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Sample records for active neural circuits

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

    Kreiner, David S.

    2012-01-01

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

  2. Genetic control of active neural circuits

    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.

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

    Jennings, Joshua H.; Stuber, Garret D.

    2014-01-01

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

  4. Activity-dependent modulation of neural circuit synaptic connectivity

    Tessier, Charles R.; Kendal Broadie

    2009-01-01

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

  5. Activity-dependent modulation of neural circuit synaptic connectivity

    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.

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

    Caleb Andrew Doll; Kendal eBroadie

    2014-01-01

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

  7. Demultiplexer circuit for neural stimulation

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

    2012-10-09

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

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

    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.

  9. Auto-programmable impulse neural circuits

    Watula, D.; Meador, J.

    1990-01-01

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

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

    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.

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

    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.

  12. Classes of feedforward neural networks and their circuit complexity

    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 well-studied hierarchy of boolean circuits. Results cover both classes of sigmoid activation function networks and linear threshold networks. This provides a much needed theoretical basis for the stu...

  13. Genetic dissection of GABAergic neural circuits in mouse neocortex

    Taniguchi, Hiroki

    2014-01-01

    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 interneurons display a high degree of cellular diversity in morphology, physiology, connectivity, and gene expression. A considerable number of subtypes of GABAergic...

  14. Genetic dissection of GABAergic neural circuits in mouse neocortex

    Hiroki Taniguchi

    2014-01-01

    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 GABAer...

  15. Neurotrophin regulation of neural circuit development and function.

    Park, Hyungju; Poo, Mu-ming

    2013-01-01

    Brain-derived neurotrophic factor (BDNF)--a member of a small family of secreted proteins that includes nerve growth factor, neurotrophin 3 and neurotrophin 4--has emerged as a key regulator of neural circuit development and function. The expression, secretion and actions of BDNF are directly controlled by neural activity, and secreted BDNF is capable of mediating many activity-dependent processes in the mammalian brain, including neuronal differentiation and growth, synapse formation and plasticity, and higher cognitive functions. This Review summarizes some of the recent progress in understanding the cellular and molecular mechanisms underlying neurotrophin regulation of neural circuits. The focus of the article is on BDNF, as this is the most widely expressed and studied neurotrophin in the mammalian brain. PMID:23254191

  16. Electronic circuits modeling using artificial neural networks

    Andrejević Miona V.

    2003-01-01

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

  17. Tic disorders: neural circuits, neurochemistry, and neuroimmunology.

    Harris, Kendra; Singer, Harvey S

    2006-08-01

    The neuroanatomy and neurochemistry underlying tic disorders are thought to involve corticostriatothalamocortical circuits and dysregulation of their component neurotransmitter systems. Tourette syndrome is a tic disorder that begins in childhood and follows a waxing and waning course of tic severity. Although it is generally believed to have a genetic component, its etiology has not been fully elucidated. The clinical entity pediatric autoimmune neuropsychiatric disorders associated with streptococcal infection (PANDAS) has led some to suggest that the pathophysiology of tics in some individuals might involve a postinfectious autoimmune component. We review the neural circuits and neurochemistry of Tourette syndrome and evaluate the evidence for and against a role for autoimmunity in the expression of tics. PMID:16970869

  18. Stable chaos in fluctuation driven neural circuits

    Highlights: • Nonlinear instabilities in fluctuation driven (balanced) neural circuits are studied. • Balanced networks display chaos and stable phases at different post-synaptic widths. • Linear instabilities coexists with nonlinear ones in the chaotic regime. • Erratic motion appears also in linearly stable phase due to stable chaos. - Abstract: We study the dynamical stability of pulse coupled networks of leaky integrate-and-fire neurons against infinitesimal and finite perturbations. In particular, we compare mean versus fluctuations driven networks, the former (latter) is realized by considering purely excitatory (inhibitory) sparse neural circuits. In the excitatory case the instabilities of the system can be completely captured by an usual linear stability (Lyapunov) analysis, whereas the inhibitory networks can display the coexistence of linear and nonlinear instabilities. The nonlinear effects are associated to finite amplitude instabilities, which have been characterized in terms of suitable indicators. For inhibitory coupling one observes a transition from chaotic to non chaotic dynamics by decreasing the pulse-width. For sufficiently fast synapses the system, despite showing an erratic evolution, is linearly stable, thus representing a prototypical example of stable chaos

  19. Genetic dissection of GABAergic neural circuits in mouse neocortex

    Hiroki Taniguchi

    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.

  20. Noise Delays Bifurcation in a Positively Coupled Neural Circuit

    Gutkin, Boris; Hely, Tim; Jost, Juergen

    2000-01-01

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

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

    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.

  2. A neural circuit for angular velocity computation.

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

    2010-01-01

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

  3. A neural circuit for angular velocity computation

    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.

  4. A neuromime system for neural circuit analysis.

    Mitchell, C E; Friesen, W O

    1981-01-01

    A system of electronic analog neurons (neuromimes) for modeling the activity in small neuronal networks is described. The system consists of sixteen analogs that simulate the integrative neuronal properties at the axon hillock and sixty-four analogs that serve to simulate synaptic interactions. The neuromime properties are based on a potential model incorporating the following properties: membrane potential, threshold, refractory period, adaptation, post-inhibitory rebound, accommodation and pacemaker potential. Use of matrix switch boards provides for convenient interconnection of the neuromime elements, allowing the construction of even complex circuits. PMID:7236753

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

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

    2015-01-01

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

  6. The neural circuit basis of learning

    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

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

    Doudkin, A. A.

    2016-01-01

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

  8. Diagnostic Neural Network Systems for the Electronic Circuits

    Neural Networks is one of the most important artificial intelligent approaches for solving the diagnostic processes. This research concerns with uses the neural networks for diagnosis of the electronic circuits. Modern electronic systems contain both the analog and digital circuits. But, diagnosis of the analog circuits suffers from great complexity due to their nonlinearity. To overcome this problem, the proposed system introduces a diagnostic system that uses the neural network to diagnose both the digital and analog circuits. So, it can face the new requirements for the modern electronic systems. A fault dictionary method was implemented in the system. Experimental results are presented on three electronic systems. They are: artificial kidney, wireless network and personal computer systems. The proposed system has improved the performance of the diagnostic systems when applied for these practical cases

  9. Analog VLSI neural network integrated circuits

    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.

  10. Genetic identification of a neural circuit that suppresses appetite.

    Carter, Matthew E; Soden, Marta E; Zweifel, Larry S; Palmiter, Richard D

    2013-11-01

    Appetite suppression occurs after a meal and in conditions when it is unfavourable to eat, such as during illness or exposure to toxins. A brain region proposed to play a role in appetite suppression is the parabrachial nucleus, a heterogeneous population of neurons surrounding the superior cerebellar peduncle in the brainstem. The parabrachial nucleus is thought to mediate the suppression of appetite induced by the anorectic hormones amylin and cholecystokinin, as well as by lithium chloride and lipopolysaccharide, compounds that mimic the effects of toxic foods and bacterial infections, respectively. Hyperactivity of the parabrachial nucleus is also thought to cause starvation after ablation of orexigenic agouti-related peptide neurons in adult mice. However, the identities of neurons in the parabrachial nucleus that regulate feeding are unknown, as are the functionally relevant downstream projections. Here we identify calcitonin gene-related peptide-expressing neurons in the outer external lateral subdivision of the parabrachial nucleus that project to the laterocapsular division of the central nucleus of the amygdala as forming a functionally important circuit for suppressing appetite. Using genetically encoded anatomical, optogenetic and pharmacogenetic tools, we demonstrate that activation of these neurons projecting to the central nucleus of the amygdala suppresses appetite. In contrast, inhibition of these neurons increases food intake in circumstances when mice do not normally eat and prevents starvation in adult mice whose agouti-related peptide neurons are ablated. Taken together, our data demonstrate that this neural circuit from the parabrachial nucleus to the central nucleus of the amygdala mediates appetite suppression in conditions when it is unfavourable to eat. This neural circuit may provide targets for therapeutic intervention to overcome or promote appetite. PMID:24121436

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

    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.

  12. Sleep quality and neural circuit function supporting emotion regulation

    Minkel Jared D

    2012-12-01

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

  13. Fully integrated circuit chip of microelectronic neural bridge

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

  14. Synchrony and neural coding in cerebellar circuits

    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

  15. Computational Aspects of Feedback in Neural Circuits

    Wolfgang Maass; Prashant Joshi; Sontag, Eduardo D.

    2007-01-01

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

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

    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...... chip to solve simple classical conditioning tasks, thus verifying the design methodologies put forward in the paper....

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

    Kevin J Tracey

    2012-01-01

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

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

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

    2014-01-01

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

  19. Implementing neural architectures using analog VLSI circuits

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

    1989-01-01

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

  20. Neural circuit mechanisms of short-term memory

    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.

  1. TrakEM2 software for neural circuit reconstruction.

    Albert Cardona

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

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

    Neihart, Nathan M; Harrison, Reid R

    2005-11-01

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

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

    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.

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

    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.

  5. Neural Networks Integrated Circuit for Biomimetics MEMS Microrobot

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

    2014-01-01

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

  6. Derivation of Neural Circuits from the Similarity Matching Principle

    Pehlevan, Cengiz; Chklovskii, Dmitri

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

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

    Sanda, Pavel; Marsalek, Petr

    2012-01-24

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

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

    Williams, Leanne M

    2016-05-01

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

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

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

    2014-12-01

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

  10. X-ray microscopy for neural circuit reconstruction

    Mizutani, Haruo; Takagi, Toshihisa [Science Integration Program, University of Tokyo, Chiba (Japan); Takeda, Yoshihiro; Momose, Atsushi [Department of Advanced Materials Science, University of Tokyo, Chiba (Japan); Takeuchi, Akihisa, E-mail: mizutani@dpc.scint.u-tokyo.ac.j [Japan Synchrotron Radiation Research Institute/SPring-8, Hyogo (Japan)

    2009-09-01

    Neural circuits in the central nervous system build our various higher brain functions. However, little is known about mechanisms underlying neuronal information processing in the brain. Anatomical graph structures of real neural networks will provide us with fundamental views to elucidate them. We aim at developing a three-dimensional atlas of neural circuits using high resolution hard X-ray microscopy by synchrotron radiation. We stained neurons of a complete brain from a mouse by the Golgi-Cox method. The heavy metals used in our procedure enhanced X-ray absorption and phase contrast. 3D images of fibriform axons and dendrites of various neurons were reconstructed by back projection. X-ray microscopy with a Talbot interferometer demonstrated finer histological structures of pyramidal neurons in the hippocampus and the cerebral cortex. This observation probably serves as a foundation for achieving a mammalian Connectome Project (identifying complete wiring diagrams of the brain) with X-ray nano-tomography in the near future.

  11. X-ray microscopy for neural circuit reconstruction

    Neural circuits in the central nervous system build our various higher brain functions. However, little is known about mechanisms underlying neuronal information processing in the brain. Anatomical graph structures of real neural networks will provide us with fundamental views to elucidate them. We aim at developing a three-dimensional atlas of neural circuits using high resolution hard X-ray microscopy by synchrotron radiation. We stained neurons of a complete brain from a mouse by the Golgi-Cox method. The heavy metals used in our procedure enhanced X-ray absorption and phase contrast. 3D images of fibriform axons and dendrites of various neurons were reconstructed by back projection. X-ray microscopy with a Talbot interferometer demonstrated finer histological structures of pyramidal neurons in the hippocampus and the cerebral cortex. This observation probably serves as a foundation for achieving a mammalian Connectome Project (identifying complete wiring diagrams of the brain) with X-ray nano-tomography in the near future.

  12. Activity-dependent neural plasticity from bench to bedside.

    Ganguly, Karunesh; Poo, Mu-Ming

    2013-10-30

    Much progress has been made in understanding how behavioral experience and neural activity can modify the structure and function of neural circuits during development and in the adult brain. Studies of physiological and molecular mechanisms underlying activity-dependent plasticity in animal models have suggested potential therapeutic approaches for a wide range of brain disorders in humans. Physiological and electrical stimulations as well as plasticity-modifying molecular agents may facilitate functional recovery by selectively enhancing existing neural circuits or promoting the formation of new functional circuits. Here, we review the advances in basic studies of neural plasticity mechanisms in developing and adult nervous systems and current clinical treatments that harness neural plasticity, and we offer perspectives on future development of plasticity-based therapy. PMID:24183023

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

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

    2003-01-01

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

  14. Communication in Neural Circuits: Tools, Opportunities, and Challenges.

    Lerner, Talia N; Ye, Li; Deisseroth, Karl

    2016-03-10

    Communication, the effective delivery of information, is fundamental to life across all scales and species. Nervous systems (by necessity) may be most specifically adapted among biological tissues for high rate and complexity of information transmitted, and thus, the properties of neural tissue and principles of its organization into circuits may illuminate capabilities and limitations of biological communication. Here, we consider recent developments in tools for studying neural circuits with particular attention to defining neuronal cell types by input and output information streams-i.e., by how they communicate. Complementing approaches that define cell types by virtue of genetic promoter/enhancer properties, this communication-based approach to defining cell types operationally by input/output (I/O) relationships links structure and function, resolves difficulties associated with single-genetic-feature definitions, leverages technology for observing and testing significance of precisely these I/O relationships in intact brains, and maps onto processes through which behavior may be adapted during development, experience, and evolution. PMID:26967281

  15. Neural circuits mediating olfactory-driven behavior in fish

    Florence Kermen

    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.

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

    Chang Hao Chen

    2014-01-01

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

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

    Dejan Pecevski; Thomas Natschläger; Klaus Schuch

    2009-01-01

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

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

    Pecevski, Dejan; Natschläger, Thomas; Schuch, Klaus

    2009-01-01

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

  19. Functional neuroanatomy of anxiety: a neural circuit perspective.

    Etkin, Amit

    2010-01-01

    Anxiety is a commonly experienced subjective state that can have both adaptive and maladaptive properties. Clinical disorders of anxiety are likewise also common, and range widely in their symptomatology and consequences for the individual. Cognitive neuroscience has provided an increasingly sophisticated understanding of the processes underlying normal human emotion, and its disruption or dysregulation in clinical anxiety disorders. In this chapter, I review functional neuroimaging studies of emotion in healthy and anxiety-disordered populations. A limbic-medial prefrontal circuit is emphasized and an information processing model is proposed for the processing of negative emotion. Data on negative emotion processing in a variety of anxiety disorders are presented and integrated within an understanding of the functions of elements within the limbic-medial prefrontal circuit. These data suggest that anxiety disorders may be usefully conceptualized as differentially affecting emotional reactivity and regulatory processes--functions that involve different neurobiological mechanisms. While the neural bases of several anxiety disorders are increasingly better understood, advances have lagged significantly behind in others. Nonetheless, the conceptual framework provided by convergent findings in studies of emotional processing in normative and anxiety-disordered populations promises to yield continued insights and nuances, and will likely provide useful information in the search for etiology and novel treatments. PMID:21309113

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

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

    2015-01-01

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

  1. Neural Networks Integrated Circuit for Biomimetics MEMS Microrobot

    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. Shaping Neural Circuits by High Order Synaptic Interactions

    Ravid Tannenbaum, Neta; Burak, Yoram

    2016-01-01

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

  3. Lag Synchronization of Switched Neural Networks via Neural Activation Function and Applications in Image Encryption.

    Wen, Shiping; Zeng, Zhigang; Huang, Tingwen; Meng, Qinggang; Yao, Wei

    2015-07-01

    This paper investigates the problem of global exponential lag synchronization of a class of switched neural networks with time-varying delays via neural activation function and applications in image encryption. The controller is dependent on the output of the system in the case of packed circuits, since it is hard to measure the inner state of the circuits. Thus, it is critical to design the controller based on the neuron activation function. Comparing the results, in this paper, with the existing ones shows that we improve and generalize the results derived in the previous literature. Several examples are also given to illustrate the effectiveness and potential applications in image encryption. PMID:25594985

  4. Active components for integrated plasmonic circuits

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

    2009-01-01

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

  5. Technologies for imaging neural activity in large volumes.

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

    2016-08-26

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

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

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

    2013-08-22

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

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

    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.

  8. Large-scale multielectrode recording and stimulation of neural activity

    Large circuits of neurons are employed by the brain to encode and process information. How this encoding and processing is carried out is one of the central questions in neuroscience. Since individual neurons communicate with each other through electrical signals (action potentials), the recording of neural activity with arrays of extracellular electrodes is uniquely suited for the investigation of this question. Such recordings provide the combination of the best spatial (individual neurons) and temporal (individual action-potentials) resolutions compared to other large-scale imaging methods. Electrical stimulation of neural activity in turn has two very important applications: it enhances our understanding of neural circuits by allowing active interactions with them, and it is a basis for a large variety of neural prosthetic devices. Until recently, the state-of-the-art in neural activity recording systems consisted of several dozen electrodes with inter-electrode spacing ranging from tens to hundreds of microns. Using silicon microstrip detector expertise acquired in the field of high-energy physics, we created a unique neural activity readout and stimulation framework that consists of high-density electrode arrays, multi-channel custom-designed integrated circuits, a data acquisition system, and data-processing software. Using this framework we developed a number of neural readout and stimulation systems: (1) a 512-electrode system for recording the simultaneous activity of as many as hundreds of neurons, (2) a 61-electrode system for electrical stimulation and readout of neural activity in retinas and brain-tissue slices, and (3) a system with telemetry capabilities for recording neural activity in the intact brain of awake, naturally behaving animals. We will report on these systems, their various applications to the field of neurobiology, and novel scientific results obtained with some of them. We will also outline future directions

  9. Large-scale multielectrode recording and stimulation of neural activity

    Sher, A. [Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, CA (United States)], E-mail: sasha@scipp.ucsc.edu; Chichilnisky, E.J. [Systems Neurobiology, Salk Institute, La Jolla, CA (United States); Dabrowski, W. [AGH University of Science and Technology, Cracow (Poland); Grillo, A.A.; Grivich, M. [Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, CA (United States); Gunning, D. [University of Glasgow, Glasgow (United Kingdom); Hottowy, P. [AGH University of Science and Technology, Cracow (Poland); Kachiguine, S.; Litke, A.M. [Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, CA (United States); Mathieson, K. [University of Glasgow, Glasgow (United Kingdom); Petrusca, D. [Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, CA (United States)

    2007-09-01

    Large circuits of neurons are employed by the brain to encode and process information. How this encoding and processing is carried out is one of the central questions in neuroscience. Since individual neurons communicate with each other through electrical signals (action potentials), the recording of neural activity with arrays of extracellular electrodes is uniquely suited for the investigation of this question. Such recordings provide the combination of the best spatial (individual neurons) and temporal (individual action-potentials) resolutions compared to other large-scale imaging methods. Electrical stimulation of neural activity in turn has two very important applications: it enhances our understanding of neural circuits by allowing active interactions with them, and it is a basis for a large variety of neural prosthetic devices. Until recently, the state-of-the-art in neural activity recording systems consisted of several dozen electrodes with inter-electrode spacing ranging from tens to hundreds of microns. Using silicon microstrip detector expertise acquired in the field of high-energy physics, we created a unique neural activity readout and stimulation framework that consists of high-density electrode arrays, multi-channel custom-designed integrated circuits, a data acquisition system, and data-processing software. Using this framework we developed a number of neural readout and stimulation systems: (1) a 512-electrode system for recording the simultaneous activity of as many as hundreds of neurons, (2) a 61-electrode system for electrical stimulation and readout of neural activity in retinas and brain-tissue slices, and (3) a system with telemetry capabilities for recording neural activity in the intact brain of awake, naturally behaving animals. We will report on these systems, their various applications to the field of neurobiology, and novel scientific results obtained with some of them. We will also outline future directions.

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

    Kano, Masanobu

    2013-06-01

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

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

    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.

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

    Rayas-Sánchez, José E.

    2004-01-01

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

  13. Nonlocal mechanism for cluster synchronization in neural circuits

    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.

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

    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. PMID:18252623

  15. Correlation between stimulation strength and onset time of signal traveling within the neocortical neural circuits under caffeine application.

    Yoshimura, Hiroshi; Honjo, Miho; Sugai, Tokio; Kaneyama, Keiseki; Segami, Natsuki; Kato, Nobuo

    2011-08-01

    In general, strength of input to neocortical neural circuits affects the amplitude of postsynaptic potentials (PSPs), thereby modulating the way signals are transmitted within the circuits. Caffeine is one of the pharmacological agents able to modulate synaptic activities. The present study investigated how strength of input affects signal propagation in neocortical circuits under the application of caffeine. Spatio-temporal neural activities were observed from visual cortical slices of rats using optical recording methods with voltage-sensitive dye. Electrical stimulations were applied to white matter in the primary visual cortex with bath-application of caffeine. When the strength of stimulation was 0.3mA, signals propagated from the site of stimulation in the primary visual cortex toward the secondary visual cortex along vertical and horizontal pathways. When stimulation strength was reduced from 0.3mA to 0.07mA, start of signal propagation was delayed about 25ms without affecting field PSP amplitude or the manner of signal propagation. Conversely, co-application of caffeine and d-2-amino-5-phosphonovaleric acid (d-AP5) did not induce delays in signal start. These findings suggest that conversion of neural code from amplitude code to temporal code is inducible at the level of neocortical circuits in an N-methyl-d-aspartate (NMDA) receptor activity-dependent manner. PMID:21621566

  16. An improved superconducting neural circuit and its application for a neural network solving a combinatorial optimization problem

    We have proposed a superconducting Hopfield-type neural network for solving the N-Queens problem which is one of combinatorial optimization problems. The sigmoid-shape function of a neuron output is represented by the output of coupled SQUIDs gate consisting of a single-junction and a double-junction SQUIDs. One of the important factors for an improvement of the network performance is an improvement of a threshold characteristic of a neuron circuit. In this paper, we report an improved design of coupled SQUID gates for a superconducting neural network. A step-like function with a steep threshold at a rising edge is desirable for a neuron circuit to solve a combinatorial optimization problem. A neuron circuit is composed of two coupled SQUIDs gates with a cascade connection in order to obtain such characteristics. The designed neuron circuit is fabricated by a 2.5 kA/cm2 Nb/AlOx/Nb process. The operation of a fabricated neuron circuit is experimentally demonstrated. Moreover, we discuss about the performance of the neural network using the improved neuron circuits and delayed negative self-connections.

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

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

    2004-10-01

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

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

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

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

    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

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

    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.

  1. An Implantable Mixed Analog/Digital Neural Stimulator Circuit

    Gudnason, Gunnar; Bruun, Erik; Haugland, Morten

    This paper describes a chip for a multichannel neural stimulator for functional electrical stimulation. The chip performs all the signal processing required in an implanted neural stimulator. The power and signal transmission to the stimulator is carried out via an inductive link. From the signals...

  2. Design of 3D Active Multichannel Silicon Neural Microelectrode

    WANG Di; ZHANG Guoxiong; LI Xingfei

    2006-01-01

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

  3. In search of the neural circuits of intrinsic motivation

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

  5. Neural Circuit Flexibility in a Small Sensorimotor System

    Blitz, Dawn M.; Nusbaum, Michael P.

    2011-01-01

    Neuronal circuits underlying rhythmic behaviors (central pattern generators: CPGs) can generate rhythmic motor output without sensory input. However, sensory input is pivotal for generating behaviorally relevant CPG output. Here we discuss recent work in the decapod crustacean stomatogastric nervous system (STNS) identifying cellular and synaptic mechanisms whereby sensory inputs select particular motor outputs from CPG circuits. This includes several examples in which sensory neurons regulat...

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

    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.

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

    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. PMID:17643087

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

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

    2010-01-01

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

  9. Neck muscle afferents influence oromotor and cardiorespiratory brainstem neural circuits.

    Edwards, I J; Lall, V K; Paton, J F; Yanagawa, Y; Szabo, G; Deuchars, S A; Deuchars, J

    2015-01-01

    Sensory information arising from the upper neck is important in the reflex control of posture and eye position. It has also been linked to the autonomic control of the cardiovascular and respiratory systems. Whiplash associated disorders (WAD) and cervical dystonia, which involve disturbance to the neck region, can often present with abnormalities to the oromotor, respiratory and cardiovascular systems. We investigated the potential neural pathways underlying such symptoms. Simulating neck afferent activity by electrical stimulation of the second cervical nerve in a working heart brainstem preparation (WHBP) altered the pattern of central respiratory drive and increased perfusion pressure. Tracing central targets of these sensory afferents revealed projections to the intermedius nucleus of the medulla (InM). These anterogradely labelled afferents co-localised with parvalbumin and vesicular glutamate transporter 1 indicating that they are proprioceptive. Anterograde tracing from the InM identified projections to brain regions involved in respiratory, cardiovascular, postural and oro-facial behaviours--the neighbouring hypoglossal nucleus, facial and motor trigeminal nuclei, parabrachial nuclei, rostral and caudal ventrolateral medulla and nucleus ambiguus. In brain slices, electrical stimulation of afferent fibre tracts lateral to the cuneate nucleus monosynaptically excited InM neurones. Direct stimulation of the InM in the WHBP mimicked the response of second cervical nerve stimulation. These results provide evidence of pathways linking upper cervical sensory afferents with CNS areas involved in autonomic and oromotor control, via the InM. Disruption of these neuronal pathways could, therefore, explain the dysphagic and cardiorespiratory abnormalities which may accompany cervical dystonia and WAD. PMID:24595534

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

    Ashwani Kumar Narula

    2015-06-01

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

  11. Time series cross prediction in a single transistor chaotic circuit using neural networks

    L. Magafas

    2009-01-01

    Full Text Available In this paper we will be trying to cross predict a multivariate time series of a single transistor chaotic circuit using neural networks. For this purpose we investigate the influence of a number of first and second order near neighbors in predicting chaotic time series using a back propagation neural network. This influence is examined by changing the number of neurons in the hidden layer of a backprogation neural network with one hidden layer. The number of neurons at the input layer were equal to the embedding dimension of the corresponding strange attractor.

  12. Developmental regulation of spatio-temporal patterns of cortical circuit activation

    Trevor Charles Griffen; Arianna Maffei

    2013-01-01

    Neural circuits are refined in an experience-dependent manner during early postnatal development. How development modulates the spatio-temporal propagation of activity through cortical circuits is poorly understood. Here we use voltage sensitive dye imaging (VSD) to show that there are significant changes in the spatio-temporal patterns of intracortical signals in primary visual cortex from postnatal day 13 (P13), eye opening, to P28, the peak of the critical period for rodent visual cortical...

  13. Developmental regulation of spatio-temporal patterns of cortical circuit activation

    Griffen, Trevor C.; Wang, Lang; Fontanini, Alfredo; Maffei, Arianna

    2013-01-01

    Neural circuits are refined in an experience-dependent manner during early postnatal development. How development modulates the spatio-temporal propagation of activity through cortical circuits is poorly understood. Here we use voltage-sensitive dye imaging (VSD) to show that there are significant changes in the spatio-temporal patterns of intracortical signals in primary visual cortex (V1) from postnatal day 13 (P13), eye opening, to P28, the peak of the critical period for rodent visual cor...

  14. A Neural Circuit for Spatial Summation in Visual Cortex

    Adesnik, Hillel; Bruns, William; Taniguchi, Hiroki; Huang, Z. Josh; Scanziani, Massimo

    2012-01-01

    The response of cortical neurons to a sensory stimulus is modulated by the context. In the visual cortex, for example, stimulation of a pyramidal cell's receptive field surround can attenuate the cell’s response to a stimulus in its receptive field’s center, a phenomenon called surround suppression. Whether cortical circuits contribute to surround suppression or whether the phenomenon is entirely relayed from earlier stages of visual processing is controversial. Here we discover that, in cont...

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

    Fu, C.Y.

    1997-02-11

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

  16. Activity transport models for PWR primary circuits

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

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

    Jiayi eZhang

    2011-11-01

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

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

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

    1995-01-01

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

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

    Šanda, Pavel; Maršálek, P.

    2012-01-01

    Roč. 1434, JAN 24 (2012), s. 257-265. ISSN 0006-8993. [International Workshop on Neural Coding. Limassol, 29.10.2010-03.11.2010] R&D Projects: GA ČR(CZ) GAP103/11/0282 Grant ostatní: GA MPO(CZ) FR-TI3/869 Institutional research plan: CEZ:AV0Z50110509 Keywords : coincidence detection * directional hearing * interaural time delay * sound azimuth * interpolation model Subject RIV: FH - Neurology Impact factor: 2.879, year: 2012

  20. Neural circuits of emotion regulation: a comparison of mindfulness-based and cognitive reappraisal strategies.

    Opialla, Sarah; Lutz, Jacqueline; Scherpiet, Sigrid; Hittmeyer, Anna; Jäncke, Lutz; Rufer, Michael; Grosse Holtforth, Martin; Herwig, Uwe; Brühl, Annette B

    2015-02-01

    Dealing with one's emotions is a core skill in everyday life. Effective cognitive control strategies have been shown to be neurobiologically represented in prefrontal structures regulating limbic regions. In addition to cognitive strategies, mindfulness-associated methods are increasingly applied in psychotherapy. We compared the neurobiological mechanisms of these two strategies, i.e. cognitive reappraisal and mindfulness, during both the cued expectation and perception of negative and potentially negative emotional pictures. Fifty-three healthy participants were examined with functional magnetic resonance imaging (47 participants included in analysis). Twenty-four subjects applied mindfulness, 23 used cognitive reappraisal. On the neurofunctional level, both strategies were associated with comparable activity of the medial prefrontal cortex and the amygdala. When expecting negative versus neutral stimuli, the mindfulness group showed stronger activations in ventro- and dorsolateral prefrontal cortex, supramarginal gyrus as well as in the left insula. During the perception of negative versus neutral stimuli, the two groups only differed in an increased activity in the caudate in the cognitive group. Altogether, both strategies recruited overlapping brain regions known to be involved in emotion regulation. This result suggests that common neural circuits are involved in the emotion regulation by mindfulness-based and cognitive reappraisal strategies. Identifying differential activations being associated with the two strategies in this study might be one step towards a better understanding of differential mechanisms of change underlying frequently used psychotherapeutic interventions. PMID:24902936

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

    Zhou, Zhen

    1989-01-01

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

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

    Dejan Pecevski

    2009-05-01

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

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

    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. (semiconductor integrated circuits)

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

    Juarez, Barbara; Han, Ming-Hu

    2016-09-01

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

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

    Busche, Marc Aurel; Konnerth, Arthur

    2016-08-01

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

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

    Farid N. Najm; Michael G. Xakellis

    1998-01-01

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

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

    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

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

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

    2012-06-26

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

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

    HU Mei; WANG Hong; HU Geng; YANG Shiyuan

    2007-01-01

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

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

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

    2016-03-01

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

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

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

    2015-08-01

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

  12. Fiberless multicolor neural optoelectrode for in vivo circuit analysis

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

    2016-08-01

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

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

    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.

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

    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.

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

    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.

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

    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.

  17. New Active Digital Pixel Circuit for CMOS Image Sensor

    2001-01-01

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

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

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

  19. Developmental regulation of spatio-temporal patterns of cortical circuit activation

    Trevor Charles Griffen

    2013-01-01

    Full Text Available Neural circuits are refined in an experience-dependent manner during early postnatal development. How development modulates the spatio-temporal propagation of activity through cortical circuits is poorly understood. Here we use voltage sensitive dye imaging (VSD to show that there are significant changes in the spatio-temporal patterns of intracortical signals in primary visual cortex from postnatal day 13 (P13, eye opening, to P28, the peak of the critical period for rodent visual cortical plasticity. Upon direct stimulation of layer 4 (L4, activity spreads to L2/3 and to L5 at all ages. However, while from eye opening to the peak of the critical period, the amplitude and persistence of the voltage signal decrease, peak activation is reached more quickly and the interlaminar gain increases with age. The lateral spread of activation within layers remains unchanged throughout the time window under analysis. These developmental changes in spatio-temporal patterns of intracortical circuit activation are mediated by differences in the contributions of excitatory and inhibitory synaptic components. Our results demonstrate that after eye opening the circuit in primary visual cortex is refined through a progression of changes that shape the spatio-temporal patterns of circuit activation. Signals become more efficiently propagated across layers through developmentally regulated changes in interlaminar gain.

  20. A Circuit for Motor Cortical Modulation of Auditory Cortical Activity

    Nelson, Anders; Schneider, David M.; Takatoh, Jun; Sakurai, Katsuyasu; Wang, Fan; Mooney, Richard

    2013-01-01

    Normal hearing depends on the ability to distinguish self-generated sounds from other sounds, and this ability is thought to involve neural circuits that convey copies of motor command signals to various levels of the auditory system. Although such interactions at the cortical level are believed to facilitate auditory comprehension during movements and drive auditory hallucinations in pathological states, the synaptic organization and function of circuitry linking the motor and auditory corti...

