A simple electronic circuit realization of the tent map

Energy Technology Data Exchange (ETDEWEB)

Campos-Canton, I. [Fac. de Ciencias, Universidad Autonoma de San Luis Potosi, Alvaro Obregon 64, 78000 San Luis Potosi, SLP (Mexico)], E-mail: icampos@galia.fc.uaslp.mx; Campos-Canton, E. [Departamento de Fisico Matematicas, Universidad Autonoma de San Luis Potosi, Alvaro Obregon 64, 78000 San Luis Potosi, SLP (Mexico)], E-mail: ecamp@uaslp.mx; Murguia, J.S. [Departamento de Fisico Matematicas, Universidad Autonoma de San Luis Potosi, Alvaro Obregon 64, 78000 San Luis Potosi, SLP (Mexico)], E-mail: ondeleto@uaslp.mx; Rosu, H.C. [Division de Materiales Avanzados, Instituto Potosino de Investigacion Cientifica y Tecnologica, Camino a la presa San Jose 2055, 78216 San Luis Potosi, SLP (Mexico)], E-mail: hcr@ipicyt.edu.mx

2009-10-15

We present a very simple electronic implementation of the tent map, one of the best-known discrete dynamical systems. This is achieved by using integrated circuits and passive elements only. The experimental behavior of the tent map electronic circuit is compared with its numerical simulation counterpart. We find that the electronic circuit presents fixed points, periodicity, period doubling, chaos and intermittency that match with high accuracy the corresponding theoretical values.

Self-Organizing Neural Circuits for Sensory-Guided Motor Control

National Research Council Canada - National Science Library

Grossberg, Stephen

1999-01-01

The reported projects developed mathematical models to explain how self-organizing neural circuits that operate under continuous or intermittent sensory guidance achieve flexible and accurate control of human movement...

Beyond excitation/inhibition imbalance in multidimensional models of neural circuit changes in brain disorders.

Science.gov (United States)

O'Donnell, Cian; Gonçalves, J Tiago; Portera-Cailliau, Carlos; Sejnowski, Terrence J

2017-10-11

A leading theory holds that neurodevelopmental brain disorders arise from imbalances in excitatory and inhibitory (E/I) brain circuitry. However, it is unclear whether this one-dimensional model is rich enough to capture the multiple neural circuit alterations underlying brain disorders. Here, we combined computational simulations with analysis of in vivo two-photon Ca 2+ imaging data from somatosensory cortex of Fmr1 knock-out (KO) mice, a model of Fragile-X Syndrome, to test the E/I imbalance theory. We found that: (1) The E/I imbalance model cannot account for joint alterations in the observed neural firing rates and correlations; (2) Neural circuit function is vastly more sensitive to changes in some cellular components over others; (3) The direction of circuit alterations in Fmr1 KO mice changes across development. These findings suggest that the basic E/I imbalance model should be updated to higher dimensional models that can better capture the multidimensional computational functions of neural circuits.

Modulation of neural circuits: how stimulus context shapes innate behavior in Drosophila

OpenAIRE

Su, Chih-Ying; Wang, Jing W.

2014-01-01

Remarkable advances have been made in recent years in our understanding of innate behavior and the underlying neural circuits. In particular, a wealth of neuromodulatory mechanisms have been uncovered that can alter the input-output relationship of a hereditary neural circuit. It is now clear that this inbuilt flexibility allows animals to modify their behavioral responses according to environmental cues, metabolic demands and physiological states. Here, we discuss recent insights into how mo...

On the origin of reproducible sequential activity in neural circuits

Science.gov (United States)

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

2004-12-01

Robustness and reproducibility of sequential spatio-temporal responses is an essential feature of many neural circuits in sensory and motor systems of animals. The most common mathematical images of dynamical regimes in neural systems are fixed points, limit cycles, chaotic attractors, and continuous attractors (attractive manifolds of neutrally stable fixed points). These are not suitable for the description of reproducible transient sequential neural dynamics. In this paper we present the concept of a stable heteroclinic sequence (SHS), which is not an attractor. SHS opens the way for understanding and modeling of transient sequential activity in neural circuits. We show that this new mathematical object can be used to describe robust and reproducible sequential neural dynamics. Using the framework of a generalized high-dimensional Lotka-Volterra model, that describes the dynamics of firing rates in an inhibitory network, we present analytical results on the existence of the SHS in the phase space of the network. With the help of numerical simulations we confirm its robustness in presence of noise in spite of the transient nature of the corresponding trajectories. Finally, by referring to several recent neurobiological experiments, we discuss possible applications of this new concept to several problems in neuroscience.

