organisms to synaptic plasticity. Recently, plasticity rules depending not only on pre- and post-synaptic activity but also on a third, non-local neuromodulatory signal have emerged as key candidates to bridge the gap between the macroscopic and the microscopic level of learning. Crucial insights into this topic are expected to be gained from simulations of neural systems, as these allow...

  20. 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.

  1. An information theoretic approach for combining neural network process models.

    Science.gov (United States)

    Sridhar, D V.; Bartlett, E B.; Seagrave, R C.

    1999-07-01

    Typically neural network modelers in chemical engineering focus on identifying and using a single, hopefully optimal, neural network model. Using a single optimal model implicitly assumes that one neural network model can extract all the information available in a given data set and that the other candidate models are redundant. In general, there is no assurance that any individual model has extracted all relevant information from the data set. Recently, Wolpert (Neural Networks, 5(2), 241 (1992)) proposed the idea of stacked generalization to combine multiple models. Sridhar, Seagrave and Barlett (AIChE J., 42, 2529 (1996)) implemented the stacked generalization for neural network models by integrating multiple neural networks into an architecture known as stacked neural networks (SNNs). SNNs consist of a combination of the candidate neural networks and were shown to provide improved modeling of chemical processes. However, in Sridhar's work SNNs were limited to using a linear combination of artificial neural networks. While a linear combination is simple and easy to use, it can utilize only those model outputs that have a high linear correlation to the output. Models that are useful in a nonlinear sense are wasted if a linear combination is used. In this work we propose an information theoretic stacking (ITS) algorithm for combining neural network models. The ITS algorithm identifies and combines useful models regardless of the nature of their relationship to the actual output. The power of the ITS algorithm is demonstrated through three examples including application to a dynamic process modeling problem. The results obtained demonstrate that the SNNs developed using the ITS algorithm can achieve highly improved performance as compared to selecting and using a single hopefully optimal network or using SNNs based on a linear combination of neural networks.

  2. Neural-networks-based Modelling and a Fuzzy Neural Networks Controller of MCFC

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    Molten Carbonate Fuel Cells (MCFC) are produced with a highly efficient and clean power generation technology which will soon be widely utilized. The temperature characters of MCFC stack are briefly analyzed. A radial basis function (RBF) neural networks identification technology is applied to set up the temperature nonlinear model of MCFC stack, and the identification structure, algorithm and modeling training process are given in detail. A fuzzy controller of MCFC stack is designed. In order to improve its online control ability, a neural network trained by the I/O data of a fuzzy controller is designed. The neural networks can memorize and expand the inference rules of the fuzzy controller and substitute for the fuzzy controller to control MCFC stack online. A detailed design of the controller is given. The validity of MCFC stack modelling based on neural networks and the superior performance of the fuzzy neural networks controller are proved by Simulations.

  3. Modeling of Magneto-Rheological Damper with Neural Network

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    With the revival of magnetorheological technology research in the 1980's, its application in vehicles is increasingly focused on vibration suppression. Based on the importance of magnetorheological damper modeling, nonparametric modeling with neural network, which is a promising development in semi-active online control of vehicles with MR suspension, has been carried out in this study. A two layer neural network with 7 neurons in a hidden layer and 3 inputs and 1 output was established to simulate the behavior of MR damper at different excitation currents. In the neural network modeling, the damping force is a function of displacement, velocity and the applied current. A MR damper for vehicles is fabricated and tested by MTS; the data acquired are utilized for neural network training and validation. The application and validation show that the predicted forces of the neural network match well with the forces tested with a small variance, which demonstrates the effectiveness and precision of neural network modeling.

  4. Development of larval motor circuits in Drosophila.

    Science.gov (United States)

    Kohsaka, Hiroshi; Okusawa, Satoko; Itakura, Yuki; Fushiki, Akira; Nose, Akinao

    2012-04-01

    How are functional neural circuits formed during development? Despite recent advances in our understanding of the development of individual neurons, little is known about how complex circuits are assembled to generate specific behaviors. Here, we describe the ways in which Drosophila motor circuits serve as an excellent model system to tackle this problem. We first summarize what has been learned during the past decades on the connectivity and development of component neurons, in particular motor neurons and sensory feedback neurons. We then review recent progress in our understanding of the development of the circuits as well as studies that apply optogenetics and other innovative techniques to dissect the circuit diagram. New approaches using Drosophila as a model system are now making it possible to search for developmental rules that regulate the construction of neural circuits.

  5. Novel mathematical neural models for visual attention

    DEFF Research Database (Denmark)

    Li, Kang

    Visual attention has been extensively studied in psychology, but some fundamental questions remain controversial. We focus on two questions in this study. First, we investigate how a neuron in visual cortex responds to multiple stimuli inside the receptive eld, described by either a response...... for the visual attention theories and spiking neuron models for single spike trains. Statistical inference and model selection are performed and various numerical methods are explored. The designed methods also give a framework for neural coding under visual attention theories. We conduct both analysis on real...... system, supported by simulation study. Finally, we present the decoding of multiple temporal stimuli under these visual attention theories, also in a realistic biophysical situation with simulations....

  6. Novel mathematical neural models for visual attention

    DEFF Research Database (Denmark)

    Li, Kang

    Visual attention has been extensively studied in psychology, but some fundamental questions remain controversial. We focus on two questions in this study. First, we investigate how a neuron in visual cortex responds to multiple stimuli inside the receptive eld, described by either a response...... for the visual attention theories and spiking neuron models for single spike trains. Statistical inference and model selection are performed and various numerical methods are explored. The designed methods also give a framework for neural coding under visual attention theories. We conduct both analysis on real...... system, supported by simulation study. Finally, we present the decoding of multiple temporal stimuli under these visual attention theories, also in a realistic biophysical situation with simulations....

  7. Trading speed and accuracy by coding time: a coupled-circuit cortical model.

    Directory of Open Access Journals (Sweden)

    Dominic Standage

    2013-04-01

    Full Text Available Our actions take place in space and time, but despite the role of time in decision theory and the growing acknowledgement that the encoding of time is crucial to behaviour, few studies have considered the interactions between neural codes for objects in space and for elapsed time during perceptual decisions. The speed-accuracy trade-off (SAT provides a window into spatiotemporal interactions. Our hypothesis is that temporal coding determines the rate at which spatial evidence is integrated, controlling the SAT by gain modulation. Here, we propose that local cortical circuits are inherently suited to the relevant spatial and temporal coding. In simulations of an interval estimation task, we use a generic local-circuit model to encode time by 'climbing' activity, seen in cortex during tasks with a timing requirement. The model is a network of simulated pyramidal cells and inhibitory interneurons, connected by conductance synapses. A simple learning rule enables the network to quickly produce new interval estimates, which show signature characteristics of estimates by experimental subjects. Analysis of network dynamics formally characterizes this generic, local-circuit timing mechanism. In simulations of a perceptual decision task, we couple two such networks. Network function is determined only by spatial selectivity and NMDA receptor conductance strength; all other parameters are identical. To trade speed and accuracy, the timing network simply learns longer or shorter intervals, driving the rate of downstream decision processing by spatially non-selective input, an established form of gain modulation. Like the timing network's interval estimates, decision times show signature characteristics of those by experimental subjects. Overall, we propose, demonstrate and analyse a generic mechanism for timing, a generic mechanism for modulation of decision processing by temporal codes, and we make predictions for experimental verification.

  8. A current model of neural circuitry active in forming mental images.

    Science.gov (United States)

    Brodziak, Andrzej

    2013-12-12

    My aim here is to formulate a compact, intuitively understandable model of neural circuits active in imagination that would be consistent with the current state of knowledge, but that would be simple enough to be able to use for teaching. I argue that such a model should be based on the recent idea of "concept neurons" and circuits of 2 separate loops necessary for recalling mental images and consolidation of memory traces of long-term memory. This paper discusses the role of the hippocampus and temporal lobe, emphasizing the essential importance of recurrent pathways and oscillations occurring in the upper layers of hierarchical neural structures, as well as oscillations in thalamo-cortical loops. The elaborated model helps explain specific processes such as imagining future situations, novel objects, and anticipated action, as well as imagination concerning oneself, which is indispensable for the sense of identity and self-awareness. I attempt to present this compact, simple model of neural circuitry active in imagination by using some intuitive, demonstrative figures.

  9. A new model of neural associative memories.

    Science.gov (United States)

    Hao, J; Vandewalle, J

    1994-03-01

    In this paper, we present a new model of discrete neural associative memories and its design rule. The most important feature of this new model is that a static mapping instead of the dynamic convergent process is used to retrieve the stored messages. The new model features a two-layer structure, with feedforward connections only and uses two kinds of neurons which implement different output functions. Another important feature is that this new model employs an extremely simple weight setup rule and all the resulted weights can only assume two different values, -1 and +1, which facilitates the VLSI implementation. Compared to the famous discrete Hopfield model designed with the well-known Hebbian rule or any other rule, the new model can guarantee all the given patterns to be stored as fixed points. Moreover, each fixed point is surrounded by an attraction basin (which is a ball in the Hamming distance sense) with the maximal possible radius. The performances of the new model are compared through some illustrative examples with those of the Hopfield associative memory designed using different methods.

  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.

  11. Stability analysis of discrete-time BAM neural networks based on standard neural network models

    Institute of Scientific and Technical Information of China (English)

    ZHANG Sen-lin; LIU Mei-qin

    2005-01-01

    To facilitate stability analysis of discrete-time bidirectional associative memory (BAM) neural networks, they were converted into novel neural network models, termed standard neural network models (SNNMs), which interconnect linear dynamic systems and bounded static nonlinear operators. By combining a number of different Lyapunov functionals with S-procedure, some useful criteria of global asymptotic stability and global exponential stability of the equilibrium points of SNNMs were derived. These stability conditions were formulated as linear matrix inequalities (LMIs). So global stability of the discrete-time BAM neural networks could be analyzed by using the stability results of the SNNMs. Compared to the existing stability analysis methods, the proposed approach is easy to implement, less conservative, and is applicable to other recurrent neural networks.

  12. Accurate neural network-based modeling for RF MEMS component synthesizing

    Science.gov (United States)

    Mohamed, Firas; Affour, Bachar

    2004-01-01

    Contrary to traditional analysis flows as expensive FEM simulation tools or inaccurate electrical models extractors, we developed MemsCompiler that implements a new real synthesis approach for RF MEMS. The new flow starts from system designer requirements and generates, in a one-click operation, a ready-to-fabricate layout (GDSII) and a passive fitted equivalent Spice circuit. Concerning the circuit, physical considerations give us an equivalent schematic in which circuit parameters values must be adjusted to fit the required performances. As to the GDSII, which constitutes the main contribution of this work, Design Of Experiment technique, used in the first version of the synthesizer, gave about 11% of dispersion and found to be unsatisfactory in some cases. A more accurate modeling was indispensable. Thus, we developed a neural networks-based modeling for circular inductors, which are considered by designers among the most stubborn components. This new modeling has shown to be very accurate: MemsCompiler produced about 3% of dispersion compared to the equivalent circuit and about 6% of dispersion for generated geometries. This modeling is flexible and could be rapidly generalized to other components.

  13. 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.

  14. SEMICONDUCTOR INTEGRATED CIRCUITS: Low power CMOS preamplifier for neural recording applications

    Science.gov (United States)

    Xu, Zhang; Weihua, Pei; Beiju, Huang; Hongda, Chen

    2010-04-01

    A fully-differential bandpass CMOS (complementary metal oxide semiconductor) preamplifier for extracellular neural recording is presented. The capacitive-coupled and capacitive-feedback topology is adopted. The preamplifier has a midband gain of 20.4 dB and a DC gain of 0. The -3 dB upper cut-off frequency of the preamplifier is 6.7 kHz. The lower cut-off frequency can be adjusted for amplifying the field or action potentials located in different bands. It has an input-referred noise of 8.2 μVrms integrated from 0.15 Hz to 6.7 kHz for recording the local field potentials and the mixed neural spikes with a power dissipation of 23.1 μW from a 3.3 V supply. A bandgap reference circuitry is also designed for providing the biasing voltage and current. The 0.22 mm2 prototype chip, including the preamplifier and its biasing circuitry, is fabricated in the 0.35-μm N-well CMOS 2P4M process.

  15. 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.

  16. 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

  17. Neural Correlates of Inflexible Behavior in the Orbitofrontal–Amygdalar Circuit after Cocaine Exposure

    Science.gov (United States)

    STALNAKER, THOMAS A.; ROESCH, MATTHEW R.; CALU, DONNA J.; BURKE, KATHRYN A.; SINGH, TEGHPAL; SCHOENBAUM, GEOFFREY

    2008-01-01

    Addiction is characterized by compulsive or inflexible behavior, observed both in the context of drug-seeking and in contexts unrelated to drugs. One possible contributor to these inflexible behaviors may be drug-induced dysfunction within circuits that support behavioral flexibility, including the basolateral amygdala (ABL) and the orbitofrontal cortex (OFC). Here we describe data demonstrating that chronic cocaine exposure causes long-lasting changes in encoding properties in the ABL and the OFC during learning and reversal in an odor-guided task. In particular, these data suggest that inflexible encoding in ABL neurons may be the proximal cause of cocaine-induced behavioral inflexibility, and that a loss of outcome-expectant encoding in OFC neurons could be a more distal contributor to this impairment. A similar mechanism of drug-induced orbitofrontal–amygdalar dysfunction may cause inflexible behavior when animals and addicts are exposed to drug-associated cues and contexts. PMID:17846156

  18. 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.

  19. PDF-1 neuropeptide signaling modulates a neural circuit for mate-searching behavior in C. elegans.

    Science.gov (United States)

    Barrios, Arantza; Ghosh, Rajarshi; Fang, Chunhui; Emmons, Scott W; Barr, Maureen M

    2012-12-01

    Appetitive behaviors require complex decision making that involves the integration of environmental stimuli and physiological needs. C. elegans mate searching is a male-specific exploratory behavior regulated by two competing needs: food and reproductive appetite. We found that the pigment dispersing factor receptor (PDFR-1) modulates the circuit that encodes the male reproductive drive that promotes male exploration following mate deprivation. PDFR-1 and its ligand, PDF-1, stimulated mate searching in the male, but not in the hermaphrodite. pdf-1 was required in the gender-shared interneuron AIM, and the receptor acted in internal and external environment-sensing neurons of the shared nervous system (URY, PQR and PHA) to produce mate-searching behavior. Thus, the pdf-1 and pdfr-1 pathway functions in non-sex-specific neurons to produce a male-specific, goal-oriented exploratory behavior. Our results indicate that secretin neuropeptidergic signaling is involved in regulating motivational internal states.

  20. A hybrid neural network model for consciousness

    Institute of Scientific and Technical Information of China (English)

    蔺杰; 金小刚; 杨建刚

    2004-01-01

    A new framework for consciousness is introduced based upon traditional artificial neural network models. This framework reflects explicit connections between two parts of the brain: one global working memory and distributed modular cerebral networks relating to specific brain functions. Accordingly this framework is composed of three layers,physical mnemonic layer and abstract thinking layer,which cooperate together through a recognition layer to accomplish information storage and cognition using algorithms of how these interactions contribute to consciousness:(1)the reception process whereby cerebral subsystems group distributed signals into coherent object patterns;(2)the partial recognition process whereby patterns from particular subsystems are compared or stored as knowledge; and(3)the resonant learning process whereby global workspace stably adjusts its structure to adapt to patterns' changes. Using this framework,various sorts of human actions can be explained,leading to a general approach for analyzing brain functions.

  1. A hybrid neural network model for consciousness

    Institute of Scientific and Technical Information of China (English)

    蔺杰; 金小刚; 杨建刚

    2004-01-01

    A new framework for consciousness is introduced based upon traditional artificial neural network models. This framework reflects explicit connections between two parts of the brain: one global working memory and distributed modular cerebral networks relating to specific brain functions. Accordingly this framework is composed of three layers, physical mnemonic layer and abstract thinking layer, which cooperate together through a recognition layer to accomplish information storage and cognition using algorithms of how these interactions contribute to consciousness: (l) the reception process whereby cerebral subsystems group distributed signals into coherent object patterns; (2) the partial recognition process whereby patterns from particular subsystems are compared or stored as knowledge; and (3) the resonant learning process whereby global workspace stably adjusts its structure to adapt to patterns' changes. Using this framework, various sorts of human actions can be explained, leading to a general approach for analyzing brain functions.

  2. Neural mass model-based tracking of anesthetic brain states

    NARCIS (Netherlands)

    Kuhlmann, Levin; Freestone, Dean R.; Manton, Jonathan H.; Heyse, Bjorn; Vereecke, Hugo E. M.; Lipping, Tarmo; Struys, Michel M. R. F.; Liley, David T. J.

    2016-01-01

    Neural mass model-based tracking of brain states from electroencephalographic signals holds the promise of simultaneously tracking brain states while inferring underlying physiological changes in various neuroscientific and clinical applications. Here, neural mass model-based tracking of brain state

  3. Delay modeling of bipolar ECL/EFL (Emitter-Coupled Logic/Emitter-Follower-Logic) circuits

    Science.gov (United States)

    Yang, Andrew T.