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

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

    2016-03-01

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

  2. Predicting conversion time of circuit design file by artificial neural networks

    Jang, Sung-Hoon; Lee, Jee-Hyong; Ahn, Byoung-Sup; Ki, Won-Tai; Choi, Ji-Hyeon; Woo, Sang-Gyun; Cho, Han-Ku

    2008-03-01

    GDSII is a data format of the circuit design file for producing semiconductor. GDSII is also used as a transfer format for fabricating photo mask as well. As design rules are getting smaller and RET (Resolution Enhancement Technology) is getting more complicated, the time of converting GDSII to a mask data format has been increased, which influences the period of mask production. Photo mask shops all over the world are widely using computer clusters which are connected through a network, that is, called distributed computing method, to reduce the converting time. Commonly computing resource for conversion is assigned based on the input file size. However, the result of experiments showed that the input file size was improper to predict the computing resource usage. In this paper, we propose the methodology of artificial intelligence with considering the properties of GDSII file to handle circuit design files more efficiently. The conversion time will be optimized by controlling the hardware resource for data conversion as long as the conversion time is predictable through analyzing the design data. Neural networks are used to predict the conversion time for this research. In this paper, the application of neural networks for the time prediction will be discussed and experimental results will be shown with comparing to statistical model based approaches.

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

    Zhang, Yimeng; Li, Xiong; Samonds, Jason M; Lee, Tai Sing

    2016-03-01

    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

  4. Visual experience modulates spatio-temporal dynamics of circuit activation

    Arianna Maffei

    2011-06-01

    Full Text Available Persistent reduction in sensory drive in early development results in multiple plastic changes of different cortical synapses. How these experience-dependent modifications affect the spatio-temporal dynamics of signal propagation in neocortical circuits is poorly understood. Here we demonstrate that brief visual deprivation significantly affects the propagation of electrical signals in the primary visual cortex. The spatio-temporal spread of circuit activation upon direct stimulation of its input layer (Layer 4 is reduced, as is the activation of Layer 2/3 – the main recipient of the output from Layer 4. Our data suggest that the decrease in spatio-temporal activation of L2/3 depends on reduced L4 output, and is not intrinsically generated within L2/3. The data shown here suggest that changes in the synaptic components of the visual cortical circuit result not only in alteration of local integration of excitatory and inhibitory inputs, but also in a significant decrease in overall circuit activation. Furthermore, our data indicate a differential effect of visual deprivation on L4 and L2/3, suggesting that while feedforward activation of L2/3 is reduced, its activation by long range, within layer inputs is unaltered. Thus, brief visual deprivation induces experience-dependent circuit re-organization by modulating not only circuit excitability, but also the spatio-temporal patterns of cortical activation within and between layers.

  5. Analysis of complex neural circuits with nonlinear multidimensional hidden state models.

    Friedman, Alexander; Slocum, Joshua F; Tyulmankov, Danil; Gibb, Leif G; Altshuler, Alex; Ruangwises, Suthee; Shi, Qinru; Toro Arana, Sebastian E; Beck, Dirk W; Sholes, Jacquelyn E C; Graybiel, Ann M

    2016-06-01

    A universal need in understanding complex networks is the identification of individual information channels and their mutual interactions under different conditions. In neuroscience, our premier example, networks made up of billions of nodes dynamically interact to bring about thought and action. Granger causality is a powerful tool for identifying linear interactions, but handling nonlinear interactions remains an unmet challenge. We present a nonlinear multidimensional hidden state (NMHS) approach that achieves interaction strength analysis and decoding of networks with nonlinear interactions by including latent state variables for each node in the network. We compare NMHS to Granger causality in analyzing neural circuit recordings and simulations, improvised music, and sociodemographic data. We conclude that NMHS significantly extends the scope of analyses of multidimensional, nonlinear networks, notably in coping with the complexity of the brain. PMID:27222584

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

    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.

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

    Tamvacakis, Arianna N.

    2016-01-01

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

  8. Manipulating neural activity in physiologically classified neurons: triumphs and challenges.

    Gore, Felicity; Schwartz, Edmund C; Salzman, C Daniel

    2015-09-19

    Understanding brain function requires knowing both how neural activity encodes information and how this activity generates appropriate responses. Electrophysiological, imaging and immediate early gene immunostaining studies have been instrumental in identifying and characterizing neurons that respond to different sensory stimuli, events and motor actions. Here we highlight approaches that have manipulated the activity of physiologically classified neurons to determine their role in the generation of behavioural responses. Previous experiments have often exploited the functional architecture observed in many cortical areas, where clusters of neurons share response properties. However, many brain structures do not exhibit such functional architecture. Instead, neurons with different response properties are anatomically intermingled. Emerging genetic approaches have enabled the identification and manipulation of neurons that respond to specific stimuli despite the lack of discernable anatomical organization. These approaches have advanced understanding of the circuits mediating sensory perception, learning and memory, and the generation of behavioural responses by providing causal evidence linking neural response properties to appropriate behavioural output. However, significant challenges remain for understanding cognitive processes that are probably mediated by neurons with more complex physiological response properties. Currently available strategies may prove inadequate for determining how activity in these neurons is causally related to cognitive behaviour. PMID:26240431

  9. Diffusion models and neural activity

    Ricciardi, L. M.; Lánský, Petr

    London : Nature publishing group, 2003 - (Nadel, L.), s. 968-972 ISBN 0-333-79261-0 R&D Projects: GA ČR GA309/02/0168 Institutional research plan: CEZ:AV0Z5011922 Keywords : Neuronal activity, Diffusion model Subject RIV: ED - Physiology

  10. Neural Dysfunction in Cognitive Control Circuits in Persons at Clinical High-Risk for Psychosis.

    Colibazzi, Tiziano; Horga, Guillermo; Wang, Zhishun; Huo, Yuankai; Corcoran, Cheryl; Klahr, Kristin; Brucato, Gary; Girgis, Ragy; Gill, Kelly; Abi-Dargham, Anissa; Peterson, Bradley S

    2016-04-01

    Cognitive control, a set of functions that develop throughout adolescence, is important in the pathogenesis of psychotic disorders. Whether cognitive control has a role in conferring vulnerability for the development of psychotic illness is still unknown. The aim of this study was to investigate the neural systems supporting cognitive control in individuals deemed to be potentially prodromal for psychotic illness. We recruited 56 participants at clinical high-risk (CHR) for psychosis based on the Structured Interview for Psychosis-Risk Syndromes (SIPS) and 49 healthy controls. Twelve of the CHR participants eventually developed psychosis. We compared functional magnetic resonance imaging (fMRI) BOLD signal during the performance of the Simon task. We tested for differences between CHR individuals and controls in conflict-related functional activity. In the CHR group when compared with controls, we detected smaller conflict-related activations in several cortical areas, including the Dorsolateral Prefrontal Cortex (DLPFC). Furthermore, conflict-related activations in the DLPFC of those CHR individuals who ultimately developed psychosis (CHR converters) were smaller than in non-converters (CHR non-converters). Higher levels of conflict-related activation were associated with better social and role outcome. Risk for psychosis was associated at the neural level with reduced conflict-related brain activity. This neural phenotype appears correlated within the DLPFC with the development of psychosis and with functional outcome. PMID:26354046

  11. Visual Experience Modulates Spatio-Temporal Dynamics of Circuit Activation

    Wang, Lang; Fontanini, Alfredo; Maffei, Arianna

    2011-01-01

    Persistent reduction in sensory drive in early development results in multiple plastic changes of different cortical synapses. How these experience-dependent modifications affect the spatio-temporal dynamics of signal propagation in neocortical circuits is poorly understood. Here we demonstrate that brief visual deprivation significantly affects the propagation of electrical signals in the primary visual cortex. The spatio-temporal spread of circuit activation upon direct stimulation of its i...

  12. Visual experience modulates spatio-temporal dynamics of circuit activation

    Arianna Maffei

    2011-01-01

    Persistent reduction in sensory drive in early development results in multiple plastic changes of different cortical synapses. How these experience-dependent modifications affect the spatio-temporal dynamics of signal propagation in neocortical circuits is poorly understood. Here we demonstrate that brief visual deprivation significantly affects the propagation of electrical signals in the primary visual cortex. The spatio-temporal spread of circuit activation upon direct stimulation of its i...

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

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

    2010-01-01

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

  14. Information transmission in oscillatory neural activity

    Koepsell, Kilian

    2008-01-01

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

  15. Neural activity associated with self-reflection

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

    2012-01-01

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

  16. Measurements of the Effects of Smoke on Active Circuits

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

  17. Measurements of the Effects of Smoke on Active Circuits

    Tanaka, T.J.

    1999-02-09

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

  18. Measurements of the effects of smoke on active circuits

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

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

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

    2002-01-01

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

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

    Priebe, Nicholas J; McGee, Aaron W.

    2014-01-01

    Genetic programs controlling ontogeny drive many of the essential connectivity patterns within the brain. Yet it is activity, derived from the experience of interacting with the world, that sculpts the precise circuitry of the central nervous system. Such experience-dependent plasticity has been observed throughout the brain but has been most extensively studied in the neocortex. A prime example of this refinement of neural circuitry is found in primary visual cortex (V1), where functional co...

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

    Nicholas Priebe; McGee, Aaron W.

    2014-01-01

    Genetic programs controlling ontogeny drive many of the essential connectivity patterns within the brain. Yet it is activity, derived from the experience of interacting with the world, that sculpts the precise circuitry of the central nervous system. Such experience-dependent plasticity has been observed throughout the brain but has been most extensively studied in the neocortex. A prime example of this refinement of neural circuitry is found in primary visual cortex (V1), where functional c...

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

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

    2010-01-01

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

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

    Nicholas Priebe

    2014-10-01

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

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

    Priebe, Nicholas J; McGee, Aaron W

    2014-01-01

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

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

    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. PMID:26411923

  6. Optical Neural Interfaces

    Warden, Melissa R.; Cardin, Jessica A.; Deisseroth, Karl

    2014-01-01

    Genetically encoded optical actuators and indicators have changed the landscape of neuroscience, enabling targetable control and readout of specific components of intact neural circuits in behaving animals. Here, we review the development of optical neural interfaces, focusing on hardware designed for optical control of neural activity, integrated optical control and electrical readout, and optical readout of population and single-cell neural activity in freely moving mammals.

  7. Neural mechanisms of the nucleus accumbens circuit in reward and aversive learning.

    Hikida, Takatoshi; Morita, Makiko; Macpherson, Tom

    2016-07-01

    The basal ganglia are key neural substrates not only for motor function, but also cognitive functions including reward and aversive learning. Critical for these processes are the functional role played by two projection neurons within nucleus accumbens (NAc); the D1- and D2-expressing neurons. Recently, we have developed a novel reversible neurotransmission blocking technique that specifically blocks neurotransmission from NAc D1- and D2-expressing neurons, allowing for in vivo analysis. In this review, we outline the functional dissociation of NAc D1- and D2-expressing neurons of the basal ganglia in reward and aversive learning, as well as drug addiction. These studies have revealed the importance of activation of NAc D1 receptors for reward learning and drug addiction, and inactivation of NAc D2 receptors for aversive learning and flexibility. Based on these findings, we propose a neural mechanism, in which dopamine neurons in the ventral tegmental area that send inputs to the NAc work as a switch between D1- and D2-expressing neurons. These basal ganglia neural mechanisms will give us new insights into the pathophysiology of neuropsychiatric diseases. PMID:26827817

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

    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.

  9. What genetic model organisms offer the study of behavior and neural circuits.

    White, Benjamin H

    2016-06-01

    The past decade has witnessed the development of powerful, genetically encoded tools for manipulating and monitoring neuronal function in freely moving animals. These tools are most readily deployed in genetic model organisms and efforts to map the circuits that govern behavior have increasingly focused on worms, flies, zebrafish, and mice. The traditional virtues of these animals for genetic studies in terms of small size, short generation times, and ease of animal husbandry in a laboratory setting have facilitated rapid progress, and the neural basis of an increasing number of behaviors is being established at cellular resolution in each of these animals. The depth and breadth of this analysis should soon offer a significantly more comprehensive understanding of how the circuitry underlying behavior is organized in particular animals and promises to help answer long-standing questions that have waited for such a brain-wide perspective on nervous system function. The comprehensive understanding achieved in genetic model animals is thus likely to make them into paradigmatic examples that will serve as touchstones for comparisons to understand how behavior is organized in other animals, including ourselves. PMID:27328841

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

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

    2011-01-01

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

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

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

    2016-05-31

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

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

    Marco eZanon

    2013-11-01

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

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

    Haichen eNiu

    2015-03-01

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

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

    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.

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

    Krasavin, Alexey V.; Zayats, Anatoly V.

    2015-01-01

    In this article a comprehensive figures of merit for both passive and active plasmonic circuit components are introduced, benchmarking their performance for the realisation of high-bandwidth optical data communication in electronic chips. For the first time the figure of merit for passive plasmonic interconnects has been derived in terms of ultimate global characteristics of the plasmonic circuitry, particularly bandwidth and power consumption densities. Then, these parameters were linked to ...

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

    Orli Yarom

    2008-01-01

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

  17. Identifying Emotions on the Basis of Neural Activation.

    Kassam, Karim S; Markey, Amanda R; Cherkassky, Vladimir L; Loewenstein, George; Just, Marcel Adam

    2013-01-01

    We attempt to determine the discriminability and organization of neural activation corresponding to the experience of specific emotions. Method actors were asked to self-induce nine emotional states (anger, disgust, envy, fear, happiness, lust, pride, sadness, and shame) while in an fMRI scanner. Using a Gaussian Naïve Bayes pooled variance classifier, we demonstrate the ability to identify specific emotions experienced by an individual at well over chance accuracy on the basis of: 1) neural activation of the same individual in other trials, 2) neural activation of other individuals who experienced similar trials, and 3) neural activation of the same individual to a qualitatively different type of emotion induction. Factor analysis identified valence, arousal, sociality, and lust as dimensions underlying the activation patterns. These results suggest a structure for neural representations of emotion and inform theories of emotional processing. PMID:23840392

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

    Felipe Gerhard

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

  19. An active control synchronization for two modified Chua circuits

    Li, Guo-Hui

    2005-03-01

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

  20. An active control synchronization for two modified Chua circuits

    Li Guo-Hui

    2005-01-01

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

  1. Active Snubber Circuit for High Power Inverter Leg

    Rasmussen, Tonny Wederberg; Johansen, Morten Holst

    2009-01-01

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

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

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

    2001-01-01

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

  3. Active Match Load Circuit Intended for Testing Piezoelectric Transformers

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

    2012-01-01

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

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

    Hanlon, Colleen A; Canterberry, Melanie

    2012-01-01

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

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

    Gregg W. Crabtree

    2014-11-01

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

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

    Wiebke Potjans

    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.

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

    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. PMID:21151370

  8. Active quenching circuit for a InGaAs single-photon avalanche diode

    We present a novel gated operation active quenching circuit (AQC). In order to simulate the quenching circuit a complete SPICE model of a InGaAs SPAD is set up according to the I–V characteristic measurement results of the detector. The circuit integrated with aROIC (readout integrated circuit) is fabricated in an CSMC 0.5 μm CMOS process and then hybrid packed with the detector. Chip measurement results show that the functionality of the circuit is correct and the performance is suitable for practical system applications. (semiconductor integrated circuits)

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

    Krasavin, Alexey V

    2015-01-01

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

  10. Neural Activity Reveals Preferences Without Choices

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

    2014-01-01

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

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

    Ramizi Mohamed; Mahidur R. Sarker; Azah Mohamed

    2014-01-01

    This study presents the performance evaluation a developed energy harvesting interface circuit in active technique. The energy harvesting interface circuit for micro-power applications uses equivalent voltage of the piezoelectric materials have been developed and simulated. Circuit designs and simulation results are presented for a conventional diode rectifier with voltage doubler in passive technique. Most of the existing techniques are mainly passive-based energy harvesting circuits. Genera...

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

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

    2015-12-01

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

  13. Evaluating a genetically encoded optical sensor of neural activity using electrophysiology in intact adult fruit flies

    Gilles Laurent

    2007-11-01

    Full Text Available Genetically encoded optical indicators hold the promise of enabling non-invasive monitoring of activity in identified neurons in behaving organisms. However, the interpretation of images of brain activity produced using such sensors is not straightforward. Several recent studies of sensory coding used G-CaMP 1.3-a calcium sensor-as an indicator of neural activity; some of these studies characterized the imaged neurons as having narrow tuning curves, a conclusion not always supported by parallel electrophysiological studies. To better understand the possible cause of these conflicting results, we performed simultaneous in vivo 2-photon imaging and electrophysiological recording of G-CaMP 1.3 expressing neurons in the antennal lobe (AL of intact fruitflies. We find that G-CaMP has a relatively high threshold, that its signal often fails to capture spiking response kinetics, and that it can miss even high instantaneous rates of activity if those are not sustained. While G-CaMP can be misleading, it is clearly useful for the identification of promising neural targets: when electrical activity is well above the sensor's detection threshold, its signal is fairly well correlated with mean firing rate and G-CaMP does not appear to alter significantly the responses of neurons that express it. The methods we present should enable any genetically encoded sensor, activator, or silencer to be evaluated in an intact neural circuit in vivo in Drosophila.

  14. Active Engine Mounting Control Algorithm Using Neural Network

    Fadly Jashi Darsivan

    2009-01-01

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

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

    Okado, Yoko; Stark, Craig E.L.

    2005-01-01

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

  16. Learning to Discern Images Modifies Neural Activity

    Gregor Rainer; Han Lee; Logothetis, Nikos K.

    2004-01-01

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

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

    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.

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

    Brent eAsrican

    2013-11-01

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

  19. Zebrafish foxc1a drives appendage-specific neural circuit development

    Banerjee, Santanu; Hayer, Katharina; Hogenesch, John B.; Granato, Michael

    2015-01-01

    Neural connectivity between the spinal cord and paired appendages is key to the superior locomotion of tetrapods and aquatic vertebrates. In contrast to nerves that innervate axial muscles, those innervating appendages converge at a specialized structure, the plexus, where they topographically reorganize before navigating towards their muscle targets. Despite its importance for providing appendage mobility, the genetic program that drives nerve convergence at the plexus, as well as the functi...

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

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

    2009-01-01

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

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

    Basilis Zikopoulos

    2013-01-01

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

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

    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 respon...

  3. Cortical Correlates of Low-Level Perception: From Neural Circuits to Percepts.

    Frégnac, Yves; Bathellier, Brice

    2015-10-01

    Low-level perception results from neural-based computations, which build a multimodal skeleton of unconscious or self-generated inferences on our environment. This review identifies bottleneck issues concerning the role of early primary sensory cortical areas, mostly in rodent and higher mammals (cats and non-human primates), where perception substrates can be searched at multiple scales of neural integration. We discuss the limitation of purely bottom-up approaches for providing realistic models of early sensory processing and the need for identification of fast adaptive processes, operating within the time of a percept. Future progresses will depend on the careful use of comparative neuroscience (guiding the choices of experimental models and species adapted to the questions under study), on the definition of agreed-upon benchmarks for sensory stimulation, on the simultaneous acquisition of neural data at multiple spatio-temporal scales, and on the in vivo identification of key generic integration and plasticity algorithms validated experimentally and in simulations. PMID:26447576

  4. Optical imaging of neural and hemodynamic brain activity

    Schei, Jennifer Lynn

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

  5. Neural Network-Based Active Control for Offshore Platforms

    周亚军; 赵德有

    2003-01-01

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

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

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

    2014-12-01

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

  7. High Accuracy Human Activity Monitoring using Neural network

    Sharma, Annapurna; Chung, Wan-Young

    2011-01-01

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

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

    Ramizi Mohamed

    2014-10-01

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

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

    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.

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

    Basilis eZikopoulos

    2013-09-01

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

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

    Xingchao Wang

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

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

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

    2016-05-01

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

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

    W. L. C. Rutten

    2006-01-01

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

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

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

    2016-02-19

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

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

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

    2015-09-01

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

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

    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

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

    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.

  18. A Realization of SC-CNN-Based Circuit Using FTFN

    Günay, Enis; UZUNHİSARCIKLI, Esma; KILIÇ, Recai; ALÇI, Mustafa

    2005-01-01

    In this paper, a realization of the State Controlled Cellular Neural Network (SC-CNN)-based circuit using Four Terminal Floating Nullor (FTFN) as active element is presented. In this realization, a new version of autonomous Chua's circuit has been considered using FTFN realization of SC-CNN-based circuit. The performance of the proposed SC-CNN-based circuit is demonstrated by PSpice simulations.

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

    Wang, Xiao-Jing

    2016-01-01

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

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

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

    2004-05-01

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

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

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

    2015-11-01

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

  2. Neural Activity When People Solve Verbal Problems with Insight

    Jung-Beeman Mark

    2004-01-01

    Full Text Available People sometimes solve problems with a unique process called insight, accompanied by an "Aha!" experience. It has long been unclear whether different cognitive and neural processes lead to insight versus noninsight solutions, or if solutions differ only in subsequent subjective feeling. Recent behavioral studies indicate distinct patterns of performance and suggest differential hemispheric involvement for insight and noninsight solutions. Subjects solved verbal problems, and after each correct solution indicated whether they solved with or without insight. We observed two objective neural correlates of insight. Functional magnetic resonance imaging (Experiment 1 revealed increased activity in the right hemisphere anterior superior temporal gyrus for insight relative to noninsight solutions. The same region was active during initial solving efforts. Scalp electroencephalogram recordings (Experiment 2 revealed a sudden burst of high-frequency (gamma-band neural activity in the same area beginning 0.3 s prior to insight solutions. This right anterior temporal area is associated with making connections across distantly related information during comprehension. Although all problem solving relies on a largely shared cortical network, the sudden flash of insight occurs when solvers engage distinct neural and cognitive processes that allow them to see connections that previously eluded them.

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

    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

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

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

    2012-01-01

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

  5. Early interfaced neural activity from chronic amputated nerves

    Kshitija Garde

    2009-05-01

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

  6. Application of neural networks to seismic active control

    An exploratory study on seismic active control using an artificial neural network (ANN) is presented in which a singledegree-of-freedom (SDF) structural system is controlled by a trained neural network. A feed-forward neural network and the backpropagation training method are used in the study. In backpropagation training, the learning rate is determined by ensuring the decrease of the error function at each training cycle. The training patterns for the neural net are generated randomly. Then, the trained ANN is used to compute the control force according to the control algorithm. The control strategy proposed herein is to apply the control force at every time step to destroy the build-up of the system response. The ground motions considered in the simulations are the N21E and N69W components of the Lake Hughes No. 12 record that occurred in the San Fernando Valley in California on February 9, 1971. Significant reduction of the structural response by one order of magnitude is observed. Also, it is shown that the proposed control strategy has the ability to reduce the peak that occurs during the first few cycles of the time history. These promising results assert the potential of applying ANNs to active structural control under seismic loads

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

    2002-01-01

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

  8. Dynamical criticality in the collective activity of a neural population

    Mora, Thierry

    The past decade has seen a wealth of physiological data suggesting that neural networks may behave like critical branching processes. Concurrently, the collective activity of neurons has been studied using explicit mappings to classic statistical mechanics models such as disordered Ising models, allowing for the study of their thermodynamics, but these efforts have ignored the dynamical nature of neural activity. I will show how to reconcile these two approaches by learning effective statistical mechanics models of the full history of the collective activity of a neuron population directly from physiological data, treating time as an additional dimension. Applying this technique to multi-electrode recordings from retinal ganglion cells, and studying the thermodynamics of the inferred model, reveals a peak in specific heat reminiscent of a second-order phase transition.

  9. Perception Neural Networks for Active Noise Control Systems

    Wang Xiaoli

    2012-11-01

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

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

    Jarcho, Johanna M; Romer, Adrienne L; Shechner, Tomer; Galvan, Adriana; Guyer, Amanda E; Leibenluft, Ellen; Pine, Daniel S; Nelson, Eric E

    2015-06-01

    Social anxiety disorder typically begins in adolescence, a sensitive period for brain development, when increased complexity and salience of peer relationships requires novel forms of social learning. Disordered social learning in adolescence may explain how brain dysfunction promotes social anxiety. Socially anxious adolescents (n = 15) and adults (n = 19) and non-anxious adolescents (n = 24) and adults (n = 32) predicted, then received, social feedback from high and low-value peers while undergoing functional magnetic resonance imaging (fMRI). A surprise recall task assessed memory biases for feedback. Neural correlates of social evaluation prediction errors (PEs) were assessed by comparing engagement to expected and unexpected positive and negative feedback. For socially anxious adolescents, but not adults or healthy participants of either age group, PEs elicited heightened striatal activity and negative fronto-striatal functional connectivity. This occurred selectively to unexpected positive feedback from high-value peers and corresponded with impaired memory for social feedback. While impaired memory also occurred in socially-anxious adults, this impairment was unrelated to brain-based PE activity. Thus, social anxiety in adolescence may relate to altered neural correlates of PEs that contribute to impaired learning about social feedback. Small samples necessitate replication. Nevertheless, results suggest that the relationship between learning and fronto-striatal function may attenuate as development progresses. PMID:25933410

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

    Johanna M. Jarcho

    2015-06-01

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

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

    Chih-Lung Shen

    2012-12-01

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

  13. The effects of gratitude expression on neural activity.

    Kini, Prathik; Wong, Joel; McInnis, Sydney; Gabana, Nicole; Brown, Joshua W

    2016-03-01

    Gratitude is a common aspect of social interaction, yet relatively little is known about the neural bases of gratitude expression, nor how gratitude expression may lead to longer-term effects on brain activity. To address these twin issues, we recruited subjects who coincidentally were entering psychotherapy for depression and/or anxiety. One group participated in a gratitude writing intervention, which required them to write letters expressing gratitude. The therapy-as-usual control group did not perform a writing intervention. After three months, subjects performed a "Pay It Forward" task in the fMRI scanner. In the task, subjects were repeatedly endowed with a monetary gift and then asked to pass it on to a charitable cause to the extent they felt grateful for the gift. Operationalizing gratitude as monetary gifts allowed us to engage the subjects and quantify the gratitude expression for subsequent analyses. We measured brain activity and found regions where activity correlated with self-reported gratitude experience during the task, even including related constructs such as guilt motivation and desire to help as statistical controls. These were mostly distinct from brain regions activated by empathy or theory of mind. Also, our between groups cross-sectional study found that a simple gratitude writing intervention was associated with significantly greater and lasting neural sensitivity to gratitude - subjects who participated in gratitude letter writing showed both behavioral increases in gratitude and significantly greater neural modulation by gratitude in the medial prefrontal cortex three months later. PMID:26746580

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

    Radwan, Ahmed Gomaa

    2011-10-01

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

  15. 78 FR 37203 - Authorization of Production Activity; Subzone 196A; TTI, Inc. (Electromechanical and Circuit...

    2013-06-20

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

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

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

    2002-01-01

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

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

    Verhagen, L.A.W.

    2009-01-01

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

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

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

    2015-12-01

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

  19. Activated sludge process based on artificial neural network

    张文艺; 蔡建安

    2002-01-01

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

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

    Jaramillo, Santiago; Pearlmutter, Barak A.

    2007-01-01

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

  1. Efficient universal computing architectures for decoding neural activity.

    Benjamin I Rapoport

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

  2. Simulation of Aorta Artery Aneurysms Using Active Electronic Circuit

    Kamran Hassani

    2007-01-01

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

  3. Comparison of bidirectional power electronics with unidirectional topologies using active discharging circuits for feeding DEAP transducer

    Hoffstadt, Thorben; Maas, Jürgen

    2015-04-01

    To enable a continuous operation of a DEAP transducer, the feeding power electronics must provide the capability to charge and discharge the transducer to enable a continuous voltage adjustment. While in case of energy harvesting applications a bidirectional power electronics is mandatory, for actuator applications also unidirectional power electronics with active discharging circuits can be used. Thus, in this contribution a bidirectional flyback-converter is compared to a unidirectional with different discharging circuits. For this purpose, the design of a resistive and an inductive-resistive discharging circuit is proposed, that are connected in parallel to the DEAP and activated when required. Modulation schemes for both discharging circuits are derived that enable a continuous voltage control. Based on realized prototypes of the investigated topologies the different converters are finally compared to each other.

  4. Loss compensation in Metamaterials through embedding of active transistor based negative differential resistance circuits

    Xu, Wangren; Padilla, Willie J.; Sonkusale, Sameer

    2012-01-01

    This paper presents an all-electronic approach for loss compensation in metamaterials. This is achieved by embedding active-transistors based negative differential resistance (NDR) circuits in each unit cell of the metamaterial lattice. NDR circuits provide tunable loss compensation over a broad frequency range limited only by the maximum operating frequency of transistors that is reaching terahertz values in newer semiconductor processes. Design, simulation and experimental results of metama...

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

    Sheng-Jun Wang

    2011-06-01

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

  6. Motor neuron activation in peripheral nerves using infrared neural stimulation

    Peterson, E. J.; Tyler, D. J.

    2014-02-01

    Objective. Localized activation of peripheral axons may improve selectivity of peripheral nerve interfaces. Infrared neural stimulation (INS) employs localized delivery to activate neural tissue. This study investigated INS to determine whether localized delivery limited functionality in larger mammalian nerves. Approach. The rabbit sciatic nerve was stimulated extraneurally with 1875 nm wavelength infrared light, electrical stimulation, or a combination of both. Infrared-sensitive regions (ISR) of the nerve surface and electromyogram (EMG) recruitment of the Medial Gastrocnemius, Lateral Gastrocnemius, Soleus, and Tibialis Anterior were the primary output measures. Stimulation applied included infrared-only, electrical-only, and combined infrared and electrical. Main results. 81% of nerves tested were sensitive to INS, with 1.7 ± 0.5 ISR detected per nerve. INS was selective to a single muscle within 81% of identified ISR. Activation energy threshold did not change significantly with stimulus power, but motor activation decreased significantly when radiant power was decreased. Maximum INS levels typically recruited up to 2-9% of any muscle. Combined infrared and electrical stimulation differed significantly from electrical recruitment in 7% of cases. Significance. The observed selectivity of INS indicates that it may be useful in augmenting rehabilitation, but significant challenges remain in increasing sensitivity and response magnitude to improve the functionality of INS.

  7. Sparing of the hippocampus, limbic circuit and neural stem cell compartment during partial brain radiotherapy for glioma: a dosimetric feasibility study

    The aim of this study was to assess the feasibility of sparing contralateral or bilateral neural stem cell (NSC) compartment, hippocampus and limbic circuit during partial brain radiotherapy (PBRT). Treatment plans were generated for five hemispheric high-grade gliomas, five hemispheric low-grade gliomas and two brainstem gliomas (12 patients). For each, standard intensity-modulated radiotherapy (IMRT) plans were generated, as well as IMRT plans which spared contralateral (hemispheric cases) or bilateral (brainstem cases) limbic circuit, hippocampus, and NSC. Biologically equivalent dose for late effects (BEDlate effects) was generated for limbic circuit, hippocampus and NSC. Per cent relative reduction in mean physical dose and BED was calculated for each plan (standard vs. sparing). We were able to reduce physical dose and BEDlate effects to these critical structures by 23.5–56.8% and 23.6–66%, respectively. It is possible to spare contralateral limbic circuit, NSC and hippocampus during PBRT for both high- and low-grade gliomas using IMRT, and to spare the hippocampus bilaterally during PBRT for brainstem low-grade gliomas. This approach may reduce late cognitive sequelae of cranial radiotherapy.

  8. Infrared neural stimulation fails to evoke neural activity in the deaf guinea pig cochlea.

    Thompson, Alexander C; Fallon, James B; Wise, Andrew K; Wade, Scott A; Shepherd, Robert K; Stoddart, Paul R

    2015-06-01

    At present there is some debate as to the processes by which infrared neural stimulation (INS) activates neurons in the cochlea, as the lasers used for INS can potentially generate a range of secondary stimuli e.g. an acoustic stimulus is produced when the light is absorbed by water. To clarify whether INS in the cochlea requires functioning hair cells and to explore the potential relevance to cochlear implants, experiments using INS were performed in the cochleae of both normal hearing and profoundly deaf guinea pigs. A response to laser stimulation was readily evoked in normal hearing cochlea. However, no response was evoked in any profoundly deaf cochleae, for either acute or chronic deafening, contrary to previous work where a response was observed after acute deafening with ototoxic drugs. A neural response to electrical stimulation was readily evoked in all cochleae after deafening. The absence of a response from optical stimuli in profoundly deaf cochleae suggests that the response from INS in the cochlea is hair cell mediated. PMID:25796297

  9. Reduction of activation and contamination of reactor circuits

    After investigation of the metal loss rate of INCOLOY 800 samples by several test series performes at 3420C and 150 bar with different oxygen contents in demineralized water (deionate), the protective gold plating applied on the inner side of the first autoclave system had to be renewed. Within the period of reporting the second autoclave system (same dimensions as the first autoclave system) also provided with an inner gold plating was nearly completed. Also a small sized third autoclave system has been completed which allows to study the metal loss rate to pressurized water of standard stainless steel, the material making up 15% of the inner surface of a primary reactor circuit. Reduction of the oxygen content in deionate by noble gas sweeping, which is much more effective than chemical reduction by hydrazine, was improved in combination with the first autoclave system. Apparently the minimum oxygen content of 0,05 mg/kg required for primary loop water of pressurized reactors can still be lowered by the factor of 20. (orig./RW)

  10. Sociocultural patterning of neural activity during self-reflection

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

    2014-01-01

    Chinese than in Danish participants. Moreover, the group difference in TPJ activity was mediated by a measure of a cultural value (i.e., interdependence of self-construal). Our findings suggest that individuals in different sociocultural contexts may learn and/or adopt distinct strategies for self......Western cultures encourage self-construals independent of social contexts whereas East Asian cultures foster interdependent self-construals that rely on how others perceive the self. How are culturally specific self-construals mediated by the human brain? Using functional MRI, we monitored neural......-reflection by changing the weight of the mPFC and TPJ in the social brain network....