FUZZY NEURAL NETWORK FOR OBJECT IDENTIFICATION ON INTEGRATED CIRCUIT LAYOUTS

Directory of Open Access Journals (Sweden)

A. A. Doudkin

2015-01-01

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

Molecular annotation of integrative feeding neural circuits.

Science.gov (United States)

Pérez, Cristian A; Stanley, Sarah A; Wysocki, Robert W; Havranova, Jana; Ahrens-Nicklas, Rebecca; Onyimba, Frances; Friedman, Jeffrey M

2011-02-02

The identity of higher-order neurons and circuits playing an associative role to control feeding is unknown. We injected pseudorabies virus, a retrograde tracer, into masseter muscle, salivary gland, and tongue of BAC-transgenic mice expressing GFP in specific neural populations and identified several CNS regions that project multisynaptically to the periphery. MCH and orexin neurons were identified in the lateral hypothalamus, and Nurr1 and Cnr1 in the amygdala and insular/rhinal cortices. Cholera toxin β tracing showed that insular Nurr1(+) and Cnr1(+) neurons project to the amygdala or lateral hypothalamus, respectively. Finally, we show that cortical Cnr1(+) neurons show increased Cnr1 mRNA and c-Fos expression after fasting, consistent with a possible role for Cnr1(+) neurons in feeding. Overall, these studies define a general approach for identifying specific molecular markers for neurons in complex neural circuits. These markers now provide a means for functional studies of specific neuronal populations in feeding or other complex behaviors. Copyright © 2011 Elsevier Inc. All rights reserved.

The Neural Circuits that Generate Tics in Gilles de la Tourette Syndrome

Science.gov (United States)

Wang, Zhishun; Maia, Tiago V.; Marsh, Rachel; Colibazzi, Tiziano; Gerber, Andrew; Peterson, Bradley S.

2014-01-01

Objective To study neural activity and connectivity within cortico-striato-thalamo-cortical circuits and to reveal circuit-based neural mechanisms that govern tic generation in Tourette syndrome. Method We acquired fMRI data from 13 participants with Tourette syndrome and 21 controls during spontaneous or simulated tics. We used independent component analysis with hierarchical partner matching to isolate neural activity within functionally distinct regions of cortico-striato-thalamo-cortical circuits. We used Granger causality to investigate causal interactions among these regions. Results We found that the Tourette group exhibited stronger neural activity and interregional causality than controls throughout all portions of the motor pathway including sensorimotor cortex, putamen, pallidum, and substania nigra. Activity in these areas correlated positively with the severity of tic symptoms. Activity within the Tourette group was stronger during spontaneous tics than during voluntary tics in somatosensory and posterior parietal cortices, putamen, and amygdala/hippocampus complex, suggesting that activity in these regions may represent features of the premonitory urges that generate spontaneous tic behaviors. In contrast, activity was weaker in the Tourette group than in controls within portions of cortico-striato-thalamo-cortical circuits that exert top-down control over motor pathways (caudate and anterior cingulate cortex), and progressively less activity in these regions accompanied more severe tic symptoms, suggesting that faulty activity in these circuits may fail to control tic behaviors or the premonitory urges that generate them. Conclusions Our findings taken together suggest that tics are caused by the combined effects of excessive activity in motor pathways and reduced activation in control portions of cortico-striato-thalamo-cortical circuits. PMID:21955933

δ-Protocadherins: Organizers of neural circuit assembly.

Science.gov (United States)

Light, Sarah E W; Jontes, James D

2017-09-01

The δ-protocadherins comprise a small family of homophilic cell adhesion molecules within the larger cadherin superfamily. They are essential for neural development as mutations in these molecules give rise to human neurodevelopmental disorders, such as schizophrenia and epilepsy, and result in behavioral defects in animal models. Despite their importance to neural development, a detailed understanding of their mechanisms and the ways in which their loss leads to changes in neural function is lacking. However, recent results have begun to reveal roles for the δ-protocadherins in both regulation of neurogenesis and lineage-dependent circuit assembly, as well as in contact-dependent motility and selective axon fasciculation. These evolutionarily conserved mechanisms could have a profound impact on the robust assembly of the vertebrate nervous system. Future work should be focused on unraveling the molecular mechanisms of the δ-protocadherins and understanding how this family functions broadly to regulate neural development. Copyright © 2017 Elsevier Ltd. All rights reserved.