    1986-08-01

    This report deals with the development of a delay-time model for timing simulation of large circuits consisting of Bipolar ECL(Emitter-Coupled Logic) and EFL (Emitter-Follower-Logic) networks. This model can provide adequate information on the performance of the circuits with a minimum expenditure of computation time. This goal is achieved by the use of proper circuit transient models on which analytical delay expressions can be derived with accurate results. The delay-model developed in this report is general enough to handle complex digital circuits with multiple inputs or/and multiple levels. The important effects of input slew rate are also included in the model.

  4. Backpropagation Neural Network Modeling for Fault Location in Transmission Line 150 kV

    Directory of Open Access Journals (Sweden)

    Azriyenni Narwan

    2014-03-01

    Full Text Available In this topic research was provided about the backpropagation neural network to detect fault location in transmission line 150 kV between substation to substation. The distance relay is one of the good protective device and safety devices that often used on transmission line 150 kV. The disturbances in power system are used distance relay protection equipment in the transmission line. However, it needs more increasing large load and network systems are increasing complex. The protection system use the digital control, in order to avoid the error calculation of the distance relay impedance settings and spent time will be more efficient. Then backpropagation neural network is a computational model that uses the training process that can be used to solve the problem of work limitations of distance protection relays. The backpropagation neural network does not have limitations cause of the impedance range setting. If the output gives the wrong result, so the correct of the weights can be minimized and also the response of galat, the backpropagation neural network is expected to be closer to the correct value. In the end, backpropagation neural network modeling is expected to detect the fault location and identify operational output current circuit breaker was tripped it. The tests are performance with interconnected system 150 kV of Riau Region.

  5. Exchange Rate Prediction using Neural – Genetic Model

    Directory of Open Access Journals (Sweden)

    MECHGOUG Raihane

    2012-10-01

    Full Text Available Neural network have successfully used for exchange rate forecasting. However, due to a large number of parameters to be estimated empirically, it is not a simple task to select the appropriate neural network architecture for exchange rate forecasting problem.Researchers often overlook the effect of neural network parameters on the performance of neural network forecasting. The performance of neural network is critically dependant on the learning algorithms, thenetwork architecture and the choice of the control parameters. Even when a suitable setting of parameters (weight can be found, the ability of the resulting network to generalize the data not seen during learning may be far from optimal. For these reasons it seemslogical and attractive to apply genetic algorithms. Genetic algorithms may provide a useful tool for automating the design of neural network. The empirical results on foreign exchange rate prediction indicate that the proposed hybrid model exhibits effectively improved accuracy, when is compared with some other time series forecasting models.

  6. Runoff Modelling in Urban Storm Drainage by Neural Networks

    DEFF Research Database (Denmark)

    Rasmussen, Michael R.; Brorsen, Michael; Schaarup-Jensen, Kjeld

    1995-01-01

    network is used to compute flow or water level at selected points in the sewer system, and to forecast the flow from a small residential area. The main advantages of the neural network are the build-in self calibration procedure and high speed performance, but the neural network cannot be used to extract......A neural network is used to simulate folw and water levels in a sewer system. The calibration of th neural network is based on a few measured events and the network is validated against measureed events as well as flow simulated with the MOUSE model (Lindberg and Joergensen, 1986). The neural...... knowledge of the runoff process. The neural network was found to simulate 150 times faster than e.g. the MOUSE model....

  7. Sex differences in the neural circuit that mediates female sexual receptivity

    Science.gov (United States)

    Flanagan-Cato, Loretta M.

    2011-01-01

    Female sexual behavior in rodents, typified by the lordosis posture, is hormone-dependent and sex-specific. Ovarian hormones control this behavior via receptors in the hypothalamic ventromedial nucleus (VMH). This review considers the sex differences in the morphology, neurochemistry and neural circuitry of the VMH to gain insights into the mechanisms that control lordosis. The VMH is larger in males compared with females, due to more synaptic connections. Another sex difference is the responsiveness to estradiol, with males exhibiting muted, and in some cases reverse, effects compared with females. The lack of lordosis in males may be explained by differences in synaptic organization or estrogen responsiveness, or both, in the VMH. However, given that damage to other brain regions unmasks lordosis behavior in males, a male-typical VMH is unlikely the main factor that prevents lordosis. In females, key questions remain regarding the mechanisms whereby ovarian hormones modulate VMH function to promote lordosis. PMID:21338620

  8. Role of neural network models for developing speech systems

    Indian Academy of Sciences (India)

    K Sreenivasa Rao

    2011-10-01

    This paper discusses the application of neural networks for developing different speech systems. Prosodic parameters of speech at syllable level depend on positional, contextual and phonological features of the syllables. In this paper, neural networks are explored to model the prosodic parameters of the syllables from their positional, contextual and phonological features. The prosodic parameters considered in this work are duration and sequence of pitch $(F_0)$ values of the syllables. These prosody models are further examined for applications such as text to speech synthesis, speech recognition, speaker recognition and language identification. Neural network models in voice conversion system are explored for capturing the mapping functions between source and target speakers at source, system and prosodic levels. We have also used neural network models for characterizing the emotions present in speech. For identification of dialects in Hindi, neural network models are used to capture the dialect specific information from spectral and prosodic features of speech.

  9. Neural circuit of verbal humor comprehension in schizophrenia - an fMRI study.

    Science.gov (United States)

    Adamczyk, Przemysław; Wyczesany, Miroslaw; Domagalik, Aleksandra; Daren, Artur; Cepuch, Kamil; Błądziński, Piotr; Cechnicki, Andrzej; Marek, Tadeusz

    2017-01-01

    Individuals with schizophrenia exhibit problems with understanding the figurative meaning of language. This study evaluates neural correlates of diminished humor comprehension observed in schizophrenia. The study included chronic schizophrenia (SCH) outpatients (n = 20), and sex, age and education level matched healthy controls (n = 20). The fMRI punchline based humor comprehension task consisted of 60 stories of which 20 had funny, 20 nonsensical and 20 neutral (not funny) punchlines. After the punchlines were presented, the participants were asked to indicate whether the story was comprehensible and how funny it was. Three contrasts were analyzed in both groups reflecting stages of humor processing: abstract vs neutral stories - incongruity detection; funny vs abstract - incongruity resolution and elaboration; and funny vs neutral - complete humor processing. Additionally, parametric modulation analysis was performed using both subjective ratings separately. Between-group comparisons revealed that the SCH subjects had attenuated activation in the right posterior superior temporal gyrus (BA 41) in case of irresolvable incongruity processing of nonsensical puns; in the left dorsomedial middle and superior frontal gyri (BA 8/9) in case of incongruity resolution and elaboration processing of funny puns; and in the interhemispheric dorsal anterior cingulate cortex (BA 24) in case of complete processing of funny puns. Additionally, during comprehensibility ratings the SCH group showed a suppressed activity in the left dorsomedial middle and superior frontal gyri (BA 8/9) and revealed weaker activation during funniness ratings in the left dorsal anterior cingulate cortex (BA 24). Interestingly, these differences in the SCH group were accompanied behaviorally by a protraction of time in both types of rating responses and by indicating funny punchlines less comprehensible. Summarizing, our results indicate neural substrates of humor comprehension processing

  10. Bayesian Recurrent Neural Network for Language Modeling.

    Science.gov (United States)

    Chien, Jen-Tzung; Ku, Yuan-Chu

    2016-02-01

    A language model (LM) is calculated as the probability of a word sequence that provides the solution to word prediction for a variety of information systems. A recurrent neural network (RNN) is powerful to learn the large-span dynamics of a word sequence in the continuous space. However, the training of the RNN-LM is an ill-posed problem because of too many parameters from a large dictionary size and a high-dimensional hidden layer. This paper presents a Bayesian approach to regularize the RNN-LM and apply it for continuous speech recognition. We aim to penalize the too complicated RNN-LM by compensating for the uncertainty of the estimated model parameters, which is represented by a Gaussian prior. The objective function in a Bayesian classification network is formed as the regularized cross-entropy error function. The regularized model is constructed not only by calculating the regularized parameters according to the maximum a posteriori criterion but also by estimating the Gaussian hyperparameter by maximizing the marginal likelihood. A rapid approximation to a Hessian matrix is developed to implement the Bayesian RNN-LM (BRNN-LM) by selecting a small set of salient outer-products. The proposed BRNN-LM achieves a sparser model than the RNN-LM. Experiments on different corpora show the robustness of system performance by applying the rapid BRNN-LM under different conditions.

  11. Neural Network Model for the Constitutive Relations of Soil

    Institute of Scientific and Technical Information of China (English)

    Zeng Jing; Wang Jing-tao

    2003-01-01

    The soil constitutive relation is one of the important issues in soil mechanics. It is very difficult to establish mathematical models because of the complexity of soil mechanical behavior. We propose a new method of neural network analysis for establishing soil constitutive models. Based on triaxial experiments of sand in the laboratory, the nonlinear constitutive models of sand expressed by the neural network were set up. In comparison with Duncan-Chang's model, the neural network method for sand modeling has been proved to be more convenient, accurate and it has a strong fault-tolerance function.

  12. Circuit modeling of graphene absorber in terahertz band

    Science.gov (United States)

    Taghvaee, Hamid Reza; Nasari, Hadiseh; Abrishamian, Mohammad Sadegh

    2017-01-01

    Here we develop and extend a transmission line method (TLM) to analyze the performance of graphene assisted metamaterial (GM) devices working in the terahertz (THz) band. We demonstrate that a circuit model can be presented for different parts of the device including graphene and also the patterned metallic sheet by analyzing the distribution of surface induced current. In pursuit of evaluating the efficiency and accuracy of our proposed method, we compare its results, obtained from an easy to implement MATLAB code for a typical GM absorber with those obtained from full wave simulations. The excellence of the proposed method in terms of computation time (showing more than 3 orders of magnitude reduction in run time) and memory resource besides producing results with acceptable agreement with the results of full wave simulation (with an error less than 5%) versus incident angle, dielectric thickness and chemical potential, nominates it as a promising approach to simulate other graphene-based devices.

  13. A new equivalent circuit model for micro electroporation systems

    KAUST Repository

    Shagoshtasbi, Hooman

    2011-02-01

    Electroporation (EP) is a unique biotechnique in which intense electric pulses are applied on the cell membrane to temporarily generate nanoscale electropores and to increase the membrane permeability for the delivery of exogenous biomolecules or drugs. We propose a new equivalent circuit model with 8 electric components to predict the electrodynamic response of a micro EP system. As the permeability of the cell membrane increases, the membrane resistance decreases. The numerical simulations of the transmembrane current responses to different applied voltages (1∼6V) are consistent with the experimental results using HeLa cells. Besides, the transmembrane voltage as a function of applied voltages is determined as well. These transmembrane current and voltage responses can be extremely useful for the design of new generation of micro EP systems for transfection of large DNA molecules in the future. © 2011 IEEE.

  14. A Multiple—Valued Algebra for Modeling MOS VLSI Circuits at Switch—Level

    Institute of Scientific and Technical Information of China (English)

    胡谋

    1992-01-01

    A multiple-valued algebra for modeling MOS VLSI circuits at switch-level is proposed in this paper,Its structure and properties are studied.This algebra can be used to transform a MOS digital circuit to a swith-level algebraic expression so as to generate the truth table for the circuit and to derive a Boolean expression for it.In the paper,methods to construct a switch-level algebraic expression for a circuit and methods to simplify expressions are given.This algebra provides a new tool for MOS VLSI circuit design and analysis.

  15. Anatomical characterization of cre driver mice for neural circuit mapping and manipulation

    Directory of Open Access Journals (Sweden)

    Julie Ann Harris

    2014-07-01

    Full Text Available Significant advances in circuit-level analyses of the brain require tools that allow for labeling, modulation of gene expression, and monitoring and manipulation of cellular activity in specific cell types and/or anatomical regions. Large-scale projects and individual laboratories have produced hundreds of gene-specific promoter-driven Cre mouse lines invaluable for enabling genetic access to subpopulations of cells in the brain. However, the potential utility of each line may not be fully realized without systematic whole brain characterization of transgene expression patterns. We established a high-throughput in situ hybridization, imaging and data processing pipeline to describe whole brain gene expression patterns in Cre driver mice. Currently, anatomical data from over 100 Cre driver lines are publicly available via the Allen Institute’s Transgenic Characterization database, which can be used to assist researchers in choosing the appropriate Cre drivers for functional, molecular, or connectional studies of different regions and/or cell types in the brain.

  16. Comparison of Gompertz and neural network models of broiler growth.

    Science.gov (United States)

    Roush, W B; Dozier, W A; Branton, S L

    2006-04-01

    Neural networks offer an alternative to regression analysis for biological growth modeling. Very little research has been conducted to model animal growth using artificial neural networks. Twenty-five male chicks (Ross x Ross 308) were raised in an environmental chamber. Body weights were determined daily and feed and water were provided ad libitum. The birds were fed a starter diet (23% CP and 3,200 kcal of ME/kg) from 0 to 21 d, and a grower diet (20% CP and 3,200 kcal of ME/ kg) from 22 to 70 d. Dead and female birds were not included in the study. Average BW of 18 birds were used as the data points for the growth curve to be modeled. Training data consisted of alternate-day weights starting with the first day. Validation data consisted of BW at all other age periods. Comparison was made between the modeling by the Gompertz nonlinear regression equation and neural network modeling. Neural network models were developed with the Neuroshell Predictor. Accuracy of the models was determined by mean square error (MSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE), and bias. The Gompertz equation was fit for the data. Forecasting error measurements were based on the difference between the model and the observed values. For the training data, the lowest MSE, MAD, MAPE, and bias were noted for the neural-developed neural network. For the validation data, the lowest MSE and MAD were noted with the genetic algorithm-developed neural network. Lowest bias was for the neural-developed network. As measured by bias, the Gompertz equation underestimated the values whereas the neural- and genetic-developed neural networks produced little or no overestimation of the observed BW responses. Past studies have attempted to interpret the biological significance of the estimates of the parameters of an equation. However, it may be more practical to ignore the relevance of parameter estimates and focus on the ability to predict responses.

  17. Neural and Neural Gray-Box Modeling for Entry Temperature Prediction in a Hot Strip Mill

    Science.gov (United States)

    Barrios, José Angel; Torres-Alvarado, Miguel; Cavazos, Alberto; Leduc, Luis

    2011-10-01

    In hot strip mills, initial controller set points have to be calculated before the steel bar enters the mill. Calculations rely on the good knowledge of rolling variables. Measurements are available only after the bar has entered the mill, and therefore they have to be estimated. Estimation of process variables, particularly that of temperature, is of crucial importance for the bar front section to fulfill quality requirements, and the same must be performed in the shortest possible time to preserve heat. Currently, temperature estimation is performed by physical modeling; however, it is highly affected by measurement uncertainties, variations in the incoming bar conditions, and final product changes. In order to overcome these problems, artificial intelligence techniques such as artificial neural networks and fuzzy logic have been proposed. In this article, neural network-based systems, including neural-based Gray-Box models, are applied to estimate scale breaker entry temperature, given its importance, and their performance is compared to that of the physical model used in plant. Several neural systems and several neural-based Gray-Box models are designed and tested with real data. Taking advantage of the flexibility of neural networks for input incorporation, several factors which are believed to have influence on the process are also tested. The systems proposed in this study were proven to have better performance indexes and hence better prediction capabilities than the physical models currently used in plant.

  18. Neural network models: Insights and prescriptions from practical applications

    Energy Technology Data Exchange (ETDEWEB)

    Samad, T. [Honeywell Technology Center, Minneapolis, MN (United States)

    1995-12-31

    Neural networks are no longer just a research topic; numerous applications are now testament to their practical utility. In the course of developing these applications, researchers and practitioners have been faced with a variety of issues. This paper briefly discusses several of these, noting in particular the rich connections between neural networks and other, more conventional technologies. A more comprehensive version of this paper is under preparation that will include illustrations on real examples. Neural networks are being applied in several different ways. Our focus here is on neural networks as modeling technology. However, much of the discussion is also relevant to other types of applications such as classification, control, and optimization.

  19. 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. PMID:26199869

  20. 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.

  1. Computational modeling of neural plasticity for self-organization of neural networks.

    Science.gov (United States)

    Chrol-Cannon, Joseph; Jin, Yaochu

    2014-11-01

    Self-organization in biological nervous systems during the lifetime is known to largely occur through a process of plasticity that is dependent upon the spike-timing activity in connected neurons. In the field of computational neuroscience, much effort has been dedicated to building up computational models of neural plasticity to replicate experimental data. Most recently, increasing attention has been paid to understanding the role of neural plasticity in functional and structural neural self-organization, as well as its influence on the learning performance of neural networks for accomplishing machine learning tasks such as classification and regression. Although many ideas and hypothesis have been suggested, the relationship between the structure, dynamics and learning performance of neural networks remains elusive. The purpose of this article is to review the most important computational models for neural plasticity and discuss various ideas about neural plasticity's role. Finally, we suggest a few promising research directions, in particular those along the line that combines findings in computational neuroscience and systems biology, and their synergetic roles in understanding learning, memory and cognition, thereby bridging the gap between computational neuroscience, systems biology and computational intelligence.