  11. Kohonen Neural Network Stress Detection Using Only Electrodermal Activity Features

    BORNOIU, I.-V.

    2014-08-01

    Full Text Available This paper presents a method for identifying human stress levels by using a Kohonen neural network. The study focuses on differentiating between a relaxed and a stressed state and it presents a series of parameters (skin conductance response signal power, skin conductance response signal frequency, skin conductance level gradient, response rise time and response amplitude extracted only from the electrodermal activity signal. A very strict recording protocol was used to minimize the artifacts caused by the bad connection between electrodes and skin. A stress inducing method is presented that can be used to replicate results in laboratory conditions.

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

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

    2016-05-01

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

  13. Imaging evolutionarily conserved neural networks: preferential activation of the olfactory system by food-related odor.

    Kulkarni, Praveen; Stolberg, Tara; Sullivanjr, J M; Ferris, Craig F

    2012-04-21

    Rodents routinely forge and rely on hippocampal-dependent spatial memory to guide them to sources of caloric rich food in their environment. Has evolution affected the olfactory system and its connections to the hippocampus and limbic cortex, so rodents have an innate sensitivity to energy rich food and their location? To test this notion, we used functional magnetic resonance imaging in awake rats to observe changes in brain activity in response to four odors: benzaldehyde (almond odor), isoamyl acetate (banana odor), methyl benzoate (rosy odor), and limonene (citrus odor). We chose the almond odor because nuts are high in calories and would be expected to convey greater valance as compared to the other odors. Moreover, the standard food chow is devoid of nuts, so laboratory bred rats would not have any previous exposure to this food. Activation maps derived from computational analysis using a 3D segmented rat MRI atlas were dramatically different between odors. Animals exposed to banana, rosy and citrus odors showed modest activation of the primary olfactory system, hippocampus and limbic cortex. However, animals exposed to almond showed a robust increase in brain activity in the primary olfactory system particularly the main olfactory bulb, anterior olfactory nucleus and tenia tecta. The most significant difference in brain activation between odors was observed in the hippocampus and limbic cortex. These findings show that fMRI can be used to identify neural circuits that have an innate sensitivity to environmental stimuli that may help in an animal's survival. PMID:22343130

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

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

  15. An adaptive active control for the modified Chua's circuit

    In this Letter, it is shown that a couple of the modified Chua's systems with different parameters and initial conditions can be synchronized using active control when the values of parameters both in drive system and response system are known aforehand. Furthermore, an adaptive active control approach is proposed based on Lyapunov stability theory to make the states of two identical Chua's systems with unknown constant parameters be asymptotically synchronized. In addition, the proposed adaptive active control method guarantees that the designed controller is independent to those uncertain parameters. Simulation results by using both active control and adaptive active control are provided, and the feasibility and effectiveness of the proposed adaptive active control are demonstrated

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

    A. Ayotunde Ajayi

    2014-01-01

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

  17. Visual avoidance in phobia: particularities in neural activity, autonomic responding, and cognitive risk evaluations

    Tatjana Aue

    2013-05-01

    Full Text Available We investigated the neural mechanisms and the autonomic and cognitive responses associated with visual avoidance behavior in spider phobia. Spider phobic and control participants imagined visiting different forest locations with the possibility of encountering spiders, snakes, or birds (neutral reference category. In each experimental trial, participants saw a picture of a forest location followed by a picture of a spider, snake, or bird, and then rated their personal risk of encountering these animals in this context, as well as their fear. The greater the visual avoidance of spiders that a phobic participant demonstrated (as measured by eye tracking, the higher were her autonomic arousal and neural activity in the amygdala, orbitofrontal cortex (OFC, anterior cingulate cortex (ACC, and precuneus at picture onset. Visual avoidance of spiders in phobics also went hand in hand with subsequently reduced cognitive risk of encounters. Control participants, in contrast, displayed a positive relationship between gaze duration toward spiders, on the one hand, and autonomic responding, as well as OFC, ACC, and precuneus activity, on the other hand. In addition, they showed reduced encounter risk estimates when they looked longer at the animal pictures. Our data are consistent with the idea that one reason for phobics to avoid phobic information may be grounded in heightened activity in the fear circuit, which signals potential threat. Because of the absence of alternative efficient regulation strategies, visual avoidance may then function to down-regulate cognitive risk evaluations for threatening information about the phobic stimuli. Control participants, in contrast, may be characterized by a different coping style, whereby paying visual attention to potentially threatening information may help them to actively down-regulate cognitive evaluations of risk.

  18. Striatal plasticity and basal ganglia circuit function

    Kreitzer, Anatol C.; Malenka, Robert C.

    2008-01-01

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

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

    Perry Danielle

    2005-09-01

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

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

    Tang, Yi; Blaabjerg, Frede

    2014-01-01

    Active power decoupling techniques are promising solutions for capacitance reduction in single-phase AC/DC or DC/AC systems. This paper proposes a novel circuit topology which can realize the power decoupling function without adding additional active switches into the circuit. Also, the proposed...... topology does not require additional passive component, e.g. inductors or film capacitors for ripple energy storage because this task can be accomplished by the dc-link capacitors themselves, and therefore its implementation cost can be minimized. Another unique feature of the proposed topology...

  1. Transcriptional Regulatory Circuits Controlling Brown Fat Development and Activation

    Seale, Patrick

    2015-01-01

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

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

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

    2005-01-01

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

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

    Laviolette, Steven R

    2007-01-01

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

  4. Activity-dependent plasticity in the isolated embryonic avian brainstem following manipulations of rhythmic spontaneous neural activity.

    Vincen-Brown, Michael A; Revill, Ann L; Pilarski, Jason Q

    2016-07-15

    When rhythmic spontaneous neural activity (rSNA) first appears in the embryonic chick brainstem and cranial nerve motor axons it is principally driven by nicotinic neurotransmission (NT). At this early age, the nicotinic acetylcholine receptor (nAChR) agonist nicotine is known to critically disrupt rSNA at low concentrations (0.1-0.5μM), which are levels that mimic the blood plasma levels of a fetus following maternal cigarette smoking. Thus, we quantified the effect of persistent exposure to exogenous nicotine on rSNA using an in vitro developmental model. We found that rSNA was eliminated by continuous bath application of exogenous nicotine, but rSNA recovered activity within 6-12h despite the persistent activation and desensitization of nAChRs. During the recovery period rSNA was critically driven by chloride-mediated membrane depolarization instead of nicotinic NT. To test whether this observed compensation was unique to the antagonism of nicotinic NT or whether the loss of spiking behavior also played a role, we eliminated rSNA by lowering overall excitatory drive with a low [K(+)]o superfusate. In this context, rSNA again recovered, although the recovery time was much quicker, and exhibited a lower frequency, higher duration, and an increase in the number of bursts per episode when compared to control embryos. Importantly, we show that the main compensatory response to lower overall excitatory drive, similar to nicotinergic block, is a result of potentiated chloride mediated membrane depolarization. These results support increasing evidence that early neural circuits sense spiking behavior to maintain primordial bioelectric rhythms. Understanding the nature of developmental plasticity in the nervous system, especially versions that preserve rhythmic behaviors following clinically meaningful environmental stimuli, both normal and pathological, will require similar studies to determine the consequences of feedback compensation at more mature chronological ages

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

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

    2016-01-01

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

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

    Halassa, Michael M.; Haydon, Philip G.

    2011-01-01

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

  7. Artificial Neural Network Characteristic For Neutron Activation Analysis

    Neutron activation analysis (NAA) is one of analysis methods for identification of elements from material. Irradiated unknown material could be identified by gamma spectrum pattern analysis. The recognition process will be done easily if we have a smart system. One of the smart system choices was artificial neural network (ANN). The gamma spectrum emitted from radioactive nuclide has specific pattern, therefore smart system will try to classify the input data. Firstly, Hp-Ge detector detects gamma radiation from material, then the gamma radiations is counted by multi channel analysis instrument (MCA). The smart system based ANN system was tested to identify 50 material, in which the system has been trained by using one data only for each classifications. The result showed that the ANN appreciates 100% identification capability or has a good performance

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

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

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

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

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

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

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

    2009-01-01

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

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

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

    2006-01-01

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

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

    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.…

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

    Oğuz ÜSTÜN

    2009-03-01

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

  14. Activation product transport in the helium cooling circuit of the SEAFP Plant Model 1

    This paper presents results using the steady-state activation transport and deposition code TRAP. Activation of the helium coolant and pipe wall deposits are calculated at important locations of the primary circuit loop of the SEAFP Plant Model 1. The dominant sources of active material in the coolant comprise a nuclear sputtering mechanism and direct generation in the coolant due to the neutron-coolant interaction. Coolant activity behavior is shown to be dictated by the volatile nuclides whereas surface activity behavior is dictated by the non-volatiles. Resulting hazards are estimated to be extremely small

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

    Chiara Begliomini

    2014-09-01

    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.

  16. Can Neural Activity Propagate by Endogenous Electrical Field?

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

    2015-12-01

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

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

    Naoki Matsuo

    2009-09-01

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

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

    Hui Li

    2014-03-01

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

  19. Adaptive RBF Neural Network Control for Three-Phase Active Power Filter

    Juntao Fei; Zhe Wang

    2013-01-01

    An adaptive radial basis function (RBF) neural network control system for three‐phase active power filter (APF) is proposed to eliminate harmonics. Compensation current is generated to track command current so as to eliminate the harmonic current of non‐linear load and improve the quality of the power system. The asymptotical stability of the APF system can be guaranteed with the proposed adaptive neural network strategy. The parameters of the neural network can be adaptively updated to achie...

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

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

    2009-08-01

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

  1. Sensitive red protein calcium indicators for imaging neural activity.

    Dana, Hod; Mohar, Boaz; Sun, Yi; Narayan, Sujatha; Gordus, Andrew; Hasseman, Jeremy P; Tsegaye, Getahun; Holt, Graham T; Hu, Amy; Walpita, Deepika; Patel, Ronak; Macklin, John J; Bargmann, Cornelia I; Ahrens, Misha B; Schreiter, Eric R; Jayaraman, Vivek; Looger, Loren L; Svoboda, Karel; Kim, Douglas S

    2016-01-01

    Genetically encoded calcium indicators (GECIs) allow measurement of activity in large populations of neurons and in small neuronal compartments, over times of milliseconds to months. Although GFP-based GECIs are widely used for in vivo neurophysiology, GECIs with red-shifted excitation and emission spectra have advantages for in vivo imaging because of reduced scattering and absorption in tissue, and a consequent reduction in phototoxicity. However, current red GECIs are inferior to the state-of-the-art GFP-based GCaMP6 indicators for detecting and quantifying neural activity. Here we present improved red GECIs based on mRuby (jRCaMP1a, b) and mApple (jRGECO1a), with sensitivity comparable to GCaMP6. We characterized the performance of the new red GECIs in cultured neurons and in mouse, Drosophila, zebrafish and C. elegans in vivo. Red GECIs facilitate deep-tissue imaging, dual-color imaging together with GFP-based reporters, and the use of optogenetics in combination with calcium imaging. PMID:27011354

  2. Generalized activity equations for spiking neural network dynamics

    Michael A Buice

    2013-11-01

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

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

    Müller-Pinzler, L.

    2016-01-01

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

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

    Crabtree, Gregg W.; Gogos, Joseph A.

    2014-01-01

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

  5. Detecting the single line to ground short circuit fault in the submarine’s power system using the artificial neural network

    Behniafar Ali

    2013-01-01

    Full Text Available The electric marine instruments are newly inserted in the trade and industry, for which the existence of an equipped and reliable power system is necessitated. One of the features of such a power system is that it cannot have an earth system causing the protection relays not to be able to detect the single line to ground short circuit fault. While on the other hand, the occurrence of another similar fault at the same time can lead to the double line fault and thereby the tripping of relays and shortening of vital loads. This in turn endangers the personals' security and causes the loss of military plans. From the above considerations, it is inferred that detecting the single line to ground fault in the marine instruments is of a special importance. In this way, this paper intends to detect the single line to ground fault in the power systems of the marine instruments using the wavelet transform and Multi-Layer Perceptron (MLP neural network. In the numerical analysis, several different types of short circuit faults are simulated on several marine power systems and the proposed approach is applied to detect the single line to ground fault. The results are of a high quality and preciseness and perfectly demonstrate the effectiveness of the proposed approach.

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

    卢纯; 石秉学

    2001-01-01

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

  7. Holographically patterned activation using photo-absorber induced neural-thermal stimulation

    Farah, Nairouz; Zoubi, Alaa; Matar, Suhail; Golan, Lior; Marom, Anat; Butson, Christopher R.; Brosh, Inbar; Shoham, Shy

    2013-10-01

    Objective. Patterned photo-stimulation offers a promising path towards the effective control of distributed neuronal circuits. Here, we demonstrate the feasibility and governing principles of spatiotemporally patterned microscopic photo-absorber induced neural-thermal stimulation (PAINTS) based on light absorption by exogenous extracellular photo-absorbers. Approach. We projected holographic light patterns from a green continuous-wave (CW) or an IR femtosecond laser onto exogenous photo-absorbing particles dispersed in the vicinity of cultured rat cortical cells. Experimental results are compared to predictions of a temperature-rate model (where membrane currents follow I ∝ dT/dt). Main results. The induced microscopic photo-thermal transients have sub-millisecond thermal relaxation times and stimulate adjacent cells. PAINTS activation thresholds for different laser pulse durations (0.02 to 1 ms) follow the Lapicque strength-duration formula, but with different chronaxies and minimal threshold energy levels for the two excitation lasers (an order of magnitude lower for the IR system <50 nJ). Moreover, the empirical thresholds for the CW system are found to be in good agreement with detailed simulations of the temperature-rate model, but are generally lower for the IR system, suggesting an auxiliary excitation mechanism. Significance. Holographically patterned PAINTS could potentially provide a means for minimally intrusive control over neuronal dynamics with a high level of spatial and temporal selectivity.

  8. Wireless micropower instrumentation for multimodal acquisition of electrical and chemical neural activity.

    Mollazadeh, M; Murari, K; Cauwenberghs, G; Thakor, N

    2009-12-01

    The intricate coupling between electrical and chemical activity in neural pathways of the central nervous system, and the implication of this coupling in neuropathologies, such as Parkinson's disease, motivates simultaneous monitoring of neurochemical and neuropotential signals. However, to date, neurochemical sensing has been lacking in integrated clinical instrumentation as well as in brain-computer interfaces (BCI). Here, we present an integrated system capable of continuous acquisition of data modalities in awake, behaving subjects. It features one channel each of a configurable neuropotential and a neurochemical acquisition system. The electrophysiological channel is comprised of a 40-dB gain, fully differential amplifier with tunable bandwidth from 140 Hz to 8.2 kHz. The amplifier offers input-referred noise below 2 muV rms for all bandwidth settings. The neurochemical module features a picoampere sensitivity potentiostat with a dynamic range spanning six decades from picoamperes to microamperes. Both systems have independent on-chip, configurable DeltaSigma analog-to-digital converters (ADCs) with programmable digital gain and resolution. The system was also interfaced to a wireless power harvesting and telemetry module capable of powering up the circuits, providing clocks for ADC operation, and telemetering out the data at up to 32 kb/s over 3.5 cm with a bit-error rate of less than 10(-5). Characterization and experimental results from the electrophysiological and neurochemical modules as well as the full system are presented. PMID:23853286

  9. A mathematical model relating cortical oxygenated and deoxygenated hemoglobin flows and volumes to neural activity

    Cornelius, Nathan R.; Nishimura, Nozomi; Suh, Minah; Schwartz, Theodore H.; Doerschuk, Peter C.

    2015-08-01

    Objective. To describe a toolkit of components for mathematical models of the relationship between cortical neural activity and space-resolved and time-resolved flows and volumes of oxygenated and deoxygenated hemoglobin motivated by optical intrinsic signal imaging (OISI). Approach. Both blood flow and blood volume and both oxygenated and deoxygenated hemoglobin and their interconversion are accounted for. Flow and volume are described by including analogies to both resistive and capacitive electrical circuit elements. Oxygenated and deoxygenated hemoglobin and their interconversion are described by generalization of Kirchhoff's laws based on well-mixed compartments. Main results. Mathematical models built from this toolkit are able to reproduce experimental single-stimulus OISI results that are described in papers from other research groups and are able to describe the response to multiple-stimuli experiments as a sublinear superposition of responses to the individual stimuli. Significance. The same assembly of tools from the toolkit but with different parameter values is able to describe effects that are considered distinctive, such as the presence or absence of an initial decrease in oxygenated hemoglobin concentration, indicating that the differences might be due to unique parameter values in a subject rather than different fundamental mechanisms.

  10. Accurate Wavelet Neural Network for Efficient Controlling of an Active Magnetic Bearing System

    Youssef Harkouss

    2010-01-01

    Full Text Available Problem statement: The synthesis of a command by the neural network has an excellent advantage over the classical one such as PID. This study presented a fast and accurate Wavelet Neural Network (WNN approach for efficient controlling of an Active Magnetic Bearing (AMB system. Approach: The proposed approach combined neural network with the wavelet theory. Wavelet theory may be exploited in deriving a good initialization for the neural network and thus improved convergence of the learning algorithm. Results: We tested two control systems based on three types of neural controllers: Multiplayer Perceptron (MLP controller, RBF Neural Network (RBFNN controller and WNN controller. The simulation results show that the proposed WNN controller provides better performance comparing with standard PID controller, MLP and RBFNN controllers. Conclusion: The proposed WNN approach was shown to be useful in controlling nonlinear dynamic mechanical system, such as the AMB system used in this study.

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

    Yaswant, Vaddi; Kumar, Amit; Sambandan, Sanjiv

    2016-07-01

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

  12. Sleep protects excitatory cortical circuits against oxidative damage.

    Schulze, Georg

    2004-01-01

    Activity in excitatory cortical pathways increases the oxidative metabolism of the brain and the risk of oxidative damage. Oxyradicals formed during periods of activity are mopped up by neural pools of nuclear factor kappa-B resulting in their activation and translocation to cell nuclei. During waking hours, glucocorticoids inhibit transactivation by nuclear factor kappa-B, increase central norepinephrine release, and elevate expression of prostaglandin D2. The build-up of nuclear factor kappa-B and prostaglandin D2 produces sleep pressures leading to sleep onset, normally gated by circadian melatonin release. During slow wave sleep nuclear factor kappa-B induces transcription of synaptogenic and antioxidant products and synaptic remodeling follows. Synaptically remodeled neural circuits have modified conductivity patterns and timescales and need to be resynchronized with existing unmodified neural circuits. The resynchronization process, mediated by theta rhythm, occurs during rapid eye movement sleep and is orchestrated from pontine centers. Resynchronization of remodeled neural circuits produces dreams. The waking state results upon successful resynchronization. Rapid eye movement sleep deprivation results in a lack of resynchronization and leads to cognitive inefficiencies. The model presented here proposes that the primary purpose of sleep is to protect cortical circuits against oxidative damage by reducing cortical activity and by remodeling and resynchronizing cortical circuits during this period of reduced activity to sustain new patterns of activation more effectively. PMID:15236776

  13. An application of multilayer neural network on hepatitis disease diagnosis using approximations of sigmoid activation function

    Onursal Çetin; Feyzullah Temurtaş; Şenol Gülgönül

    2015-01-01

    Objective: Implementation of multilayer neural network (MLNN) with sigmoid activation function for the diagnosis of hepatitis disease.Methods: Artificial neural networks (ANNs) are efficient tools currently in common use for medical diagnosis. In hardware based architectures activation functions play an important role in ANN behavior. Sigmoid function is the most frequently used activation function because of its smooth response. Thus, sigmoid function and its close approximations were implem...

  14. Role of emergent neural activity in visual map development

    Ackman, James B.; Crair, Michael C.

    2013-01-01

    The initial structural and functional development of visual circuits in reptiles, birds, and mammals happens independent of sensory experience. After eye opening, visual experience further refines and elaborates circuits that are critical for normal visual function. Innate genetic programs that code for gradients of molecules provide gross positional information for developing nerve cells, yet much of the cytoarchitectural complexity and synaptogenesis of neurons depends on calcium influx, ne...

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

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

    2016-06-01

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

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

    Migliori, Benjamin John

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

  17. Frequency translating phase conjugation circuit for active retrodirective antenna array. [microwave transmission

    Chernoff, R. (Inventor)

    1980-01-01

    An active retrodirective antenna array which has central phasing from a reference antenna element through a "tree" structured network of transmission lines utilizes a number of phase conjugate circuits (PCCs) at each node and a phase reference regeneration circuit (PRR) at each node except the initial node. Each node virtually coincides with an element of the array. A PCC generates the exact conjugate phase of an incident signal using a phase locked loop which combines the phases in an up converter, divides the sum by 2 and mixes the result with the phase in a down converter for phase detection. The PRR extracts the phase from the conjugate phase. Both the PCC and the PRR are not only exact but also free from mixer degeneracy.

  18. Nonlinear dynamics of neural delayed feedback

    Longtin, A.

    1990-01-01

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

  19. Anisotropy of ongoing neural activity in the primate visual cortex

    Maier A

    2014-09-01

    Full Text Available Alexander Maier,1 Michele A Cox,1 Kacie Dougherty,1 Brandon Moore,1 David A Leopold2 1Department of Psychology, College of Arts and Science, Vanderbilt University, Nashville, TN, USA; 2Section on Cognitive Neurophysiology and Imaging, National Institute of Mental Health, National Institute of Health, Bethesda, MD, USA Abstract: The mammalian neocortex features distinct anatomical variation in its tangential and radial extents. This review consolidates previously published findings from our group in order to compare and contrast the spatial profile of neural activity coherence across these distinct cortical dimensions. We focus on studies of ongoing local field potential (LFP data obtained simultaneously from multiple sites in the primary visual cortex in two types of experiments in which electrode contacts were spaced either along the cortical surface or at different laminar positions. These studies demonstrate that across both dimensions the coherence of ongoing LFP fluctuations diminishes as a function of interelectrode distance, although the nature and spatial scale of this falloff is very different. Along the cortical surface, the overall LFP coherence declines gradually and continuously away from a given position. In contrast, across the cortical layers, LFP coherence is discontinuous and compartmentalized as a function of depth. Specifically, regions of high LFP coherence fall into discrete superficial and deep laminar zones, with an abrupt discontinuity between the granular and infragranular layers. This spatial pattern of ongoing LFP coherence is similar when animals are at rest and when they are engaged in a behavioral task. These results point to the existence of partially segregated laminar zones of cortical processing that extend tangentially within the laminar compartments and are thus oriented orthogonal to the cortical columns. We interpret these electrophysiological observations in light of the known anatomical organization of

  20. Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition

    Francisco Javier Ordóñez; Daniel Roggen

    2016-01-01

    Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we pro...

  1. Tracking cortical entrainment in neural activity: Auditory processes in human temporal cortex

    Thwaites, Andrew; Nimmo-Smith, Ian; Fonteneau, Elisabeth; Patterson, Roy D.; Buttery, Paula; Marslen-Wilson, William D.

    2015-01-01

    A primary objective for cognitive neuroscience is to identify how features of the sensory environment are encoded in neural activity. Current auditory models of loudness perception can be used to make detailed predictions about the neural activity of the cortex as an individual listens to speech. We used two such models (loudness-sones and loudness-phons), varying in their psychophysiological realism, to predict the instantaneous loudness contours produced by 480 isolated words. These two set...

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

    Boushel, Robert Christopher; Calbet, J A; Rådegran, G; Sondergaard, H; Wagner, Poul Erik; Saltin, B

    2001-01-01

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

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

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

    2007-01-01

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

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

    Sindhu R; Dr Shilpa Mehta

    2015-01-01

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

  5. Dopamine D1 receptor activation rescues extinction impairments in low-estrogen female rats and induces cortical layer-specific activation changes in prefrontal-amygdala circuits.

    Rey, Colin D; Lipps, Jennifer; Shansky, Rebecca M

    2014-04-01

    Post-traumatic stress disorder (PTSD) is twice as common in women as in men; it is a major public health problem whose neurobiological basis is unknown. In preclinical studies using fear conditioning and extinction paradigms, women and female animals with low estrogen levels exhibit impaired extinction retrieval, but the mechanisms that underlie these hormone-based discrepancies have not been identified. There is much evidence that estrogen can modulate dopaminergic transmission, and here we tested the hypothesis that dopamine-estrogen interactions drive extinction processes in females. Intact male and female rats were trained on cued fear conditioning, and received an intraperitoneal injection of a D1 agonist or vehicle before extinction learning. As reported previously, females that underwent extinction during low estrogen estrous phases (estrus/metaestrus/diestrus (EMD)) froze more during extinction retrieval than those that had been in the high-estrogen phase (proestrus; PRO). However, D1 stimulation reversed this relationship, impairing extinction retrieval in PRO and enhancing it in EMD. We also combined retrograde tracing and fluorescent immunohistochemistry to measure c-fos expression in infralimbic (IL) projections to the basolateral area of the amygdala (BLA), a neural pathway known to be critical to extinction retrieval. Again we observed diverging, estrous-dependent effects; SKF treatment induced a positive correlation between freezing and IL-BLA circuit activation in EMD animals, and a negative correlation in PRO animals. These results show for the first time that hormone-dependent extinction deficits can be overcome with non-hormone-based interventions, and suggest a circuit-specific mechanism by which these behavioral effects occur. PMID:24343528

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

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

    2006-01-01

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

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

    Miyamoto, Daisuke; Murayama, Masanori

    2016-02-01

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

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

    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.

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

    Okado, Yoko; Stark, Craig E. L.

    2005-01-01

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

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

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

    2012-01-01

    Magnetic Resonance Imaging (MRI) of nuclei other than 1H (e.g. 13C) allows for characterisation of metabolic processes. Imaging of such nuclei, however, requires development of sensitive MRI coils. This paper describes the design of surface receive coils for 13C imaging in small animals. The design...... is based on application-specified coil profile and includes impedance matching and balancing circuits. Active decoupling is implemented in order to minimize the influence of the receiving coil on the homogeneity of the transmit-coil field. Measurement results for a coil prototype are presented...

  11. Fidelity of frequency and phase entrainment of circuit-level spike activity during DBS.

    Agnesi, Filippo; Muralidharan, Abirami; Baker, Kenneth B; Vitek, Jerrold L; Johnson, Matthew D

    2015-08-01

    High-frequency stimulation is known to entrain spike activity downstream and upstream of several clinical deep brain stimulation (DBS) targets, including the cerebellar-receiving area of thalamus (VPLo), subthalamic nucleus (STN), and globus pallidus (GP). Less understood are the fidelity of entrainment to each stimulus pulse, whether entrainment patterns are stationary over time, and how responses differ among DBS targets. In this study, three rhesus macaques were implanted with a single DBS lead in VPLo, STN, or GP. Single-unit spike activity was recorded in the resting state in motor cortex during VPLo DBS, in GP during STN DBS, and in STN and pallidal-receiving area of motor thalamus (VLo) during GP DBS. VPLo DBS induced time-locked spike activity in 25% (n = 15/61) of motor cortex cells, with entrained cells following 7.5 ± 7.4% of delivered pulses. STN DBS entrained spike activity in 26% (n = 8/27) of GP cells, which yielded time-locked spike activity for 8.7 ± 8.4% of stimulus pulses. GP DBS entrained 67% (n = 14/21) of STN cells and 32% (n = 19/59) of VLo cells, which showed a higher fraction of pulses effectively inhibiting spike activity (82.0 ± 9.6% and 86.1 ± 16.6%, respectively). Latency of phase-locked spike activity increased over time in motor cortex (58%, VPLo DBS) and to a lesser extent in GP (25%, STN DBS). In contrast, the initial inhibitory phase observed in VLo and STN during GP DBS remained stable following stimulation onset. Together, these data suggest that circuit-level entrainment is low-pass filtered during high-frequency stimulation, most notably for glutamatergic pathways. Moreover, phase entrainment is not stationary or consistent at the circuit level for all DBS targets. PMID:26084905

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

    Nazli eEmadi

    2014-11-01

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

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

    SHEN Yanjun; WANG Bingwen

    2004-01-01

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

  14. Progress in neural plasticity

    POO; Mu-Ming

    2010-01-01

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

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

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

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

    Mohit

    2015-01-01

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

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

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

    2005-02-01

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

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

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

    2008-01-01

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

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

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

    2011-01-01

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

  20. Relation of obesity to neural activation in response to food commercials

    Gearhardt, Ashley N; Yokum, Sonja; Stice, Eric; Harris, Jennifer L.; Brownell, Kelly D.

    2013-01-01

    Adolescents view thousands of food commercials annually, but the neural response to food advertising and its association with obesity is largely unknown. This study is the first to examine how neural response to food commercials differs from other stimuli (e.g. non-food commercials and television show) and to explore how this response may differ by weight status. The blood oxygen level-dependent functional magnetic resonance imaging activation was measured in 30 adolescents ranging from lean ...

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

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

    2008-01-01

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

  2. The impact of cancer on the neural activity

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

    2015-01-01

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

  3. Early Interfaced Neural Activity from Chronic Amputated Nerves

    Garde, Kshitija; Keefer, Edward; Botterman, Barry; Galvan, Pedro; Romero, Mario I.

    2009-01-01

    Direct interfacing of transected peripheral nerves with advanced robotic prosthetic devices has been proposed as a strategy for achieving natural motor control and sensory perception of such bionic substitutes, thus fully functionally replacing missing limbs in amputees. Multi-electrode arrays placed in the brain and peripheral nerves have been used successfully to convey neural control of prosthetic devices to the user. However, reactive gliosis, micro hemorrhages, axonopathy and excessive i...

  4. Early interfaced neural activity from chronic amputated nerves

    Kshitija Garde; Barry Botterman; Pedro Galvan

    2009-01-01

    Direct interfacing of transected peripheral nerves with advanced robotic prosthetic devices has been proposed as a strategy for achieving natural motor control and sensory perception of such bionic substitutes, thus fully functionally replacing missing limbs in amputees. Multi-electrode arrays placed in the brain and peripheral nerves have been used successfully to convey neural control of prosthetic devices to the user. However, reactive gliosis, micro hemorrhages, axonopathy and excessive i...

  5. A novel active T-type three-level converter with open-circuit fault-tolerant control

    Choi, Uimin; Blaabjerg, Frede

    This paper proposes a novel active T-type three-level converter concept with fault-tolerant control. With the proposed topology, the converter can be operated continuously under open-circuit faults. It is shown that the proposed topology not only can keep its outputs under open-circuit fault......-point devices have the highest thermal stresses. The feasibility and effectiveness of the proposed topology concept are verified by the simulation results....

  6. Temporal coupling between stimulus-evoked neural activity and hemodynamic responses from individual cortical columns

    Using previously published data from the whisker barrel cortex of anesthetized rodents (Berwick et al 2008 J. Neurophysiol. 99 787-98) we investigated whether highly spatially localized stimulus-evoked cortical hemodynamics responses displayed a linear time-invariant (LTI) relationship with neural activity. Presentation of stimuli to individual whiskers of 2 s and 16 s durations produced hemodynamics and neural activity spatially localized to individual cortical columns. Two-dimensional optical imaging spectroscopy (2D-OIS) measured hemoglobin responses, while multi-laminar electrophysiology recorded neural activity. Hemoglobin responses to 2 s stimuli were deconvolved with underlying evoked neural activity to estimate impulse response functions which were then convolved with neural activity evoked by 16 s stimuli to generate predictions of hemodynamic responses. An LTI system more adequately described the temporal neuro-hemodynamics coupling relationship for these spatially localized sensory stimuli than in previous studies that activated the entire whisker cortex. An inability to predict the magnitude of an initial 'peak' in the total and oxy- hemoglobin responses was alleviated when excluding responses influenced by overlying arterial components. However, this did not improve estimation of the hemodynamic responses return to baseline post-stimulus cessation.

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

    Nie, Xiaobing; Zheng, Wei Xing

    2016-03-01

    This paper addresses the problem of coexistence and dynamical behaviors of multiple equilibria for competitive neural networks. First, a general class of discontinuous nonmonotonic piecewise linear activation functions is introduced for competitive neural networks. Then based on the fixed point theorem and theory of strict diagonal dominance matrix, it is shown that under some conditions, such n -neuron competitive neural networks can have 5(n) equilibria, among which 3(n) equilibria are locally stable and the others are unstable. More importantly, it is revealed that the neural networks with the discontinuous activation functions introduced in this paper can have both more total equilibria and locally stable equilibria than the ones with other activation functions, such as the continuous Mexican-hat-type activation function and discontinuous two-level activation function. Furthermore, the 3(n) locally stable equilibria given in this paper are located in not only saturated regions, but also unsaturated regions, which is different from the existing results on multistability of neural networks with multiple level activation functions. A simulation example is provided to illustrate and validate the theoretical findings. PMID:25826814

  8. Visualization of cortical, subcortical and deep brain neural circuit dynamics during naturalistic mammalian behavior with head-mounted microscopes and chronically implanted lenses.