  2. Modeling analysis of the relationship between EEG rhythms and connectivity among different neural populations.

    Science.gov (United States)

    Ursino, Mauro; Zavaglia, Melissa

    2007-12-01

    In our research, a neural mass model consisting of four interconnected neural groups (pyramidal neurons, excitatory interneurons, inhibitory interneurons with slow synaptic kinetics, and inhibitory interneurons with fast synaptic kinetics) is used to investigate the mechanisms which cause the appearance of multiple rhythms in EEG spectra, and to assess how these rhythms can be affected by connectivity among different populations. First, we showed that a single neural population, stimulated with white noise, can oscillate with its intrinsic rhythm, and that the position of this rhythm can be finely tuned (in the alpha, beta or gamma frequency ranges), acting on the population synaptic kinetics. Subsequently, we analyzed more complex circuits, composed of two or three interconnected populations, each with a different synaptic kinetics between neural groups within a population (hence, with a different intrinsic rhythm). The results demonstrates apex that a single population can exhibit many different simultaneous rhythms, provided that some of these come from external sources (for instance, from remote regions). The analysis of coherence, and of the position of the peaks in power spectral density, reveals important information on the possible connections among populations, and is especially useful to follow temporal changes in connectivity. In perspective, the results may be of value for a deeper comprehension of the mechanisms causing EEGs rhythms, for the study of connectivity among different neural populations and for the test of neurophysiological hypotheses.

  3. Changes in University Students' Explanation Models of DC Circuits

    Science.gov (United States)

    Kokkonen, Tommi; Mäntylä, Terhi

    2017-04-01

    One well-known learning obstacle is that students rarely use the concepts in the way that scientists use them. Rather, students mix up closely related concepts and are inclined towards matter-based conceptualisations. Furthermore, some researchers have argued that certain difficulties are rooted in the student's limited repertoire of causal schemes. These two aspects are conveniently represented in the recent proposal of the systemic view of concept learning. We applied this framework in our analyses of university students' explanations of DC circuits and their use of concepts such as voltage, current and resistance. Our data consist of transcribed group interviews, which we analysed with content analysis. The results of our analysis are represented with directed graphs. Our results show that students had a rather refined ontological knowledge of the concepts. However, students relied on rather simple explanation models, but few students were able to modify their explanations during the interview. Based on the analysis, we identified three processes of change: model switch, model refinement and model elaboration. This emphasises the importance of relevant relational knowledge at a later stage of learning. This demonstrates how concept individuation and learning of relational structures occurs (and in which order) and sets forth interesting research questions for future research.

  4. Neural circuit mechanism for learning dependent on dopamine transmission: roles of striatal direct and indirect pathways in sensory discrimination.

    Science.gov (United States)

    Kobayashi, Kazuto; Fukabori, Ryoji; Nishizawa, Kayo

    2013-01-01

    The dorsal striatum in basal ganglia circuit mediates learning processes contributing to instrumental motor actions. The striatum receives excitatory inputs from many cortical areas and the thalamic nuclei and dopaminergic inputs from the ventral midbrain and projects to the output nuclei through direct and indirect pathways. The neural mechanism remains unclear as to how these striatofugal pathways control the learning processes of instrumental actions. Here, we addressed the behavioral roles of the two striatofugal pathways in the performance of sensory discrimination by using immunotoxin (IT)-mediated cell targeting. IT targeting of the striatal direct pathway in mutant mice lengthened the response time but did not affect the accuracy of the response selection in visual discrimination. Subregion-specific pathway targeting revealed a delay in motor responses generated by elimination of the direct pathway arising from the dorsomedial striatum (DMS) but not from the dorsolateral striatum (DLS). These findings indicate that the direct pathway, in particular that from the DMS, contributes to the regulation of the response time in visual discrimination. In addition, IT targeting of the striatal indirect pathway originating from the DLS in transgenic rats impaired the accuracy of response selection in auditory discrimination, whereas the response time remained normal. These data demonstrate that the DLS-derived indirect pathway plays an essential role in the control of the selection accuracy of learned motor responses. Our results suggest that striatal direct and indirect pathways act cooperatively to regulate the selection accuracy and response time of learned motor actions in the performance of discriminative learning.

  5. Primary circuit iodine model addition to IMPAIR-3

    Energy Technology Data Exchange (ETDEWEB)

    Osetek, D.J.; Louie, D.L.Y. [Los Alamos Technical Associates, Inc., Albuquerque, NM (United States); Guntay, S.; Cripps, R. [Paul Scherrer Inst. (PSI), Villigen (Switzerland)

    1996-12-01

    As part of a continuing effort to provide the U.S. Department of Energy (DOE) Advanced Reactor Severe Accident Program (ARSAP) with complete iodine analysis capability, a task was undertaken to expand the modeling of IMPAIR-3, an iodine chemistry code. The expanded code will enable the DOE to include detailed iodine behavior in the assessment of severe accident source terms used in the licensing of U.S. Advanced Light Water Reactors (ALWRs). IMPAIR-3 was developed at the Paul Scherrer Institute (PSI), Switzerland, and has been used by ARSAP for the past two years to analyze containment iodine chemistry for ALWR source term analyses. IMPAIR-3 is primarily a containment code but the iodine chemistry inside the primary circuit (the Reactor Coolant System or RCS) may influence the iodine species released into the the containment; therefore, a RCS iodine chemistry model must be implemented in IMPAIR-3 to ensure thorough source term analysis. The ARSAP source term team and the PSI IMPAIR-3 developers are working together to accomplish this task. This cooperation is divided into two phases. Phase I, taking place in 1996, involves developing a stand-alone RCS iodine chemistry program called IMPRCS (IMPAIR -Reactor Coolant System). This program models a number of the chemical and physical processes of iodine that are thought to be important at conditions of high temperature and pressure in the RCS. In Phase II, which is tentatively scheduled for 1997, IMPRCS will be implemented as a subroutine in IMPAIR-3. To ensure an efficient calculation, an interface/tracking system will be developed to control the use of the RCS model from the containment model. These two models will be interfaced in such a way that once the iodine is released from the RCS, it will no longer be tracked by the RCS model but will be tracked by the containment model. All RCS thermal-hydraulic parameters will be provided by other codes. (author) figs., tabs., refs.

  6. Modelling the permeability of polymers: a neural network approach

    NARCIS (Netherlands)

    Wessling, M.; Mulder, M.H.V.; Bos, A.; Linden, van der M.K.T.; Bos, M.; Linden, van der W.E.

    1994-01-01

    In this short communication, the prediction of the permeability of carbon dioxide through different polymers using a neural network is studied. A neural network is a numeric-mathematical construction that can model complex non-linear relationships. Here it is used to correlate the IR spectrum of a p

  7. VLSI implementation of a nonlinear neuronal model: a "neural prosthesis" to restore hippocampal trisynaptic dynamics.

    Science.gov (United States)

    Hsiao, Min-Chi; Chan, Chiu-Hsien; Srinivasan, Vijay; Ahuja, Ashish; Erinjippurath, Gopal; Zanos, Theodoros P; Gholmieh, Ghassan; Song, Dong; Wills, Jack D; LaCoss, Jeff; Courellis, Spiros; Tanguay, Armand R; Granacki, John J; Marmarelis, Vasilis Z; Berger, Theodore W

    2006-01-01

    We are developing a biomimetic electronic neural prosthesis to replace regions of the hippocampal brain area that have been damaged by disease or insult. We have used the hippocampal slice preparation as the first step in developing such a prosthesis. The major intrinsic circuitry of the hippocampus consists of an excitatory cascade involving the dentate gyrus (DG), CA3, and CA1 subregions; this trisynaptic circuit can be maintained in a transverse slice preparation. Our demonstration of a neural prosthesis for the hippocampal slice involves: (i) surgically removing CA3 function from the trisynaptic circuit by transecting CA3 axons, (ii) replacing biological CA3 function with a hardware VLSI (very large scale integration) model of the nonlinear dynamics of CA3, and (iii) through a specially designed multi-site electrode array, transmitting DG output to the hardware device, and routing the hardware device output to the synaptic inputs of the CA1 subregion, thus by-passing the damaged CA3. Field EPSPs were recorded from the CA1 dendritic zone in intact slices and "hybrid" DG-VLSI-CA1 slices. Results show excellent agreement between data from intact slices and transected slices with the hardware-substituted CA3: propagation of temporal patterns of activity from DG-->VLSI-->CA1 reproduces that observed experimentally in the biological DG-->CA3-->CA1 circuit.

  8. Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity.

    Science.gov (United States)

    Potjans, Wiebke; Morrison, Abigail; Diesmann, Markus

    2010-01-01

    A major puzzle in the field of computational neuroscience is how to relate system-level learning in higher organisms to synaptic plasticity. Recently, plasticity rules depending not only on pre- and post-synaptic activity but also on a third, non-local neuromodulatory signal have emerged as key candidates to bridge the gap between the macroscopic and the microscopic level of learning. Crucial insights into this topic are expected to be gained from simulations of neural systems, as these allow the simultaneous study of the multiple spatial and temporal scales that are involved in the problem. In particular, synaptic plasticity can be studied during the whole learning process, i.e., on a time scale of minutes to hours and across multiple brain areas. Implementing neuromodulated plasticity in large-scale network simulations where the neuromodulatory signal is dynamically generated by the network itself is challenging, because the network structure is commonly defined purely by the connectivity graph without explicit reference to the embedding of the nodes in physical space. Furthermore, the simulation of networks with realistic connectivity entails the use of distributed computing. A neuromodulated synapse must therefore be informed in an efficient way about the neuromodulatory signal, which is typically generated by a population of neurons located on different machines than either the pre- or post-synaptic neuron. Here, we develop a general framework to solve the problem of implementing neuromodulated plasticity in a time-driven distributed simulation, without reference to a particular implementation language, neuromodulator, or neuromodulated plasticity mechanism. We implement our framework in the simulator NEST and demonstrate excellent scaling up to 1024 processors for simulations of a recurrent network incorporating neuromodulated spike-timing dependent plasticity.

  9. Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity

    Directory of Open Access Journals (Sweden)

    Wiebke ePotjans

    2010-11-01

    Full Text Available A major puzzle in the field of computational neuroscience is how to relate system-level learning in higher organisms to synaptic plasticity. Recently, plasticity rules depending not only on pre- and post-synaptic activity but also on a third, non-local neuromodulatory signal have emerged as key candidates to bridge the gap between the macroscopic and the microscopic level of learning. Crucial insights into this topic are expected to be gained from simulations of neural systems, as these allow the simultaneous study of the multiple spatial and temporal scales that are involved in the problem. In particular, synaptic plasticity can be studied during the whole learning process, i.e. on a time scale of minutes to hours and across multiple brain areas. Implementing neuromodulated plasticity in large-scale network simulations where the neuromodulatory signal is dynamically generated by the network itself is challenging, because the network structure is commonly defined purely by the connectivity graph without explicit reference to the embedding of the nodes in physical space. Furthermore, the simulation of networks with realistic connectivity entails the use of distributed computing. A neuromodulated synapse must therefore be informed in an efficient way about the neuromodulatory signal, which is typically generated by a population of neurons located on different machines than either the pre- or post-synaptic neuron. Here, we develop a general framework to solve the problem of implementing neuromodulated plasticity in a time-driven distributed simulation, without reference to a particular implementation language, neuromodulator or neuromodulated plasticity mechanism. We implement our framework in the simulator NEST and demonstrate excellent scaling up to 1024 processors for simulations of a recurrent network incorporating neuromodulated spike-timing dependent plasticity.

  10. A neural population model incorporating dopaminergic neurotransmission during complex voluntary behaviors.

    Directory of Open Access Journals (Sweden)

    Stefan Fürtinger

    2014-11-01

    Full Text Available Assessing brain activity during complex voluntary motor behaviors that require the recruitment of multiple neural sites is a field of active research. Our current knowledge is primarily based on human brain imaging studies that have clear limitations in terms of temporal and spatial resolution. We developed a physiologically informed non-linear multi-compartment stochastic neural model to simulate functional brain activity coupled with neurotransmitter release during complex voluntary behavior, such as speech production. Due to its state-dependent modulation of neural firing, dopaminergic neurotransmission plays a key role in the organization of functional brain circuits controlling speech and language and thus has been incorporated in our neural population model. A rigorous mathematical proof establishing existence and uniqueness of solutions to the proposed model as well as a computationally efficient strategy to numerically approximate these solutions are presented. Simulated brain activity during the resting state and sentence production was analyzed using functional network connectivity, and graph theoretical techniques were employed to highlight differences between the two conditions. We demonstrate that our model successfully reproduces characteristic changes seen in empirical data between the resting state and speech production, and dopaminergic neurotransmission evokes pronounced changes in modeled functional connectivity by acting on the underlying biological stochastic neural model. Specifically, model and data networks in both speech and rest conditions share task-specific network features: both the simulated and empirical functional connectivity networks show an increase in nodal influence and segregation in speech over the resting state. These commonalities confirm that dopamine is a key neuromodulator of the functional connectome of speech control. Based on reproducible characteristic aspects of empirical data, we suggest a number

  11. Magnetic equivalent circuit model for unipolar hybrid excitation synchronous machine

    Directory of Open Access Journals (Sweden)

    Kupiec Emil

    2015-03-01

    Full Text Available Lately, there has been increased interest in hybrid excitation electrical machines. Hybrid excitation is a construction that combines permanent magnet excitation with wound field excitation. Within the general classification, these machines can be classified as modified synchronous machines or inductor machines. These machines may be applied as motors and generators. The complexity of electromagnetic phenomena which occur as a result of coupling of magnetic fluxes of separate excitation systems with perpendicular magnetic axis is a motivation to formulate various mathematical models of these machines. The presented paper discusses the construction of a unipolar hybrid excitation synchronous machine. The magnetic equivalent circuit model including nonlinear magnetization curves is presented. Based on this model, it is possible to determine the multi-parameter relationships between the induced voltage and magnetomotive force in the excitation winding. Particular attention has been paid to the analysis of the impact of additional stator and rotor yokes on above relationship. Induced voltage determines the remaining operating parameters of the machine, both in the motor and generator mode of operation. The analysis of chosen correlations results in an identification of the effective control range of electromotive force of the machine.

  12. Dynamic modeling and analysis of the closed-circuit grinding-classification process

    Institute of Scientific and Technical Information of China (English)

    Yunfei Chu; Wenli Xu; Weihan Wan

    2005-01-01

    Mathematical models of the grinding process are the basis of analysis, simulation and control. Most existent models including theoretical models and identification models are, however, inconvenient for direct analysis. In addition, many models pay much attention to the local details in the closed-circuit grinding process while overlooking the systematic behavior of the process as a whole. From the systematic perspective, the dynamic behavior of the whole closed-circuit grinding-classification process is considered and a first-order transfer function model describing the dynamic relation between the raw material and the product is established.The model proves that the time constant of the closed-circuit process is lager than that of the open-circuit process and reveals how physical parameters affect the process dynamic behavior. These are very helpful to understand, design and control the closed-circuit grinding-classification process.

  13. Neural and Cognitive Modeling with Networks of Leaky Integrator Units

    Science.gov (United States)

    Graben, Peter beim; Liebscher, Thomas; Kurths, Jürgen

    After reviewing several physiological findings on oscillations in the electroencephalogram (EEG) and their possible explanations by dynamical modeling, we present neural networks consisting of leaky integrator units as a universal paradigm for neural and cognitive modeling. In contrast to standard recurrent neural networks, leaky integrator units are described by ordinary differential equations living in continuous time. We present an algorithm to train the temporal behavior of leaky integrator networks by generalized back-propagation and discuss their physiological relevance. Eventually, we show how leaky integrator units can be used to build oscillators that may serve as models of brain oscillations and cognitive processes.

  14. Spike neural models (part I: The Hodgkin-Huxley model

    Directory of Open Access Journals (Sweden)

    Johnson, Melissa G.

    2017-05-01

    Full Text Available Artificial neural networks, or ANNs, have grown a lot since their inception back in the 1940s. But no matter the changes, one of the most important components of neural networks is still the node, which represents the neuron. Within spiking neural networks, the node is especially important because it contains the functions and properties of neurons that are necessary for their network. One important aspect of neurons is the ionic flow which produces action potentials, or spikes. Forces of diffusion and electrostatic pressure work together with the physical properties of the cell to move ions around changing the cell membrane potential which ultimately produces the action potential. This tutorial reviews the Hodkgin-Huxley model and shows how it simulates the ionic flow of the giant squid axon via four differential equations. The model is implemented in Matlab using Euler's Method to approximate the differential equations. By using Euler's method, an extra parameter is created, the time step. This new parameter needs to be carefully considered or the results of the node may be impaired.