    Resendez, Shanna L; Jennings, Josh H; Ung, Randall L; Namboodiri, Vijay Mohan K; Zhou, Zhe Charles; Otis, James M; Nomura, Hiroshi; McHenry, Jenna A; Kosyk, Oksana; Stuber, Garret D

    2016-03-01

    Genetically encoded calcium indicators for visualizing dynamic cellular activity have greatly expanded our understanding of the brain. However, owing to the light-scattering properties of the brain, as well as the size and rigidity of traditional imaging technology, in vivo calcium imaging has been limited to superficial brain structures during head-fixed behavioral tasks. These limitations can now be circumvented by using miniature, integrated microscopes in conjunction with an implantable microendoscopic lens to guide light into and out of the brain, thus permitting optical access to deep brain (or superficial) neural ensembles during naturalistic behaviors. Here we describe steps to conduct such imaging studies using mice. However, we anticipate that the protocol can be easily adapted for use in other small vertebrates. Successful completion of this protocol will permit cellular imaging of neuronal activity and the generation of data sets with sufficient statistical power to correlate neural activity with stimulus presentation, physiological state and other aspects of complex behavioral tasks. This protocol takes 6-11 weeks to complete. PMID:26914316

  9. Cellular and circuit models of increased resting-state network gamma activity in schizophrenia.

    White, R S; Siegel, S J

    2016-05-01

    Schizophrenia (SCZ) is a disorder characterized by positive symptoms (hallucinations, delusions), negative symptoms (blunted affect, alogia, reduced sociability, and anhedonia), as well as persistent cognitive deficits (memory, concentration, and learning). While the biology underlying subjective experiences is difficult to study, abnormalities in electroencephalographic (EEG) measures offer a means to dissect potential circuit and cellular changes in brain function. EEG is indispensable for studying cerebral information processing due to the introduction of techniques for the decomposition of event-related activity into its frequency components. Specifically, brain activity in the gamma frequency range (30-80Hz) is thought to underlie cognitive function and may be used as an endophenotype to aid in diagnosis and treatment of SCZ. In this review we address evidence indicating that there is increased resting-state gamma power in SCZ. We address how modeling this aspect of the illness in animals may help treatment development as well as providing insights into the etiology of SCZ. PMID:26577758

  10. Striatal plasticity and basal ganglia circuit function.

    Kreitzer, Anatol C; Malenka, Robert C

    2008-11-26

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

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

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

    2016-05-18

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

  12. Perceptual similarity of visual patterns predicts dynamic neural activation patterns measured with MEG.

    Wardle, Susan G; Kriegeskorte, Nikolaus; Grootswagers, Tijl; Khaligh-Razavi, Seyed-Mahdi; Carlson, Thomas A

    2016-05-15

    Perceptual similarity is a cognitive judgment that represents the end-stage of a complex cascade of hierarchical processing throughout visual cortex. Previous studies have shown a correspondence between the similarity of coarse-scale fMRI activation patterns and the perceived similarity of visual stimuli, suggesting that visual objects that appear similar also share similar underlying patterns of neural activation. Here we explore the temporal relationship between the human brain's time-varying representation of visual patterns and behavioral judgments of perceptual similarity. The visual stimuli were abstract patterns constructed from identical perceptual units (oriented Gabor patches) so that each pattern had a unique global form or perceptual 'Gestalt'. The visual stimuli were decodable from evoked neural activation patterns measured with magnetoencephalography (MEG), however, stimuli differed in the similarity of their neural representation as estimated by differences in decodability. Early after stimulus onset (from 50ms), a model based on retinotopic organization predicted the representational similarity of the visual stimuli. Following the peak correlation between the retinotopic model and neural data at 80ms, the neural representations quickly evolved so that retinotopy no longer provided a sufficient account of the brain's time-varying representation of the stimuli. Overall the strongest predictor of the brain's representation was a model based on human judgments of perceptual similarity, which reached the limits of the maximum correlation with the neural data defined by the 'noise ceiling'. Our results show that large-scale brain activation patterns contain a neural signature for the perceptual Gestalt of composite visual features, and demonstrate a strong correspondence between perception and complex patterns of brain activity. PMID:26899210

  13. Voltage Estimation in Active Distribution Grids Using Neural Networks

    Pertl, Michael; Heussen, Kai; Gehrke, Oliver;

    2016-01-01

    observability of distribution systems has to be improved. To increase the situational awareness of the power system operator data driven methods can be employed. These methods benefit from newly available data sources such as smart meters. This paper presents a voltage estimation method based on neural networks......The power flow in distribution grids is becoming more complicated as reverse power flows and undesired voltage rises might occur under particular circumstances due to integration of renewable energy sources, increasing the occurrence of critical bus voltages. To identify these critical feeders the...

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

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

    2016-01-01

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

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

    Funamizu, Akihiro; Kanzaki, Ryohei; Takahashi, Hirokazu

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

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

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

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

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

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

    2013-01-01

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

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

    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. PMID:23178904

  19. An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer

    Staudenmayer, John; Pober, David; Crouter, Scott; Bassett, David; Freedson, Patty

    2009-01-01

    The aim of this investigation was to develop and test two artificial neural networks (ANN) to apply to physical activity data collected with a commonly used uniaxial accelerometer. The first ANN model estimated physical activity metabolic equivalents (METs), and the second ANN identified activity type. Subjects (n = 24 men and 24 women, mean age = 35 yr) completed a menu of activities that included sedentary, light, moderate, and vigorous intensities, and each activity was performed for 10 mi...

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

    Jinah Han

    2015-02-01

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

  1. Competing Neural Ensembles in Motor Cortex Gate Goal-Directed Motor Output.

    Zagha, Edward; Ge, Xinxin; McCormick, David A

    2015-11-01

    Unit recordings in behaving animals have revealed the transformation of sensory to motor representations in cortical neurons. However, we still lack basic insights into the mechanisms by which neurons interact to generate such transformations. Here, we study cortical circuits related to behavioral control in mice engaged in a sensory detection task. We recorded neural activity using extracellular and intracellular techniques and analyzed the task-related neural dynamics to reveal underlying circuit processes. Within motor cortex, we find two populations of neurons that have opposing spiking patterns in anticipation of movement. From correlation analyses and circuit modeling, we suggest that these dynamics reflect neural ensembles engaged in a competition. Furthermore, we demonstrate how this competitive circuit may convert a transient, sensory stimulus into a motor command. Together, these data reveal cellular and circuit processes underlying behavioral control and establish an essential framework for future studies linking cellular activity to behavior. PMID:26593093

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

    Jarry, Pierre

    2016-01-01

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

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

    Xiaoling, Guo; Bo, Zhang; Jing, Zhang

    2015-01-01

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

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

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

    2001-02-01

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

  5. Neuralized1 Activates CPEB3: A Novel Function of Ubiquitination in Synaptic Plasticity and Memory Storage

    Pavlopoulos, Elias; Trifilieff, Pierre; Chevaleyre, Vivien; Fioriti, Luana; Zairis, Sakellarios; Pagano, Andrew; Malleret, Gaël; Kandel, Eric R.

    2011-01-01

    The cytoplasmic polyadenylation element-binding protein 3 (CPEB3), a regulator of local protein synthesis, is the mouse homologue of ApCPEB, a functional prion protein in Aplysia. Here, we provide evidence that CPEB3 is activated by Neuralized1, an E3 ubiquitin ligase. In hippocampal cultures, CPEB3 activated by Neuralized1-mediated ubiquitination leads both to the growth of new dendritic spines and to an increase of the GluA1 and GluA2 subunits of AMPA receptors, two CPEB3 targets essential ...

  6. Computational modeling of neural activities for statistical inference

    Kolossa, Antonio

    2016-01-01

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

  7. Improved training of neural networks for the nonlinear active control of sound and vibration.

    Bouchard, M; Paillard, B; Le Dinh, C T

    1999-01-01

    Active control of sound and vibration has been the subject of a lot of research in recent years, and examples of applications are now numerous. However, few practical implementations of nonlinear active controllers have been realized. Nonlinear active controllers may be required in cases where the actuators used in active control systems exhibit nonlinear characteristics, or in cases when the structure to be controlled exhibits a nonlinear behavior. A multilayer perceptron neural-network based control structure was previously introduced as a nonlinear active controller, with a training algorithm based on an extended backpropagation scheme. This paper introduces new heuristical training algorithms for the same neural-network control structure. The objective is to develop new algorithms with faster convergence speed (by using nonlinear recursive-least-squares algorithms) and/or lower computational loads (by using an alternative approach to compute the instantaneous gradient of the cost function). Experimental results of active sound control using a nonlinear actuator with linear and nonlinear controllers are presented. The results show that some of the new algorithms can greatly improve the learning rate of the neural-network control structure, and that for the considered experimental setup a neural-network controller can outperform linear controllers. PMID:18252535

  8. VLSI circuits implementing computational models of neocortical circuits.

    Wijekoon, Jayawan H B; Dudek, Piotr

    2012-09-15

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

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

    Blaabjerg, Frede; Chiarantoni, Ernesto; Aquila, Antonio Dell’; Liserre, Marco; Vergura, Silvano

    2004-01-01

    Three-phase active rectifiers based on the voltage source converter topology can successfully replace traditional thyristor based rectifiers or diode bridge plus chopper in interfacing dc-systems to the grid. However, if the application in which they are employed has a high safety issue or if there......, to the grid side stiffness and to the parameters of the controller has never been detailed considered. In this paper the experimental results of an LCL-filter-based three-phase active rectifier are analysed with the circuit theory approach. A ?virtual circuit? is synthesized in role of the digital...

  10. Genetic Enhancement of Visual Learning by Activation of Protein Kinase C Pathways in Small Groups of Rat Cortical Neurons

    Zhang, Guo-rong; Wang, Xiaodan; Kong, Lingxin; Lu, Xiu-Gui; Lee, Brian; Liu, Meng; Sun, Mei; Franklin, Corinna; Cook, Robert G.; Geller, Alfred I.

    2005-01-01

    Although learning and memory theories hypothesize that memories are encoded by specific circuits, it has proven difficult to localize learning within a cortical area. Neural network theories predict that activation of a small fraction of the neurons in a circuit can activate that circuit. Consequently, altering the physiology of a small group of neurons might potentiate a specific circuit and enhance learning, thereby localizing learning to that circuit. In this study, we activated protein ki...

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

    Abhishek eBanerjee

    2012-05-01

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

  12. An Artificial Neural Network Controller for Three-level Shunt Active Filter to Eliminate the Current Harmonics and Compensate Reactive Power

    Chennai Salim

    2011-09-01

    Full Text Available The increased use of nonlinear devices in the industry has resulted in the direct increase of harmonic distortion in power systems during these last years. Active filter systems are proposed to mitigate current harmonics generated by nonlinear loads. The conventional scheme based on a two-level voltage source inverter controlled by a hysteresis controller has several disadvantages and cannot be used for medium or high-power applications. To overcome these drawbacks and improve the APF performance, there’s a great tendency to use multilevel inverters controlled by intelligent controllers. Three level (NPC inverter is one of the most widely used topologies in various industrial applications such as machine drives and power factor compensators. On the other hand, artificial neural networks are under study and investigation in other power electronics applications. In order to gain the advantages of the three-level inverter and artificial neural networks and to reduce the complexity of classical control schemes, a new active power filter configuration controlled by two MLPNN (Multi-Layer Perceptron Neural Network is proposed in this paper. The first ANN is used to replace the PWM current controller, and the second one to maintain a constant dc link voltage across the capacitors and compensate the inverter power losses. The performance of the global system, including power and control circuits is evaluated by Matlab-Simulink and SimPowerSystem Toolbox simulation. The obtained results confirm the effectiveness of the proposed control scheme.

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

    Sindhu R

    2015-04-01

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

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

    Adriana Galvan

    2015-02-01

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

  15. Functional magnetic resonance imaging in awake transgenic fragile X rats: evidence of dysregulation in reward processing in the mesolimbic/habenular neural circuit.

    Kenkel, W M; Yee, J R; Moore, K; Madularu, D; Kulkarni, P; Gamber, K; Nedelman, M; Ferris, C F

    2016-01-01

    Anxiety and social deficits, often involving communication impairment, are fundamental clinical features of fragile X syndrome. There is growing evidence that dysregulation in reward processing is a contributing factor to the social deficits observed in many psychiatric disorders. Hence, we hypothesized that transgenic fragile X mental retardation 1 gene (fmr1) KO (FX) rats would display alterations in reward processing. To this end, awake control and FX rats were imaged for changes in blood oxygen level dependent (BOLD) signal intensity in response to the odor of almond, a stimulus to elicit the innate reward response. Subjects were 'odor naive' to this evolutionarily conserved stimulus. The resulting changes in brain activity were registered to a three-dimensional segmented, annotated rat atlas delineating 171 brain regions. Both wild-type (WT) and FX rats showed robust brain activation to a rewarding almond odor, though FX rats showed an altered temporal pattern and tended to have a higher number of voxels with negative BOLD signal change from baseline. This pattern of greater negative BOLD was especially apparent in the Papez circuit, critical to emotional processing and the mesolimbic/habenular reward circuit. WT rats showed greater positive BOLD response in the supramammillary area, whereas FX rats showed greater positive BOLD response in the dorsal lateral striatum, and greater negative BOLD response in the retrosplenial cortices, the core of the accumbens and the lateral preoptic area. When tested in a freely behaving odor-investigation paradigm, FX rats failed to show the preference for almond odor which typifies WT rats. However, FX rats showed investigation profiles similar to WT when presented with social odors. These data speak to an altered processing of this highly salient novel odor in the FX phenotype and lend further support to the notion that altered reward systems in the brain may contribute to fragile X syndrome symptomology. PMID:27003189

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

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

    2016-07-01

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

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

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

    2016-07-01

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

  18. Rapid neural regulation of muscle urokinase-like plasminogen activator as defined by nerve crush.

    Hantaï, D; Rao, J. S.; Festoff, B W

    1990-01-01

    Muscle plasminogen activators (PAs), such as urokinase-like PA and, to a lesser extent, tissue PA, increase dramatically after denervation induced by axotomy. The PA/plasmin system has also been implicated in degradation of specific components of the muscle fiber basement membrane after local activation of plasminogen. These results suggest that neural regulation of muscle extracellular matrix metabolism accompanies or precedes regeneration after injury and is mediated by activation of PAs. I...

  19. The circuit designer's companion

    Williams, Tim

    2013-01-01

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

  20. Neural circuits of eye movements during performance of the visual exploration task, which is similar to the responsive search score task, in schizophrenia patients and normal subjects

    Abnormal exploratory eye movements have been studied as a biological marker for schizophrenia. Using functional MRI (fMRI), we investigated brain activations of 12 healthy and 8 schizophrenic subjects during performance of a visual exploration task that is similar to the responsive search score task to clarify the neural basis of the abnormal exploratory eye movement. Performance data, such as the number of eye movements, the reaction time, and the percentage of correct answers showed no significant differences between the two groups. Only the normal subjects showed activations at the bilateral thalamus and the left anterior medial frontal cortex during the visual exploration tasks. In contrast, only the schizophrenic subjects showed activations at the right anterior cingulate gyms during the same tasks. The activation at the different locations between the two groups, the left anterior medial frontal cortex in normal subjects and the right anterior cingulate gyrus in schizophrenia subjects, was explained by the feature of the visual tasks. Hypoactivation at the bilateral thalamus supports a dysfunctional filtering theory of schizophrenia. (author)

  1. Neural Networks for Logic Circuits

    1998-01-01

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

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

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

    2006-01-01

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

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

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

    2016-02-01

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

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

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

    2011-01-01

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

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

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

    2007-01-01

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

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

    Eliza eCongdon

    2013-09-01

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

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

    Singh, Vandana

    2010-01-01

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

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

    YAN JiHong(闫纪红); WU Cheng(吴澄)

    2003-01-01

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

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

    Duan, Lian; Huang, Lihong; Guo, Zhenyuan

    2016-07-01

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

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

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

    2012-01-01

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

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

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

    2013-01-01

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

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

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

    2010-01-01

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

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

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

    2010-01-01

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

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

    Bhowmik, Subrata

    2011-01-01

    This paper presents a neural network based semi-active control method for a rotary type magnetorheological (MR) damper. The characteristics of the MR damper are described by the classic Bouc-Wen model, and the performance of the proposed control method is evaluated in terms of a base exited shear......-displacement trajectories. The proposed neural network controller is therefore trained based on data derived from these desired forcedisplacement curves, where the optimal relation between friction force level and response amplitude is determined explicitly by simply maximizing the damping ratio of the targeted vibration...... mode of the structure. The neural network control is then developed to reproduce the desired force based on damper displacement and velocity as network input, and it is therefore referred to as an amplitude dependent model reference control method. An inverse model of the MR damper is needed to...

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

    Bhowmik, Subrata

    2011-01-01

    This paper presents a neural network based semi-active control method for a rotary type magnetorheological (MR) damper. The characteristics of the MR damper are described by the classic Bouc-Wen model, and the performance of the proposed control method is evaluated in terms of a base exited shear......-displacement trajectories. The proposed neural network controller is therefore trained based on data derived from these desired forcedisplacement curves, where the optimal relation between friction force level and response amplitude is determined explicitly by simply maximizing the damping ratio of the targeted vibration...... mode of the structure. The neural network control is then developed to reproduce the desired force based on damper displacement and velocity as network input, and it is therefore referred to as an amplitude dependent model reference control method. An inverse model of the MR damper is needed to...

  16. 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.

    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

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

    Tang, Yi; Blaabjerg, Frede

    2015-01-01

    This letter proposes a novel circuit topology which can realize the power decoupling function without adding additional active switches into the circuit. The dc-link capacitor of a full bridge rectifier is split into two identical parts and the midpoint is connected to one leg through a filter...... component, e.g. inductors or film capacitors for ripple energy storage because this task can be accomplished by the dc-link capacitors, and therefore its implementation cost can be minimized. Another unique feature of the proposed topology is that the current stress of power semiconductors can be reduced...

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

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

    2016-01-01

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

  19. Altered temporal variance and neural synchronization of spontaneous brain activity in anesthesia.

    Huang, Zirui; Wang, Zhiyao; Zhang, Jianfeng; Dai, Rui; Wu, Jinsong; Li, Yuan; Liang, Weimin; Mao, Ying; Yang, Zhong; Holland, Giles; Zhang, Jun; Northoff, Georg

    2014-11-01

    Recent studies at the cellular and regional levels have pointed out the multifaceted importance of neural synchronization and temporal variance of neural activity. For example, neural synchronization and temporal variance has been shown by us to be altered in patients in the vegetative state (VS). This finding nonetheless leaves open the question of whether these abnormalities are specific to VS or rather more generally related to the absence of consciousness. The aim of our study was to investigate the changes of inter- and intra-regional neural synchronization and temporal variance of resting state activity in anesthetic-induced unconsciousness state. Applying an intra-subject design, we compared resting state activity in functional magnetic resonance imaging (fMRI) between awake versus anesthetized states in the same subjects. Replicating previous studies, we observed reduced functional connectivity within the default mode network (DMN) and thalamocortical network in the anesthetized state. Importantly, intra-regional synchronization as measured by regional homogeneity (ReHo) and temporal variance as measured by standard deviation (SD) of the BOLD signal were significantly reduced in especially the cortical midline regions, while increased in the lateral cortical areas in the anesthetized state. We further found significant frequency-dependent effects of SD in the thalamus, which showed abnormally high SD in Slow-5 (0.01-0.027 Hz) in the anesthetized state. Our results show for the first time of altered temporal variance of resting state activity in anesthesia. Combined with our findings in the vegetative state, these findings suggest a close relationship between temporal variance, neural synchronization and consciousness. PMID:24867379

  20. A New Training Method for Feedforward Neural Networks Based on Geometric Contraction Property of Activation Functions

    Birtea, Petre; Cernazanu-Glavan, Cosmin; Sisu, Alexandru

    2016-01-01

    We propose a new training method for a feedforward neural network having the activation functions with the geometric contraction property. The method consists of constructing a new functional that is less nonlinear in comparison with the classical functional by removing the nonlinearity of the activation functions from the output layer. We validate this new method by a series of experiments that show an improved learning speed and also a better classification error.

  1. Population-wide distributions of neural activity during perceptual decision-making.

    Wohrer, Adrien; Humphries, Mark D.; Machens, Christian K

    2013-01-01

    Cortical activity involves large populations of neurons, even when it is limited to functionally coherent areas. Electrophysiological recordings, on the other hand, involve comparatively small neural ensembles, even when modern-day techniques are used. Here we review results which have started to fill the gap between these two scales of inquiry, by shedding light on the statistical distributions of activity in large populations of cells. We put our main focus on data recorded in awake animals...

  2. Hyperinsulinemia produces both sympathetic neural activation and vasodilation in normal humans.

    Anderson, E A; Hoffman, R P; Balon, T W; Sinkey, C A; Mark, A L

    1991-01-01

    Hyperinsulinemia may contribute to hypertension by increasing sympathetic activity and vascular resistance. We sought to determine if insulin increases central sympathetic neural outflow and vascular resistance in humans. We recorded muscle sympathetic nerve activity (MSNA; microneurography, peroneal nerve), forearm blood flow (plethysmography), heart rate, and blood pressure in 14 normotensive males during 1-h infusions of low (38 mU/m2/min) and high (76 mU/m2/min) doses of insulin while hol...

  3. Neural networks with periodic and monotonic activation functions: a comparative study in classification problems

    Romero Merino, Enrique; Sopena, Josep Maria; Alquézar Mancho, René; Moliner, Joan L.

    2000-01-01

    This article discusses a number of reasons why the use of non-monotonic functions as activation functions can lead to a marked improvement in the performance of a neural network. Using a wide range of benchmarks we show that a multilayer feed-forward network using sine activation functions (and an appropriate choice of initial parameters) learns much faster than one incorporating sigmoid functions - as much as 150-500 times faster - when both types are trained with backpr...

  4. The neural processing of taste

    Katz Donald B

    2007-09-01

    Full Text Available Abstract Although there have been many recent advances in the field of gustatory neurobiology, our knowledge of how the nervous system is organized to process information about taste is still far from complete. Many studies on this topic have focused on understanding how gustatory neural circuits are spatially organized to represent information about taste quality (e.g., "sweet", "salty", "bitter", etc.. Arguments pertaining to this issue have largely centered on whether taste is carried by dedicated neural channels or a pattern of activity across a neural population. But there is now mounting evidence that the timing of neural events may also importantly contribute to the representation of taste. In this review, we attempt to summarize recent findings in the field that pertain to these issues. Both space and time are variables likely related to the mechanism of the gustatory neural code: information about taste appears to reside in spatial and temporal patterns of activation in gustatory neurons. What is more, the organization of the taste network in the brain would suggest that the parameters of space and time extend to the neural processing of gustatory information on a much grander scale.

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

    Jasbir D Upadhyaya

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

  6. Neural circuits underlying tongue movements for the prey-catching behavior in frog: distribution of primary afferent terminals on motoneurons supplying the tongue.

    Kecskes, Szilvia; Matesz, Clara; Gaál, Botond; Birinyi, András

    2016-04-01

    The hypoglossal motor nucleus is one of the efferent components of the neural network underlying the tongue prehension behavior of Ranid frogs. Although the appropriate pattern of the motor activity is determined by motor pattern generators, sensory inputs can modify the ongoing motor execution. Combination of fluorescent tracers were applied to investigate whether there are direct contacts between the afferent fibers of the trigeminal, facial, vestibular, glossopharyngeal-vagal, hypoglossal, second cervical spinal nerves and the hypoglossal motoneurons. Using confocal laser scanning microscope, we detected different number of close contacts from various sensory fibers, which were distributed unequally between the motoneurons innervating the protractor, retractor and inner muscles of the tongue. Based on the highest number of contacts and their closest location to the perikaryon, the glossopharyngeal-vagal nerves can exert the strongest effect on hypoglossal motoneurons and in agreement with earlier physiological results, they influence the protraction of the tongue. The second largest number of close appositions was provided by the hypoglossal and second cervical spinal afferents and they were located mostly on the proximal and middle parts of the dendrites of retractor motoneurons. Due to their small number and distal location, the trigeminal and vestibular terminals seem to have minor effects on direct activation of the hypoglossal motoneurons. We concluded that direct contacts between primary afferent terminals and hypoglossal motoneurons provide one of the possible morphological substrates of very quick feedback and feedforward modulation of the motor program during various stages of prey-catching behavior. PMID:25575900

  7. Concurrent multitasking: From neural activity to human cognition

    Nijboer, Menno

    2016-01-01

    Multitasking has become an important part of our daily lives. This delicate juggling act between several activities occurs when people drive, when they are working, and even when they should be paying attention in the classroom. While multitasking is typically considered as something to avoid, there are instances where we are perfectly capable at performing multiple activities concurrently. It is therefore important that we understand how multitasking works, so that we can predict when engagi...

  8. Neural portraits of perception: reconstructing face images from evoked brain activity.

    Cowen, Alan S; Chun, Marvin M; Kuhl, Brice A

    2014-07-01

    Recent neuroimaging advances have allowed visual experience to be reconstructed from patterns of brain activity. While neural reconstructions have ranged in complexity, they have relied almost exclusively on retinotopic mappings between visual input and activity in early visual cortex. However, subjective perceptual information is tied more closely to higher-level cortical regions that have not yet been used as the primary basis for neural reconstructions. Furthermore, no reconstruction studies to date have reported reconstructions of face images, which activate a highly distributed cortical network. Thus, we investigated (a) whether individual face images could be accurately reconstructed from distributed patterns of neural activity, and (b) whether this could be achieved even when excluding activity within occipital cortex. Our approach involved four steps. (1) Principal component analysis (PCA) was used to identify components that efficiently represented a set of training faces. (2) The identified components were then mapped, using a machine learning algorithm, to fMRI activity collected during viewing of the training faces. (3) Based on activity elicited by a new set of test faces, the algorithm predicted associated component scores. (4) Finally, these scores were transformed into reconstructed images. Using both objective and subjective validation measures, we show that our methods yield strikingly accurate neural reconstructions of faces even when excluding occipital cortex. This methodology not only represents a novel and promising approach for investigating face perception, but also suggests avenues for reconstructing 'offline' visual experiences-including dreams, memories, and imagination-which are chiefly represented in higher-level cortical areas. PMID:24650597

  9. Linear integrated circuits

    Young, T.

    This book is intended to be used as a textbook in a one-semester course at a variety of levels. Because of self-study features incorporated, it may also be used by practicing electronic engineers as a formal and thorough introduction to the subject. The distinction between linear and digital integrated circuits is discussed, taking into account digital and linear signal characteristics, linear and digital integrated circuit characteristics, the definitions for linear and digital circuits, applications of digital and linear integrated circuits, aspects of fabrication, packaging, and classification and numbering. Operational amplifiers are considered along with linear integrated circuit (LIC) power requirements and power supplies, voltage and current regulators, linear amplifiers, linear integrated circuit oscillators, wave-shaping circuits, active filters, DA and AD converters, demodulators, comparators, instrument amplifiers, current difference amplifiers, analog circuits and devices, and aspects of troubleshooting.

  10. Imaging the neural circuitry and chemical control of aggressive motivation

    Blanchard D Caroline

    2008-11-01

    Full Text Available Abstract Background With the advent of functional magnetic resonance imaging (fMRI in awake animals it is possible to resolve patterns of neuronal activity across the entire brain with high spatial and temporal resolution. Synchronized changes in neuronal activity across multiple brain areas can be viewed as functional neuroanatomical circuits coordinating the thoughts, memories and emotions for particular behaviors. To this end, fMRI in conscious rats combined with 3D computational analysis was used to identifying the putative distributed neural circuit involved in aggressive motivation and how this circuit is affected by drugs that block aggressive behavior. Results To trigger aggressive motivation, male rats were presented with their female cage mate plus a novel male intruder in the bore of the magnet during image acquisition. As expected, brain areas previously identified as critical in the organization and expression of aggressive behavior were activated, e.g., lateral hypothalamus, medial basal amygdala. Unexpected was the intense activation of the forebrain cortex and anterior thalamic nuclei. Oral administration of a selective vasopressin V1a receptor antagonist SRX251 or the selective serotonin reuptake inhibitor fluoxetine, drugs that block aggressive behavior, both caused a general suppression of the distributed neural circuit involved in aggressive motivation. However, the effect of SRX251, but not fluoxetine, was specific to aggression as brain activation in response to a novel sexually receptive female was unaffected. Conclusion The putative neural circuit of aggressive motivation identified with fMRI includes neural substrates contributing to emotional expression (i.e. cortical and medial amygdala, BNST, lateral hypothalamus, emotional experience (i.e. hippocampus, forebrain cortex, anterior cingulate, retrosplenial cortex and the anterior thalamic nuclei that bridge the motor and cognitive components of aggressive responding

  11. Model Integrating Fuzzy Argument with Neural Network Enhancing the Performance of Active Queue Management

    Nguyen Kim Quoc

    2015-08-01

    Full Text Available The bottleneck control by active queue management mechanisms at network nodes is essential. In recent years, some researchers have used fuzzy argument to improve the active queue management mechanisms to enhance the network performance. However, the projects using the fuzzy controller depend heavily on professionals and their parameters cannot be updated according to changes in the network, so the effectiveness of this mechanism is not high. Therefore, we propose a model combining the fuzzy controller with neural network (FNN to overcome the limitations above. Results of the training of the neural networks will find the optimal parameters for the adaptive fuzzy controller well to changes of the network. This improves the operational efficiency of the active queue management mechanisms at network nodes.

  12. Quantitative meta-analysis of neural activity in posttraumatic stress disorder

    Hayes Jasmeet P

    2012-05-01

    Full Text Available Abstract Background In recent years, neuroimaging techniques such as functional magnetic resonance imaging (fMRI and positron emission tomography (PET have played a significant role in elucidating the neural underpinnings of posttraumatic stress disorder (PTSD. However, a detailed understanding of the neural regions implicated in the disorder remains incomplete because of considerable variability in findings across studies. The aim of this meta-analysis was to identify consistent patterns of neural activity across neuroimaging study designs in PTSD to improve understanding of the neurocircuitry of PTSD. Methods We conducted a literature search for PET and fMRI studies of PTSD that were published before February 2011. The article search resulted in 79 functional neuroimaging PTSD studies. Data from 26 PTSD peer-reviewed neuroimaging articles reporting results from 342 adult patients and 342 adult controls were included. Peak activation coordinates from selected articles were used to generate activation likelihood estimate maps separately for symptom provocation and cognitive-emotional studies of PTSD. A separate meta-analysis examined the coupling between ventromedial prefrontal cortex and amygdala activity in patients. Results Results demonstrated that the regions most consistently hyperactivated in PTSD patients included mid- and dorsal anterior cingulate cortex, and when ROI studies were included, bilateral amygdala. By contrast, widespread hypoactivity was observed in PTSD including the ventromedial prefrontal cortex and the inferior frontal gyrus. Furthermore, decreased ventromedial prefrontal cortex activity was associated with increased amygdala activity. Conclusions These results provide evidence for a neurocircuitry model of PTSD that emphasizes alteration in neural networks important for salience detection and emotion regulation.

  13. Concurrent multitasking : From neural activity to human cognition

    Nijboer, Menno

    2016-01-01

    Multitasking has become an important part of our daily lives. This delicate juggling act between several activities occurs when people drive, when they are working, and even when they should be paying attention in the classroom. While multitasking is typically considered as something to avoid, there

  14. Neural control of glutamine synthetase activity in rat skeletal muscles.

    Feng, B; Konagaya, M; Konagaya, Y; Thomas, J W; Banner, C; Mill, J; Max, S R

    1990-05-01

    The mechanism of glutamine synthetase induction in rat skeletal muscle after denervation or limb immobilization was investigated. Adult male rats were subjected to midthigh section of the sciatic nerve. At 1, 2, and 5 h and 1, 2, and 7 days after denervation, rats were killed and denervated, and contralateral control soleus and plantaris muscles were excised, weighted, homogenized, and assayed for glutamine synthetase. Glutamine synthetase activity increased approximately twofold 1 h after denervation in both muscles. By 7 days postdenervation enzyme activity had increased to three times the control level in plantaris muscle and to four times the control level in soleus muscle. Increased enzyme activity after nerve section was associated with increased maximum velocity with no change in apparent Michaelis constant. Immunotitration with an antiglutamine synthetase antibody suggested that denervation caused an increase in the number of glutamine synthetase molecules in muscle. However, Northern-blot analysis revealed no increase in the steady-state level of glutamine synthetase mRNA after denervation. A mixing experiment failed to yield evidence for the presence of a soluble factor involved in regulating the activity of glutamine synthetase in denervated muscle. A combination of denervation and dexamethasone injections resulted in additive increases in glutamine synthetase. Thus the mechanism underlying increased glutamine synthetase after denervation appears to be posttranscriptional and is distinct from that of the glucocorticoid-mediated glutamine synthetase induction previously described by us. PMID:1970709

  15. Alternative Sensor System and MLP Neural Network for Vehicle Pedal Activity Estimation

    Ahmed M. Wefky

    2010-04-01

    Full Text Available It is accepted that the activity of the vehicle pedals (i.e., throttle, brake, clutch reflects the driver’s behavior, which is at least partially related to the fuel consumption and vehicle pollutant emissions. This paper presents a solution to estimate the driver activity regardless of the type, model, and year of fabrication of the vehicle. The solution is based on an alternative sensor system (regime engine, vehicle speed, frontal inclination and linear acceleration that reflects the activity of the pedals in an indirect way, to estimate that activity by means of a multilayer perceptron neural network with a single hidden layer.