  15. arXiv Modeling NNLO jet corrections with neural networks

    CERN Document Server

    Carrazza, Stefano

    2017-01-01

    We present a preliminary strategy for modeling multidimensional distributions through neural networks. We study the efficiency of the proposed strategy by considering as input data the two-dimensional next-to-next leading order (NNLO) jet k-factors distribution for the ATLAS 7 TeV 2011 data. We then validate the neural network model in terms of interpolation and prediction quality by comparing its results to alternative models.

  16. 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.

  17. Artificial neural network modeling of dissolved oxygen in reservoir.

    Science.gov (United States)

    Chen, Wei-Bo; Liu, Wen-Cheng

    2014-02-01

    The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan.

  18. Optimized Second-Order Dynamical Systems and Their RLC Circuit Models with PWL Controlled Sources

    Directory of Open Access Journals (Sweden)

    J. Brzobohaty

    2004-09-01

    Full Text Available Complementary active RLC circuit models with a voltage-controlledvoltage source (VCVS and a current-controlled current source (CCCSfor the second-order autonomous dynamical system realization areproposed. The main advantage of these equivalent circuits is the simplerelation between the state model parameters and their correspondingcircuit parameters, which leads also to simple design formulas.

  19. The single-electron tunneling junction: Modeling nanoelectronic devices in circuit theory

    NARCIS (Netherlands)

    Hoekstra, J.

    2008-01-01

    Considering modeling of nanoelectronic devices, it is argued that four really different modeling levels exist. These are the quantumphysics level, the (semi)classical-physics level, the circuit level, and the system level. The circuit level is best suited for predicting the utilization of newly prop

  20. MEMS 3-DoF gyroscope design, modeling and simulation through equivalent circuit lumped parameter model

    Energy Technology Data Exchange (ETDEWEB)

    Mian, Muhammad Umer, E-mail: umermian@gmail.com; Khir, M. H. Md.; Tang, T. B. [Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Tronoh, Perak (Malaysia); Dennis, John Ojur [Department of Fundamental & Applied Sciences, Universiti Teknologi PETRONAS, Tronoh, Perak (Malaysia); Riaz, Kashif; Iqbal, Abid [Faculty of Electronics Engineering, GIK Institute of Engineering Sciences and Technology, Topi, Khyber Pakhtunkhaw (Pakistan); Bazaz, Shafaat A. [Department of Computer Science, Center for Advance Studies in Engineering, Islamabad (Pakistan)

    2015-07-22

    Pre-fabrication, behavioural and performance analysis with computer aided design (CAD) tools is a common and fabrication cost effective practice. In light of this we present a simulation methodology for a dual-mass oscillator based 3 Degree of Freedom (3-DoF) MEMS gyroscope. 3-DoF Gyroscope is modeled through lumped parameter models using equivalent circuit elements. These equivalent circuits consist of elementary components which are counterpart of their respective mechanical components, used to design and fabricate 3-DoF MEMS gyroscope. Complete designing of equivalent circuit model, mathematical modeling and simulation are being presented in this paper. Behaviors of the equivalent lumped models derived for the proposed device design are simulated in MEMSPRO T-SPICE software. Simulations are carried out with the design specifications following design rules of the MetalMUMPS fabrication process. Drive mass resonant frequencies simulated by this technique are 1.59 kHz and 2.05 kHz respectively, which are close to the resonant frequencies found by the analytical formulation of the gyroscope. The lumped equivalent circuit modeling technique proved to be a time efficient modeling technique for the analysis of complex MEMS devices like 3-DoF gyroscopes. The technique proves to be an alternative approach to the complex and time consuming couple field analysis Finite Element Analysis (FEA) previously used.

  1. MEMS 3-DoF gyroscope design, modeling and simulation through equivalent circuit lumped parameter model

    Science.gov (United States)

    Mian, Muhammad Umer; Dennis, John Ojur; Khir, M. H. Md.; Riaz, Kashif; Iqbal, Abid; Bazaz, Shafaat A.; Tang, T. B.

    2015-07-01

    Pre-fabrication, behavioural and performance analysis with computer aided design (CAD) tools is a common and fabrication cost effective practice. In light of this we present a simulation methodology for a dual-mass oscillator based 3 Degree of Freedom (3-DoF) MEMS gyroscope. 3-DoF Gyroscope is modeled through lumped parameter models using equivalent circuit elements. These equivalent circuits consist of elementary components which are counterpart of their respective mechanical components, used to design and fabricate 3-DoF MEMS gyroscope. Complete designing of equivalent circuit model, mathematical modeling and simulation are being presented in this paper. Behaviors of the equivalent lumped models derived for the proposed device design are simulated in MEMSPRO T-SPICE software. Simulations are carried out with the design specifications following design rules of the MetalMUMPS fabrication process. Drive mass resonant frequencies simulated by this technique are 1.59 kHz and 2.05 kHz respectively, which are close to the resonant frequencies found by the analytical formulation of the gyroscope. The lumped equivalent circuit modeling technique proved to be a time efficient modeling technique for the analysis of complex MEMS devices like 3-DoF gyroscopes. The technique proves to be an alternative approach to the complex and time consuming couple field analysis Finite Element Analysis (FEA) previously used.

  2. Accurate dynamic power estimation for CMOS combinational logic circuits with real gate delay model

    Directory of Open Access Journals (Sweden)

    Omnia S. Fadl

    2016-01-01

    Full Text Available Dynamic power estimation is essential in designing VLSI circuits where many parameters are involved but the only circuit parameter that is related to the circuit operation is the nodes’ toggle rate. This paper discusses a deterministic and fast method to estimate the dynamic power consumption for CMOS combinational logic circuits using gate-level descriptions based on the Logic Pictures concept to obtain the circuit nodes’ toggle rate. The delay model for the logic gates is the real-delay model. To validate the results, the method is applied to several circuits and compared against exhaustive, as well as Monte Carlo, simulations. The proposed technique was shown to save up to 96% processing time compared to exhaustive simulation.

  3. Calculation of precise firing statistics in a neural network model

    Science.gov (United States)

    Cho, Myoung Won

    2017-08-01

    A precise prediction of neural firing dynamics is requisite to understand the function of and the learning process in a biological neural network which works depending on exact spike timings. Basically, the prediction of firing statistics is a delicate manybody problem because the firing probability of a neuron at a time is determined by the summation over all effects from past firing states. A neural network model with the Feynman path integral formulation is recently introduced. In this paper, we present several methods to calculate firing statistics in the model. We apply the methods to some cases and compare the theoretical predictions with simulation results.

  4. Simulation Model of Magnetic Levitation Based on NARX Neural Networks

    Directory of Open Access Journals (Sweden)

    Dragan Antić

    2013-04-01

    Full Text Available In this paper, we present analysis of different training types for nonlinear autoregressive neural network, used for simulation of magnetic levitation system. First, the model of this highly nonlinear system is described and after that the Nonlinear Auto Regressive eXogenous (NARX of neural network model is given. Also, numerical optimization techniques for improved network training are described. It is verified that NARX neural network can be successfully used to simulate real magnetic levitation system if suitable training procedure is chosen, and the best two training types, obtained from experimental results, are described in details.

  5. Artificial Neural Network Model for Optical Fiber Direction Coupler Design

    Institute of Scientific and Technical Information of China (English)

    李九生; 鲍振武

    2004-01-01

    A new approach to the design of the optical fiber direction coupler by using neural network is proposed. To train the artificial neural network,the coupling length is defined as the input sample, and the coupling ratio is defined as the output sample. Compared with the numerical value calculation of the theoretical formula, the error of the neural network model output is 1% less.Then, through the model, to design a broadband or a single wavelength optical fiber direction coupler becomes easy. The method is proved to be reliable, accurate and time-saving. So it is promising in the field of both investigation and application.

  6. Numeral eddy current sensor modelling based on genetic neural network

    Institute of Scientific and Technical Information of China (English)

    Yu A-Long

    2008-01-01

    This paper presents a method used to the numeral eddy current sensor modelling based on the genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network (GNN) according to measurement data. So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. The nonlinear model has the advantages of strong robustness,on-line modelling and high precision.The maximum nonlinearity error can be reduced to 0.037% by using GNN.However, the maximum nonlinearity error is 0.075% using the least square method.

  7. Role of Amygdala and Hippocampus in the Neural Circuit Subserving Conditioned Defeat in Syrian Hamsters

    Science.gov (United States)

    Markham, Chris M.; Taylor, Stacie L.; Huhman, Kim L.

    2010-01-01

    We examined the roles of the amygdala and hippocampus in the formation of emotionally relevant memories using an ethological model of conditioned fear termed conditioned defeat (CD). Temporary inactivation of the ventral, but not dorsal hippocampus (VH, DH, respectively) using muscimol disrupted the acquisition of CD, whereas pretraining VH…

  8. Role of Amygdala and Hippocampus in the Neural Circuit Subserving Conditioned Defeat in Syrian Hamsters

    Science.gov (United States)

    Markham, Chris M.; Taylor, Stacie L.; Huhman, Kim L.

    2010-01-01

    We examined the roles of the amygdala and hippocampus in the formation of emotionally relevant memories using an ethological model of conditioned fear termed conditioned defeat (CD). Temporary inactivation of the ventral, but not dorsal hippocampus (VH, DH, respectively) using muscimol disrupted the acquisition of CD, whereas pretraining VH…

  9. Is the Medial Amygdala Part of the Neural Circuit Modulating Conditioned Defeat in Syrian Hamsters?

    Science.gov (United States)

    Markham, Chris M.; Huhman, Kim L.

    2008-01-01

    Conditioned defeat is a model wherein hamsters that have previously experienced a single social defeat subsequently exhibit heightened levels of avoidance and submission in response to a smaller, non-aggressive intruder. While we have previously demonstrated the critical involvement of the basolateral and central nuclei of the amygdala in the…

  10. SEB circuit-level model in N-channel power MOSFETs; Modele pour circuits du burnout dans des MOSFETs de puissance de type N

    Energy Technology Data Exchange (ETDEWEB)

    Liu, J.; Schrimpf, R.D.; Massengill, L.; Galloway, K.F. [Vanderbilt Univ., Nashville, TN (United States)

    1999-07-01

    A Single Event Burnout (SEB) circuit model has been developed. The dependence of SEB sensitivity on various parameters is presented and compared with experimental results. The parasitic resistance and capacitance of the device as well as the circuit parameters contribute to the length of SEB pulse. Increasing the switching frequency of the power MOSFET may be a possible way to prevent SEB in applications. (authors)

  11. An Equivalent Electrical Circuit Model of Proton Exchange Membrane Fuel Cells Based on Mathematical Modelling

    Directory of Open Access Journals (Sweden)

    Dinh An Nguyen

    2012-07-01

    Full Text Available Many of the Proton Exchange Membrane Fuel Cell (PEMFC models proposed in the literature consist of mathematical equations. However, they are not adequately practical for simulating power systems. The proposed model takes into account phenomena such as activation polarization, ohmic polarization, double layer capacitance and mass transport effects present in a PEM fuel cell. Using electrical analogies and a mathematical modeling of PEMFC, the circuit model is established. To evaluate the effectiveness of the circuit model, its static and dynamic performances under load step changes are simulated and compared to the numerical results obtained by solving the mathematical model. Finally, the applicability of our model is demonstrated by simulating a practical system.

  12. Mathematical model of an integrated circuit cooling through cylindrical rods

    Directory of Open Access Journals (Sweden)

    Beltrán-Prieto Luis Antonio

    2017-01-01

    Full Text Available One of the main challenges in integrated circuits development is to propose alternatives to handle the extreme heat generated by high frequency of electrons moving in a reduced space that cause overheating and reduce the lifespan of the device. The use of cooling fins offers an alternative to enhance the heat transfer using combined a conduction-convection systems. Mathematical model of such process is important for parametric design and also to gain information about temperature distribution along the surface of the transistor. In this paper, we aim to obtain the equations for heat transfer along the chip and the fin by performing energy balance and heat transfer by conduction from the chip to the rod, followed by dissipation to the surrounding by convection. Newton's law of cooling and Fourier law were used to obtain the equations that describe the profile temperature in the rod and the surface of the chip. Ordinary differential equations were obtained and the respective analytical solutions were derived after consideration of boundary conditions. The temperature along the rod decreased considerably from the initial temperature (in contatct with the chip surface. This indicates the benefit of using a cilindrical rod to distribute the heat generated in the chip.

  13. A novel prediction method about single components of analog circuits based on complex field modeling.

    Science.gov (United States)

    Zhou, Jingyu; Tian, Shulin; Yang, Chenglin

    2014-01-01

    Few researches pay attention to prediction about analog circuits. The few methods lack the correlation with circuit analysis during extracting and calculating features so that FI (fault indicator) calculation often lack rationality, thus affecting prognostic performance. To solve the above problem, this paper proposes a novel prediction method about single components of analog circuits based on complex field modeling. Aiming at the feature that faults of single components hold the largest number in analog circuits, the method starts with circuit structure, analyzes transfer function of circuits, and implements complex field modeling. Then, by an established parameter scanning model related to complex field, it analyzes the relationship between parameter variation and degeneration of single components in the model in order to obtain a more reasonable FI feature set via calculation. According to the obtained FI feature set, it establishes a novel model about degeneration trend of analog circuits' single components. At last, it uses particle filter (PF) to update parameters for the model and predicts remaining useful performance (RUP) of analog circuits' single components. Since calculation about the FI feature set is more reasonable, accuracy of prediction is improved to some extent. Finally, the foregoing conclusions are verified by experiments.

  14. Nonlinear modeling of neural population dynamics for hippocampal prostheses

    OpenAIRE

    Song, Dong; Chan, Rosa H.M.; Vasilis Z Marmarelis; Hampson, Robert E.; Deadwyler, Sam A.; Berger, Theodore W.

    2009-01-01

    Developing a neural prosthesis for the damaged hippocampus requires restoring the transformation of population neural activities performed by the hippocampal circuitry. To bypass a damaged region, output spike trains need to be predicted from the input spike trains and then reinstated through stimulation. We formulate a multiple-input, multiple-output (MIMO) nonlinear dynamic model for the input–output transformation of spike trains. In this approach, a MIMO model comprises a series of physio...

  15. Prediction Model of Sewing Technical Condition by Grey Neural Network

    Institute of Scientific and Technical Information of China (English)

    DONG Ying; FANG Fang; ZHANG Wei-yuan

    2007-01-01

    The grey system theory and the artificial neural network technology were applied to predict the sewing technical condition. The representative parameters, such as needle, stitch, were selected. Prediction model was established based on the different fabrics' mechanical properties that measured by KES instrument. Grey relevant degree analysis was applied to choose the input parameters of the neural network. The result showed that prediction model has good precision. The average relative error was 4.08% for needle and 4.25% for stitch.

  16. FPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model.

    Science.gov (United States)

    Yaghini Bonabi, Safa; Asgharian, Hassan; Safari, Saeed; Nili Ahmadabadi, Majid

    2014-01-01

    A set of techniques for efficient implementation of Hodgkin-Huxley-based (H-H) model of a neural network on FPGA (Field Programmable Gate Array) is presented. The central implementation challenge is H-H model complexity that puts limits on the network size and on the execution speed. However, basics of the original model cannot be compromised when effect of synaptic specifications on the network behavior is the subject of study. To solve the problem, we used computational techniques such as CORDIC (Coordinate Rotation Digital Computer) algorithm and step-by-step integration in the implementation of arithmetic circuits. In addition, we employed different techniques such as sharing resources to preserve the details of model as well as increasing the network size in addition to keeping the network execution speed close to real time while having high precision. Implementation of a two mini-columns network with 120/30 excitatory/inhibitory neurons is provided to investigate the characteristic of our method in practice. The implementation techniques provide an opportunity to construct large FPGA-based network models to investigate the effect of different neurophysiological mechanisms, like voltage-gated channels and synaptic activities, on the behavior of a neural network in an appropriate execution time. Additional to inherent properties of FPGA, like parallelism and re-configurability, our approach makes the FPGA-based system a proper candidate for study on neural control of cognitive robots and systems as well.

  17. Nano-scale CMOS analog circuits models and CAD techniques for high-level design

    CERN Document Server

    Pandit, Soumya; Patra, Amit

    2014-01-01

    Reliability concerns and the limitations of process technology can sometimes restrict the innovation process involved in designing nano-scale analog circuits. The success of nano-scale analog circuit design requires repeat experimentation, correct analysis of the device physics, process technology, and adequate use of the knowledge database.Starting with the basics, Nano-Scale CMOS Analog Circuits: Models and CAD Techniques for High-Level Design introduces the essential fundamental concepts for designing analog circuits with optimal performances. This book explains the links between the physic

  18. Functionality and Robustness of Injured Connectomic Dynamics in C. elegans: Linking Behavioral Deficits to Neural Circuit Damage

    Science.gov (United States)

    Kunert, James M.; Maia, Pedro D.; Kutz, J. Nathan

    2017-01-01

    Using a model for the dynamics of the full somatic nervous system of the nematode C. elegans, we address how biological network architectures and their functionality are degraded in the presence of focal axonal swellings (FAS) arising from neurodegenerative disease and/or traumatic brain injury. Using biophysically measured FAS distributions and swelling sizes, we are able to simulate the effects of injuries on the neural dynamics of C. elegans, showing how damaging the network degrades its low-dimensional dynamical responses. We visualize these injured neural dynamics by mapping them onto the worm’s low-dimensional postures, i.e. eigenworm modes. We show that a diversity of functional deficits arise from the same level of injury on a connectomic network. Functional deficits are quantified using a statistical shape analysis, a procrustes analysis, for deformations of the limit cycles that characterize key behaviors such as forward crawling. This procrustes metric carries information on the functional outcome of injuries in the model. Furthermore, we apply classification trees to relate injury structure to the behavioral outcome. This makes testable predictions for the structure of an injury given a defined functional deficit. More critically, this study demonstrates the potential role of computational simulation studies in understanding how neuronal networks process biological signals, and how this processing is impacted by network injury. PMID:28056097

  19. Impulsive Neural Networks Algorithm Based on the Artificial Genome Model

    Directory of Open Access Journals (Sweden)

    Yuan Gao

    2014-05-01

    Full Text Available To describe gene regulatory networks, this article takes the framework of the artificial genome model and proposes impulsive neural networks algorithm based on the artificial genome model. Firstly, the gene expression and the cell division tree are applied to generate spiking neurons with specific attributes, neural network structure, connection weights and specific learning rules of each neuron. Next, the gene segment duplications and divergence model are applied to design the evolutionary algorithm of impulsive neural networks at the level of the artificial genome. The dynamic changes of developmental gene regulatory networks are controlled during the whole evolutionary process. Finally, the behavior of collecting food for autonomous intelligent agent is simulated, which is driven by nerves. Experimental results demonstrate that the algorithm in this article has the evolutionary ability on large-scale impulsive neural networks

  20. Interval standard neural network models for nonlinear systems

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    A neural-network-based robust control design is suggested for control of a class of nonlinear systems. The design approach employs a neural network, whose activation functions satisfy the sector conditions, to approximate the nonlinear system. To improve the approximation performance and to account for the parameter perturbations during operation, a novel neural network model termed standard neural network model (SNNM) is proposed. If the uncertainty is bounded, the SNNM is called an interval SNNM (ISNNM). A state-feedback control law is designed for the nonlinear system modelled by an ISNNM such that the closed-loop system is globally, robustly, and asymptotically stable. The control design equations are shown to be a set of linear matrix inequalities (LMIs) that can be easily solved by available convex optimization algorithms. An example is given to illustrate the control design procedure, and the performance of the proposed approach is compared with that of a related method reported in literature.

  1. Synaptic inputs compete during rapid formation of the calyx of Held: a new model system for neural development.

    Science.gov (United States)

    Holcomb, Paul S; Hoffpauir, Brian K; Hoyson, Mitchell C; Jackson, Dakota R; Deerinck, Thomas J; Marrs, Glenn S; Dehoff, Marlin; Wu, Jonathan; Ellisman, Mark H; Spirou, George A

    2013-08-07

    Hallmark features of neural circuit development include early exuberant innervation followed by competition and pruning to mature innervation topography. Several neural systems, including the neuromuscular junction and climbing fiber innervation of Purkinje cells, are models to study neural development in part because they establish a recognizable endpoint of monoinnervation of their targets and because the presynaptic terminals are large and easily monitored. We demonstrate here that calyx of Held (CH) innervation of its target, which forms a key element of auditory brainstem binaural circuitry, exhibits all of these characteristics. To investigate CH development, we made the first application of serial block-face scanning electron microscopy to neural development with fine temporal resolution and thereby accomplished the first time series for 3D ultrastructural analysis of neural circuit formation. This approach revealed a growth spurt of added apposed surface area (ASA)>200 μm2/d centered on a single age at postnatal day 3 in mice and an initial rapid phase of growth and competition that resolved to monoinnervation in two-thirds of cells within 3 d. This rapid growth occurred in parallel with an increase in action potential threshold, which may mediate selection of the strongest input as the winning competitor. ASAs of competing inputs were segregated on the cell body surface. These data suggest mechanisms to select "winning" inputs by regional reinforcement of postsynaptic membrane to mediate size and strength of competing synaptic inputs.

  2. Immunohistochemical analysis of a novel dehydroepiandrosterone sulfotransferase-like protein in Drosophila neural circuits.

    Science.gov (United States)

    Liu, Tzu-An; Liu, Ming-Cheh; Yang, Yuh-Shyong

    2008-02-29

    Sulfotransferase (ST)-catalyzed sulfation plays an important role in various neuronal functions such as homeostasis of catecholamine neurotransmitters and hormones. Drosophila is a popular model for the study of memory and behavioral manifestations because it is able to mimic the intricate neuroregulation and recognition in humans. However, there has been no evidence indicating that cytosolic ST(s) is(are) present in Drosophila. The aim of this study is to investigate whether or not cytosolic ST(s) is(are) expressed in the Drosophila nervous system. Immunoblot analysis demonstrated the presence of dehydroepiandrosterone (DHEA) ST-like protein in Drosophila brain and a sensitive fluorometric assay revealed its sulfating activity toward DHEA. Immunohistochemical staining demonstrated this DHEA ST-like protein to be abundant in specific neurons as well as in several bundles of nerve fibers in Drosophila. Clarification of a possible link between ST and a neurotransmitter-mediated effect may eventually aid in designing approaches for alleviating neuronal disorders in humans.

  3. [Modeling and analysis of volume conduction based on field-circuit coupling].

    Science.gov (United States)

    Tang, Zhide; Liu, Hailong; Xie, Xiaohui; Chen, Xiufa; Hou, Deming

    2012-08-01

    Numerical simulations of volume conduction can be used to analyze the process of energy transfer and explore the effects of some physical factors on energy transfer efficiency. We analyzed the 3D quasi-static electric field by the finite element method, and developed A 3D coupled field-circuit model of volume conduction basing on the coupling between the circuit and the electric field. The model includes a circuit simulation of the volume conduction to provide direct theoretical guidance for energy transfer optimization design. A field-circuit coupling model with circular cylinder electrodes was established on the platform of the software FEM3.5. Based on this, the effects of electrode cross section area, electrode distance and circuit parameters on the performance of volume conduction system were obtained, which provided a basis for optimized design of energy transfer efficiency.

  4. Testing Circuit Models for the Energies of Coronal Magnetic Field Configurations

    CERN Document Server

    Wheatland, M S

    2003-01-01

    Circuit models involving bulk currents and inductances are often used to estimate the energies of coronal magnetic field configurations, in particular configurations associated with solar flares. The accuracy of circuit models is tested by comparing calculated energies of linear force-free fields with specified boundary conditions with corresponding circuit estimates. The circuit models are found to provide reasonable (order of magnitude) estimates for the energies of the non-potential components of the fields, and to reproduce observed functional dependences of the energies. However, substantial departure from the circuit estimates is observed for large values of the force-free parameter, and this is attributed to the influence of the non-potential component of the field on the path taken by the current.

  5. Parameter Identification of Equivalent Circuit Models for Li-ion Batteries Based on Tree Seeds Algorithm

    Science.gov (United States)

    Chen, W. J.; Tan, X. J.; Cai, M.

    2017-07-01

    Parameter identification method of equivalent circuit models for Li-ion batteries using the advanced tree seeds algorithm is proposed. On one hand, since the electrochemical models are not suitable for the design of battery management system, the equivalent circuit models are commonly adopted for on-board applications. On the other hand, by building up the objective function for optimization, the tree seeds algorithm can be used to identify the parameters of equivalent circuit models. Experimental verifications under different profiles demonstrate the suggested method can achieve a better result with lower complexity, more accuracy and robustness, which make it a reasonable alternative for other identification algorithms.

  6. Neural network models of learning and categorization in multigame experiments

    Directory of Open Access Journals (Sweden)

    Davide eMarchiori

    2011-12-01

    Full Text Available Previous research has shown that regret-driven neural networks predict behavior in repeated completely mixed games remarkably well, substantially equating the performance of the most accurate established models of learning. This result prompts the question of what is the added value of modeling learning through neural networks. We submit that this modeling approach allows for models that are able to distinguish among and respond differently to different payoff structures. Moreover, the process of categorization of a game is implicitly carried out by these models, thus without the need of any external explicit theory of similarity between games. To validate our claims, we designed and ran two multigame experiments in which subjects faced, in random sequence, different instances of two completely mixed 2x2 games. Then, we tested on our experimental data two regret-driven neural network models, and compared their performance with that of other established models of learning and Nash equilibrium.

  7. Modeling superconducting networks containing Josephson junctions by means of PC-based circuit simulation software

    Energy Technology Data Exchange (ETDEWEB)

    Blackburn, J.A. (Department of Physics and Computing, Wilfrid Laurier University, Waterloo, ON (Canada)); Smith, H.J.T. (Department of Physics, University of Waterloo, Waterloo, ON (Canada))

    1990-09-01

    Software packages are now available with which complex analog electronic circuits can be simulated on desktop computers. Using Micro Cap III it is demonstrated that the modeling capabilities of such software can be extended to include {ital superconducting} networks by means of an appropriate equivalent circuit for a Josephson junction.

  8. An Analytical Gate-All-Around MOSFET Model for Circuit Simulation

    Directory of Open Access Journals (Sweden)

    Kuan-Chou Lin

    2015-01-01

    Full Text Available A generic charge-based compact model for undoped (lightly doped quadruple-gate (QG and cylindrical-gate MOSFETs using Verilog-A is developed. This model is based on the exact solution of Poisson’s equation with scale length. The fundamental DC and charging currents of QG MOSFETs are physically and analytically calculated. In addition, as the Verilog-A modeling is portable for different circuit simulators, the modeling scheme provides a useful tool for circuit designers.

  9. Analog and VLSI circuits

    CERN Document Server

    Chen, Wai-Kai

    2009-01-01

    Featuring hundreds of illustrations and references, this book provides the information on analog and VLSI circuits. It focuses on analog integrated circuits, presenting the knowledge on monolithic device models, analog circuit cells, high performance analog circuits, RF communication circuits, and PLL circuits.

  10. Analysis and modeling of Fano resonances using equivalent circuit elements.

    Science.gov (United States)

    Lv, Bo; Li, Rujiang; Fu, Jiahui; Wu, Qun; Zhang, Kuang; Chen, Wan; Wang, Zhefei; Ma, Ruyu

    2016-08-22

    Fano resonance presents an asymmetric line shape formed by an interference of a continuum coupled with a discrete autoionized state. In this paper, we show several simple circuits for Fano resonances from the stable-input impedance mechanism, where the elements consisting of inductors and capacitors are formulated for various resonant modes, and the resistor represents the damping of the oscillators. By tuning the pole-zero of the input impedance, a simple circuit with only three passive components e.g. two inductors and one capacitor, can exhibit asymmetric resonance with arbitrary Q-factors flexiblely. Meanwhile, four passive components can exhibit various resonances including the Lorentz-like and reversely electromagnetically induced transparency (EIT) formations. Our work not only provides an intuitive understanding of Fano resonances, but also pave the way to realize Fano resonaces using simple circuit elements.

  11. Variable cluster analysis method for building neural network model

    Institute of Scientific and Technical Information of China (English)

    王海东; 刘元东

    2004-01-01

    To address the problems that input variables should be reduced as much as possible and explain output variables fully in building neural network model of complicated system, a variable selection method based on cluster analysis was investigated. Similarity coefficient which describes the mutual relation of variables was defined. The methods of the highest contribution rate, part replacing whole and variable replacement are put forwarded and deduced by information theory. The software of the neural network based on cluster analysis, which can provide many kinds of methods for defining variable similarity coefficient, clustering system variable and evaluating variable cluster, was developed and applied to build neural network forecast model of cement clinker quality. The results show that all the network scale, training time and prediction accuracy are perfect. The practical application demonstrates that the method of selecting variables for neural network is feasible and effective.

  12. Adaptation of an Evolutionary Algorithm in Modeling Electric Circuits

    Directory of Open Access Journals (Sweden)

    J. Hájek

    2010-01-01

    Full Text Available This paper describes the influence of setting control parameters of a differential evolutionary algorithm (DE and the influence of adapting these parameters on the simulation of electric circuits and their components. Various DE algorithm strategies are investigated, and also the influence of adapting the controlling parameters (Cr, F during simulation and the effect of sample size. Optimizing an equivalent circuit diagram is chosen as a test task. Several strategies and settings of a DE algorithm are evaluated according to their convergence to the right solution. 

  13. Intelligent Intrusion Detection System Model Using Rough Neural Network

    Institute of Scientific and Technical Information of China (English)

    YAN Huai-zhi; HU Chang-zhen; TAN Hui-min

    2005-01-01

    A model of intelligent intrusion detection based on rough neural network (RNN), which combines the neural network and rough set, is presented. It works by capturing network packets to identify network intrusions or malicious attacks using RNN with sub-nets. The sub-net is constructed by detection-oriented signatures extracted using rough set theory to detect different intrusions. It is proved that RNN detection method has the merits of adaptive, high universality,high convergence speed, easy upgrading and management.

  14. Circuit models and SPICE macro-models for quantum Hall effect devices

    CERN Document Server

    Ortolano, Massimo

    2015-01-01

    Quantum Hall effect (QHE) devices are a pillar of modern quantum electrical metrology. Electrical networks including one or more QHE elements can be used as quantum resistance and impedance standards. The analysis of these networks allows metrologists to evaluate the effect of the inevitable parasitic parameters on their performance as standards. This paper presents a systematic analysis of the various circuit models for QHE elements proposed in the literature, and the development of a new model. This last model is particularly suited to be employed with the analogue electronic circuit simulator SPICE. The SPICE macro-model and examples of SPICE simulations, validated by comparison with the corresponding analytical solution and/or experimental data, are provided.

  15. Detection of Internal Short Circuit in Lithium Ion Battery Using Model-Based Switching Model Method

    Directory of Open Access Journals (Sweden)

    Minhwan Seo

    2017-01-01

    Full Text Available Early detection of an internal short circuit (ISCr in a Li-ion battery can prevent it from undergoing thermal runaway, and thereby ensure battery safety. In this paper, a model-based switching model method (SMM is proposed to detect the ISCr in the Li-ion battery. The SMM updates the model of the Li-ion battery with ISCr to improve the accuracy of ISCr resistance R I S C f estimates. The open circuit voltage (OCV and the state of charge (SOC are estimated by applying the equivalent circuit model, and by using the recursive least squares algorithm and the relation between OCV and SOC. As a fault index, the R I S C f is estimated from the estimated OCVs and SOCs to detect the ISCr, and used to update the model; this process yields accurate estimates of OCV and R I S C f . Then the next R I S C f is estimated and used to update the model iteratively. Simulation data from a MATLAB/Simulink model and experimental data verify that this algorithm shows high accuracy of R I S C f estimates to detect the ISCr, thereby helping the battery management system to fulfill early detection of the ISCr.

  16. Stimulus-dependent maximum entropy models of neural population codes.

    Science.gov (United States)

    Granot-Atedgi, Einat; Tkačik, Gašper; Segev, Ronen; Schneidman, Elad

    2013-01-01

    Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input. For large populations, direct sampling of these distributions is impossible, and so we must rely on constructing appropriate models. We show here that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli, dependencies between cells play an important encoding role. We introduce the stimulus-dependent maximum entropy (SDME) model-a minimal extension of the canonical linear-nonlinear model of a single neuron, to a pairwise-coupled neural population. We find that the SDME model gives a more accurate account of single cell responses and in particular significantly outperforms uncoupled models in reproducing the distributions of population codewords emitted in response to a stimulus. We show how the SDME model, in conjunction with static maximum entropy models of population vocabulary, can be used to estimate information-theoretic quantities like average surprise and information transmission in a neural population.

  17. Stimulus-dependent maximum entropy models of neural population codes.

    Directory of Open Access Journals (Sweden)

    Einat Granot-Atedgi

    Full Text Available Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input. For large populations, direct sampling of these distributions is impossible, and so we must rely on constructing appropriate models. We show here that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli, dependencies between cells play an important encoding role. We introduce the stimulus-dependent maximum entropy (SDME model-a minimal extension of the canonical linear-nonlinear model of a single neuron, to a pairwise-coupled neural population. We find that the SDME model gives a more accurate account of single cell responses and in particular significantly outperforms uncoupled models in reproducing the distributions of population codewords emitted in response to a stimulus. We show how the SDME model, in conjunction with static maximum entropy models of population vocabulary, can be used to estimate information-theoretic quantities like average surprise and information transmission in a neural population.