  16. Progress Towards Biocompatible Intracortical Microelectrodes for Neural Interfacing Applications

    Jorfi, Mehdi; Skousen, John L.; Weder, Christoph; Capadona, Jeffrey R.

    2014-01-01

    To ensure long-term consistent neural recordings, next-generation intracortical microelectrodes are being developed with an increased emphasis on reducing the neuro-inflammatory response. The increased emphasis stems from the improved understanding of the multifaceted role that inflammation may play in disrupting both biologic and abiologic components of the overall neural interface circuit. To combat neuro-inflammation and improve recording quality, the field is actively progressing from tra...

  17. Optimal Recognition Method of Human Activities Using Artificial Neural Networks

    Oniga Stefan

    2015-12-01

    Full Text Available The aim of this research is an exhaustive analysis of the various factors that may influence the recognition rate of the human activity using wearable sensors data. We made a total of 1674 simulations on a publically released human activity database by a group of researcher from the University of California at Berkeley. In a previous research, we analyzed the influence of the number of sensors and their placement. In the present research we have examined the influence of the number of sensor nodes, the type of sensor node, preprocessing algorithms, type of classifier and its parameters. The final purpose is to find the optimal setup for best recognition rates with lowest hardware and software costs.

  18. Optimal Recognition Method of Human Activities Using Artificial Neural Networks

    Oniga, Stefan; József, Sütő

    2015-12-01

    The aim of this research is an exhaustive analysis of the various factors that may influence the recognition rate of the human activity using wearable sensors data. We made a total of 1674 simulations on a publically released human activity database by a group of researcher from the University of California at Berkeley. In a previous research, we analyzed the influence of the number of sensors and their placement. In the present research we have examined the influence of the number of sensor nodes, the type of sensor node, preprocessing algorithms, type of classifier and its parameters. The final purpose is to find the optimal setup for best recognition rates with lowest hardware and software costs.

  19. Measuring circuits

    Graf, Rudolf F

    1996-01-01

    This series of circuits provides designers with a quick source for measuring circuits. Why waste time paging through huge encyclopedias when you can choose the topic you need and select any of the specialized circuits sorted by application?This book in the series has 250-300 practical, ready-to-use circuit designs, with schematics and brief explanations of circuit operation. The original source for each circuit is listed in an appendix, making it easy to obtain additional information.Ready-to-use circuits.Grouped by application for easy look-up.Circuit source listings

  20. Circuit theory

    This book is divided into fourteen chapters, which deals with circuit theory of basis, sinusoidal alternating current on cycle and frequency, basics current circuit about R.L, C circuit and resonant circuit, current power, general linear circuit, inductive coupling circuit and vector locus on an alternating current bridge and mutual inductance and coupling coefficient, multiphase alternating current and method of symmetrical coordinates, non-sinusoidal alternating current, two terminal network, four terminal network, transient of circuits, distributed line circuit constant, frequency characteristic and a filter and Laplace transformation.

  1. Environmental layout complexity affects neural activity during navigation in humans.

    Slone, Edward; Burles, Ford; Iaria, Giuseppe

    2016-05-01

    Navigating large-scale surroundings is a fundamental ability. In humans, it is commonly assumed that navigational performance is affected by individual differences, such as age, sex, and cognitive strategies adopted for orientation. We recently showed that the layout of the environment itself also influences how well people are able to find their way within it, yet it remains unclear whether differences in environmental complexity are associated with changes in brain activity during navigation. We used functional magnetic resonance imaging to investigate how the brain responds to a change in environmental complexity by asking participants to perform a navigation task in two large-scale virtual environments that differed solely in interconnection density, a measure of complexity defined as the average number of directional choices at decision points. The results showed that navigation in the simpler, less interconnected environment was faster and more accurate relative to the complex environment, and such performance was associated with increased activity in a number of brain areas (i.e. precuneus, retrosplenial cortex, and hippocampus) known to be involved in mental imagery, navigation, and memory. These findings provide novel evidence that environmental complexity not only affects navigational behaviour, but also modulates activity in brain regions that are important for successful orientation and navigation. PMID:26990572

  2. A Granger causality measure for point process models of ensemble neural spiking activity.

    Sanggyun Kim

    2011-03-01

    Full Text Available The ability to identify directional interactions that occur among multiple neurons in the brain is crucial to an understanding of how groups of neurons cooperate in order to generate specific brain functions. However, an optimal method of assessing these interactions has not been established. Granger causality has proven to be an effective method for the analysis of the directional interactions between multiple sets of continuous-valued data, but cannot be applied to neural spike train recordings due to their discrete nature. This paper proposes a point process framework that enables Granger causality to be applied to point process data such as neural spike trains. The proposed framework uses the point process likelihood function to relate a neuron's spiking probability to possible covariates, such as its own spiking history and the concurrent activity of simultaneously recorded neurons. Granger causality is assessed based on the relative reduction of the point process likelihood of one neuron obtained excluding one of its covariates compared to the likelihood obtained using all of its covariates. The method was tested on simulated data, and then applied to neural activity recorded from the primary motor cortex (MI of a Felis catus subject. The interactions present in the simulated data were predicted with a high degree of accuracy, and when applied to the real neural data, the proposed method identified causal relationships between many of the recorded neurons. This paper proposes a novel method that successfully applies Granger causality to point process data, and has the potential to provide unique physiological insights when applied to neural spike trains.

  3. Differences in neural activity when processing emotional arousal and valence in autism spectrum disorders.

    Tseng, Angela; Wang, Zhishun; Huo, Yuankai; Goh, Suzanne; Russell, James A; Peterson, Bradley S

    2016-02-01

    Individuals with autism spectrum disorders (ASD) often have difficulty recognizing and interpreting facial expressions of emotion, which may impair their ability to navigate and communicate successfully in their social, interpersonal environments. Characterizing specific differences between individuals with ASD and their typically developing (TD) counterparts in the neural activity subserving their experience of emotional faces may provide distinct targets for ASD interventions. Thus we used functional magnetic resonance imaging (fMRI) and a parametric experimental design to identify brain regions in which neural activity correlated with ratings of arousal and valence for a broad range of emotional faces. Participants (51 ASD, 84 TD) were group-matched by age, sex, IQ, race, and socioeconomic status. Using task-related change in blood-oxygen-level-dependent (BOLD) fMRI signal as a measure, and covarying for age, sex, FSIQ, and ADOS scores, we detected significant differences across diagnostic groups in the neural activity subserving the dimension of arousal but not valence. BOLD-signal in TD participants correlated inversely with ratings of arousal in regions associated primarily with attentional functions, whereas BOLD-signal in ASD participants correlated positively with arousal ratings in regions commonly associated with impulse control and default-mode activity. Only minor differences were detected between groups in the BOLD signal correlates of valence ratings. Our findings provide unique insight into the emotional experiences of individuals with ASD. Although behavioral responses to face-stimuli were comparable across diagnostic groups, the corresponding neural activity for our ASD and TD groups differed dramatically. The near absence of group differences for valence correlates and the presence of strong group differences for arousal correlates suggest that individuals with ASD are not atypical in all aspects of emotion-processing. Studying these similarities

  4. Relation of obesity to neural activation in response to food commercials.

    Gearhardt, Ashley N; Yokum, Sonja; Stice, Eric; Harris, Jennifer L; Brownell, Kelly D

    2014-07-01

    Adolescents view thousands of food commercials annually, but the neural response to food advertising and its association with obesity is largely unknown. This study is the first to examine how neural response to food commercials differs from other stimuli (e.g. non-food commercials and television show) and to explore how this response may differ by weight status. The blood oxygen level-dependent functional magnetic resonance imaging activation was measured in 30 adolescents ranging from lean to obese in response to food and non-food commercials imbedded in a television show. Adolescents exhibited greater activation in regions implicated in visual processing (e.g. occipital gyrus), attention (e.g. parietal lobes), cognition (e.g. temporal gyrus and posterior cerebellar lobe), movement (e.g. anterior cerebellar cortex), somatosensory response (e.g. postcentral gyrus) and reward [e.g. orbitofrontal cortex and anterior cingulate cortex (ACC)] during food commercials. Obese participants exhibited less activation during food relative to non-food commercials in neural regions implicated in visual processing (e.g. cuneus), attention (e.g. posterior cerebellar lobe), reward (e.g. ventromedial prefrontal cortex and ACC) and salience detection (e.g. precuneus). Obese participants did exhibit greater activation in a region implicated in semantic control (e.g. medial temporal gyrus). These findings may inform current policy debates regarding the impact of food advertising to minors. PMID:23576811

  5. Data Fusion Using Different Activation Functions in Artificial Neural Networks for Vehicular Navigation

    MALLESWARAN M,

    2010-12-01

    Full Text Available Global positioning System (GPS and Inertial Navigation System (INS data can be integrated together to provide a reliable navigation. GPS/INS data integration provides reliable navigation solutions by overcoming each of their shortcomings, including signal blockage for GPS and increase in position errors with time for INS. This paper aims to provide GPS/INS data integration utilizing Artificial Neural Network (ANN architecture. This architecture is based on Feed Forward Neural Networks, which generally includes Radial Basis Function (RBF neural network and Back Propagation neural network (BPN. These are systematic methods for training multi-layer artificial networks. The BPN-ANN and RBF-ANN modules are trained to predict the INS position error and provide accurate positioning of the moving vehicle. This paper also compares performance of theGPS/INS data integration system by using different activation function like Bipolar Sigmoidal Function (BPSF, Binary Sigmoidal Function (BISF, Hyperbolic Tangential Function (HTF and Gaussian Function (GF in BPN-ANN and using Gaussian function in RBF-ANN.

  6. Deep neural nets as a method for quantitative structure-activity relationships.

    Ma, Junshui; Sheridan, Robert P; Liaw, Andy; Dahl, George E; Svetnik, Vladimir

    2015-02-23

    Neural networks were widely used for quantitative structure-activity relationships (QSAR) in the 1990s. Because of various practical issues (e.g., slow on large problems, difficult to train, prone to overfitting, etc.), they were superseded by more robust methods like support vector machine (SVM) and random forest (RF), which arose in the early 2000s. The last 10 years has witnessed a revival of neural networks in the machine learning community thanks to new methods for preventing overfitting, more efficient training algorithms, and advancements in computer hardware. In particular, deep neural nets (DNNs), i.e. neural nets with more than one hidden layer, have found great successes in many applications, such as computer vision and natural language processing. Here we show that DNNs can routinely make better prospective predictions than RF on a set of large diverse QSAR data sets that are taken from Merck's drug discovery effort. The number of adjustable parameters needed for DNNs is fairly large, but our results show that it is not necessary to optimize them for individual data sets, and a single set of recommended parameters can achieve better performance than RF for most of the data sets we studied. The usefulness of the parameters is demonstrated on additional data sets not used in the calibration. Although training DNNs is still computationally intensive, using graphical processing units (GPUs) can make this issue manageable. PMID:25635324

  7. A nonlinear neural fir filter with an adaptive activation function

    Lee Su Goh

    2003-01-01

    Full Text Available An adaptive amplitude normalized nonlinear gradient descent (AANNGD algorithm for the class of nonlinear finite impulse response (FIR adaptive filters (dynamical perception is introduced. This is achieved by making the amplitude of the nonlinear activation function gradient adaptive. The proposed learning algorithm is suitable for processing of nonlinear and nonstationary signals with a large dynamical range, and removes the unwanted effect of saturation nonlinearities. For rigor, sensitivity analysis is performed and the improved performance of the AANNGD algorithm over the standard LMS, NGD, NNGD, the fully adaptive NNGD (FANNGD and the sign algorithm is verified by simulations on nonlinear and nonstationary inputs with large dynamics.

  8. Spontaneous neural activity during human slow wave sleep

    Dang-Vu, Thien Thanh; Schabus, Manuel; Desseilles, Martin; Albouy, Geneviève; Boly, Mélanie; Darsaud, Annabelle; Gais, Steffen; Rauchs, Géraldine; Sterpenich, Virginie; Vandewalle, Gilles; Carrier, Julie; Moonen, Gustave; Balteau, Evelyne; Degueldre, Christian; Luxen, André

    2008-01-01

    Slow wave sleep (SWS) is associated with spontaneous brain oscillations that are thought to participate in sleep homeostasis and to support the processing of information related to the experiences of the previous awake period. At the cellular level, during SWS, a slow oscillation (140 μV) and delta waves (75–140 μV) during SWS in 14 non-sleep-deprived normal human volunteers. Significant increases in activity were associated with these waves in several cortical areas, including the inferior f...

  9. Mutual information and self-control of a fully-connected low-activity neural network

    Bollé, D.; Carreta, D. Dominguez

    2000-11-01

    A self-control mechanism for the dynamics of a three-state fully connected neural network is studied through the introduction of a time-dependent threshold. The self-adapting threshold is a function of both the neural and the pattern activity in the network. The time evolution of the order parameters is obtained on the basis of a recently developed dynamical recursive scheme. In the limit of low activity the mutual information is shown to be the relevant parameter in order to determine the retrieval quality. Due to self-control an improvement of this mutual information content as well as an increase of the storage capacity and an enlargement of the basins of attraction are found. These results are compared with numerical simulations.

  10. Single-cell transcriptome analyses reveal signals to activate dormant neural stem cells.

    Luo, Yuping; Coskun, Volkan; Liang, Aibing; Yu, Juehua; Cheng, Liming; Ge, Weihong; Shi, Zhanping; Zhang, Kunshan; Li, Chun; Cui, Yaru; Lin, Haijun; Luo, Dandan; Wang, Junbang; Lin, Connie; Dai, Zachary; Zhu, Hongwen; Zhang, Jun; Liu, Jie; Liu, Hailiang; deVellis, Jean; Horvath, Steve; Sun, Yi Eve; Li, Siguang

    2015-05-21

    The scarcity of tissue-specific stem cells and the complexity of their surrounding environment have made molecular characterization of these cells particularly challenging. Through single-cell transcriptome and weighted gene co-expression network analysis (WGCNA), we uncovered molecular properties of CD133(+)/GFAP(-) ependymal (E) cells in the adult mouse forebrain neurogenic zone. Surprisingly, prominent hub genes of the gene network unique to ependymal CD133(+)/GFAP(-) quiescent cells were enriched for immune-responsive genes, as well as genes encoding receptors for angiogenic factors. Administration of vascular endothelial growth factor (VEGF) activated CD133(+) ependymal neural stem cells (NSCs), lining not only the lateral but also the fourth ventricles and, together with basic fibroblast growth factor (bFGF), elicited subsequent neural lineage differentiation and migration. This study revealed the existence of dormant ependymal NSCs throughout the ventricular surface of the CNS, as well as signals abundant after injury for their activation. PMID:26000486

  11. QSAR study on estrogenic activity of structurally diverse compounds using generalized regression neural network

    JI Li; WANG XiaoDong; LUO Si; QIN Liang; YANG XvShu; LIU ShuShen; WANG LianSheng

    2008-01-01

    Computer-based quantitative structure-activity relationship (QSAR) model has been becoming a powerful tool in understanding the structural requirements for chemicals to bind the estrogen receptor (ER), designing drugs for human estrogen replacement therapy, and identifying potential estrogenic endocrine disruptors, in this study, a simple yet powerful neural network technique, generalized regression neural network (GRNN) was used to develop a QSAR model based on 131 structurally diverse estrogens (training set). Only nine descriptors calculated solely from the molecular structures of compounds selected by objective and subjective feature selections were used as inputs of the GRNN model. The predictive power of the built model was found to be comparable to that of the more traditional techniques but requiring significantly easy implementation and a shorter computation-time. The obtained result indicates that the proposed GRNN model is robust and satisfactory, and can provide a feasible and practical tool for the rapid screening of the estrogenic activity of organic compounds.

  12. Category-based induction from similarity of neural activation.

    Weber, Matthew J; Osherson, Daniel

    2014-03-01

    The idea that similarity might be an engine of inductive inference dates back at least as far as David Hume. However, Hume's thesis is difficult to test without begging the question, since judgments of similarity may be infected by inferential processes. We present a one-parameter model of category-based induction that generates predictions about arbitrary statements of conditional probability over a predicate and a set of items. The prediction is based on the unconditional probabilities and similarities that characterize that predicate and those items. To test Hume's thesis, we collected brain activation from various regions of the ventral visual stream during a categorization task that did not invite comparison of categories. We then calculated the similarity of those activation patterns using a simple measure of vectorwise similarity and supplied those similarities to the model. The model's outputs correlated well with subjects' judgments of conditional probability. Our results represent a promising first step toward confirming Hume's thesis; similarity, assessed without reference to induction, may well drive inductive inference. PMID:24254747

  13. Integrated Circuits for Analog Signal Processing

    2013-01-01

      This book presents theory, design methods and novel applications for integrated circuits for analog signal processing.  The discussion covers a wide variety of active devices, active elements and amplifiers, working in voltage mode, current mode and mixed mode.  This includes voltage operational amplifiers, current operational amplifiers, operational transconductance amplifiers, operational transresistance amplifiers, current conveyors, current differencing transconductance amplifiers, etc.  Design methods and challenges posed by nanometer technology are discussed and applications described, including signal amplification, filtering, data acquisition systems such as neural recording, sensor conditioning such as biomedical implants, actuator conditioning, noise generators, oscillators, mixers, etc.   Presents analysis and synthesis methods to generate all circuit topologies from which the designer can select the best one for the desired application; Includes design guidelines for active devices/elements...

  14. Cholinergic Circuit Control of Postnatal Neurogenesis

    Asrican, Brent; Paez-Gonzalez, Patricia; Erb, Joshua; Kuo, Chay T.

    2016-01-01

    New neuron addition via continued neurogenesis in the postnatal/adult mammalian brain presents a distinct form of nervous system plasticity. During embryonic development, precise temporal and spatial patterns of neurogenesis are necessary to create the nervous system architecture. Similar between embryonic and postnatal stages, neurogenic proliferation is regulated by neural stem cell (NSC)-intrinsic mechanisms layered upon cues from their local microenvironmental niche. Following developmental assembly, it remains relatively unclear what may be the key driving forces that sustain continued production of neurons in the postnatal/adult brain. Recent experimental evidence suggests that patterned activity from specific neural circuits can also directly govern postnatal/adult neurogenesis. Here, we review experimental findings that revealed cholinergic modulation, and how patterns of neuronal activity and acetylcholine release may differentially or synergistically activate downstream signaling in NSCs. Higher-order excitatory and inhibitory inputs regulating cholinergic neuron firing, and their implications in neurogenesis control are also considered.

  15. On the solar energetic events, neural networks and forecasts of geomagnetic activity

    Hejda, Pavel; Bochníček, Josef; Valach, F.; Revallo, M.

    Sofia : International Scientific Conference SGEM, 2009, s. 685-692. ISBN 978-954-91818-1-4. [International multidisciplinary scientific geo-conference and expo /9./. Albena (BG), 14.06.2009-19.06.2009] R&D Projects: GA AV ČR(CZ) IAA300120608; GA MŠk OC09070 Institutional research plan: CEZ:AV0Z30120515 Keywords : solar energetic events * geomagnetic activity * artificial neural network Subject RIV: DE - Earth Magnetism, Geodesy, Geography

  16. A dynamic neural field architecture for a pro-active assistant robot

    Pinheiro, Manuel; Bicho, E.; Erlhagen, Wolfram

    2010-01-01

    We present a control architecture for non-verbal HRI that allows an assistant robot to have a pro-active and anticipatory behavior. The architecture implements the coordination of actions and goals among the human, that needs help, and the robot as a dynamic process that integrates contextual cues, shared task knowledge and predicted outcome of the human motor behavior. The robot control architecture is formalized by a coupled system of dynamic neural fields representing a distributed network...

  17. Neural network analysis of electrodynamic activity of yeast cells around 1 kHz

    This paper deals with data analysis of electrodynamic activity of two mutants of yeast cells, cell cycle of which is synchronized and non-synchronized, respectively. We used data already published by Jelinek et al. and treat them with data mining method based on the multilayer neural network. Intersection of data mining and statistical distribution of the noise shows significant difference between synchronized and non-synchronized yeasts not only in total power, but also discrete frequencies.

  18. Social status alters defeat-induced neural activation in Syrian hamsters

    Morrison, Kathleen E.; Curry, Daniel W.; Cooper, Matthew A.

    2012-01-01

    While exposure to social stress leads to increased depression-like and anxiety-like behavior, some individuals are more vulnerable than others to these stress-induced changes in behavior. Prior social experience is one factor that can modulate how individuals respond to stressful events. In this study we investigated whether experience-dependent resistance to the behavioral consequences of social defeat was associated with a specific pattern of neural activation. We paired weight-matched male...

  19. Social Exclusion in Middle Childhood: Rejection Events, Slow-wave Neural Activity and Ostracism Distress

    Crowley, Michael J.; Wu, Jia; MOLFESE, PETER J.; Mayes, Linda C.

    2010-01-01

    This study examined neural activity with event-related potentials (ERPs) in middle childhood during a computer-simulated ball-toss game, Cyberball. Experiencing fair play initially, children were ultimately excluded by the other players. We focused specifically on “not my turn” events within fair play and rejection events within social exclusion. Dense-array ERPs revealed that rejection events are perceived rapidly. Condition differences (“not my turn” vs. rejection) were evident in a posteri...

  20. Strong geomagnetic activity forecast by neural networks under dominant southern orientation of the interplanetary magnetic field

    Valach, F.; Bochníček, Josef; Hejda, Pavel; Revallo, M.

    2014-01-01

    Roč. 53, č. 4 (2014), s. 589-598. ISSN 0273-1177 R&D Projects: GA AV ČR(CZ) IAA300120608; GA MŠk OC09070 Institutional support: RVO:67985530 Keywords : geomagnetic activity * interplanetary magnetic field * artificial neural network * ejection of coronal mass * X-ray flares Subject RIV: DE - Earth Magnetism, Geodesy, Geography Impact factor: 1.358, year: 2014

  1. Human facial neural activities and gesture recognition for machine-interfacing applications

    Hamedi M

    2011-12-01

    Full Text Available M Hamedi1, Sh-Hussain Salleh2, TS Tan2, K Ismail2, J Ali3, C Dee-Uam4, C Pavaganun4, PP Yupapin51Faculty of Biomedical and Health Science Engineering, Department of Biomedical Instrumentation and Signal Processing, University of Technology Malaysia, Skudai, 2Centre for Biomedical Engineering Transportation Research Alliance, 3Institute of Advanced Photonics Science, Nanotechnology Research Alliance, University of Technology Malaysia (UTM, Johor Bahru, Malaysia; 4College of Innovative Management, Valaya Alongkorn Rajabhat University, Pathum Thani, 5Nanoscale Science and Engineering Research Alliance (N'SERA, Advanced Research Center for Photonics, Faculty of Science, King Mongkut's Institute of Technology Ladkrabang, Bangkok, ThailandAbstract: The authors present a new method of recognizing different human facial gestures through their neural activities and muscle movements, which can be used in machine-interfacing applications. Human–machine interface (HMI technology utilizes human neural activities as input controllers for the machine. Recently, much work has been done on the specific application of facial electromyography (EMG-based HMI, which have used limited and fixed numbers of facial gestures. In this work, a multipurpose interface is suggested that can support 2–11 control commands that can be applied to various HMI systems. The significance of this work is finding the most accurate facial gestures for any application with a maximum of eleven control commands. Eleven facial gesture EMGs are recorded from ten volunteers. Detected EMGs are passed through a band-pass filter and root mean square features are extracted. Various combinations of gestures with a different number of gestures in each group are made from the existing facial gestures. Finally, all combinations are trained and classified by a Fuzzy c-means classifier. In conclusion, combinations with the highest recognition accuracy in each group are chosen. An average accuracy

  2. Distinct neural activity associated with focused-attention meditation and loving-kindness meditation

    Lee, Tatia M. C.; Leung, Mei-Kei; Hou, Wai-Kai; Tang, Joey C. Y.; Yin, Jing; So, Kwok-Fai; Lee, Chack-Fan; Chan, Chetwyn C. H.

    2012-01-01

    This study examined the dissociable neural effects of ānāpānasati (focused-attention meditation, FAM) and mettā (loving-kindness meditation, LKM) on BOLD signals during cognitive (continuous performance test, CPT) and affective (emotion-processing task, EPT, in which participants viewed affective pictures) processing. Twenty-two male Chinese expert meditators (11 FAM experts, 11 LKM experts) and 22 male Chinese novice meditators (11 FAM novices, 11 LKM novices) had their brain activity monito...

  3. Abnormal Task Modulation of Oscillatory Neural Activity in Schizophrenia

    Elisa C Dias

    2013-08-01

    Full Text Available Schizophrenia patients have deficits in cognitive function that are a core feature of the disorder. AX-CPT is commonly used to study cognition in schizophrenia, and patients have characteristic pattern of behavioral and ERP response. In AX-CPT subjects respond when a flashed cue A is followed by a target X, ignoring other letter combinations. Patients show reduced hit rate to go trials, and increased false alarms to sequences that require inhibition of a prepotent response. EEG recordings show reduced sensory (P1/N1, as well as later cognitive components (N2, P3, CNV. Behavioral deficits correlate most strongly with sensory dysfunction. Oscillatory analyses provide critical information regarding sensory/cognitive processing over and above standard ERP analyses. Recent analyses of induced oscillatory activity in single trials during AX-CPT in healthy volunteers showed characteristic response patterns in theta, alpha and beta frequencies tied to specific sensory and cognitive processes. Alpha and beta modulated during the trials and beta modulation over the frontal cortex correlated with reaction time. In this study, EEG data was obtained from 18 schizophrenia patients and 13 controls during AX-CPT performance, and single trial decomposition of the signal yielded power in the target wavelengths.Significant task-related event-related desynchronization (ERD was observed in both alpha and beta frequency bands over parieto-occipital cortex related to sensory encoding of the cue. This modulation was reduced in patients for beta, but not for alpha. In addition, significant beta ERD was observed over motor cortex, related to motor preparation for the response, and was also reduced in patients. These findings demonstrate impaired dynamic modulation of beta frequency rhythms in schizophrenia, and suggest that failures of oscillatory activity may underlie impaired sensory information processing in schizophrenia that in turn contributes to cognitive deficits.

  4. Spontaneous neural activity in the primary visual cortex of retinal degenerated rats.

    Wang, Yi; Chen, Ke; Xu, Ping; Ng, Tsz Kin; Chan, Leanne Lai Hang

    2016-06-01

    Retinal degeneration (RD) models have been widely used to study retinal degenerative diseases for a long time. The biological and electrophysiological presentations of changes in the retina during degeneration progress have been well investigated; thus, the present study is aimed at investigating the electrophysiological effects of RD in the primary visual cortex. We extracellularly recorded the spontaneous neural activities in the primary visual cortex of RD rats. The firing rate, interspike interval (ISI) and Lempel-Ziv (LZ) complexity of spontaneous neural activities were subsequently analyzed. When compared to the control group, it was found that the neurons in primary visual cortex of the RD model fired more frequently. In addition, there was a decrease in LZ complexity of spontaneous neural firing in the RD model. These results suggest that the progress of RD may not only affect the retina itself but also the primary visual cortex, which may result in an unbalanced inhibition-excitation system as well as the decreased arising rate of new patterns of spontaneous activities. PMID:27132087

  5. Dynamics of modularity of neural activity in the brain during development

    Deem, Michael; Chen, Man

    2014-03-01

    Theory suggests that more modular systems can have better response functions at short times. This theory suggests that greater cognitive performance may be achieved for more modular neural activity, and that modularity of neural activity may, therefore, likely increase with development in children. We study the relationship between age and modularity of brain neural activity in developing children. The value of modularity calculated from fMRI data is observed to increase during childhood development and peak in young adulthood. We interpret these results as evidence of selection for plasticity in the cognitive function of the human brain. We present a model to illustrate how modularity can provide greater cognitive performance at short times and enhance fast, low-level, automatic cognitive processes. Conversely, high-level, effortful, conscious cognitive processes may not benefit from modularity. We use quasispecies theory to predict how the average modularity evolves with age, given a fitness function extracted from the model. We suggest further experiments exploring the effect of modularity on cognitive performance and suggest that modularity may be a potential biomarker for injury, rehabilitation, or disease.

  6. A Neuro-Fuzzy Technique for Implementing the Half-Adder Circuit Using the CANFIS Model

    Lakra, Sachin; Prasad, T. V.; Sharma, Deepak; Atrey, Shree Harsh; Sharma, Anubhav

    2012-01-01

    A Neural Network, in general, is not considered to be a good solver of mathematical and binary arithmetic problems. However, networks have been developed for such problems as the XOR circuit. This paper presents a technique for the implementation of the Half-adder circuit using the CoActive Neuro-Fuzzy Inference System (CANFIS) Model and attempts to solve the problem using the NeuroSolutions 5 Simulator. The paper gives the experimental results along with the interpretations and possible appl...

  7. Development of quench detection/protection system based on active power method for superconducting magnet by using capacitor circuit

    Highlights: ► The authors have presented an active power method for quench detection. ► A method for improving its characteristics using a capacitor circuit was proposed. ► Quench detection/protection test for a Bi2223 superconducting coil was carried out. ► The proposed method was more useful than the conventional one. -- Abstract: When a quench occurs in a superconducting magnet, excessive joule heating in normal region may damage the magnet. It is necessary to detect the quench as soon as possible and discharge magnetic energy stored in the magnet. The authors have presented a quench detection/protection system based on an active power method which detects the quench regardless of a self-inductive and mutual-inductive voltages and electromagnetic noise. In the conventional active power method, the inductive voltages are removed by cancel coils. In this paper, the authors propose a method to cancel an inductive voltage using a capacitor circuit. The quench detection/protection system becomes more precise and smaller than the conventional system through the capacitor circuit

  8. Recent Advances in Neural Recording Microsystems

    Benoit Gosselin

    2011-04-01

    Full Text Available The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field.

  9. Noise influence on spike activation in a Hindmarsh–Rose small-world neural network

    Zhe, Sun; Micheletto, Ruggero

    2016-07-01

    We studied the role of noise in neural networks, especially focusing on its relation to the propagation of spike activity in a small sized system. We set up a source of information using a single neuron that is constantly spiking. This element called initiator x o feeds spikes to the rest of the network that is initially quiescent and subsequently reacts with vigorous spiking after a transitional period of time. We found that noise quickly suppresses the initiator’s influence and favors spontaneous spike activity and, using a decibel representation of noise intensity, we established a linear relationship between noise amplitude and the interval from the initiator’s first spike and the rest of the network activation. We studied the same process with networks of different sizes (number of neurons) and found that the initiator x o has a measurable influence on small networks, but as the network grows in size, spontaneous spiking emerges disrupting its effects on networks of more than about N = 100 neurons. This suggests that the mechanism of internal noise generation allows information transmission within a small neural neighborhood, but decays for bigger network domains. We also analyzed the Fourier spectrum of the whole network membrane potential and verified that noise provokes the reduction of main θ and α peaks before transitioning into chaotic spiking. However, network size does not reproduce a similar phenomena; instead we recorded a reduction in peaks’ amplitude, a better sharpness and definition of Fourier peaks, but not the evident degeneration to chaos observed with increasing external noise. This work aims to contribute to the understanding of the fundamental mechanisms of propagation of spontaneous spiking in neural networks and gives a quantitative assessment of how noise can be used to control and modulate this phenomenon in Hindmarsh‑Rose (H‑R) neural networks.

  10. Rejuvenation of MPTP-induced human neural precursor cell senescence by activating autophagy

    Zhu, Liang [East Hospital, Tongji University School of Medicine, Shanghai (China); Dong, Chuanming [East Hospital, Tongji University School of Medicine, Shanghai (China); Department of Anatomy and Neurobiology, The Jiangsu Key Laboratory of Neuroregeneration, Nantong University, Nantong (China); Sun, Chenxi; Ma, Rongjie; Yang, Danjing [East Hospital, Tongji University School of Medicine, Shanghai (China); Zhu, Hongwen, E-mail: hongwen_zhu@hotmail.com [Tianjin Hospital, Tianjin Academy of Integrative Medicine, Tianjin (China); Xu, Jun, E-mail: xunymc2000@yahoo.com [East Hospital, Tongji University School of Medicine, Shanghai (China)

    2015-08-21

    Aging of neural stem cell, which can affect brain homeostasis, may be caused by many cellular mechanisms. Autophagy dysfunction was found in aged and neurodegenerative brains. However, little is known about the relationship between autophagy and human neural stem cell (hNSC) aging. The present study used 1-methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine (MPTP) to treat neural precursor cells (NPCs) derived from human embryonic stem cell (hESC) line H9 and investigate related molecular mechanisms involved in this process. MPTP-treated NPCs were found to undergo premature senescence [determined by increased senescence-associated-β-galactosidase (SA-β-gal) activity, elevated intracellular reactive oxygen species level, and decreased proliferation] and were associated with impaired autophagy. Additionally, the cellular senescence phenotypes were manifested at the molecular level by a significant increase in p21 and p53 expression, a decrease in SOD2 expression, and a decrease in expression of some key autophagy-related genes such as Atg5, Atg7, Atg12, and Beclin 1. Furthermore, we found that the senescence-like phenotype of MPTP-treated hNPCs was rejuvenated through treatment with a well-known autophagy enhancer rapamycin, which was blocked by suppression of essential autophagy gene Beclin 1. Taken together, these findings reveal the critical role of autophagy in the process of hNSC aging, and this process can be reversed by activating autophagy. - Highlights: • We successfully establish hESC-derived neural precursor cells. • MPTP treatment induced senescence-like state in hESC-derived NPCs. • MPTP treatment induced impaired autophagy of hESC-derived NPCs. • MPTP-induced hESC-derived NPC senescence was rejuvenated by activating autophagy.