  18. An Improved Nonlinear Circuit Model for GaAs Gunn Diode in W-Band Oscillator

    Science.gov (United States)

    Zhang, Bo; Fan, Yong; Zhang, Yonghong

    An improved nonlinear circuit model for a GaAs Gunn diode in an oscillator is proposed based on the physical mechanism of the diode. This model interprets the nonlinear harmonic character on the Gunn diode. Its equivalent nonlinear circuit of which can assist in the design of the Gunn oscillator and help in the analysis of the fundamental and harmonic characteristics of the GaAs Gunn diode. The simulation prediction and the experiment of the Gunn oscillator show the feasibility of the nonlinear circuit model for the GaAs Gunn oscillator.

  19. Dynamic companion harmonic circuit models for analysis of power systems with embedded power electronics devices

    Energy Technology Data Exchange (ETDEWEB)

    Garcia, H.; Madrigal, M. [Programa de Graduados e Investigacion en Ingenieria Electrica, Instituto Tecnologico de Morelia, Morelia (Mexico); Vyakaranam, B.; Rarick, R.; Villaseca, F.E. [Department of Electrical and Computer Engineering, Cleveland State University, OH (United States)

    2011-02-15

    In this paper a methodology that extends the dynamic harmonic domain (DHD) analysis of large networks is presented. The method combines DHD analysis and discrete companion circuit modeling resulting in a powerful analytic technique called dynamic companion harmonic circuit modeling. It provides for a complete dynamic harmonic analysis of the system while preserving the advantages of discrete companion circuit models. The methodology is illustrated by its application to a three-node power system, where reactive power compensation is achieved using a fixed-capacitor, thyristor-controlled reactor (FC-TCR) and its control system. (author)

  20. Functional reorganization of motor and limbic circuits after exercise training in a rat model of bilateral parkinsonism.

    Directory of Open Access Journals (Sweden)

    Zhuo Wang

    in decreases in rCBF in the medial prefrontal cortex (cingulate, prelimbic, infralimbic. Our results in this PD rat model uniquely highlight the breadth of functional reorganizations in motor and limbic circuits following lesion and long-term, aerobic exercise, and provide a framework for understanding the neural substrates underlying exercise-based neurorehabilitation.

  1. Predictive modeling of dental pain using neural network.

    Science.gov (United States)

    Kim, Eun Yeob; Lim, Kun Ok; Rhee, Hyun Sill

    2009-01-01

    The mouth is a part of the body for ingesting food that is the most basic foundation and important part. The dental pain predicted by the neural network model. As a result of making a predictive modeling, the fitness of the predictive modeling of dental pain factors was 80.0%. As for the people who are likely to experience dental pain predicted by the neural network model, preventive measures including proper eating habits, education on oral hygiene, and stress release must precede any dental treatment.

  2. Three-Dimensional Electro-Thermal Verilog-A Model of Power MOSFET for Circuit Simulation

    Science.gov (United States)

    Chvála, A.; Donoval, D.; Marek, J.; Príbytný, P.; Molnár, M.; Mikolášek, M.

    2014-04-01

    New original circuit model for the power device based on interactive coupling of electrical and thermal properties is described. The thermal equivalent network for a three-dimensional heat flow is presented. Designed electro-thermal MOSFET model for circuit simulations with distributed properties and three-dimensional thermal equivalent network is used for simulation of multipulse unclamped inductive switching (UIS) test of device robustness. The features and the limitations of the new model are analyzed and presented.

  3. Neuromorphic silicon neuron circuits

    Directory of Open Access Journals (Sweden)

    Giacomo eIndiveri

    2011-05-01

    Full Text Available Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain-machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance based Hodgkin-Huxley models to bi-dimensional generalized adaptive Integrate and Fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips.

  4. Models of Innate Neural Attractors and Their Applications for Neural Information Processing.

    Science.gov (United States)

    Solovyeva, Ksenia P; Karandashev, Iakov M; Zhavoronkov, Alex; Dunin-Barkowski, Witali L

    2015-01-01

    In this work we reveal and explore a new class of attractor neural networks, based on inborn connections provided by model molecular markers, the molecular marker based attractor neural networks (MMBANN). Each set of markers has a metric, which is used to make connections between neurons containing the markers. We have explored conditions for the existence of attractor states, critical relations between their parameters and the spectrum of single neuron models, which can implement the MMBANN. Besides, we describe functional models (perceptron and SOM), which obtain significant advantages over the traditional implementation of these models, while using MMBANN. In particular, a perceptron, based on MMBANN, gets specificity gain in orders of error probabilities values, MMBANN SOM obtains real neurophysiological meaning, the number of possible grandma cells increases 1000-fold with MMBANN. MMBANN have sets of attractor states, which can serve as finite grids for representation of variables in computations. These grids may show dimensions of d = 0, 1, 2,…. We work with static and dynamic attractor neural networks of the dimensions d = 0 and 1. We also argue that the number of dimensions which can be represented by attractors of activities of neural networks with the number of elements N = 10(4) does not exceed 8.

  5. An ART neural network model of discrimination shift learning

    NARCIS (Netherlands)

    Raijmakers, M.E.J.; Coffey, E.; Stevenson, C.; Winkel, J.; Berkeljon, A.; Taatgen, N.; van Rijn, H.

    2009-01-01

    We present an ART-based neural network model (adapted from [2]) of the development of discrimination-shift learning that models the trial-by-trial learning process in great detail. In agreement with the results of human participants (4-20 years of age) in [1] the model revealed two distinct learning

  6. Study on neural network model for calculating subsidence factor

    Institute of Scientific and Technical Information of China (English)

    GUO Wen-bing; ZHANG Jie

    2007-01-01

    The major factors influencing subsidence factor were comprehensively analyzed. Then the artificial neural network model for calculating subsidence factor was set up with the theory of artificial neural network (ANN). A large amount of data from observation stations in China was collected and used as learning and training samples to train and test the artificial neural network model. The calculated results of the ANN model and the observed values were compared and analyzed in this paper. The results demonstrate that many factors can be considered in this model and the result is more precise and closer to observed values to calculate the subsidence factor by the ANN model. It can satisfy the need of engineering.

  7. Stability of a neural predictive controller scheme on a neural model

    DEFF Research Database (Denmark)

    Luther, Jim Benjamin; Sørensen, Paul Haase

    2009-01-01

    In previous works presenting various forms of neural-network-based predictive controllers, the main emphasis has been on the implementation aspects, i.e. the development of a robust optimization algorithm for the controller, which will be able to perform in real time. However, the stability issue...... has not been addressed specifically for these controllers. On the other hand a number of results concerning the stability of receding horizon controllers on a nonlinear system exist. In this paper we present a proof of stability for a predictive controller controlling a neural network model...

  8. Estimation of the frequency border for tolerable use of the approximate models of transmission lines at the circuit analysis of printed circuit boards

    Directory of Open Access Journals (Sweden)

    Sirotко V. K.

    2010-10-01

    Full Text Available The required frequency border was defined by means of comparison of amplitude signals on receiving end of printed circuit boards transmission lines calculated using more exact electrodynamic models. For the contemporary printed circuit boards materials and designs the frequency border makes 3 GHz.

  9. Modelling Framework of a Neural Object Recognition

    Directory of Open Access Journals (Sweden)

    Aswathy K S

    2016-02-01

    Full Text Available In many industrial, medical and scientific image processing applications, various feature and pattern recognition techniques are used to match specific features in an image with a known template. Despite the capabilities of these techniques, some applications require simultaneous analysis of multiple, complex, and irregular features within an image as in semiconductor wafer inspection. In wafer inspection discovered defects are often complex and irregular and demand more human-like inspection techniques to recognize irregularities. By incorporating neural network techniques such image processing systems with much number of images can be trained until the system eventually learns to recognize irregularities. The aim of this project is to develop a framework of a machine-learning system that can classify objects of different category. The framework utilizes the toolboxes in the Matlab such as Computer Vision Toolbox, Neural Network Toolbox etc.

  10. About Nested Circuits Markov in one Parametric Queueing Model

    Directory of Open Access Journals (Sweden)

    Rafik A. Simonyan

    2013-01-01

    Full Text Available In operation the single-channel queuing system with several Poisson entering flows and with Kleynrok's parametric discipline is considered. The Markov circuit which is received on a basis a vector of processes of the maximum priorities of flows of calls is completely studied

  11. Diagnosing process faults using neural network models

    Energy Technology Data Exchange (ETDEWEB)

    Buescher, K.L.; Jones, R.D.; Messina, M.J.

    1993-11-01

    In order to be of use for realistic problems, a fault diagnosis method should have the following three features. First, it should apply to nonlinear processes. Second, it should not rely on extensive amounts of data regarding previous faults. Lastly, it should detect faults promptly. The authors present such a scheme for static (i.e., non-dynamic) systems. It involves using a neural network to create an associative memory whose fixed points represent the normal behavior of the system.

  12. Solving linear integer programming problems by a novel neural model.

    Science.gov (United States)

    Cavalieri, S

    1999-02-01

    The paper deals with integer linear programming problems. As is well known, these are extremely complex problems, even when the number of integer variables is quite low. Literature provides examples of various methods to solve such problems, some of which are of a heuristic nature. This paper proposes an alternative strategy based on the Hopfield neural network. The advantage of the strategy essentially lies in the fact that hardware implementation of the neural model allows for the time required to obtain a solution so as not depend on the size of the problem to be solved. The paper presents a particular class of integer linear programming problems, including well-known problems such as the Travelling Salesman Problem and the Set Covering Problem. After a brief description of this class of problems, it is demonstrated that the original Hopfield model is incapable of supplying valid solutions. This is attributed to the presence of constant bias currents in the dynamic of the neural model. A demonstration of this is given and then a novel neural model is presented which continues to be based on the same architecture as the Hopfield model, but introduces modifications thanks to which the integer linear programming problems presented can be solved. Some numerical examples and concluding remarks highlight the solving capacity of the novel neural model.

  13. Artificial neural networks modeling gene-environment interaction

    Directory of Open Access Journals (Sweden)

    Günther Frauke

    2012-05-01

    Full Text Available Abstract Background Gene-environment interactions play an important role in the etiological pathway of complex diseases. An appropriate statistical method for handling a wide variety of complex situations involving interactions between variables is still lacking, especially when continuous variables are involved. The aim of this paper is to explore the ability of neural networks to model different structures of gene-environment interactions. A simulation study is set up to compare neural networks with standard logistic regression models. Eight different structures of gene-environment interactions are investigated. These structures are characterized by penetrance functions that are based on sigmoid functions or on combinations of linear and non-linear effects of a continuous environmental factor and a genetic factor with main effect or with a masking effect only. Results In our simulation study, neural networks are more successful in modeling gene-environment interactions than logistic regression models. This outperfomance is especially pronounced when modeling sigmoid penetrance functions, when distinguishing between linear and nonlinear components, and when modeling masking effects of the genetic factor. Conclusion Our study shows that neural networks are a promising approach for analyzing gene-environment interactions. Especially, if no prior knowledge of the correct nature of the relationship between co-variables and response variable is present, neural networks provide a valuable alternative to regression methods that are limited to the analysis of linearly separable data.

  14. 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.

  15. Modeling and simulation of carbon nanotube field effect transistor and its circuit application

    Science.gov (United States)

    Singh, Amandeep; Saini, Dinesh Kumar; Agarwal, Dinesh; Aggarwal, Sajal; Khosla, Mamta; Raj, Balwinder

    2016-07-01

    The carbon nanotube field effect transistor (CNTFET) is modelled for circuit application. The model is based on the transport mechanism and it directly relates the transport mechanism with the chirality. Also, it does not consider self consistent equations and thus is used to develop the HSPICE compatible circuit model. For validation of the model, it is applied to the top gate CNTFET structure and the MATLAB simulation results are compared with the simulations of a similar structure created in NanoTCAD ViDES. For demonstrating the circuit compatibility of the model, two circuits viz. inverter and SRAM are designed and simulated in HSPICE. Finally, SRAM performance metrics are compared with those of device simulations from Nano TCAD ViDES.

  16. An equivalent circuit model for transmitting capacitive micromachined ultrasonic transducers in collapse mode.

    Science.gov (United States)

    Olcum, Selim; Yamaner, F Yalcin; Bozkurt, Ayhan; Köymen, Hayrettin; Atalar, Abdullah

    2011-07-01

    The collapse mode of operation of capacitive micromachined ultrasonic transducers (CMUTs) was shown to be a very effective way to achieve high output pressures. However, no accurate analytical or equivalent circuit model exists for understanding the mechanics and limits of the collapse mode. In this work, we develop an equivalent nonlinear electrical circuit that can accurately simulate the mechanical behavior of a CMUT with given dimensions and mechanical parameters under any large or small signal electrical excitation, including the collapse mode. The static and dynamic deflections of a plate predicted from the model are compared with finite element simulations. The equivalent circuit model can estimate the static deflection and transient behavior of a CMUT plate to within 5% accuracy. The circuit model is in good agreement with experimental results of pulse excitation applied to fabricated CMUTs. The model is suitable as a powerful design and optimization tool for collapsed and uncollapsed CMUTs.

  17. An Integrated Magnetic Circuit Model and Finite Element Model Approach to Magnetic Bearing Design

    Science.gov (United States)

    Provenza, Andrew J.; Kenny, Andrew; Palazzolo, Alan B.

    2003-01-01

    A code for designing magnetic bearings is described. The code generates curves from magnetic circuit equations relating important bearing performance parameters. Bearing parameters selected from the curves by a designer to meet the requirements of a particular application are input directly by the code into a three-dimensional finite element analysis preprocessor. This means that a three-dimensional computer model of the bearing being developed is immediately available for viewing. The finite element model solution can be used to show areas of magnetic saturation and make more accurate predictions of the bearing load capacity, current stiffness, position stiffness, and inductance than the magnetic circuit equations did at the start of the design process. In summary, the code combines one-dimensional and three-dimensional modeling methods for designing magnetic bearings.

  18. A neural network model for credit risk evaluation.

    Science.gov (United States)

    Khashman, Adnan

    2009-08-01

    Credit scoring is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. This paper presents a credit risk evaluation system that uses a neural network model based on the back propagation learning algorithm. We train and implement the neural network to decide whether to approve or reject a credit application, using seven learning schemes and real world credit applications from the Australian credit approval datasets. A comparison of the system performance under the different learning schemes is provided, furthermore, we compare the performance of two neural networks; with one and two hidden layers following the ideal learning scheme. Experimental results suggest that neural networks can be effectively used in automatic processing of credit applications.

  19. 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.

  20. From Boolean Network Model to Continuous Model Helps in Design of Functional Circuits

    Science.gov (United States)

    Zhang, Dongliang; Wu, Jiayi; Ouyang, Qi

    2015-01-01

    Computational circuit design with desired functions in a living cell is a challenging task in synthetic biology. To achieve this task, numerous methods that either focus on small scale networks or use evolutionary algorithms have been developed. Here, we propose a two-step approach to facilitate the design of functional circuits. In the first step, the search space of possible topologies for target functions is reduced by reverse engineering using a Boolean network model. In the second step, continuous simulation is applied to evaluate the performance of these topologies. We demonstrate the usefulness of this method by designing an example biological function: the SOS response of E. coli. Our numerical results show that the desired function can be faithfully reproduced by candidate networks with different parameters and initial conditions. Possible circuits are ranked according to their robustness against perturbations in parameter and gene expressions. The biological network is among the candidate networks, yet novel designs can be generated. Our method provides a scalable way to design robust circuits that can achieve complex functions, and makes it possible to uncover design principles of biological networks. PMID:26061094

  1. Quantum game simulator, using the circuit model of quantum computation

    Science.gov (United States)

    Vlachos, Panagiotis; Karafyllidis, Ioannis G.

    2009-10-01

    We present a general two-player quantum game simulator that can simulate any two-player quantum game described by a 2×2 payoff matrix (two strategy games).The user can determine the payoff matrices for both players, their strategies and the amount of entanglement between their initial strategies. The outputs of the simulator are the expected payoffs of each player as a function of the other player's strategy parameters and the amount of entanglement. The simulator also produces contour plots that divide the strategy spaces of the game in regions in which players can get larger payoffs if they choose to use a quantum strategy against any classical one. We also apply the simulator to two well-known quantum games, the Battle of Sexes and the Chicken game. Program summaryProgram title: Quantum Game Simulator (QGS) Catalogue identifier: AEED_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEED_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 3416 No. of bytes in distributed program, including test data, etc.: 583 553 Distribution format: tar.gz Programming language: Matlab R2008a (C) Computer: Any computer that can sufficiently run Matlab R2008a Operating system: Any system that can sufficiently run Matlab R2008a Classification: 4.15 Nature of problem: Simulation of two player quantum games described by a payoff matrix. Solution method: The program calculates the matrices that comprise the Eisert setup for quantum games based on the quantum circuit model. There are 5 parameters that can be altered. We define 3 of them as constant. We play the quantum game for all possible values for the other 2 parameters and store the results in a matrix. Unusual features: The software provides an easy way of simulating any two-player quantum games. Running time: Approximately

  2. Mathematical Model of Thyristor Inverter Including a Series-parallel Resonant Circuit

    Directory of Open Access Journals (Sweden)

    Miroslaw Luft

    2008-01-01

    Full Text Available The article presents a mathematical model of thyristor inverter including a series-parallel resonant circuit with theaid of state variable method. Maple procedures are used to compute current and voltage waveforms in the inverter.