  11. Rejuvenation of MPTP-induced human neural precursor cell senescence by activating autophagy

    Aging of neural stem cell, which can affect brain homeostasis, may be caused by many cellular mechanisms. Autophagy dysfunction was found in aged and neurodegenerative brains. However, little is known about the relationship between autophagy and human neural stem cell (hNSC) aging. The present study used 1-methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine (MPTP) to treat neural precursor cells (NPCs) derived from human embryonic stem cell (hESC) line H9 and investigate related molecular mechanisms involved in this process. MPTP-treated NPCs were found to undergo premature senescence [determined by increased senescence-associated-β-galactosidase (SA-β-gal) activity, elevated intracellular reactive oxygen species level, and decreased proliferation] and were associated with impaired autophagy. Additionally, the cellular senescence phenotypes were manifested at the molecular level by a significant increase in p21 and p53 expression, a decrease in SOD2 expression, and a decrease in expression of some key autophagy-related genes such as Atg5, Atg7, Atg12, and Beclin 1. Furthermore, we found that the senescence-like phenotype of MPTP-treated hNPCs was rejuvenated through treatment with a well-known autophagy enhancer rapamycin, which was blocked by suppression of essential autophagy gene Beclin 1. Taken together, these findings reveal the critical role of autophagy in the process of hNSC aging, and this process can be reversed by activating autophagy. - Highlights: • We successfully establish hESC-derived neural precursor cells. • MPTP treatment induced senescence-like state in hESC-derived NPCs. • MPTP treatment induced impaired autophagy of hESC-derived NPCs. • MPTP-induced hESC-derived NPC senescence was rejuvenated by activating autophagy

  12. Orphan nuclear receptor TLX activates Wnt/β-catenin signalling to stimulate neural stem cell proliferation and self-renewal

    Qu, Qiuhao; Sun, Guoqiang; Li, Wenwu; Yang, Su; Ye, Peng; Zhao, Chunnian; Yu, Ruth T.; Gage, Fred H; Evans, Ronald M; Shi, Yanhong

    2009-01-01

    The nuclear receptor TLX (also known as NR2E1) is essential for adult neural stem cell self-renewal; however, the molecular mechanisms involved remain elusive. Here we show that TLX activates the canonical Wnt/β-catenin pathway in adult mouse neural stem cells. Furthermore, we demonstrate that Wnt/β-catenin signalling is important in the proliferation and self-renewal of adult neural stem cells in the presence of epidermal growth factor and fibroblast growth factor. Wnt7a and active β-catenin...

  13. Perceptual Salience and Reward Both Influence Feedback-Related Neural Activity Arising from Choice.

    Lou, Bin; Hsu, Wha-Yin; Sajda, Paul

    2015-09-23

    expected reward. Here, we use electroencephelography to identify trial-by-trial neural activity of perceived stimulus salience, showing that this activity can be combined with the value of choice options to form a representation of expected reward. Our results provide insight into the neural processing governing the interaction between salience and value and the formation of subjective expected reward and prediction error. This work is potentially important for identifying neural markers of abnormal sensory/value processing, as is seen in some cases of psychiatric illnesses. PMID:26400937

  14. Subliminal versus supraliminal stimuli activate neural responses in anterior cingulate cortex, fusiform gyrus and insula: a meta-analysis of fMRI studies

    Meneguzzo, Paolo; Tsakiris, Manos; Schioth, Helgi B.; Dan J Stein; Brooks, Samantha J.

    2014-01-01

    Background Non-conscious neural activation may underlie various psychological functions in health and disorder. However, the neural substrates of non-conscious processing have not been entirely elucidated. Examining the differential effects of arousing stimuli that are consciously, versus unconsciously perceived will improve our knowledge of neural circuitry involved in non-conscious perception. Here we conduct preliminary analyses of neural activation in studies that have used both sublimina...

  15. Active vibration control of flexible cantilever plates using piezoelectric materials and artificial neural networks

    Abdeljaber, Osama; Avci, Onur; Inman, Daniel J.

    2016-02-01

    The study presented in this paper introduces a new intelligent methodology to mitigate the vibration response of flexible cantilever plates. The use of the piezoelectric sensor/actuator pairs for active control of plates is discussed. An intelligent neural network based controller is designed to control the optimal voltage applied on the piezoelectric patches. The control technique utilizes a neurocontroller along with a Kalman Filter to compute the appropriate actuator command. The neurocontroller is trained based on an algorithm that incorporates a set of emulator neural networks which are also trained to predict the future response of the cantilever plate. Then, the neurocontroller is evaluated by comparing the uncontrolled and controlled responses under several types of dynamic excitations. It is observed that the neurocontroller reduced the vibration response of the flexible cantilever plate significantly; the results demonstrated the success and robustness of the neurocontroller independent of the type and distribution of the excitation force.

  16. The BDNF Val66Met Polymorphism Influences Reading Ability and Patterns of Neural Activation in Children.

    Jasińska, Kaja K; Molfese, Peter J; Kornilov, Sergey A; Mencl, W Einar; Frost, Stephen J; Lee, Maria; Pugh, Kenneth R; Grigorenko, Elena L; Landi, Nicole

    2016-01-01

    Understanding how genes impact the brain's functional activation for learning and cognition during development remains limited. We asked whether a common genetic variant in the BDNF gene (the Val66Met polymorphism) modulates neural activation in the young brain during a critical period for the emergence and maturation of the neural circuitry for reading. In animal models, the bdnf variation has been shown to be associated with the structure and function of the developing brain and in humans it has been associated with multiple aspects of cognition, particularly memory, which are relevant for the development of skilled reading. Yet, little is known about the impact of the Val66Met polymorphism on functional brain activation in development, either in animal models or in humans. Here, we examined whether the BDNF Val66Met polymorphism (dbSNP rs6265) is associated with children's (age 6-10) neural activation patterns during a reading task (n = 81) using functional magnetic resonance imaging (fMRI), genotyping, and standardized behavioral assessments of cognitive and reading development. Children homozygous for the Val allele at the SNP rs6265 of the BDNF gene outperformed Met allele carriers on reading comprehension and phonological memory, tasks that have a strong memory component. Consistent with these behavioral findings, Met allele carriers showed greater activation in reading-related brain regions including the fusiform gyrus, the left inferior frontal gyrus and left superior temporal gyrus as well as greater activation in the hippocampus during a word and pseudoword reading task. Increased engagement of memory and spoken language regions for Met allele carriers relative to Val/Val homozygotes during reading suggests that Met carriers have to exert greater effort required to retrieve phonological codes. PMID:27551971

  17. Artificial neural networks in the evaluation of the radioactive waste drums activity

    The mathematical techniques are becoming more important to solve geometry and standard identification problems. The gamma spectrometry of radioactive waste drums would be a complex solution problem. The main difficulty is the detectors calibration for this geometry; the waste is not homogeneously distributed inside the drums, therefore there are many possible combinations between the activity and the position of these radionuclides inside the drums, making the preparation of calibration standards impracticable. This work describes the development of a methodology to estimate the activity of a 200 L radioactive waste drum, as well as a mapping of the waste distribution, using Artificial Neural Network. The neural network data set entry obtaining was based on the possible detection efficiency combination with 10 sources activities varying from 0 to 74 x 103 Bq. The set up consists of a 200 L drum divided in 5 layers. Ten detectors were positioned all the way through a parallel line to the drum axis, from 15 cm of its surface. The Cesium -137 radionuclide source was used. The 50 efficiency obtained values (10 detectors and 5 layers), combined with the 10 source intensities resulted in a 100,000 lines for 15 columns matrix, with all the possible combinations of source intensity and the Cs-137 position in the 5 layers of the drum. This archive was divided in 2 parts to compose the set of training: input and target files. The MatLab 7.0 module of neural networks was used for training. The net architecture has 10 neurons in the input layer, 18 in the hidden layer and 5 in the output layer. The training algorithm was the 'traincgb' and after 300 'epoch s' the medium square error was 0.00108172. This methodology allows knowing the detection positions answers in a heterogeneous distribution of radionuclides inside a 200 L waste drum; in consequence it is possible to estimate the total activity of the drum in the training neural network limits. The results accuracy depends on

  18. Brain-machine interface circuits and systems

    Zjajo, Amir

    2016-01-01

    This book provides a complete overview of significant design challenges in respect to circuit miniaturization and power reduction of the neural recording system, along with circuit topologies, architecture trends, and (post-silicon) circuit optimization algorithms. The introduced novel circuits for signal conditioning, quantization, and classification, as well as system configurations focus on optimized power-per-area performance, from the spatial resolution (i.e. number of channels), feasible wireless data bandwidth and information quality to the delivered power of implantable system.

  19. Thinking about the thoughts of others; temporal and spatial neural activation during false belief reasoning.

    Mossad, Sarah I; AuCoin-Power, Michelle; Urbain, Charline; Smith, Mary Lou; Pang, Elizabeth W; Taylor, Margot J

    2016-07-01

    Theory of Mind (ToM) is the ability to understand the perspectives, mental states and beliefs of others in order to anticipate their behaviour and is therefore crucial to social interactions. Although fMRI has been widely used to establish the neural networks implicated in ToM, little is known about the timing of ToM-related brain activity. We used magnetoencephalography (MEG) to measure the neural processes underlying ToM, as MEG provides very accurate timing and excellent spatial localization of brain processes. We recorded MEG activity during a false belief task, a reliable measure of ToM, in twenty young adults (10 females). MEG data were recorded in a 151 sensor CTF system (MISL, Coquitlam, BC) and data were co-registered to each participant's MRI (Siemens 3T) for source reconstruction. We found stronger right temporoparietal junction (rTPJ) activations in the false belief condition from 150ms to 225ms, in the right precuneus from 275ms to 375ms, in the right inferior frontal gyrus from 200ms to 300ms and the superior frontal gyrus from 300ms to 400ms. Our findings extend the literature by demonstrating the timing and duration of neural activity in the main regions involved in the "mentalizing" network, showing that activations related to false belief in adults are predominantly right lateralized and onset around 100ms. The sensitivity of MEG will allow us to determine spatial and temporal differences in the brain processes in ToM in younger populations or those who demonstrate deficits in this ability. PMID:27039146

  20. TOUCHING MOMENTS: DESIRE MODULATES THE NEURAL ANTICIPATION OF ACTIVE ROMANTIC CARESS

    Sjoerd J.H. Ebisch

    2014-02-01

    Full Text Available A romantic caress is a basic expression of affiliative behavior and a primary reinforcer. Given its inherent affective valence, its performance also would imply the prediction of reward values. For example, touching a person for whom one has strong passionate feelings likely is motivated by a strong desire for physical contact and associated with the anticipation of hedonic experiences. The present study aims at investigating how the anticipatory neural processes of active romantic caress are modulated by the intensity of the desire for affective contact as reflected by passionate feelings for the other. Functional magnetic resonance imaging scanning was performed in romantically involved partners using a paradigm that allowed to isolate the specific anticipatory representations of active romantic caress, compared with control caress, while testing for the relationship between neural activity and measures of feelings of passionate love for the other. The results demonstrated that right posterior insula activity in anticipation of romantic caress significantly co-varied with the intensity of desire for union with the other. This effect was independent of the sensory-affective properties of the performed touch, like its pleasantness. Furthermore, functional connectivity analysis showed that the same posterior insula cluster interacted with brain regions related to sensory-motor functions as well as to the processing and anticipation of reward. The findings provide insight on the neural substrate mediating between the desire for and the performance of romantic caress. In particular, we propose that anticipatory activity patterns in posterior insula may modulate subsequent sensory-affective processing of skin-to-skin contact.

  1. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

    Francisco Javier Ordóñez

    2016-01-01

    Full Text Available Human activity recognition (HAR tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i is suitable for multimodal wearable sensors; (ii can perform sensor fusion naturally; (iii does not require expert knowledge in designing features; and (iv explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation.

  2. A Neural Mechanism for Nonconscious Activation of Conditioned Placebo and Nocebo Responses.

    Jensen, Karin B; Kaptchuk, Ted J; Chen, Xiaoyan; Kirsch, Irving; Ingvar, Martin; Gollub, Randy L; Kong, Jian

    2015-10-01

    Fundamental aspects of human behavior operate outside of conscious awareness. Yet, theories of conditioned responses in humans, such as placebo and nocebo effects on pain, have a strong emphasis on conscious recognition of contextual cues that trigger the response. Here, we investigated the neural pathways involved in nonconscious activation of conditioned pain responses, using functional magnetic resonance imaging in healthy participants. Nonconscious compared with conscious activation of conditioned placebo analgesia was associated with increased activation of the orbitofrontal cortex, a structure with direct connections to affective brain regions and basic reward processing. During nonconscious nocebo, there was increased activation of the thalamus, amygdala, and hippocampus. In contrast to previous assumptions about conditioning in humans, our results show that conditioned pain responses can be elicited independently of conscious awareness and our results suggest a hierarchical activation of neural pathways for nonconscious and conscious conditioned responses. Demonstrating that the human brain has a nonconscious mechanism for responding to conditioned cues has major implications for the role of associative learning in behavioral medicine and psychiatry. Our results may also open up for novel approaches to translational animal-to-human research since human consciousness and animal cognition is an inherent paradox in all behavioral science. PMID:25452576

  3. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

    Ordóñez, Francisco Javier; Roggen, Daniel

    2016-01-01

    Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation. PMID:26797612

  4. GaAs Optoelectronic Integrated-Circuit Neurons

    Lin, Steven H.; Kim, Jae H.; Psaltis, Demetri

    1992-01-01

    Monolithic GaAs optoelectronic integrated circuits developed for use as artificial neurons. Neural-network computer contains planar arrays of optoelectronic neurons, and variable synaptic connections between neurons effected by diffraction of light from volume hologram in photorefractive material. Basic principles of neural-network computers explained more fully in "Optoelectronic Integrated Circuits For Neural Networks" (NPO-17652). In present circuits, devices replaced by metal/semiconductor field effect transistors (MESFET's), which consume less power.

  5. Human activities recognition by head movement using partial recurrent neural network

    Tan, Henry C. C.; Jia, Kui; De Silva, Liyanage C.

    2003-06-01

    Traditionally, human activities recognition has been achieved mainly by the statistical pattern recognition methods or the Hidden Markov Model (HMM). In this paper, we propose a novel use of the connectionist approach for the recognition of ten simple human activities: walking, sitting down, getting up, squatting down and standing up, in both lateral and frontal views, in an office environment. By means of tracking the head movement of the subjects over consecutive frames from a database of different color image sequences, and incorporating the Elman model of the partial recurrent neural network (RNN) that learns the sequential patterns of relative change of the head location in the images, the proposed system is able to robustly classify all the ten activities performed by unseen subjects from both sexes, of different race and physique, with a recognition rate as high as 92.5%. This demonstrates the potential of employing partial RNN to recognize complex activities in the increasingly popular human-activities-based applications.

  6. Hypoxia activates a latent circuit for processing gustatory information in C. elegans

    Pocock, Roger; Hobert, Oliver

    2010-01-01

    Dedicated neuronal circuits enable animals to engage in specific behavioral responses to environmental stimuli. We demonstrate here that hypoxic stress enhances gustatory sensory perception via novel circuitry in the nematode Caenorhabditis elegans. The hypoxia-inducible transcription factor HIF-1 upregulates serotonin (5-HT) expression in specific sensory neurons not normally required for chemosensation. 5-HT subsequently signals to promote hypoxia-enhanced sensory perception using a metabot...

  7. Light activated escape circuits : a behavior and neurophysiology lab module using Drosophila optogenetics

    Titlow, Joshua S.; Johnson, Bruce R.; Pulver, Stefan R.

    2015-01-01

    The neural networks that control escape from predators often show very clear relationships between defined sensory inputs and stereotyped motor outputs. This feature provides unique opportunities for researchers, but it also provides novel opportunities for neuroscience educators. Here we introduce new teaching modules using adult Drosophila that have been engineered to express csChrimson, a red-light sensitive channelrhodopsin, in specific sets of neurons and muscles mediating visually guide...

  8. Mild blast events alter anxiety, memory, and neural activity patterns in the anterior cingulate cortex.

    Xie, Kun; Kuang, Hui; Tsien, Joe Z

    2013-01-01

    There is a general interest in understanding of whether and how exposure to emotionally traumatizing events can alter memory function and anxiety behaviors. Here we have developed a novel laboratory-version of mild blast exposure comprised of high decibel bomb explosion sound coupled with strong air blast to mice. This model allows us to isolate the effects of emotionally fearful components from those of traumatic brain injury or bodily injury typical associated with bomb blasts. We demonstrate that this mild blast exposure is capable of impairing object recognition memory, increasing anxiety in elevated O-maze test, and resulting contextual generalization. Our in vivo neural ensemble recording reveal that such mild blast exposures produced diverse firing changes in the anterior cingulate cortex, a region processing emotional memory and inhibitory control. Moreover, we show that these real-time neural ensemble patterns underwent post-event reverberations, indicating rapid consolidation of those fearful experiences. Identification of blast-induced neural activity changes in the frontal brain may allow us to better understand how mild blast experiences result in abnormal changes in memory functions and excessive fear generalization related to post-traumatic stress disorder. PMID:23741416

  9. Neural activation during processing of aversive faces predicts treatment outcome in alcoholism.

    Charlet, Katrin; Schlagenhauf, Florian; Richter, Anne; Naundorf, Karina; Dornhof, Lina; Weinfurtner, Christopher E J; König, Friederike; Walaszek, Bernadeta; Schubert, Florian; Müller, Christian A; Gutwinski, Stefan; Seissinger, Annette; Schmitz, Lioba; Walter, Henrik; Beck, Anne; Gallinat, Jürgen; Kiefer, Falk; Heinz, Andreas

    2014-05-01

    Neuropsychological studies reported decoding deficits of emotional facial expressions in alcohol-dependent patients, and imaging studies revealed reduced prefrontal and limbic activation during emotional face processing. However, it remains unclear whether this reduced neural activation is mediated by alcohol-associated volume reductions and whether it interacts with treatment outcome. We combined analyses of neural activation during an aversive face-cue-comparison task and local gray matter volumes (GM) using Biological Parametric Mapping in 33 detoxified alcohol-dependent patients and 33 matched healthy controls. Alcoholics displayed reduced activation toward aversive faces-neutral shapes in bilateral fusiform gyrus [FG; Brodmann areas (BA) 18/19], right middle frontal gyrus (BA46/47), right inferior parietal gyrus (BA7) and left cerebellum compared with controls, which were explained by GM differences (except for cerebellum). Enhanced functional activation in patients versus controls was found in left rostral anterior cingulate cortex (ACC) and medial frontal gyrus (BA10/11), even after GM reduction control. Increased ACC activation correlated significantly with less (previous) lifetime alcohol intake [Lifetime Drinking History (LDH)], longer abstinence and less subsequent binge drinking in patients. High LDH appear to impair treatment outcome via its neurotoxicity on ACC integrity. Thus, high activation of the rostral ACC elicited by affective faces appears to be a resilience factor predicting better treatment outcome. Although no group differences were found, increased FG activation correlated with patients' higher LDH. Because high LDH correlated with worse task performance for facial stimuli in patients, elevated activation in the fusiform 'face' area may reflect inefficient compensatory activation. Therapeutic interventions (e.g. emotion evaluation training) may enable patients to cope with social stress and to decrease relapses after detoxification. PMID

  10. Model of Reentrant Ventricular Tachycardia based upon Infarct Border Zone Geometry Predicts Reentrant Circuit Features as Determined by Activation Mapping

    Ciaccio, Edward J; Ashikaga, Hiroshi; Kaba, Riyaz A; Cervantes, Daniel; Hopenfeld, Bruce; Wit, Andrew L; Peters, Nicholas S; McVeigh, Elliot R; Garan, Hasan; Coromilas, James

    2008-01-01

    Background Infarct border zone (IBZ) geometry likely affects inducibility and characteristics of postinfarction reentrant ventricular tachycardia, but the connection has not been established. Objective To determine characteristics of post infarction ventricular tachycardia in the IBZ. Methods A geometric model describing the relationship between IBZ geometry and wavefront propagation in reentrant circuits was developed. Based on the formulation, slow conduction and block was expected to coincide with areas where IBZ thickness (T) is minimal and the local spatial gradient in thickness (ΔT) is maximal, so that the degree of wavefront curvature ρ ∝ ΔT/T is maximal. Regions of fastest conduction velocity were predicted to coincide with areas of minimum ΔT. In seven arrhythmogenic postinfarction canine heart experiments, tachycardia was induced by programmed stimulation, and activation maps were constructed from multichannel recordings. IBZ thickness was measured in excised hearts from histologic analysis or magnetic resonance imaging. Reentrant circuit properties were predicted from IBZ geometry and compared with ventricular activation maps following tachycardia induction. Results Mean IBZ thickness was 231±140µm at the reentry isthmus and 1440±770µm in the outer pathway (p<0.001). Mean curvature ρ was 1.63±0.45mm−1 at functional block line locations, 0.71±0.18mm−1 at isthmus entrance-exit points, and 0.33±0.13mm−1 in the outer reentrant circuit pathway. The mean conduction velocity about the circuit during reentrant tachycardia was 0.32±0.04mm/ms at entrance-exit points, 0.42±0.13mm/ms for the entire outer pathway, and 0.64±0.16mm/ms at outer pathway regions with minimum ΔT. Model sensitivity and specificity to detect isthmus location was 75.0±5.7% and 97.2±0.7%. Conclusions Reentrant circuit features as determined by activation mapping can be predicted on the basis of IBZ geometrical relationships. PMID:17675078

  11. Refractory Neuron Circuits

    Sarpeshkar, Rahul; Watts, Lloyd; Mead, Carver

    1992-01-01

    Neural networks typically use an abstraction of the behaviour of a biological neuron, in which the continuously varying mean firing rate of the neuron is presumed to carry information about the neuron's time-varying state of excitation. However, the detailed timing of action potentials is known to be important in many biological systems. To build electronic models of such systems, one must have well-characterized neuron circuits that capture the essential behaviour of real neur...

  12. ACUTE EFFECTS OF THREE DIFFERENT CIRCUIT WEIGHT TRAINING PROTOCOLS ON BLOOD LACTATE, HEART RATE, AND RATING OF PERCEIVED EXERTION IN RECREATIONALLY ACTIVE WOMEN

    Brook L. Skidmore

    2012-12-01

    Full Text Available Interval and circuit weight training are popular training methods for maximizing time-efficiency, and are purported to deliver greater physiological benefits faster than traditional training methods. Adding interval training into a circuit weight-training workout may further enhance the benefits of circuit weight training by placing increased demands upon the cardiovascular system. Our purpose was to compare acute effects of three circuit weight training protocols 1 traditional circuit weight training, 2 aerobic circuit weight training, and 3 combined circuit weight-interval training on blood lactate (BLA, heart rate (HR, and ratings of perceived exertion (RPE. Eleven recreationally active women completed 7 exercise sessions. Session 1 included measurements of height, weight, estimated VO2max, and 13 repetition maximum (RM testing of the weight exercises. Sessions 2-4 were held on non-consecutive days for familiarization with traditional circuit weight training (TRAD, aerobic circuit weight training (ACWT, and combined circuit weight-interval training (CWIT protocols. In sessions 5-7, TRAD, ACWT, and CWIT were performed in a randomized order > 72 hr apart for measures of BLA, HR, and RPE at pre-exercise and following each of three mini-circuit weight training stations. Repeated-measures ANOVAs yielded significant interactions (p < 0.05 in BLA, HR, and RPE. Combined circuit weight- interval training (CWIT produced higher BLA (7.31 ± 0.37 vs. TRAD: 3.99 ± 0.26, ACWT: 4.54 ± 0.31 mmol.L-1, HR (83.51 ± 1.18 vs. TRAD: 70.42 ± 1.67, ACWT: 74.13 ± 1.43 beats.min-1 and RPE (8.14 ± 0.41 vs. TRAD: 5.06 ± 0.43, ACWT: 6.15 ± 0.42 at all measures. Aerobic circuit weight training (ACWT elicited greater RPE than traditional circuit weight training (TRAD at all measures. Including combined circuit weight-interval training (CWIT workouts into exercise programming may enhance fitness benefits and maximize time-efficiency more so than traditional circuit

  13. Chaotic memristive circuit: equivalent circuit realization and dynamical analysis

    Bao Bo-Cheng; Xu Jian-Ping; Zhou Guo-Hua; Ma Zheng-Hua; Zou Ling

    2011-01-01

    In this paper,a practical equivalent circuit of an active flux-controlled memristor characterized by smooth piecewise-quadratic nonlinearity is designed and an experimental chaotic memristive circuit is implemented.The chaotic memristive circuit has an equilibrium set and its stability is dependent on the initial state of the memristor.The initial state-dependent and the circuit parameter-dependent dynamics of the chaotic memristive circuit are investigated via phase portraits,bifurcation diagrams and Lyapunov exponents.Both experimental and simulation results validate the proposed equivalent circuit realization of the active flux-controlled memristor.

  14. A flexible organic active matrix circuit fabricated using novel organic thin film transistors and organic light-emitting diodes

    Gutiérrez-Heredia, Gerardo

    2010-10-04

    We present an active matrix circuit fabricated on plastic (polyethylene naphthalene, PEN) and glass substrates using organic thin film transistors and organic capacitors to control organic light-emitting diodes (OLEDs). The basic circuit is fabricated using two pentacene-based transistors and a capacitor using a novel aluminum oxide/parylene stack (Al2O3/ parylene) as the dielectric for both the transistor and the capacitor. We report that our circuit can deliver up to 15 μA to each OLED pixel. To achieve 200 cd m-2 of brightness a 10 μA current is needed; therefore, our approach can initially deliver 1.5× the required current to drive a single pixel. In contrast to parylene-only devices, the Al2O 3/parylene stack does not fail after stressing at a field of 1.7 MV cm-1 for >10 000 s, whereas \\'parylene only\\' devices show breakdown at approximately 1000 s. Details of the integration scheme are presented. © 2010 IOP Publishing Ltd.

  15. Emergence of spatially heterogeneous burst suppression in a neural field model of electrocortical activity

    Ingo eBojak

    2015-02-01

    Full Text Available Burst suppression in the electroencephalogram (EEG is a well described phenomenon that occurs during deep anaesthesia, as well as in a variety of congenital and acquired brain insults. Classically it is thought of as spatially synchronous, quasi-periodic bursts of high amplitude EEG separated by low amplitude activity. However, its characterisation as a ``global brain state'' has been challenged by recent results obtained with intracranial electrocortigraphy. Not only does it appear that burst suppression activity is highly asynchronous across cortex, but also that it may occur in isolated regions of circumscribed spatial extent. Here we outline a realistic neural field model for burst suppression by adding a slow process of synaptic resource depletion and recovery, which is able to reproduce qualitatively the empirically observed features during general anaesthesia at the whole cortex level. Simulations reveal heterogeneous bursting over the model cortex and complex spatiotemporal dynamics during simulated anaesthetic action, and provide forward predictions of neuroimaging signals for subsequent empirical comparisons and more detailed characterisation.Because burst suppression corresponds to a dynamical end-point of brain activity, theoretically accounting for its spatiotemporal emergence will vitally contribute to efforts aimed at clarifying whether a common physiological trajectory is induced by the actions of general anaesthetic agents. We have taken a first step in this direction by showing that a neural field model can qualitatively match recent experimental data that indicate spatial differentiation of burst suppression activity across cortex.

  16. Right hemisphere neural activations in the recall of waking fantasies and of dreams.

    Benedetti, Francesco; Poletti, Sara; Radaelli, Daniele; Ranieri, Rebecca; Genduso, Valeria; Cavallotti, Simone; Castelnovo, Anna; Smeraldi, Enrico; Scarone, Silvio; D'Agostino, Armando

    2015-10-01

    The story-like organization of dreams is characterized by a pervasive bizarreness of events and actions that resembles psychotic thought, and largely exceeds that observed in normal waking fantasies. Little is known about the neural correlates of the confabulatory narrative construction of dreams. In this study, dreams, fantasies elicited by ambiguous pictorial stimuli, and non-imaginative first- and third-person narratives from healthy participants were recorded, and were then studied for brain blood oxygen level-dependent functional magnetic resonance imaging on a 3.0-Tesla scanner while listening to their own narrative reports and attempting a retrieval of the corresponding experience. In respect to non-bizarre reports of daytime activities, the script-driven recall of dreams and fantasies differentially activated a right hemisphere network including areas in the inferior frontal gyrus, and superior and middle temporal gyrus. Neural responses were significantly greater for fantasies than for dreams in all regions, and inversely proportional to the degree of bizarreness observed in narrative reports. The inferior frontal gyrus, superior and middle temporal gyrus have been implicated in the semantic activation, integration and selection needed to build a coherent story representation and to resolve semantic ambiguities; in deductive and inferential reasoning; in self- and other-perspective taking, theory of mind, moral and autobiographical reasoning. Their degree of activation could parallel the level of logical robustness or inconsistency experienced when integrating information and mental representations in the process of building fantasy and dream narratives. PMID:25871325

  17. Bioimpedance Harmonic Analysis as a Diagnostic Tool to Assess Regional Circulation and Neural Activity

    Mudraya, I. S.; Revenko, S. V.; Khodyreva, L. A.; Markosyan, T. G.; Dudareva, A. A.; Ibragimov, A. R.; Romich, V. V.; Kirpatovsky, V. I.

    2013-04-01

    The novel technique based on harmonic analysis of bioimpedance microvariations with original hard- and software complex incorporating a high-resolution impedance converter was used to assess the neural activity and circulation in human urinary bladder and penis in patients with pelvic pain, erectile dysfunction, and overactive bladder. The therapeutic effects of shock wave therapy and Botulinum toxin detrusor injections were evaluated quantitatively according to the spectral peaks at low 0.1 Hz frequency (M for Mayer wave), respiratory (R) and cardiac (C) rhythms with their harmonics. Enhanced baseline regional neural activity identified according to M and R peaks was found to be presumably sympathetic in pelvic pain patients, and parasympathetic - in patients with overactive bladder. Total pulsatile activity and pulsatile resonances found in the bladder as well as in the penile spectrum characterised regional circulation and vascular tone. The abnormal spectral parameters characteristic of the patients with genitourinary diseases shifted to the norm in the cases of efficient therapy. Bioimpedance harmonic analysis seems to be a potent tool to assess regional peculiarities of circulatory and autonomic nervous activity in the course of patient treatment.

  18. Bioimpedance Harmonic Analysis as a Diagnostic Tool to Assess Regional Circulation and Neural Activity

    The novel technique based on harmonic analysis of bioimpedance microvariations with original hard- and software complex incorporating a high-resolution impedance converter was used to assess the neural activity and circulation in human urinary bladder and penis in patients with pelvic pain, erectile dysfunction, and overactive bladder. The therapeutic effects of shock wave therapy and Botulinum toxin detrusor injections were evaluated quantitatively according to the spectral peaks at low 0.1 Hz frequency (M for Mayer wave), respiratory (R) and cardiac (C) rhythms with their harmonics. Enhanced baseline regional neural activity identified according to M and R peaks was found to be presumably sympathetic in pelvic pain patients, and parasympathetic – in patients with overactive bladder. Total pulsatile activity and pulsatile resonances found in the bladder as well as in the penile spectrum characterised regional circulation and vascular tone. The abnormal spectral parameters characteristic of the patients with genitourinary diseases shifted to the norm in the cases of efficient therapy. Bioimpedance harmonic analysis seems to be a potent tool to assess regional peculiarities of circulatory and autonomic nervous activity in the course of patient treatment.

  19. Analytically tractable studies of traveling waves of activity in integrate-and-fire neural networks

    Zhang, Jie; Osan, Remus

    2016-05-01

    In contrast to other large-scale network models for propagation of electrical activity in neural tissue that have no analytical solutions for their dynamics, we show that for a specific class of integrate and fire neural networks the acceleration depends quadratically on the instantaneous speed of the activity propagation. We use this property to analytically compute the network spike dynamics and to highlight the emergence of a natural time scale for the evolution of the traveling waves. These results allow us to examine other applications of this model such as the effect that a nonconductive gap of tissue has on further activity propagation. Furthermore we show that activity propagation also depends on local conditions for other more general connectivity functions, by converting the evolution equations for network dynamics into a low-dimensional system of ordinary differential equations. This approach greatly enhances our intuition into the mechanisms of the traveling waves evolution and significantly reduces the simulation time for this class of models.

  20. Forecast and restoration of geomagnetic activity indices by using the software-computational neural network complex

    Barkhatov, Nikolay; Revunov, Sergey

    2010-05-01

    It is known that currently used indices of geomagnetic activity to some extent reflect the physical processes occurring in the interaction of the perturbed solar wind with Earth's magnetosphere. Therefore, they are connected to each other and with the parameters of near-Earth space. The establishment of such nonlinear connections is interest. For such purposes when the physical problem is complex or has many parameters the technology of artificial neural networks is applied. Such approach for development of the automated forecast and restoration method of geomagnetic activity indices with the establishment of creative software-computational neural network complex is used. Each neural network experiments were carried out at this complex aims to search for a specific nonlinear relation between the analyzed indices and parameters. At the core of the algorithm work program a complex scheme of the functioning of artificial neural networks (ANN) of different types is contained: back propagation Elman network, feed forward network, fuzzy logic network and Kohonen layer classification network. Tools of the main window of the complex (the application) the settings used by neural networks allow you to change: the number of hidden layers, the number of neurons in the layer, the input and target data, the number of cycles of training. Process and the quality of training the ANN is a dynamic plot of changing training error. Plot of comparison of network response with the test sequence is result of the network training. The last-trained neural network with established nonlinear connection for repeated numerical experiments can be run. At the same time additional training is not executed and the previously trained network as a filter input parameters get through and output parameters with the test event are compared. At statement of the large number of different experiments provided the ability to run the program in a "batch" mode is stipulated. For this purpose the user a

  1. A cortical circuit for gain control by behavioral state

    Fu, Yu; Tucciarone, Jason M.; Espinosa, J. Sebastian; Sheng, Nengyin; Darcy, Daniel P.; Nicoll, Roger A.; Huang, Z. Josh; Stryker, Michael P

    2014-01-01

    The brain’s response to sensory input is strikingly modulated by behavioral state. Notably, the visual response of mouse primary visual cortex (V1) is enhanced by locomotion, a tractable and accessible example of a time-locked change in cortical state. The neural circuits that transmit behavioral state to sensory cortex to produce this modulation are unknown. In vivo calcium imaging of behaving animals revealed that locomotion activates vasoactive intestinal peptide (VIP)-positive neurons in ...

  2. Cognitive emotion regulation in children: Reappraisal of emotional faces modulates neural source activity in a frontoparietal network

    Ida Wessing

    2015-06-01

    Full Text Available Emotion regulation has an important role in child development and psychopathology. Reappraisal as cognitive regulation technique can be used effectively by children. Moreover, an ERP component known to reflect emotional processing called late positive potential (LPP can be modulated by children using reappraisal and this modulation is also related to children's emotional adjustment. The present study seeks to elucidate the neural generators of such LPP effects. To this end, children aged 8–14 years reappraised emotional faces, while neural activity in an LPP time window was estimated using magnetoencephalography-based source localization. Additionally, neural activity was correlated with two indexes of emotional adjustment and age. Reappraisal reduced activity in the left dorsolateral prefrontal cortex during down-regulation and enhanced activity in the right parietal cortex during up-regulation. Activity in the visual cortex decreased with increasing age, more adaptive emotion regulation and less anxiety. Results demonstrate that reappraisal changed activity within a frontoparietal network in children. Decreasing activity in the visual cortex with increasing age is suggested to reflect neural maturation. A similar decrease with adaptive emotion regulation and less anxiety implies that better emotional adjustment may be associated with an advance in neural maturation.

  3. Sex differences in neural activation to facial expressions denoting contempt and disgust.

    André Aleman

    Full Text Available The facial expression of contempt has been regarded to communicate feelings of moral superiority. Contempt is an emotion that is closely related to disgust, but in contrast to disgust, contempt is inherently interpersonal and hierarchical. The aim of this study was twofold. First, to investigate the hypothesis of preferential amygdala responses to contempt expressions versus disgust. Second, to investigate whether, at a neural level, men would respond stronger to biological signals of interpersonal superiority (e.g., contempt than women. We performed an experiment using functional magnetic resonance imaging (fMRI, in which participants watched facial expressions of contempt and disgust in addition to neutral expressions. The faces were presented as distractors in an oddball task in which participants had to react to one target face. Facial expressions of contempt and disgust activated a network of brain regions, including prefrontal areas (superior, middle and medial prefrontal gyrus, anterior cingulate, insula, amygdala, parietal cortex, fusiform gyrus, occipital cortex, putamen and thalamus. Contemptuous faces did not elicit stronger amygdala activation than did disgusted expressions. To limit the number of statistical comparisons, we confined our analyses of sex differences to the frontal and temporal lobes. Men displayed stronger brain activation than women to facial expressions of contempt in the medial frontal gyrus, inferior frontal gyrus, and superior temporal gyrus. Conversely, women showed stronger neural responses than men to facial expressions of disgust. In addition, the effect of stimulus sex differed for men versus women. Specifically, women showed stronger responses to male contemptuous faces (as compared to female expressions, in the insula and middle frontal gyrus. Contempt has been conceptualized as signaling perceived moral violations of social hierarchy, whereas disgust would signal violations of physical purity. Thus, our

  4. Stability in Switched Cohen-Grossberg Neural Networks with Mixed Time Delays and Non-Lipschitz Activation Functions

    Qiangqiang Guo; Ning Li; Kewang Wang; Chongyang Wu; Guohua Xu; Huaiqin Wu

    2012-01-01

    The stability for the switched Cohen-Grossberg neural networks with mixed time delays and α-inverse Hölder activation functions is investigated under the switching rule with the average dwell time property. By applying multiple Lyapunov-Krasovskii functional approach and linear matrix inequality (LMI) technique, a delay-dependent sufficient criterion is achieved to ensure such switched neural networks to be globally exponentially stable in terms of LMIs, and the exponential decay estimation i...

  5. Neural activity in the suprachiasmatic circadian clock of nocturnal mice anticipating a daytime meal.

    Dattolo, T; Coomans, C P; van Diepen, H C; Patton, D F; Power, S; Antle, M C; Meijer, J H; Mistlberger, R E

    2016-02-19

    Circadian rhythms in mammals are regulated by a system of circadian oscillators that includes a light-entrainable pacemaker in the suprachiasmatic nucleus (SCN) and food-entrainable oscillators (FEOs) elsewhere in the brain and body. In nocturnal rodents, the SCN promotes sleep in the day and wake at night, while FEOs promote an active state in anticipation of a predictable daily meal. For nocturnal animals to anticipate a daytime meal, wake-promoting signals from FEOs must compete with sleep-promoting signals from the SCN pacemaker. One hypothesis is that FEOs impose a daily rhythm of inhibition on SCN output that is timed to permit the expression of activity prior to a daytime meal. This hypothesis predicts that SCN activity should decrease prior to the onset of anticipatory activity and remain suppressed through the scheduled mealtime. To assess the hypothesis, neural activity in the SCN of mice anticipating a 4-5-h daily meal in the light period was measured using FOS immunohistochemistry and in vivo multiple unit electrophysiology. SCN FOS, quantified by optical density, was significantly reduced at the expected mealtime in food-anticipating mice with access to a running disk, compared to ad libitum-fed and acutely fasted controls. Group differences were not significant when FOS was quantified by other methods, or in mice without running disks. SCN electrical activity was markedly decreased during locomotion in some mice but increased in others. Changes in either direction were concurrent with locomotion, were not specific to food anticipation, and were not sustained during longer pauses. Reduced FOS indicates a net suppression of SCN activity that may depend on the intensity or duration of locomotion. The timing of changes in SCN activity relative to locomotion suggests that any effect of FEOs on SCN output is mediated indirectly, by feedback from neural or systemic correlates of locomotion. PMID:26701294

  6. A Voltage Mode Memristor Bridge Synaptic Circuit with Memristor Emulators

    Leon Chua

    2012-03-01

    Full Text Available A memristor bridge neural circuit which is able to perform signed synaptic weighting was proposed in our previous study, where the synaptic operation was verified via software simulation of the mathematical model of the HP memristor. This study is an extension of the previous work advancing toward the circuit implementation where the architecture of the memristor bridge synapse is built with memristor emulator circuits. In addition, a simple neural network which performs both synaptic weighting and summation is built by combining memristor emulators-based synapses and differential amplifier circuits. The feasibility of the memristor bridge neural circuit is verified via SPICE simulations.

  7. Integration of Optical Manipulation and Electrophysiological Tools to Modulate and Record Activity in Neural Networks

    Difato, F.; Schibalsky, L.; Benfenati, F.; Blau, A.

    2011-07-01

    We present an optical system that combines IR (1064 nm) holographic optical tweezers with a sub-nanosecond-pulsed UV (355 nm) laser microdissector for the optical manipulation of single neurons and entire networks both on transparent and non-transparent substrates in vitro. The phase-modulated laser beam can illuminate the sample concurrently or independently from above or below assuring compatibility with different types of microelectrode array and patch-clamp electrophysiology. By combining electrophysiological and optical tools, neural activity in response to localized stimuli or injury can be studied and quantified at sub-cellular, cellular, and network level.

  8. Circuits and filters handbook

    Chen, Wai-Kai

    2003-01-01

    A bestseller in its first edition, The Circuits and Filters Handbook has been thoroughly updated to provide the most current, most comprehensive information available in both the classical and emerging fields of circuits and filters, both analog and digital. This edition contains 29 new chapters, with significant additions in the areas of computer-aided design, circuit simulation, VLSI circuits, design automation, and active and digital filters. It will undoubtedly take its place as the engineer's first choice in looking for solutions to problems encountered in the design, analysis, and behavi

  9. Functional and structural specific roles of activity-driven BDNF within circuits formed by single spiny stellate neurons of the barrel cortex

    Qian-Quan eSun

    2014-11-01

    Full Text Available Brain derived neurotrophic factor (BDNF plays key roles in several neurodevelopmental disorders and actions of pharmacological treatments. However it is uncealr how specific BDNF’s effects are on diffeerent circuit components. Current studies have largely focused on the role of BDNF in modification of synaptic development. The precise roles of BDNF in the refinement of a functional circuit in vivo remain unclear. Val66Met polymorphism of BDNF may be associated with increased risk for cognitive impairments and is mediated at least in part by activity-dependent trafficking and/or secretion of BDNF. Using mutant mice that lacked activity-driven BDNF expression (bdnf-KIV, we previously reported that experience regulation of the cortical GABAergic network is mediated by activity-driven BDNF expression. Here, we demonstrate that activity-driven BDNF’s effects on circuits formed by the layer IV spiny stellate cells are highly specific. Structurally, dendritic but not axonal morphology was altered in the mutant. Physiologically, GABAergic but not glutamatergic synapses were severely affected. The effects on GABA transmission occurs via presynaptic alteration of calcium-dependent release probability. These results suggest that neuronal activity through activity-driven BDNF expression, can selectively regulate specific features of layer IV circuits in vivo. We postulate that the role of activity-dependent BDNF is to modulate the computational ability of circuits that relate to the gain control (i.e. feed-forward inhibition; whereas the basic wiring of circuits relevant to the sensory pathway is spared. Gain control modulation within cortical circuits has broad impact on cognitive processing and brain state-transitions. Cognitive behavior and mode is determined by brain states, thus the studying of circuit alteration by endogenous BDNF provides insights into the cellular and molecular mechanisms of diseases mediated by BDNF.

  10. Detecting the single line to ground short circuit fault in the submarine’s power system using the artificial neural network

    Behniafar Ali; Darabi Ahmad; Banejad Mahdi; Baghayipour Mohammadreza

    2013-01-01

    The electric marine instruments are newly inserted in the trade and industry, for which the existence of an equipped and reliable power system is necessitated. One of the features of such a power system is that it cannot have an earth system causing the protection relays not to be able to detect the single line to ground short circuit fault. While on the other hand, the occurrence of another similar fault at the same time can lead to the double line fault a...

  11. Shaping prestimulus neural activity with auditory rhythmic stimulation improves the temporal allocation of attention

    Pincham, Hannah L.; Cristoforetti, Giulia; Facoetti, Andrea; Szűcs, Dénes

    2016-01-01

    Human attention fluctuates across time, and even when stimuli have identical physical characteristics and the task demands are the same, relevant information is sometimes consciously perceived and at other times not. A typical example of this phenomenon is the attentional blink, where participants show a robust deficit in reporting the second of two targets (T2) in a rapid serial visual presentation (RSVP) stream. Previous electroencephalographical (EEG) studies showed that neural correlates of correct T2 report are not limited to the RSVP period, but extend before visual stimulation begins. In particular, reduced oscillatory neural activity in the alpha band (8-12 Hz) before the onset of the RSVP has been linked to lower T2 accuracy. We therefore examined whether auditory rhythmic stimuli presented at a rate of 10 Hz (within the alpha band) could increase oscillatory alpha-band activity and improve T2 performance in the attentional blink time window. Behaviourally, the auditory rhythmic stimulation worked to enhance T2 accuracy. This enhanced perception was associated with increases in the posterior T2-evoked N2 component of the event-related potentials and this effect was observed selectively at lag 3. Frontal and posterior oscillatory alpha-band activity was also enhanced during auditory stimulation in the pre-RSVP period and positively correlated with T2 accuracy. These findings suggest that ongoing fluctuations can be shaped by sensorial events to improve the allocation of attention in time. PMID:26986506

  12. Shaping prestimulus neural activity with auditory rhythmic stimulation improves the temporal allocation of attention.

    Ronconi, Luca; Pincham, Hannah L; Cristoforetti, Giulia; Facoetti, Andrea; Szűcs, Dénes

    2016-05-01

    Human attention fluctuates across time, and even when stimuli have identical physical characteristics and the task demands are the same, relevant information is sometimes consciously perceived and at other times not. A typical example of this phenomenon is the attentional blink, where participants show a robust deficit in reporting the second of two targets (T2) in a rapid serial visual presentation (RSVP) stream. Previous electroencephalographical (EEG) studies showed that neural correlates of correct T2 report are not limited to the RSVP period, but extend before visual stimulation begins. In particular, reduced oscillatory neural activity in the alpha band (8-12 Hz) before the onset of the RSVP has been linked to lower T2 accuracy. We therefore examined whether auditory rhythmic stimuli presented at a rate of 10 Hz (within the alpha band) could increase oscillatory alpha-band activity and improve T2 performance in the attentional blink time window. Behaviourally, the auditory rhythmic stimulation worked to enhance T2 accuracy. This enhanced perception was associated with increases in the posterior T2-evoked N2 component of the event-related potentials and this effect was observed selectively at lag 3. Frontal and posterior oscillatory alpha-band activity was also enhanced during auditory stimulation in the pre-RSVP period and positively correlated with T2 accuracy. These findings suggest that ongoing fluctuations can be shaped by sensorial events to improve the allocation of attention in time. PMID:26986506

  13. Artificial neural network and multiple regression model for nickel(II) adsorption on powdered activated carbons.

    Hema, M; Srinivasan, K

    2011-07-01

    Nickel removal efficiency of powered activated carbons of coconut oilcake, neem oilcake and commercial carbon was investigated by using artificial neural network. The effective parameters for the removal of nickel (%R) by adsorption process, which included the pH, contact time (T), distinctiveness of activated carbon (Cn), amount of activated carbon (Cw) and initial concentration of nickel (Co) were investigated. Levenberg-Marquardt (LM) Back-propagation algorithm is used to train the network. The network topology was optimized by varying number of hidden layer and number of neurons in hidden layer. The model was developed in terms of training; validation and testing of experimental data, the test subsets that each of them contains 60%, 20% and 20% of total experimental data, respectively. Multiple regression equation was developed for nickel adsorption system and the output was compared with both simulated and experimental outputs. Standard deviation (SD) with respect to experimental output was quite higher in the case of regression model when compared with ANN model. The obtained experimental data best fitted with the artificial neural network. PMID:23029923

  14. 基于有源广义忆阻的无感混沌电路研究∗%Inductorless chaotic circuit based on active generalized memristors

    2015-01-01

    Equivalently implementing a generalized memristor by using common components and then making a nonlinear circuit with a reliable property, are conducive to experimentally exhibit the nonlinear phenomena of the memristive chaotic circuit and show practical applications in generating chaotic signals. Firstly, based on a memristive diode bridge circuit, a new first-order actively generalized memristor emulator is constructed with no grounded restriction and ease to realize. The mathematical model of the emulator is established and its fingerprints are analyzed by the pinched hysteresis loops with different sinusoidal voltage stimuli. The results verified by experimental measurements indicate that the emulator uses only one operational amplifier and nine elementary electronic circuit elements and is an active voltage-controlled generalized memristor. Secondly, by parallelly connecting the proposed emulator to a capacitor and then linearly coupling with an RC bridge oscillator, a memristor based chaotic circuit without any inductance element is constructed. The dynamical model of the inductorless memristive chaotic circuit is established and the phase portraits of the chaotic attractor with typical circuit parameters are obtained numerically. The dissipativity, equilibrium points, and stabilities are derived, which indicate that in the phase space of the inductorless memristive chaotic circuit there exists a dissipative area where are distributed two unstable nonzero saddle-foci and a non-dissipative area containing an unstable origin saddle point. Furthermore, by utilizing the bifurcation diagram, Lyapunov exponent spectra, and phase portraits, the dynamical behaviors of the inductorless memristive chaotic circuit are investigated. Results show that with the evolution of the parameter value of the coupling resistor, the complex nonlinear phenomena of the coexisting bifurcation modes and coexisting attractors under two different initial conditions of the state variables

  15. Passive and active RF-microwave circuits course and exercises with solutions

    Jarry, Pierre

    2015-01-01

    Microwave and radiofrequency (RF) circuits play an important role in communication systems. Due to the proliferation of radar, satellite, and mobile wireless systems, there is a need for design methods that can satisfy the ever increasing demand for accuracy, reliability, and fast development times. This book explores the principal elements for receiving and emitting signals between Earth stations, satellites, and RF (mobile phones) in four parts; the theory and realization of couplers, computation and realization of microwave and RF filters, amplifiers and microwave and RF oscillators. Pas

  16. Common features of neural activity during singing and sleep periods in a basal ganglia nucleus critical for vocal learning in a juvenile songbird.

    Shin Yanagihara

    Full Text Available Reactivations of waking experiences during sleep have been considered fundamental neural processes for memory consolidation. In songbirds, evidence suggests the importance of sleep-related neuronal activity in song system motor pathway nuclei for both juvenile vocal learning and maintenance of adult song. Like those in singing motor nuclei, neurons in the basal ganglia nucleus Area X, part of the basal ganglia-thalamocortical circuit essential for vocal plasticity, exhibit singing-related activity. It is unclear, however, whether Area X neurons show any distinctive spiking activity during sleep similar to that during singing. Here we demonstrate that, during sleep, Area X pallidal neurons exhibit phasic spiking activity, which shares some firing properties with activity during singing. Shorter interspike intervals that almost exclusively occurred during singing in awake periods were also observed during sleep. The level of firing variability was consistently higher during singing and sleep than during awake non-singing states. Moreover, deceleration of firing rate, which is considered to be an important firing property for transmitting signals from Area X to the thalamic nucleus DLM, was observed mainly during sleep as well as during singing. These results suggest that songbird basal ganglia circuitry may be involved in the off-line processing potentially critical for vocal learning during sensorimotor learning phase.

  17. Architecture and development of olivocerebellar circuit topography

    Stacey L Reeber

    2013-01-01

    Full Text Available The cerebellum has a simple tri-laminar structure that is comprised of relatively few cell types. Yet, its internal micro-circuitry is anatomically, biochemically, and functionally complex. The most striking feature of cerebellar circuit complexity is its compartmentalized topography. Each cell type within the cerebellar cortex is organized into an exquisite map; molecular expression patterns, dendrite projections, and axon terminal fields divide the medial-lateral axis of the cerebellum into topographic sagittal zones. Here, we discuss the mechanisms that establish zones and highlight how gene expression and neural activity contribute to cerebellar pattern formation. We focus on the olivocerebellar system because its developmental mechanisms are becoming clear, its topographic termination patterns are very precise, and its contribution to zonal function is debated. This review deconstructs the architecture and development of the olivocerebellar pathway to provide an update on how brain circuit maps form and function.

  18. A Role of Phase-Resetting in Coordinating Large Scale Neural Networks During Attention and Goal-Directed Behavior.

    Voloh, Benjamin; Womelsdorf, Thilo

    2016-01-01

    Short periods of oscillatory activation are ubiquitous signatures of neural circuits. A broad range of studies documents not only their circuit origins, but also a fundamental role for oscillatory activity in coordinating information transfer during goal directed behavior. Recent studies suggest that resetting the phase of ongoing oscillatory activity to endogenous or exogenous cues facilitates coordinated information transfer within circuits and between distributed brain areas. Here, we review evidence that pinpoints phase resetting as a critical marker of dynamic state changes of functional networks. Phase resets: (1) set a "neural context" in terms of narrow band frequencies that uniquely characterizes the activated circuits; (2) impose coherent low frequency phases to which high frequency activations can synchronize, identifiable as cross-frequency correlations across large anatomical distances; (3) are critical for neural coding models that depend on phase, increasing the informational content of neural representations; and (4) likely originate from the dynamics of canonical E-I circuits that are anatomically ubiquitous. These multiple signatures of phase resets are directly linked to enhanced information transfer and behavioral success. We survey how phase resets re-organize oscillations in diverse task contexts, including sensory perception, attentional stimulus selection, cross-modal integration, Pavlovian conditioning, and spatial navigation. The evidence we consider suggests that phase-resets can drive changes in neural excitability, ensemble organization, functional networks, and ultimately, overt behavior. PMID:27013986

  19. A role of phase-resetting in coordinating large scale neural oscillations during attention and goal-directed behavior

    Benjamin eVoloh

    2016-03-01

    Full Text Available Short periods of oscillatory activation are ubiquitous signatures of neural circuits. A broad range of studies documents not only their circuit origins, but also a fundamental role for oscillatory activity in coordinating information transfer during goal directed behavior. Recent studies suggest that resetting the phase of ongoing oscillatory activity to endogenous or exogenous cues facilitates coordinated information transfer within circuits and between distributed brain areas. Here, we review evidence that pinpoints phase resetting as a critical marker of dynamic state changes of functional networks. Phase resets (1 set a neural context in terms of narrow band frequencies that uniquely characterizes the activated circuits, (2 impose coherent low frequency phases to which high frequency activations can synchronize, identifiable as cross-frequency correlations across large anatomical distances, (3 are critical for neural coding models that depend on phase, increasing the informational content of neural representations, and (4 likely originate from the dynamics of canonical E-I circuits that are anatomically ubiquitous. These multiple signatures of phase resets are directly linked to enhanced information transfer and behavioral success. We survey how phase resets re-organize oscillations in diverse task contexts, including sensory perception, attentional stimulus selection, cross-modal integration, Pavlovian conditioning, and spatial navigation. The evidence we consider suggests that phase-resets can drive changes in neural excitability, ensemble organization, functional networks, and ultimately, overt behavior.

  20. Musical molecules: the molecular junction as an active component in audio distortion circuits

    Molecular junctions that have a non-linear current–voltage characteristic consistent with quantum mechanical tunneling are demonstrated as analog audio clipping elements in overdrive circuits widely used in electronic music, particularly with electric guitars. The performance of large-area molecular junctions fabricated at the wafer level is compared to currently standard semiconductor diode clippers, showing a difference in the sound character. The harmonic distributions resulting from the use of traditional and molecular clipping elements are reported and discussed, and differences in performance are noted that result from the underlying physics that controls the electronic properties of each clipping component. In addition, the ability to tune the sound using the molecular junction is demonstrated. Finally, the hybrid circuit is compared to an overdriven tube amplifier, which has been the standard reference electric guitar clipped tone for over 60 years. In order to investigate the feasibility of manufacturing molecular junctions for use in commercial applications, devices are fabricated using a low-density format at the wafer level, where 38 dies per wafer, each containing two molecular junctions, are made with exceptional non-shorted yield (99.4%, representing 718 out of 722 tested devices) without requiring clean room facilities. (paper)

  1. Musical molecules: the molecular junction as an active component in audio distortion circuits

    Bergren, Adam Johan; Zeer-Wanklyn, Lucas; Semple, Mitchell; Pekas, Nikola; Szeto, Bryan; McCreery, Richard L.

    2016-03-01

    Molecular junctions that have a non-linear current-voltage characteristic consistent with quantum mechanical tunneling are demonstrated as analog audio clipping elements in overdrive circuits widely used in electronic music, particularly with electric guitars. The performance of large-area molecular junctions fabricated at the wafer level is compared to currently standard semiconductor diode clippers, showing a difference in the sound character. The harmonic distributions resulting from the use of traditional and molecular clipping elements are reported and discussed, and differences in performance are noted that result from the underlying physics that controls the electronic properties of each clipping component. In addition, the ability to tune the sound using the molecular junction is demonstrated. Finally, the hybrid circuit is compared to an overdriven tube amplifier, which has been the standard reference electric guitar clipped tone for over 60 years. In order to investigate the feasibility of manufacturing molecular junctions for use in commercial applications, devices are fabricated using a low-density format at the wafer level, where 38 dies per wafer, each containing two molecular junctions, are made with exceptional non-shorted yield (99.4%, representing 718 out of 722 tested devices) without requiring clean room facilities.

  2. Clustering the Sources of EEG Activity during Motor Imagery by Attractor Neural Network with Increasing Activity (ANNIA)

    Bobrov, P.; Frolov, A.; Húsek, Dušan; Snášel, V.

    Cham: Springer, 2014 - (Krömer, P.; Abraham, A.; Snášel, V.), s. 183-191. (Advances in Intelligent Systems and Computing. 303). ISBN 978-3-319-08155-7. ISSN 2194-5357. [IBICA 2014. International Conference on Innovations in Bio-Inspired Computing and Applications /5./. Ostrava (CZ), 23.06.2014-25.06.2014] Grant ostatní: GA MŠk(CZ) ED1.1.00/02.0070; GA MŠk(CZ) EE.2.3.20.0073 Institutional support: RVO:67985807 Keywords : brain computer interface * motor imagery * independent component analysis * attractor neural network with increasing activity Subject RIV: IN - Informatics, Computer Science

  3. The relation of ongoing brain activity, evoked neural responses, and cognition

    Sepideh Sadaghiani

    2010-06-01

    Full Text Available Ongoing brain activity has been observed since the earliest neurophysiological recordings and is found over a wide range of temporal and spatial scales. It is characterized by remarkably large spontaneous modulations. Here, we review evidence for the functional role of these ongoing activity fluctuations and argue that they constitute an essential property of the neural architecture underlying cognition. The role of spontaneous activity fluctuations is probably best understood when considering both their spatiotemporal structure and their functional impact on cognition. We first briefly argue against a ‘segregationist’ view on ongoing activity, both in time and space, countering this view with an emphasis on integration within a hierarchical spatiotemporal organization of intrinsic activity. We then highlight the flexibility and context-sensitivity of intrinsic functional connectivity that suggest its involvement in functionally relevant information processing. This role in information processing is pursued by reviewing how ongoing brain activity interacts with afferent and efferent information exchange of the brain with its environment. We focus on the relationship between the variability of ongoing and evoked brain activity, and review recent reports that tie ongoing brain activity fluctuations to variability in human perception and behavior. Finally, these observations are discussed within the framework of the free-energy principle which – applied to human brain function - provides a theoretical account for a non-random, coordinated interaction of ongoing and evoked activity in perception and behaviour.

  4. Differences in neural activation for object-directed grasping in chimpanzees and humans.

    Hecht, Erin E; Murphy, Lauren E; Gutman, David A; Votaw, John R; Schuster, David M; Preuss, Todd M; Orban, Guy A; Stout, Dietrich; Parr, Lisa A

    2013-08-28

    The human faculty for object-mediated action, including tool use and imitation, exceeds that of even our closest primate relatives and is a key foundation of human cognitive and cultural uniqueness. In humans and macaques, observing object-directed grasping actions activates a network of frontal, parietal, and occipitotemporal brain regions, but differences in human and macaque activation suggest that this system has been a focus of selection in the primate lineage. To study the evolution of this system, we performed functional neuroimaging in humans' closest living relatives, chimpanzees. We compare activations during performance of an object-directed manual grasping action, observation of the same action, and observation of a mimed version of the action that consisted of only movements without results. Performance and observation of the same action activated a distributed frontoparietal network similar to that reported in macaques and humans. Like humans and unlike macaques, these regions were also activated by observing movements without results. However, in a direct chimpanzee/human comparison, we also identified unique aspects of human neural responses to observed grasping. Chimpanzee activation showed a prefrontal bias, including significantly more activity in ventrolateral prefrontal cortex, whereas human activation was more evenly distributed across more posterior regions, including significantly more activation in ventral premotor cortex, inferior parietal cortex, and inferotemporal cortex. This indicates a more "bottom-up" representation of observed action in the human brain and suggests that the evolution of tool use, social learning, and cumulative culture may have involved modifications of frontoparietal interactions. PMID:23986247

  5. Critical and resonance phenomena in neural networks

    Goltsev, A. V.; Lopes, M. A.; Lee, K.-E.; Mendes, J. F. F.

    2013-01-01

    Brain rhythms contribute to every aspect of brain function. Here, we study critical and resonance phenomena that precede the emergence of brain rhythms. Using an analytical approach and simulations of a cortical circuit model of neural networks with stochastic neurons in the presence of noise, we show that spontaneous appearance of network oscillations occurs as a dynamical (non-equilibrium) phase transition at a critical point determined by the noise level, network structure, the balance between excitatory and inhibitory neurons, and other parameters. We find that the relaxation time of neural activity to a steady state, response to periodic stimuli at the frequency of the oscillations, amplitude of damped oscillations, and stochastic fluctuations of neural activity are dramatically increased when approaching the critical point of the transition.

  6. Trait self-esteem and neural activities related to self-evaluation and social feedback.

    Yang, Juan; Xu, Xiaofan; Chen, Yu; Shi, Zhenhao; Han, Shihui

    2016-01-01

    Self-esteem has been associated with neural responses to self-reflection and attitude toward social feedback but in different brain regions. The distinct associations might arise from different tasks or task-related attitudes in the previous studies. The current study aimed to clarify these by investigating the association between self-esteem and neural responses to evaluation of one's own personality traits and of others' opinion about one's own personality traits. We scanned 25 college students using functional MRI during evaluation of oneself or evaluation of social feedback. Trait self-esteem was measured using the Rosenberg self-esteem scale after scanning. Whole-brain regression analyses revealed that trait self-esteem was associated with the bilateral orbitofrontal activity during evaluation of one's own positive traits but with activities in the medial prefrontal cortex, posterior cingulate, and occipital cortices during evaluation of positive social feedback. Our findings suggest that trait self-esteem modulates the degree of both affective processes in the orbitofrontal cortex during self-reflection and cognitive processes in the medial prefrontal cortex during evaluation of social feedback. PMID:26842975

  7. Altered Brain Activities Associated with Neural Repetition Effects in Mild Cognitive Impairment Patients.

    Yu, Jing; Li, Rui; Jiang, Yang; Broster, Lucas S; Li, Juan

    2016-05-11

    Older adults with mild cognitive impairment (MCI) manifest impaired explicit memory. However, studies on implicit memory such as repetition effects in persons with MCI have been limited. In the present study, 17 MCI patients and 16 healthy normal controls (NC) completed a modified delayed-match-to-sample task while undergoing functional magnetic resonance imaging. We aim to examine the neural basis of repetition; specifically, to elucidate whether and how repetition-related brain responses are altered in participants with MCI. When repeatedly rejecting distracters, both NC and MCI showed similar behavioral repetition effects; however, in both whole-brain and region-of-interest analyses of functional data, persons with MCI showed reduced repetition-driven suppression in the middle occipital and middle frontal gyrus. Further, individual difference analysis found that activation in the left middle occipital gyrus was positively correlated with rejecting reaction time and negatively correlated with accuracy rate, suggesting a predictor of repetition behavioral performance. These findings provide new evidence to support the view that neural mechanisms of repetition effect are altered in MCI who manifests compensatory repetition-related brain activities along with their neuropathology. PMID:27176074

  8. Neural activation during imitation with or without performance feedback: An fMRI study.

    Zhang, Kaihua; Wang, Hui; Dong, Guangheng; Wang, Mengxing; Zhang, Jilei; Zhang, Hui; Meng, Weixia; Du, Xiaoxia

    2016-08-26

    In our daily lives, we often receive performance feedback (PF) during imitative learning, and we adjust our behaviors accordingly to improve performance. However, little is known regarding the neural mechanisms underlying this learning process. We hypothesized that appropriate PF would enhance neural activation or recruit additional brain areas during subsequent action imitation. Pictures of 20 different finger gestures without any social meaning were shown to participants from the first-person perspective. Imitation with or without PF was investigated by functional magnetic resonance imaging in 30 healthy subjects. The PF was given by a real person or by a computer. PF from a real person induced hyperactivation of the parietal lobe (precuneus and cuneus), cingulate cortex (posterior and anterior), temporal lobe (superior and transverse temporal gyri), and cerebellum (posterior and anterior lobes) during subsequent imitation. The positive PF and negative PF from a real person, induced the activation of more brain areas during the following imitation. The hyperactivation of the cerebellum, posterior cingulate cortex, precuneus, and cuneus suggests that the subjects exhibited enhanced motor control and visual attention during imitation after PF. Additionally, random PF from a computer had a small effect on the next imitation. We suggest that positive and accurate PF may be helpful for imitation learning. PMID:27422729

  9. QSAR study on estrogenic activity of structurally diverse compounds using generalized regression neural network

    2008-01-01

    Computer-based quantitative structure-activity relationship (QSAR) model has been becoming a pow- erful tool in understanding the structural requirements for chemicals to bind the estrogen receptor (ER), designing drugs for human estrogen replacement therapy, and identifying potential estrogenic endo- crine disruptors. In this study, a simple yet powerful neural network technique, generalized regression neural network (GRNN) was used to develop a QSAR model based on 131 structurally diverse estro- gens (training set). Only nine descriptors calculated solely from the molecular structures of com- pounds selected by objective and subjective feature selections were used as inputs of the GRNN model. The predictive power of the built model was found to be comparable to that of the more traditional techniques but requiring significantly easy implementation and a shorter computation-time. The ob- tained result indicates that the proposed GRNN model is robust and satisfactory, and can provide a feasible and practical tool for the rapid screening of the estrogenic activity of organic compounds.