  3. Mechanisms of left-right coordination in mammalian locomotor pattern generation circuits: a mathematical modeling view.

    Directory of Open Access Journals (Sweden)

    Yaroslav I Molkov

    2015-05-01

    Full Text Available The locomotor gait in limbed animals is defined by the left-right leg coordination and locomotor speed. Coordination between left and right neural activities in the spinal cord controlling left and right legs is provided by commissural interneurons (CINs. Several CIN types have been genetically identified, including the excitatory V3 and excitatory and inhibitory V0 types. Recent studies demonstrated that genetic elimination of all V0 CINs caused switching from a normal left-right alternating activity to a left-right synchronized "hopping" pattern. Furthermore, ablation of only the inhibitory V0 CINs (V0D subtype resulted in a lack of left-right alternation at low locomotor frequencies and retaining this alternation at high frequencies, whereas selective ablation of the excitatory V0 neurons (V0V subtype maintained the left-right alternation at low frequencies and switched to a hopping pattern at high frequencies. To analyze these findings, we developed a simplified mathematical model of neural circuits consisting of four pacemaker neurons representing left and right, flexor and extensor rhythm-generating centers interacting via commissural pathways representing V3, V0D, and V0V CINs. The locomotor frequency was controlled by a parameter defining the excitation of neurons and commissural pathways mimicking the effects of N-methyl-D-aspartate on locomotor frequency in isolated rodent spinal cord preparations. The model demonstrated a typical left-right alternating pattern under control conditions, switching to a hopping activity at any frequency after removing both V0 connections, a synchronized pattern at low frequencies with alternation at high frequencies after removing only V0D connections, and an alternating pattern at low frequencies with hopping at high frequencies after removing only V0V connections. We used bifurcation theory and fast-slow decomposition methods to analyze network behavior in the above regimes and transitions between them

  4. A retinal circuit model accounting for wide-field amacrine cells

    OpenAIRE

    SAĞLAM, Murat; Hayashida, Yuki; Murayama, Nobuki

    2008-01-01

    In previous experimental studies on the visual processing in vertebrates, higher-order visual functions such as the object segregation from background were found even in the retinal stage. Previously, the “linear–nonlinear” (LN) cascade models have been applied to the retinal circuit, and succeeded to describe the input-output dynamics for certain parts of the circuit, e.g., the receptive field of the outer retinal neurons. And recently, some abstract models composed of LN cascades as the cir...

  5. Artificial Neural Networks for Modeling Knowing and Learning in Science.

    Science.gov (United States)

    Roth, Wolff-Michael

    2000-01-01

    Advocates artificial neural networks as models for cognition and development. Provides an example of how such models work in the context of a well-known Piagetian developmental task and school science activity: balance beam problems. (Contains 59 references.) (Author/WRM)

  6. Discriminative training of self-structuring hidden control neural models

    DEFF Research Database (Denmark)

    Sørensen, Helge Bjarup Dissing; Hartmann, Uwe; Hunnerup, Preben

    1995-01-01

    This paper presents a new training algorithm for self-structuring hidden control neural (SHC) models. The SHC models were trained non-discriminatively for speech recognition applications. Better recognition performance can generally be achieved, if discriminative training is applied instead. Thus...

  7. Artificial Neural Networks for Modeling Knowing and Learning in Science.

    Science.gov (United States)

    Roth, Wolff-Michael

    2000-01-01

    Advocates artificial neural networks as models for cognition and development. Provides an example of how such models work in the context of a well-known Piagetian developmental task and school science activity: balance beam problems. (Contains 59 references.) (Author/WRM)

  8. A Neural Network Model of Retrieval-Induced Forgetting

    Science.gov (United States)

    Norman, Kenneth A.; Newman, Ehren L.; Detre, Greg

    2007-01-01

    Retrieval-induced forgetting (RIF) refers to the finding that retrieving a memory can impair subsequent recall of related memories. Here, the authors present a new model of how the brain gives rise to RIF in both semantic and episodic memory. The core of the model is a recently developed neural network learning algorithm that leverages regular…

  9. A Novel Prediction Method about Single Components of Analog Circuits Based on Complex Field Modeling

    Directory of Open Access Journals (Sweden)

    Jingyu Zhou

    2014-01-01

    Full Text Available Few researches pay attention to prediction about analog circuits. The few methods lack the correlation with circuit analysis during extracting and calculating features so that FI (fault indicator calculation often lack rationality, thus affecting prognostic performance. To solve the above problem, this paper proposes a novel prediction method about single components of analog circuits based on complex field modeling. Aiming at the feature that faults of single components hold the largest number in analog circuits, the method starts with circuit structure, analyzes transfer function of circuits, and implements complex field modeling. Then, by an established parameter scanning model related to complex field, it analyzes the relationship between parameter variation and degeneration of single components in the model in order to obtain a more reasonable FI feature set via calculation. According to the obtained FI feature set, it establishes a novel model about degeneration trend of analog circuits’ single components. At last, it uses particle filter (PF to update parameters for the model and predicts remaining useful performance (RUP of analog circuits’ single components. Since calculation about the FI feature set is more reasonable, accuracy of prediction is improved to some extent. Finally, the foregoing conclusions are verified by experiments.

  10. The Use of Neural Network Technology to Model Swimming Performance

    Science.gov (United States)

    Silva, António José; Costa, Aldo Manuel; Oliveira, Paulo Moura; Reis, Victor Machado; Saavedra, José; Perl, Jurgen; Rouboa, Abel; Marinho, Daniel Almeida

    2007-01-01

    The aims of the present study were: to identify the factors which are able to explain the performance in the 200 meters individual medley and 400 meters front crawl events in young swimmers, to model the performance in those events using non-linear mathematic methods through artificial neural networks (multi-layer perceptrons) and to assess the neural network models precision to predict the performance. A sample of 138 young swimmers (65 males and 73 females) of national level was submitted to a test battery comprising four different domains: kinanthropometric evaluation, dry land functional evaluation (strength and flexibility), swimming functional evaluation (hydrodynamics, hydrostatic and bioenergetics characteristics) and swimming technique evaluation. To establish a profile of the young swimmer non-linear combinations between preponderant variables for each gender and swim performance in the 200 meters medley and 400 meters font crawl events were developed. For this purpose a feed forward neural network was used (Multilayer Perceptron) with three neurons in a single hidden layer. The prognosis precision of the model (error lower than 0.8% between true and estimated performances) is supported by recent evidence. Therefore, we consider that the neural network tool can be a good approach in the resolution of complex problems such as performance modeling and the talent identification in swimming and, possibly, in a wide variety of sports. Key pointsThe non-linear analysis resulting from the use of feed forward neural network allowed us the development of four performance models.The mean difference between the true and estimated results performed by each one of the four neural network models constructed was low.The neural network tool can be a good approach in the resolution of the performance modeling as an alternative to the standard statistical models that presume well-defined distributions and independence among all inputs.The use of neural networks for sports

  11. Neural network emulation of a rainfall-runoff model

    Directory of Open Access Journals (Sweden)

    R. J. Abrahart

    2007-02-01

    Full Text Available The potential of an artificial neural network to perform simple non-linear hydrological transformations is examined. Four neural network models were developed to emulate different facets of a recognised non-linear hydrological transformation equation that possessed a small number of variables and contained no temporal component. The modeling process was based on a set of uniform random distributions. The cloning operation facilitated a direct comparison with the exact equation-based relationship. It also provided broader information about the power of a neural network to emulate existing equations and model non-linear relationships. Several comparisons with least squares multiple linear regression were performed. The first experiment involved a direct emulation of the Xinanjiang Rainfall-Runoff Model. The next two experiments were designed to assess the competencies of two neural solutions that were developed on a reduced number of inputs. This involved the omission and conflation of previous inputs. The final experiment used derived variables to model intrinsic but otherwise concealed internal relationships that are of hydrological interest. Two recent studies have suggested that neural solutions offer no worthwhile improvements in comparison to traditional weighted linear transfer functions for capturing the non-linear nature of hydrological relationships. Yet such fundamental properties are intrinsic aspects of catchment processes that cannot be excluded or ignored. The results from the four experiments that are reported in this paper are used to challenge the interpretations from these two earlier studies and thus further the debate with regards to the appropriateness of neural networks for hydrological modelling.

  12. The shared circuits model (SCM): how control, mirroring, and simulation can enable imitation, deliberation, and mindreading.

    Science.gov (United States)

    Hurley, Susan

    2008-02-01

    Imitation, deliberation, and mindreading are characteristically human sociocognitive skills. Research on imitation and its role in social cognition is flourishing across various disciplines. Imitation is surveyed in this target article under headings of behavior, subpersonal mechanisms, and functions of imitation. A model is then advanced within which many of the developments surveyed can be located and explained. The shared circuits model (SCM) explains how imitation, deliberation, and mindreading can be enabled by subpersonal mechanisms of control, mirroring, and simulation. It is cast at a middle, functional level of description, that is, between the level of neural implementation and the level of conscious perceptions and intentional actions. The SCM connects shared informational dynamics for perception and action with shared informational dynamics for self and other, while also showing how the action/perception, self/other, and actual/possible distinctions can be overlaid on these shared informational dynamics. It avoids the common conception of perception and action as separate and peripheral to central cognition. Rather, it contributes to the situated cognition movement by showing how mechanisms for perceiving action can be built on those for active perception.;>;>The SCM is developed heuristically, in five layers that can be combined in various ways to frame specific ontogenetic or phylogenetic hypotheses. The starting point is dynamic online motor control, whereby an organism is closely attuned to its embedding environment through sensorimotor feedback. Onto this are layered functions of prediction and simulation of feedback, mirroring, simulation of mirroring, monitored inhibition of motor output, and monitored simulation of input. Finally, monitored simulation of input specifying possible actions plus inhibited mirroring of such possible actions can generate information about the possible as opposed to actual instrumental actions of others, and the

  13. A Right Coprime Factorization of Neural State Space Models

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon

    2007-01-01

    In recent years, various methods for identification of nonlinear systems in closed loop using open-loop approaches have received considerable attention. However, these methods rely on differentially coprime factorizations of the nonlinear plants, which can be difficult to compute in practice....... To address this issue, this paper presents various technical results leading up to explicit formulae for right coprime factorizations of neural state space models, i.e., nonlinear system models represented in state space using neural networks, which satisfy a Bezout identity. ...

  14. Fuzzy Entropy: Axiomatic Definition and Neural Networks Model

    Institute of Scientific and Technical Information of China (English)

    QINGMing; CAOYue; HUANGTian-min

    2004-01-01

    The measure of uncertainty is adopted as a measure of information. The measures of fuzziness are known as fuzzy information measures. The measure of a quantity of fuzzy information gained from a fuzzy set or fuzzy system is known as fuzzy entropy. Fuzzy entropy has been focused and studied by many researchers in various fields. In this paper, firstly, the axiomatic definition of fuzzy entropy is discussed. Then, neural networks model of fuzzy entropy is proposed, based on the computing capability of neural networks. In the end, two examples are discussed to show the efficiency of the model.

  15. Superior model for fault tolerance computation in designing nano-sized circuit systems

    Energy Technology Data Exchange (ETDEWEB)

    Singh, N. S. S., E-mail: narinderjit@petronas.com.my; Muthuvalu, M. S., E-mail: msmuthuvalu@gmail.com [Fundamental and Applied Sciences Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak (Malaysia); Asirvadam, V. S., E-mail: vijanth-sagayan@petronas.com.my [Electrical and Electronics Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak (Malaysia)

    2014-10-24

    As CMOS technology scales nano-metrically, reliability turns out to be a decisive subject in the design methodology of nano-sized circuit systems. As a result, several computational approaches have been developed to compute and evaluate reliability of desired nano-electronic circuits. The process of computing reliability becomes very troublesome and time consuming as the computational complexity build ups with the desired circuit size. Therefore, being able to measure reliability instantly and superiorly is fast becoming necessary in designing modern logic integrated circuits. For this purpose, the paper firstly looks into the development of an automated reliability evaluation tool based on the generalization of Probabilistic Gate Model (PGM) and Boolean Difference-based Error Calculator (BDEC) models. The Matlab-based tool allows users to significantly speed-up the task of reliability analysis for very large number of nano-electronic circuits. Secondly, by using the developed automated tool, the paper explores into a comparative study involving reliability computation and evaluation by PGM and, BDEC models for different implementations of same functionality circuits. Based on the reliability analysis, BDEC gives exact and transparent reliability measures, but as the complexity of the same functionality circuits with respect to gate error increases, reliability measure by BDEC tends to be lower than the reliability measure by PGM. The lesser reliability measure by BDEC is well explained in this paper using distribution of different signal input patterns overtime for same functionality circuits. Simulation results conclude that the reliability measure by BDEC depends not only on faulty gates but it also depends on circuit topology, probability of input signals being one or zero and also probability of error on signal lines.

  16. Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe

    Directory of Open Access Journals (Sweden)

    Eli eShlizerman

    2014-08-01

    Full Text Available The antennal lobe (AL, olfactory processing center in insects, is able to process stimuli into distinct neural activity patterns, called olfactory neural codes. To model their dynamics we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a dynamic neuronal network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons (modeled as firing-rate units, and is capable of producing unique olfactory neural codes for the tested odorants. To construct the network, we (i design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (ii characterize scent recognition, i.e., decision-making based on olfactory signals and (iii infer the wiring of the neural circuit, the connectome of the AL. We show that the constructed model is consistent with biological observations, such as contrast enhancement and robustness to noise. The study suggests a data-driven approach to answer a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns.

  17. Transformation of Neural State Space Models into LFT Models for Robust Control Design

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Trangbæk, Klaus

    2000-01-01

    This paper considers the extraction of linear state space models and uncertainty models from neural networks trained as state estimators with direct application to robust control. A new method for writing a neural state space model in a linear fractional transformation form in a non...

  18. On a fractal LC-electric circuit modeled by local fractional calculus

    Science.gov (United States)

    Yang, Xiao-Jun; Machado, J. A. Tenreiro; Cattani, Carlo; Gao, Feng

    2017-06-01

    A non-differentiable model of the LC-electric circuit described by a local fractional differential equation of fractal dimensional order is addressed in this article. From the fractal electrodynamics point of view, the relaxation oscillator, defined on Cantor sets in LC-electric circuit, and its exact solution using the local fractional Laplace transform are obtained. Comparative results among local fractional derivative, Riemann-Liouville fractional derivative and conventional derivative are discussed. Local fractional calculus is proposed as a new tool suitable for the study of a large class of electric circuits.

  19. Alterations in the neural circuits from peripheral afferents to the spinal cord: possible implications for diabetic polyneuropathy in streptozotocin-induced type 1 diabetic rats

    Directory of Open Access Journals (Sweden)

    Zhen-Zhen eKou

    2014-01-01

    Full Text Available Diabetic polyneuropathy (DPN presents as a wide variety of sensorimotor symptoms and affects approximately 50% of diabetic patients. Changes in the neural circuits may occur in the early stages in diabetes and are implicated in the development of DPN. Therefore, we aimed to detect changes in the expression of isolectin B4 (IB4, the marker for nonpeptidergic unmyelinated fibers and their cell bodies and calcitonin gene-related peptide (CGRP, the marker for peptidergic fibers and their cell bodies in the dorsal root ganglion (DRG and spinal cord of streptozotocin (STZ-induced type 1 diabetic rats showing alterations in sensory and motor function. We also used cholera toxin B subunit (CTB to show the morphological changes of the myelinated fibers and motor neurons. STZ-induced diabetic rats exhibited hyperglycemia, decreased body weight gain, mechanical allodynia and impaired locomotor activity. In the DRG and spinal dorsal horn, IB4-labeled structures decreased, but both CGRP immunostaining and CTB labeling increased from day 14 to day 28 in diabetic rats. In spinal ventral horn, CTB labeling decreased in motor neurons in diabetic rats. Treatment with intrathecal injection of insulin at the early stages of DPN could alleviate mechanical allodynia and impaired locomotor activity in diabetic rats. The results suggest that the alterations of the neural circuits between spinal nerve and spinal cord via the DRG and ventral root might be involved in DPN.

  20. 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.