  10. Adaptive Neural-Sliding Mode Control of Active Suspension System for Camera Stabilization

    Feng Zhao

    2015-01-01

    Full Text Available The camera always suffers from image instability on the moving vehicle due to the unintentional vibrations caused by road roughness. This paper presents a novel adaptive neural network based on sliding mode control strategy to stabilize the image captured area of the camera. The purpose is to suppress vertical displacement of sprung mass with the application of active suspension system. Since the active suspension system has nonlinear and time varying characteristics, adaptive neural network (ANN is proposed to make the controller robustness against systematic uncertainties, which release the model-based requirement of the sliding model control, and the weighting matrix is adjusted online according to Lyapunov function. The control system consists of two loops. The outer loop is a position controller designed with sliding mode strategy, while the PID controller in the inner loop is to track the desired force. The closed loop stability and asymptotic convergence performance can be guaranteed on the basis of the Lyapunov stability theory. Finally, the simulation results show that the employed controller effectively suppresses the vibration of the camera and enhances the stabilization of the entire camera, where different excitations are considered to validate the system performance.

  11. Patterns of neural and behavioral activity in freely-moving Navanax inermis (Mollusca; Opisthobranchia).

    Leonard, J L

    1992-01-01

    As part of an ongoing neuroethological study of complex behavior in the opisthobranch mollusc, Navanax inermis, I have extended the available gross anatomical descriptions and used cuff electrodes to obtain chronic recordings from whole nerves or connectives. The major anatomical findings concern a) finer branches of the pedal nerves, particularly P3C P4 and P5; b) the distribution of nerves from the abdominal and subintestinal ganglia; and c) a possible neurohaemal area of the supraintestinal ganglion. With cuff electrodes it has been possible to get good quality recordings (often with spikes in the mv range) during the full repertoire of sexual, predatory and cannibalistic behaviors. The high degree of cryptic neural activity and the fact that in Navanax behaviors are not mutually exclusive, make it difficult to identify one-to-one correspondences between behaviors and neural patterns, However, there is an apparent correlation between the activity of a very large unit(s) on P5 and an exploratory behavior, the Face-Down head posture when it is directed at the substrate rather than prey, or a conspecific. PMID:1299122

  12. Error-related electromyographic activity over the corrugator supercilii is associated with neural performance monitoring.

    Elkins-Brown, Nathaniel; Saunders, Blair; Inzlicht, Michael

    2016-02-01

    Emerging research in social and affective neuroscience has implicated a role for affect and motivation in performance monitoring and cognitive control. No study, however, has investigated whether facial electromyography (EMG) over the corrugator supercilii-a measure associated with negative affect and the exertion of effort-is related to neural performance monitoring. Here, we explored these potential relationships by simultaneously measuring the error-related negativity, error positivity (Pe), and facial EMG over the corrugator supercilii muscle during a punished, inhibitory control task. We found evidence for increased facial EMG activity over the corrugator immediately following error responses, and this activity was related to the Pe for both between- and within-subject analyses. These results are consistent with the idea that early, avoidance-motivated processes are associated with performance monitoring, and that such processes may also be related to orienting toward errors, the emergence of error awareness, or both. PMID:26470645

  13. Optimal waist-to-hip ratios in women activate neural reward centers in men.

    Platek, Steven M; Singh, Devendra

    2010-01-01

    Secondary sexual characteristics convey information about reproductive potential. In the same way that facial symmetry and masculinity, and shoulder-to-hip ratio convey information about reproductive/genetic quality in males, waist-to-hip-ratio (WHR) is a phenotypic cue to fertility, fecundity, neurodevelopmental resources in offspring, and overall health, and is indicative of "good genes" in women. Here, using fMRI, we found that males show activation in brain reward centers in response to naked female bodies when surgically altered to express an optimal (approximately 0.7) WHR with redistributed body fat, but relatively unaffected body mass index (BMI). Relative to presurgical bodies, brain activation to postsurgical bodies was observed in bilateral orbital frontal cortex. While changes in BMI only revealed activation in visual brain substrates, changes in WHR revealed activation in the anterior cingulate cortex, an area associated with reward processing and decision-making. When regressing ratings of attractiveness on brain activation, we observed activation in forebrain substrates, notably the nucleus accumbens, a forebrain nucleus highly involved in reward processes. These findings suggest that an hourglass figure (i.e., an optimal WHR) activates brain centers that drive appetitive sociality/attention toward females that represent the highest-quality reproductive partners. This is the first description of a neural correlate implicating WHR as a putative honest biological signal of female reproductive viability and its effects on men's neurological processing. PMID:20140088

  14. Negative stereotype activation alters interaction between neural correlates of arousal, inhibition and cognitive control.

    Forbes, Chad E; Cox, Christine L; Schmader, Toni; Ryan, Lee

    2012-10-01

    Priming negative stereotypes of African Americans can bias perceptions toward novel Black targets, but less is known about how these perceptions ultimately arise. Examining how neural regions involved in arousal, inhibition and control covary when negative stereotypes are activated can provide insight into whether individuals attempt to downregulate biases. Using fMRI, White egalitarian-motivated participants were shown Black and White faces at fast (32 ms) or slow (525 ms) presentation speeds. To create a racially negative stereotypic context, participants listened to violent and misogynistic rap (VMR) in the background. No music (NM) and death metal (DM) were used as control conditions in separate blocks. Fast exposure of Black faces elicited amygdala activation in the NM and VMR conditions (but not DM), that also negatively covaried with activation in prefrontal regions. Only in VMR, however, did amygdala activation for Black faces persist during slow exposure and positively covary with activation in dorsolateral prefrontal cortex while negatively covarying with activation in orbitofrontal cortex. Findings suggest that contexts that prime negative racial stereotypes seem to hinder the downregulation of amygdala activation that typically occurs when egalitarian perceivers are exposed to Black faces. PMID:21954239

  15. Anti-glycated activity prediction of polysaccharides from two guava fruits using artificial neural networks.

    Yan, Chunyan; Lee, Jinsheng; Kong, Fansheng; Zhang, Dezhi

    2013-10-15

    High-efficiency ultrasonic treatment was used to extract the polysaccharides of Psidium guajava (PPG) and Psidium littorale (PPL). The aims of this study were to compare polysaccharide activities from these two guavas, as well as to investigate the relationship between ultrasonic conditions and anti-glycated activity. A mathematical model of anti-glycated activity was constructed with the artificial neural network (ANN) toolbox of MATLAB software. Response surface plots showed the correlation between ultrasonic conditions and bioactivity. The optimal ultrasonic conditions of PPL for the highest anti-glycated activity were predicted to be 256 W, 60 °C, and 12 min, and the predicted activity was 42.2%. The predicted highest anti-glycated activity of PPG was 27.2% under its optimal predicted ultrasonic condition. The experimental result showed that PPG and PPL possessed anti-glycated and antioxidant activities, and those of PPL were greater. The experimental data also indicated that ANN had good prediction and optimization capability. PMID:23987324

  16. The Mind Grows Circuits

    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.

  17. A logical molecular circuit for programmable and autonomous regulation of protein activity using DNA aptamer-protein interactions

    Han, Da; Zhu, Zhi; Wu, Cuichen; Peng, Lu; Zhou, Leiji; Gulbakan, Basri; Zhu, Guizhi; Williams, Kathryn R.; Tan, Weihong

    2012-01-01

    Researchers increasingly envision an important role for artificial biochemical circuits in biological engineering, much like electrical circuits in electrical engineering. Similar to electrical circuits, which control electromechanical devices, biochemical circuits could be utilized as a type of servomechanism to control nanodevices in vitro, monitor chemical reactions in situ, or regulate gene expressions in vivo.1 As a consequence of their relative robustness and potential applicability for...

  18. Determination of DPPH free radical scavenging activity: application of artificial neural networks.

    Musa, Khalid Hamid; Abdullah, Aminah; Al-Haiqi, Ahmed

    2016-03-01

    A new computational approach for the determination of 2,2-diphenyl-1-picrylhydrazyl free radical scavenging activity (DPPH-RSA) in food is reported, based on the concept of machine learning. Trolox standard was mix with DPPH at different concentrations to produce different colors from purple to yellow. Artificial neural network (ANN) was trained on a typical set of images of the DPPH radical reacting with different levels of Trolox. This allowed the neural network to classify future images of any sample into the correct class of RSA level. The ANN was then able to determine the DPPH-RSA of cinnamon, clove, mung bean, red bean, red rice, brown rice, black rice and tea extract and the results were compared with data obtained using a spectrophotometer. The application of ANN correlated well to the spectrophotometric classical procedure and thus do not require the use of spectrophotometer, and it could be used to obtain semi-quantitative results of DPPH-RSA. PMID:26471610

  19. DETECTING ACTIVE GALACTIC NUCLEI USING MULTI-FILTER IMAGING DATA. II. INCORPORATING ARTIFICIAL NEURAL NETWORKS

    Dong, X. Y.; De Robertis, M. M., E-mail: xydong@yorku.ca [Physics and Astronomy Department, York University, Toronto, ON M3J 1P3 (Canada)

    2013-10-01

    This is the second paper of the series Detecting Active Galactic Nuclei Using Multi-filter Imaging Data. In this paper we review shapelets, an image manipulation algorithm, which we employ to adjust the point-spread function (PSF) of galaxy images. This technique is used to ensure the image in each filter has the same and sharpest PSF, which is the preferred condition for detecting AGNs using multi-filter imaging data as we demonstrated in Paper I of this series. We apply shapelets on Canada-France-Hawaii Telescope Legacy Survey Wide Survey ugriz images. Photometric parameters such as effective radii, integrated fluxes within certain radii, and color gradients are measured on the shapelets-reconstructed images. These parameters are used by artificial neural networks (ANNs) which yield: photometric redshift with an rms of 0.026 and a regression R-value of 0.92; galaxy morphological types with an uncertainty less than 2 T types for z ≤ 0.1; and identification of galaxies as AGNs with 70% confidence, star-forming/starburst (SF/SB) galaxies with 90% confidence, and passive galaxies with 70% confidence for z ≤ 0.1. The incorporation of ANNs provides a more reliable technique for identifying AGN or SF/SB candidates, which could be very useful for large-scale multi-filter optical surveys that also include a modest set of spectroscopic data sufficient to train neural networks.

  20. DETECTING ACTIVE GALACTIC NUCLEI USING MULTI-FILTER IMAGING DATA. II. INCORPORATING ARTIFICIAL NEURAL NETWORKS

    This is the second paper of the series Detecting Active Galactic Nuclei Using Multi-filter Imaging Data. In this paper we review shapelets, an image manipulation algorithm, which we employ to adjust the point-spread function (PSF) of galaxy images. This technique is used to ensure the image in each filter has the same and sharpest PSF, which is the preferred condition for detecting AGNs using multi-filter imaging data as we demonstrated in Paper I of this series. We apply shapelets on Canada-France-Hawaii Telescope Legacy Survey Wide Survey ugriz images. Photometric parameters such as effective radii, integrated fluxes within certain radii, and color gradients are measured on the shapelets-reconstructed images. These parameters are used by artificial neural networks (ANNs) which yield: photometric redshift with an rms of 0.026 and a regression R-value of 0.92; galaxy morphological types with an uncertainty less than 2 T types for z ≤ 0.1; and identification of galaxies as AGNs with 70% confidence, star-forming/starburst (SF/SB) galaxies with 90% confidence, and passive galaxies with 70% confidence for z ≤ 0.1. The incorporation of ANNs provides a more reliable technique for identifying AGN or SF/SB candidates, which could be very useful for large-scale multi-filter optical surveys that also include a modest set of spectroscopic data sufficient to train neural networks

  1. Periodicity and global exponential stability of generalized Cohen-Grossberg neural networks with discontinuous activations and mixed delays.

    Wang, Dongshu; Huang, Lihong

    2014-03-01

    In this paper, we investigate the periodic dynamical behaviors for a class of general Cohen-Grossberg neural networks with discontinuous right-hand sides, time-varying and distributed delays. By means of retarded differential inclusions theory and the fixed point theorem of multi-valued maps, the existence of periodic solutions for the neural networks is obtained. After that, we derive some sufficient conditions for the global exponential stability and convergence of the neural networks, in terms of nonsmooth analysis theory with generalized Lyapunov approach. Without assuming the boundedness (or the growth condition) and monotonicity of the discontinuous neuron activation functions, our results will also be valid. Moreover, our results extend previous works not only on discrete time-varying and distributed delayed neural networks with continuous or even Lipschitz continuous activations, but also on discrete time-varying and distributed delayed neural networks with discontinuous activations. We give some numerical examples to show the applicability and effectiveness of our main results. PMID:24406667

  2. Neural substrates underlying the passive observation and active control of translational egomotion.

    Huang, Ruey-Song; Chen, Ching-Fu; Sereno, Martin I

    2015-03-11

    Moving or static obstacles often get in the way while walking in daily life. Avoiding obstacles involves both perceptual processing of motion information and controlling appropriate defensive movements. Several higher-level motion areas, including the ventral intraparietal area (VIP), medial superior temporal area, parieto-insular vestibular cortex (PIVC), areas V6 and V6A, and cingulate sulcus visual area, have been identified in humans by passive viewing of optic flow patterns that simulate egomotion and object motion. However, the roles of these areas in the active control of egomotion in the real world remain unclear. Here, we used functional magnetic resonance imaging (fMRI) to map the neural substrates underlying the passive observation and active control of translational egomotion in humans. A wide-field virtual reality environment simulated a daily scenario where doors randomly swing outward while walking in a hallway. The stimuli of door-dodging events were essentially the same in two event-related fMRI experiments, which compared passive and active dodges in response to swinging doors. Passive dodges were controlled by a computer program, while active dodges were controlled by the subject. Passive dodges activated several higher-level areas distributed across three dorsal motion streams in the temporal, parietal, and cingulate cortex. Active dodges most strongly activated the temporal-vestibular stream, with peak activation located in the right PIVC. Other higher-level motion areas including VIP showed weaker to no activation in active dodges. These results suggest that PIVC plays an active role in sensing and guiding translational egomotion that moves an observer aside from impending obstacles. PMID:25762672

  3. Data on the impact of SSRIs and depression symptoms on the neural activities in obsessive-compulsive disorder at rest.

    Chen, Yunhui; Juhas, Michal; Greenshaw, Andrew J; Hu, Qiang; Meng, Xin; Cui, Hongsheng; Ding, Yongzhuo; Kang, Lu; Zhang, Yubo; Wang, Yuhua; Cui, Guangcheng; Li, Ping

    2016-09-01

    The data provided here related to our research article (Chen et al., 2016) [1]. We provide whole-brain intrinsic functional connectivity patterns in obsessive-compulsive disorder at resting-state [1]. This article also provides supplementary information to our research article, i.e., between - group comparisons of the effect of selective serotonin reuptake inhibitors (SSRIs) and combined depression symptoms on resting-state neural activities in obsessive-compulsive disorder. The data presented here provide novel insights into the effect of SSRIs and combined depression symptoms on the neural activities at rest. PMID:27504477

  4. Heterotypic Signals from Neural HSF-1 Separate Thermotolerance from Longevity.

    Douglas, Peter M; Baird, Nathan A; Simic, Milos S; Uhlein, Sarah; McCormick, Mark A; Wolff, Suzanne C; Kennedy, Brian K; Dillin, Andrew

    2015-08-18

    Integrating stress responses across tissues is essential for the survival of multicellular organisms. The metazoan nervous system can sense protein-misfolding stress arising in different subcellular compartments and initiate cytoprotective transcriptional responses in the periphery. Several subcellular compartments possess a homotypic signal whereby the respective compartment relies on a single signaling mechanism to convey information within the affected cell to the same stress-responsive pathway in peripheral tissues. In contrast, we find that the heat shock transcription factor, HSF-1, specifies its mode of transcellular protection via two distinct signaling pathways. Upon thermal stress, neural HSF-1 primes peripheral tissues through the thermosensory neural circuit to mount a heat shock response. Independent of this thermosensory circuit, neural HSF-1 activates the FOXO transcription factor, DAF-16, in the periphery and prolongs lifespan. Thus a single transcription factor can coordinate different stress response pathways to specify its mode of protection against changing environmental conditions. PMID:26257177

  5. Heterotypic Signals from Neural HSF-1 Separate Thermotolerance from Longevity

    Peter M. Douglas

    2015-08-01

    Full Text Available Integrating stress responses across tissues is essential for the survival of multicellular organisms. The metazoan nervous system can sense protein-misfolding stress arising in different subcellular compartments and initiate cytoprotective transcriptional responses in the periphery. Several subcellular compartments possess a homotypic signal whereby the respective compartment relies on a single signaling mechanism to convey information within the affected cell to the same stress-responsive pathway in peripheral tissues. In contrast, we find that the heat shock transcription factor, HSF-1, specifies its mode of transcellular protection via two distinct signaling pathways. Upon thermal stress, neural HSF-1 primes peripheral tissues through the thermosensory neural circuit to mount a heat shock response. Independent of this thermosensory circuit, neural HSF-1 activates the FOXO transcription factor, DAF-16, in the periphery and prolongs lifespan. Thus a single transcription factor can coordinate different stress response pathways to specify its mode of protection against changing environmental conditions.

  6. Seasonal prediction of tropical cyclone activity over the north Indian Ocean using three artificial neural networks

    Nath, Sankar; Kotal, S. D.; Kundu, P. K.

    2016-03-01

    Three artificial neural network (ANN) methods, namely, multilayer perceptron (MLP), radial basis function (RBF) and generalized regression neural network (GRNN) are utilized to predict the seasonal tropical cyclone (TC) activity over the north Indian Ocean (NIO) during the post-monsoon season (October, November, December). The frequency of TC and large-scale climate variables derived from NCEP/NCAR reanalysis dataset of resolution 2.5° × 2.5° were analyzed for the period 1971-2013. Data for the years 1971-2002 were used for the development of the models, which were tested with independent sample data for the year 2003-2013. Using the correlation analysis, the five large-scale climate variables, namely, geopotential height at 500 hPa, relative humidity at 500 hPa, sea-level pressure, zonal wind at 700 hPa and 200 hPa for the preceding month September, are selected as potential predictors of the post-monsoon season TC activity. The result reveals that all the three different ANN methods are able to provide satisfactory forecast in terms of the various metrics, such as root mean-square error (RMSE), standard deviation (SD), correlation coefficient (r), and bias and index of agreement (d). Additionally, leave-one-out cross validation (LOOCV) method is also performed and the forecast skill is evaluated. The results show that the MLP model is found to be superior to the other two models (RBF, GRNN). The (MLP) is expected to be very useful to operational forecasters for prediction of TC activity.

  7. Altered neural activity in the `when' pathway during temporal processing in fragile X premutation carriers

    Kim, So-Yeon; Tassone, Flora; Simon, Tony J.; Rivera, Susan M.

    2016-01-01

    Mutations of the fragile X mental retardation 1 (FMR1) gene are the genetic cause of fragile X syndrome (FXS). Large expansions of the CGG repeat (>200 repeats) consequently result in transcriptional silencing of the FMR1 gene and deficiency/absence of the FMR1 protein (FMRP). Carriers with a premutation allele (55–200 of CGG repeats) are often associated with mildly reduced levels of FMRP and/or elevated levels of FMR1 mRNA. Recent studies have shown that infants with FXS exhibit severely reduced resolution of temporal attention, whereas spatial resolution of attention is not impaired. Following from these findings in the full mutation, the current study used fMRI to examine whether premutation carriers would exhibit atypical temporal processing at behavioral and/or neural levels. Using spatial and temporal working memory (SWM and TWM) tasks, separately tagging spatial and temporal processing, we demonstrated that neurotypical adults showed greater activation in the `when pathway' (i.e., the right temporoparietal junction: TPJ) during TWM retrieval than SWM retrieval. However, premutation carriers failed to show this increased involvement of the right TPJ during retrieval of temporal information. Further, multiple regression analyses on right TPJ activation and FMR1 gene expression (i.e., CGG repeat size and FMR1 mRNA) suggests that elevated FMR1 mRNA level is a powerful predictor accounting for reduced right TPJ activation associated with temporal processing in premutation carriers. In conclusion, the current study provides the first evidence on altered neural correlates of temporal processing in adults with the premutation, explained by their FMR1 gene expression. PMID:24398265

  8. Habenula circuit development: past, present and future

    Carlo Antonio Beretta

    2012-04-01

    Full Text Available The habenular neural circuit is attracting increasing attention from researchers in fields as diverse as neuroscience, medicine, behavior, development and evolution. Recent studies have revealed that this part of the limbic system in the dorsal diencephalon is involved in reward, addiction and other behaviors and its impairment is associated with various neurological conditions and diseases. Since the initial description of the Dorsal Diencephalic Conduction system (DDC with the habenulae in its center at the end of the 19th century, increasingly sophisticated techniques have resolved much of its anatomy and have shown that these pathways relay information from different parts of the forebrain to the tegmentum, midbrain and hindbrain. The first part of this review gives a brief historical overview on how the improving experimental approaches have allowed the stepwise uncovering of much of the architecture of the habenula circuit as we know it today. Our brain distributes tasks differentially between left and right and it has become a paradigm that this functional lateralization is a universal feature of vertebrates. Moreover, task dependent differential brain activities have been linked to anatomical differences across the left-right axis in humans. A good way to further explore this fundamental issue will be to study the functional consequences of subtle changes in neural network formation, which requires that we fully understand DDC system development. As the habenular circuit is evolutionarily highly conserved, researchers have the option to perform such difficult experiments in more experimentally amenable vertebrate systems. Indeed, research in the last decade has shown that the zebrafish is well suited for the study of DDC system development and the phenomenon of functional lateralization. We will critically discuss the advantages of the zebrafish model, available techniques and others that are needed to fully understand habenular circuit

  9. Unbalanced Neuronal Circuits in Addiction

    Volkow, Nora D; Wang, Gen-Jack; Tomasi, Dardo; Baler, Ruben D.

    2013-01-01

    Through sequential waves of drug-induced neurochemical stimulation, addiction co-opts the brain's neuronal circuits that mediate reward, motivation, , to behavioral inflexibility and a severe disruption of self-control and compulsive drug intake. Brain imaging technologies have allowed neuroscientists to map out the neural landscape of addiction in the human brain and to understand how drugs modify it.

  10. Effects of detergent on calcium-activated neutral proteinase (CANP) of neural and non-neural tissues in rat. A comparative study

    Homogenates of brain, liver, kidney, heart and skeletal muscle of rat were prepared in 0.32 M-sucrose containing 2 mM EDTA. The CANP activity was assayed using 14C-azocasein as substrate in 50 mM Tris acetate buffer, pH 7.4, 0.1% Triton X-100 and 5 mM-β-mercaptoethanol, with and without CaCl2. Addition to CNS membranes of other non-ionic detergents including sodium deoxycholate, β-D-thiogluco-pyranoside, and cetyltrimethyl-ammonium bromide activated the enzyme to varying extent depending on the detergent concentration. The ionic detergent sodium dodecyl sulfate abolished CANP activity completely in all preparations and this effect could not be reversed by non-ionic detergents. The most interesting feature of the Triton X-100 effect was a ten-fold increase of CNS CANP activity whereas non-neural CANP was not at all induced by Triton. CNS CANP was found mainly in the particulate fraction and only 30% in cytosol. In contrast, non-neural CANP was present mainly in cytosol. These results suggest that the bulk of CANP is membrane bound in CNS and differs from other tissue where it remains cytosolic

  11. Neurotrophins and spinal circuit function

    Lorne M. Mendell

    2014-01-01

    Work early in the last century emphasized the stereotyped activity of spinal circuits based on studies of reflexes. However, the last several decades have focused on the plasticity of these spinal circuits. These considerations began with studies of the effects of monoamines on descending and reflex circuits. In recent years new classes of compounds called growth factors that are found in peripheral nerves and the spinal cord have been shown to affect circuit behavior in the spinal cord. In t...

  12. Cascaded active silicon microresonator array cross-connect circuits for WDM networks-on-chip

    Poon, Andrew W.; Xu, Fang; Luo, Xianshu

    2008-02-01

    We propose a design of an optical switch on a silicon chip comprising a 5 × 5 array of cascaded waveguide-crossing-coupled microring resonator-based switches for photonic networks-on-chip applications. We adopt our recently demonstrated design of multimode-interference (MMI)-based wire waveguide crossings, instead of conventional plain waveguide crossings, for the merits of low loss and low crosstalk. The microring resonator is integrated with a lateral p-i-n diode for carrier-injection-based GHz-speed on-off switching. All 25 microring resonators are assumed to be identical within a relatively wide resonance line width. The optical circuit switch can employ a single wavelength channel or multiple wavelength channels that are spaced by the microring resonator free spectral range. We analyze the potential performance of the proposed photonic network in terms of (i) light path cross-connections loss budget, and (ii) DC on-off power consumption for establishing a light path. As a proof-of-concept, our initial experiments on cascaded passive silicon MMI-crossing-coupled microring resonators demonstrate 3.6-Gbit/s non-return-to-zero data transmissions at on- and off-resonance wavelengths.

  13. Effects of selective serotonin reuptake inhibition on neural activity related to risky decisions and monetary rewards in healthy males

    Macoveanu, Julian; Fisher, Patrick M; Haahr, Mette E;

    2014-01-01

    functional MRI (fMRI) to investigate how a three-week fluoxetine intervention influences neural activity related to risk taking and reward processing. Employing a double-blinded parallel-group design, 29 healthy young males were randomly assigned to receive 3 weeks of a daily dose of 40 mg fluoxetine or...... placebo, the SSRI intervention did not alter individual risk-choice preferences, but modified neural activity during decision-making and reward processing: During the choice phase, SSRI reduced the neural response to increasing risk in lateral orbitofrontal cortex, a key structure for value-based decision-making...... involvement of the normally functioning 5HT-system in decision-making under risk and processing of monetary rewards. The data suggest that prolonged SSRI treatment might reduce emotional engagement by reducing the impact of risk during decision-making or the impact of reward during outcome evaluation....

  14. TEMPORAL AND CONCENTRATION DEPENDENT ESTRADIOL EFFECTS ON NEURAL PATHWAYS MEDIATING SEXUAL RECEPTIVITY

    Micevych, Paul; Sinchak, Kevin

    2013-01-01

    The acceptance of estradiol signaling through receptors found in the cell membrane, as well as, the nucleus has provided for a re-examination of timing and location of estradiol actions on neural circuits mediating sexual receptivity (lordosis). Estradiol membrane signaling involves the transactivation of metabotropic glutamate receptors (mGluR) that transduce steroid information through PKC signaling cascades producing rapid activation of lordosis regulating circuits. It has been known for s...

  15. Bayesian Inference for Neural Electromagnetic Source Localization: Analysis of MEG Visual Evoked Activity

    We have developed a Bayesian approach to the analysis of neural electromagnetic (MEG/EEG) data that can incorporate or fuse information from other imaging modalities and addresses the ill-posed inverse problem by sarnpliig the many different solutions which could have produced the given data. From these samples one can draw probabilistic inferences about regions of activation. Our source model assumes a variable number of variable size cortical regions of stimulus-correlated activity. An active region consists of locations on the cortical surf ace, within a sphere centered on some location in cortex. The number and radi of active regions can vary to defined maximum values. The goal of the analysis is to determine the posterior probability distribution for the set of parameters that govern the number, location, and extent of active regions. Markov Chain Monte Carlo is used to generate a large sample of sets of parameters distributed according to the posterior distribution. This sample is representative of the many different source distributions that could account for given data, and allows identification of probable (i.e. consistent) features across solutions. Examples of the use of this analysis technique with both simulated and empirical MEG data are presented

  16. Bayesian Inference for Neural Electromagnetic Source Localization: Analysis of MEG Visual Evoked Activity

    George, J.S.; Schmidt, D.M.; Wood, C.C.

    1999-02-01

    We have developed a Bayesian approach to the analysis of neural electromagnetic (MEG/EEG) data that can incorporate or fuse information from other imaging modalities and addresses the ill-posed inverse problem by sarnpliig the many different solutions which could have produced the given data. From these samples one can draw probabilistic inferences about regions of activation. Our source model assumes a variable number of variable size cortical regions of stimulus-correlated activity. An active region consists of locations on the cortical surf ace, within a sphere centered on some location in cortex. The number and radi of active regions can vary to defined maximum values. The goal of the analysis is to determine the posterior probability distribution for the set of parameters that govern the number, location, and extent of active regions. Markov Chain Monte Carlo is used to generate a large sample of sets of parameters distributed according to the posterior distribution. This sample is representative of the many different source distributions that could account for given data, and allows identification of probable (i.e. consistent) features across solutions. Examples of the use of this analysis technique with both simulated and empirical MEG data are presented.

  17. Encoding of fear learning and memory in distributed neuronal circuits.

    Herry, Cyril; Johansen, Joshua P

    2014-12-01

    How sensory information is transformed by learning into adaptive behaviors is a fundamental question in neuroscience. Studies of auditory fear conditioning have revealed much about the formation and expression of emotional memories and have provided important insights into this question. Classical work focused on the amygdala as a central structure for fear conditioning. Recent advances, however, have identified new circuits and neural coding strategies mediating fear learning and the expression of fear behaviors. One area of research has identified key brain regions and neuronal coding mechanisms that regulate the formation, specificity and strength of fear memories. Other work has discovered critical circuits and neuronal dynamics by which fear memories are expressed through a medial prefrontal cortex pathway and coordinated activity across interconnected brain regions. Here we review these recent advances alongside prior work to provide a working model of the extended circuits and neuronal coding mechanisms mediating fear learning and memory. PMID:25413091

  18. Chinese Speech Recognition Model Based on Activation of the State Feedback Neural Network

    李先志; 孙义和

    2001-01-01

    This paper proposes a simplified novel speech recognition model, the state feedback neuralnetwork activation model (SFNNAM), which is developed based on the characteristics of Chinese speechstructure. The model assumes that the current state of speech is only a correction of the last previous state.According to the "C-V"(Consonant-Vowel) structure of the Chinese language, a speech segmentation methodis also implemented in the SFNNAM model. This model has a definite physical meaning grounded on thestructure of the Chinese language and is easily implemented in very large scale integrated circuit (VLSI). In thespeech recognition experiment, less calculations were need than in the hidden Markov models (HMM) basedalgorithm. The recognition rate for Chinese numbers was 93.5% for the first candidate and 99.5% for the firsttwo candidates.``

  19. Topological defect launches 3D mound in the active nematic sheet of neural progenitors

    Kawaguchi, Kyogo; Sano, Masaki

    2016-01-01

    Cultured stem cells have become a standard platform not only for regenerative medicine and developmental biology but also for biophysical studies. Yet, the characterization of cultured stem cells at the level of morphology and macroscopic patterns resulting from cell-to-cell interactions remain largely qualitative, even though they are the simplest features observed in everyday experiments. Here we report that neural progenitor cells (NPCs), which are multipotent stem cells that give rise to cells in the central nervous system, rapidly glide and stochastically reverse its velocity while locally aligning with neighboring cells, thus showing features of an active nematic system. Within the two-dimensional nematic pattern, we find interspaced topological defects with +1/2 and -1/2 charges. Remarkably, we identified rapid cell accumulation leading to three-dimensional mounds at the +1/2 topological defects. Single-cell level imaging around the defects allowed quantification of the evolving cell density, clarifyin...

  20. Attentional Dissociation in Hypnosis And Neural Connectivity: Preliminary Evidence from Bilateral Electrodermal Activity.

    Bob, Petr; Siroka, Ivana

    2016-01-01

    According to recent findings, interhemispheric interactions and information connectivity represent crucial mechanisms used in processing information across various sensory modalities. To study these interactions, the authors measured bilateral electrodermal activity (EDA) in 33 psychiatric outpatients. The results show that, during congruent Stroop stimuli in hypnosis, the patients with higher hypnotizability manifest a decreased level of interhemispheric information transfer measured by pointwise transinformation (PTI) that was calculated from left and right EDA records. These results show that specific shifts of attentional focus during hypnosis are related to changes of interhemispheric interactions that may be reflected in neural connectivity calculated from the bilateral EDA measurement. This attentional shift may cause dissociated attentional control disturbing integrative functions of consciousness and contextual experiences. PMID:27267677