  1. Lumped modeling with circuit elements for nonreciprocal magnetoelectric tunable band-pass filter

    Science.gov (United States)

    Li, Xiao-Hong; Zhou, Hao-Miao; Zhang, Qiu-shi; Hu, Wen-Wen

    2016-11-01

    This paper presents a lumped equivalent circuit model of the nonreciprocal magnetoelectric tunable microwave band-pass filter. The reciprocal coupled-line circuit is based on the converse magnetoelectric effect of magnetoelectric composites, includes the electrical tunable equivalent factor of the piezoelectric layer, and is established by the introduced lumped elements, such as radiation capacitance, radiation inductance, and coupling inductance, according to the transmission characteristics of the electromagnetic wave and magnetostatic wave in an inverted-L-shaped microstrip line and ferrite slab. The nonreciprocal transmission property of the filter is described by the introduced T-shaped circuit containing controlled sources. Finally, the lumped equivalent circuit of a nonreciprocal magnetoelectric tunable microwave band-pass filter is given and the lumped parameters are also expressed. When the deviation angles of the ferrite slab are respectively 0° and 45°, the corresponding magnetoelectric devices are respectively a reciprocal device and a nonreciprocal device. The curves of S parameter obtained by the lumped equivalent circuit model and electromagnetic simulation are in good agreement with the experimental results. When the deviation angle is between 0° and 45°, the maximum value of the S parameter predicted by the lumped equivalent circuit model is in good agreement with the experimental result. The comparison results of the paper show that the lumped equivalent circuit model is valid. Further, the effect of some key material parameters on the performance of devices is predicted by the lumped equivalent circuit model. The research can provide the theoretical basis for the design and application of nonreciprocal magnetoelectric tunable devices. Project supported by the National Natural Science Foundation of China (Grant Nos. 11172285, 11472259, and 11302217) and the Natural Science Foundation of Zhejiang Province, China (Grant No. LR13A020002).

  2. Modelling and control PEMFC using fuzzy neural networks

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    Proton exchange membrane generation technology is highly efficient, clean and considered as the most hopeful "green" power technology. The operating principles of proton exchange membrane fuel cell (PEMFC) system involve thermodynamics, electrochemistry, hydrodynamics and mass transfer theory, which comprise a complex nonlinear system, for which it is difficult to establish a mathematical model and control online. This paper first simply analyzes the characters of the PEMFC; and then uses the approach and self-study ability of artificial neural networks to build the model of the nonlinear system, and uses the adaptive neural-networks fuzzy infer system (ANFIS) to build the temperature model of PEMFC which is used as the reference model of the control system, and adjusts the model parameters to control it online. The model and control are implemented in SIMULINK environment. Simulation results showed that the test data and model agreed well, so it will be very useful for optimal and real-time control of PEMFC system.

  3. New equivalent-electrical circuit model and a practical measurement method for human body impedance.

    Science.gov (United States)

    Chinen, Koyu; Kinjo, Ichiko; Zamami, Aki; Irei, Kotoyo; Nagayama, Kanako

    2015-01-01

    Human body impedance analysis is an effective tool to extract electrical information from tissues in the human body. This paper presents a new measurement method of impedance using armpit electrode and a new equivalent circuit model for the human body. The lowest impedance was measured by using an LCR meter and six electrodes including armpit electrodes. The electrical equivalent circuit model for the cell consists of resistance R and capacitance C. The R represents electrical resistance of the liquid of the inside and outside of the cell, and the C represents high frequency conductance of the cell membrane. We propose an equivalent circuit model which consists of five parallel high frequency-passing CR circuits. The proposed equivalent circuit represents alpha distribution in the impedance measured at a lower frequency range due to ion current of the outside of the cell, and beta distribution at a high frequency range due to the cell membrane and the liquid inside cell. The calculated values by using the proposed equivalent circuit model were consistent with the measured values for the human body impedance.

  4. Circuital Model for Post Coupler Stabilization in a Drift Tube Linac

    CERN Document Server

    Grespan, F; Ramberger, S; Vretenar, M

    2010-01-01

    Linac4 Drift Tube Linac (DTL) cavities will be equipped with Post Couplers (PCs) for field stabilization. The study presented in this paper starts with the analysis of 2D and 3D simulations of post couplers in order to develop an equivalent circuit model which can explain the post coupler stabilization working principle and define a tuning strategy for DTL cavities. Simulations and equivalent circuit results have been verified by measurements on the Linac4 DTL prototypes at CERN.

  5. Quasi-linear vacancy dynamics modeling and circuit analysis of the bipolar memristor.

    Science.gov (United States)

    Abraham, Isaac

    2014-01-01

    The quasi-linear transport equation is investigated for modeling the bipolar memory resistor. The solution accommodates vacancy and circuit level perspectives on memristance. For the first time in literature the component resistors that constitute the contemporary dual variable resistor circuit model are quantified using vacancy parameters and derived from a governing partial differential equation. The model describes known memristor dynamics even as it generates new insight about vacancy migration, bottlenecks to switching speed and elucidates subtle relationships between switching resistance range and device parameters. The model is shown to comply with Chua's generalized equations for the memristor. Independent experimental results are used throughout, to validate the insights obtained from the model. The paper concludes by implementing a memristor-capacitor filter and compares its performance to a reference resistor-capacitor filter to demonstrate that the model is usable for practical circuit analysis.

  6. Superconducting high current magnetic Circuit: Design and Parameter Estimation of a Simulation Model

    CERN Document Server

    Kiefer, Alexander; Reich, Werner Dr

    The Large Hadron Collider (LHC) utilizes superconducting main dipole magnets that bend the trajectory of the particle beams. In order to adjust the not completely homogeneous magnetic feld of the main dipole magnets, amongst others, sextupole correctcorrector magnets are used. In one of the 16 corrector magnet circuits placed in the LHC, 154 of these sextupole corrector magnets (MCS) are connected in series. This circuit extends on a 3.35 km tunnel section of the LHC. In 2015, at one of the 16 circuits a fault was detected. The simulation of this circuit is helpful for fnding the fault by applying alternating current at different frequencies. Within this Thesis a PSpice model for the simulation of the superconducting corrector magnet circuit was designed. The physical properties of the circuit and its elements were analyzed and implemented. For the magnets and bus-bars, sub-circuits were created which reflect the parasitic effects of electrodynamics and electrostats. The inductance values and capacitance valu...

  7. Modeling of methane emissions using artificial neural network approach

    Directory of Open Access Journals (Sweden)

    Stamenković Lidija J.

    2015-01-01

    Full Text Available The aim of this study was to develop a model for forecasting CH4 emissions at the national level, using Artificial Neural Networks (ANN with broadly available sustainability, economical and industrial indicators as their inputs. ANN modeling was performed using two different types of architecture; a Backpropagation Neural Network (BPNN and a General Regression Neural Network (GRNN. A conventional multiple linear regression (MLR model was also developed in order to compare model performance and assess which model provides the best results. ANN and MLR models were developed and tested using the same annual data for 20 European countries. The ANN model demonstrated very good performance, significantly better than the MLR model. It was shown that a forecast of CH4 emissions at the national level using the ANN model can be made successfully and accurately for a future period of up to two years, thereby opening the possibility to apply such a modeling technique which can be used to support the implementation of sustainable development strategies and environmental management policies. [Projekat Ministarstva nauke Republike Srbije, br. 172007

  8. Models of Acetylcholine and Dopamine Signals Differentially Improve Neural Representations

    Science.gov (United States)

    Holca-Lamarre, Raphaël; Lücke, Jörg; Obermayer, Klaus

    2017-01-01

    Biological and artificial neural networks (ANNs) represent input signals as patterns of neural activity. In biology, neuromodulators can trigger important reorganizations of these neural representations. For instance, pairing a stimulus with the release of either acetylcholine (ACh) or dopamine (DA) evokes long lasting increases in the responses of neurons to the paired stimulus. The functional roles of ACh and DA in rearranging representations remain largely unknown. Here, we address this question using a Hebbian-learning neural network model. Our aim is both to gain a functional understanding of ACh and DA transmission in shaping biological representations and to explore neuromodulator-inspired learning rules for ANNs. We model the effects of ACh and DA on synaptic plasticity and confirm that stimuli coinciding with greater neuromodulator activation are over represented in the network. We then simulate the physiological release schedules of ACh and DA. We measure the impact of neuromodulator release on the network's representation and on its performance on a classification task. We find that ACh and DA trigger distinct changes in neural representations that both improve performance. The putative ACh signal redistributes neural preferences so that more neurons encode stimulus classes that are challenging for the network. The putative DA signal adapts synaptic weights so that they better match the classes of the task at hand. Our model thus offers a functional explanation for the effects of ACh and DA on cortical representations. Additionally, our learning algorithm yields performances comparable to those of state-of-the-art optimisation methods in multi-layer perceptrons while requiring weaker supervision signals and interacting with synaptically-local weight updates. PMID:28690509

  9. Mechanistic Modeling of Genetic Circuits for ArsR Arsenic Regulation.

    Science.gov (United States)

    Berset, Yves; Merulla, Davide; Joublin, Aurélie; Hatzimanikatis, Vassily; van der Meer, Jan R

    2017-02-24

    Bioreporters are living cells that generate an easily measurable signal in the presence of a chemical compound. They acquire their functionality from synthetic gene circuits, the configuration of which defines the response signal and signal-to-noise ratio. Bioreporters based on the Escherichia coli ArsR system have raised significant interest for quantifying arsenic pollution, but they need to be carefully optimized to accurately work in the required low concentration range (1-10 μg arsenite L(-1)). To better understand the general functioning of ArsR-based genetic circuits, we developed a comprehensive mechanistic model that was empirically tested and validated in E. coli carrying different circuit configurations. The model accounts for the different elements in the circuits (proteins, DNA, chemical species), and their detailed affinities and interactions, and predicts the (fluorescent) output from the bioreporter cell as a function of arsenite concentration. The model was parametrized using existing ArsR biochemical data, and then complemented by parameter estimations from the accompanying experimental data using a scatter search algorithm. Model predictions and experimental data were largely coherent for feedback and uncoupled circuit configurations, different ArsR alleles, promoter strengths, and presence or absence of arsenic efflux in the bioreporters. Interestingly, the model predicted a particular useful circuit variant having steeper response at low arsenite concentrations, which was experimentally confirmed and may be useful as arsenic bioreporter in the field. From the extensive validation we expect the mechanistic model to further be a useful framework for detailed modeling of other synthetic circuits.

  10. A comprehensive equivalent circuit model of all-vanadium redox flow battery for power system analysis

    Science.gov (United States)

    Zhang, Yu; Zhao, Jiyun; Wang, Peng; Skyllas-Kazacos, Maria; Xiong, Binyu; Badrinarayanan, Rajagopalan

    2015-09-01

    Electrical equivalent circuit models demonstrate excellent adaptability and simplicity in predicting the electrical dynamic response of the all-vanadium redox flow battery (VRB) system. However, only a few publications that focus on this topic are available. The paper presents a comprehensive equivalent circuit model of VRB for system level analysis. The least square method is used to identify both steady-state and dynamic characteristics of VRB. The inherent features of the flow battery such as shunt current, ion diffusion and pumping energy consumption are also considered. The proposed model consists of an open-circuit voltage source, two parasitic shunt bypass circuits, a 1st order resistor-capacitor network and a hydraulic circuit model. Validated with experimental data, the proposed model demonstrates excellent accuracy. The mean-error of terminal voltage and pump consumption are 0.09 V and 0.49 W respectively. Based on the proposed model, self-discharge and system efficiency are studied. An optimal flow rate which maximizes the system efficiency is identified. Finally, the dynamic responses of the proposed VRB model under step current profiles are presented. Variables such as SOC and stack terminal voltage can be provided.

  11. Batch Process Modelling and Optimal Control Based on Neural Network Models

    Institute of Scientific and Technical Information of China (English)

    Jie Zhang

    2005-01-01

    This paper presents several neural network based modelling, reliable optimal control, and iterative learning control methods for batch processes. In order to overcome the lack of robustness of a single neural network, bootstrap aggregated neural networks are used to build reliable data based empirical models. Apart from improving the model generalisation capability, a bootstrap aggregated neural network can also provide model prediction confidence bounds. A reliable optimal control method by incorporating model prediction confidence bounds into the optimisation objective function is presented. A neural network based iterative learning control strategy is presented to overcome the problem due to unknown disturbances and model-plant mismatches. The proposed methods are demonstrated on a simulated batch polymerisation process.

  12. Improved neural network modeling of inverse lens distortion

    CSIR Research Space (South Africa)

    De Villiers, JP

    2011-04-01

    Full Text Available Inverse lens distortion modelling allows one to find the pixel in a distorted image which corresponds to a known point in object space, such as may be produced by a RADAR. This paper extends recent work using neural networks as a compromise between...

  13. THE USE OF NEURAL NETWORK TECHNOLOGY TO MODEL SWIMMING PERFORMANCE

    Directory of Open Access Journals (Sweden)

    António José Silva

    2007-03-01

    Full Text Available The aims of the present study were: to identify the factors which are able to explain the performance in the 200 meters individual medley and 400 meters front crawl events in young swimmers, to model the performance in those events using non-linear mathematic methods through artificial neural networks (multi-layer perceptrons and to assess the neural network models precision to predict the performance. A sample of 138 young swimmers (65 males and 73 females of national level was submitted to a test battery comprising four different domains: kinanthropometric evaluation, dry land functional evaluation (strength and flexibility, swimming functional evaluation (hydrodynamics, hydrostatic and bioenergetics characteristics and swimming technique evaluation. To establish a profile of the young swimmer non-linear combinations between preponderant variables for each gender and swim performance in the 200 meters medley and 400 meters font crawl events were developed. For this purpose a feed forward neural network was used (Multilayer Perceptron with three neurons in a single hidden layer. The prognosis precision of the model (error lower than 0.8% between true and estimated performances is supported by recent evidence. Therefore, we consider that the neural network tool can be a good approach in the resolution of complex problems such as performance modeling and the talent identification in swimming and, possibly, in a wide variety of sports

  14. Introducing Artificial Neural Networks through a Spreadsheet Model

    Science.gov (United States)

    Rienzo, Thomas F.; Athappilly, Kuriakose K.

    2012-01-01

    Business students taking data mining classes are often introduced to artificial neural networks (ANN) through point and click navigation exercises in application software. Even if correct outcomes are obtained, students frequently do not obtain a thorough understanding of ANN processes. This spreadsheet model was created to illuminate the roles of…

  15. A Constructive Neural-Network Approach to Modeling Psychological Development

    Science.gov (United States)

    Shultz, Thomas R.

    2012-01-01

    This article reviews a particular computational modeling approach to the study of psychological development--that of constructive neural networks. This approach is applied to a variety of developmental domains and issues, including Piagetian tasks, shift learning, language acquisition, number comparison, habituation of visual attention, concept…

  16. A model of interval timing by neural integration.

    Science.gov (United States)

    Simen, Patrick; Balci, Fuat; de Souza, Laura; Cohen, Jonathan D; Holmes, Philip

    2011-06-22

    We show that simple assumptions about neural processing lead to a model of interval timing as a temporal integration process, in which a noisy firing-rate representation of time rises linearly on average toward a response threshold over the course of an interval. Our assumptions include: that neural spike trains are approximately independent Poisson processes, that correlations among them can be largely cancelled by balancing excitation and inhibition, that neural populations can act as integrators, and that the objective of timed behavior is maximal accuracy and minimal variance. The model accounts for a variety of physiological and behavioral findings in rodents, monkeys, and humans, including ramping firing rates between the onset of reward-predicting cues and the receipt of delayed rewards, and universally scale-invariant response time distributions in interval timing tasks. It furthermore makes specific, well-supported predictions about the skewness of these distributions, a feature of timing data that is usually ignored. The model also incorporates a rapid (potentially one-shot) duration-learning procedure. Human behavioral data support the learning rule's predictions regarding learning speed in sequences of timed responses. These results suggest that simple, integration-based models should play as prominent a role in interval timing theory as they do in theories of perceptual decision making, and that a common neural mechanism may underlie both types of behavior.

  17. Pragmatic Bootstrapping: A Neural Network Model of Vocabulary Acquisition

    Science.gov (United States)

    Caza, Gregory A.; Knott, Alistair

    2012-01-01

    The social-pragmatic theory of language acquisition proposes that children only become efficient at learning the meanings of words once they acquire the ability to understand the intentions of other agents, in particular the intention to communicate (Akhtar & Tomasello, 2000). In this paper we present a neural network model of word learning which…

  18. Bilingual Lexical Interactions in an Unsupervised Neural Network Model

    Science.gov (United States)

    Zhao, Xiaowei; Li, Ping

    2010-01-01

    In this paper we present an unsupervised neural network model of bilingual lexical development and interaction. We focus on how the representational structures of the bilingual lexicons can emerge, develop, and interact with each other as a function of the learning history. The results show that: (1) distinct representations for the two lexicons…

  19. A Constructive Neural-Network Approach to Modeling Psychological Development

    Science.gov (United States)

    Shultz, Thomas R.

    2012-01-01

    This article reviews a particular computational modeling approach to the study of psychological development--that of constructive neural networks. This approach is applied to a variety of developmental domains and issues, including Piagetian tasks, shift learning, language acquisition, number comparison, habituation of visual attention, concept…

  20. Pragmatic Bootstrapping: A Neural Network Model of Vocabulary Acquisition

    Science.gov (United States)

    Caza, Gregory A.; Knott, Alistair

    2012-01-01

    The social-pragmatic theory of language acquisition proposes that children only become efficient at learning the meanings of words once they acquire the ability to understand the intentions of other agents, in particular the intention to communicate (Akhtar & Tomasello, 2000). In this paper we present a neural network model of word learning which…