Optogenetic interrogation of neural circuits: technology for probing mammalian brain structures

Science.gov (United States)

Zhang, Feng; Gradinaru, Viviana; Adamantidis, Antoine R; Durand, Remy; Airan, Raag D; de Lecea, Luis; Deisseroth, Karl

2015-01-01

Elucidation of the neural substrates underlying complex animal behaviors depends on precise activity control tools, as well as compatible readout methods. Recent developments in optogenetics have addressed this need, opening up new possibilities for systems neuroscience. Interrogation of even deep neural circuits can be conducted by directly probing the necessity and sufficiency of defined circuit elements with millisecond-scale, cell type-specific optical perturbations, coupled with suitable readouts such as electrophysiology, optical circuit dynamics measures and freely moving behavior in mammals. Here we collect in detail our strategies for delivering microbial opsin genes to deep mammalian brain structures in vivo, along with protocols for integrating the resulting optical control with compatible readouts (electrophysiological, optical and behavioral). The procedures described here, from initial virus preparation to systems-level functional readout, can be completed within 4–5 weeks. Together, these methods may help in providing circuit-level insight into the dynamics underlying complex mammalian behaviors in health and disease. PMID:20203662

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

Science.gov (United States)

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

2011-12-08

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

Neural Circuit Mechanisms of Social Behavior.

Science.gov (United States)

Chen, Patrick; Hong, Weizhe

2018-04-04

We live in a world that is largely socially constructed, and we are constantly involved in and fundamentally influenced by a broad array of complex social interactions. Social behaviors among conspecifics, either conflictive or cooperative, are exhibited by all sexually reproducing animal species and are essential for the health, survival, and reproduction of animals. Conversely, impairment in social function is a prominent feature of several neuropsychiatric disorders, such as autism spectrum disorders and schizophrenia. Despite the importance of social behaviors, many fundamental questions remain unanswered. How is social sensory information processed and integrated in the nervous system? How are different social behavioral decisions selected and modulated in brain circuits? Here we discuss conceptual issues and recent advances in our understanding of brain regions and neural circuit mechanisms underlying the regulation of social behaviors. Copyright © 2018 Elsevier Inc. All rights reserved.

The primary visual cortex in the neural circuit for visual orienting

Science.gov (United States)

Zhaoping, Li

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

Railway track circuit fault diagnosis using recurrent neural networks

NARCIS (Netherlands)

de Bruin, T.D.; Verbert, K.A.J.; Babuska, R.

2017-01-01

Timely detection and identification of faults in railway track circuits are crucial for the safety and availability of railway networks. In this paper, the use of the long-short-term memory (LSTM) recurrent neural network is proposed to accomplish these tasks based on the commonly available

Neural net generated seismic facies map and attribute facies map

International Nuclear Information System (INIS)

Addy, S.K.; Neri, P.

1998-01-01

The usefulness of 'seismic facies maps' in the analysis of an Upper Wilcox channel system in a 3-D survey shot by CGG in 1995 in Lavaca county in south Texas was discussed. A neural net-generated seismic facies map is a quick hydrocarbon exploration tool that can be applied regionally as well as on a prospect scale. The new technology is used to classify a constant interval parallel to a horizon in a 3-D seismic volume based on the shape of the wiggle traces using a neural network technology. The tool makes it possible to interpret sedimentary features of a petroleum deposit. The same technology can be used in regional mapping by making 'attribute facies maps' in which various forms of amplitude attributes, phase attributes or frequency attributes can be used

Explicit logic circuits discriminate neural states.

Directory of Open Access Journals (Sweden)

Lane Yoder

Full Text Available The magnitude and apparent complexity of the brain's connectivity have left explicit networks largely unexplored. As a result, the relationship between the organization of synaptic connections and how the brain processes information is poorly understood. A recently proposed retinal network that produces neural correlates of color vision is refined and extended here to a family of general logic circuits. For any combination of high and low activity in any set of neurons, one of the logic circuits can receive input from the neurons and activate a single output neuron whenever the input neurons have the given activity state. The strength of the output neuron's response is a measure of the difference between the smallest of the high inputs and the largest of the low inputs. The networks generate correlates of known psychophysical phenomena. These results follow directly from the most cost-effective architectures for specific logic circuits and the minimal cellular capabilities of excitation and inhibition. The networks function dynamically, making their operation consistent with the speed of most brain functions. The networks show that well-known psychophysical phenomena do not require extraordinarily complex brain structures, and that a single network architecture can produce apparently disparate phenomena in different sensory systems.

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

Science.gov (United States)

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

2014-01-01

Major depression is characterized by a cluster of symptoms that includes hopelessness, low mood, feelings of worthlessness and inability to experience pleasure. The lifetime prevalence of major depression approaches 20%, yet current treatments are often inadequate both because of associated side effects and because they are ineffective for many people. In basic research, animal models are often used to study depression. Typically, experimental animals are exposed to acute or chronic stress to generate a variety of depression-like symptoms. Despite its clinical importance, very little is known about the cellular and neural circuits that mediate these symptoms. Recent advances in circuit-targeted approaches have provided new opportunities to study the neuropathology of mood disorders such as depression and anxiety. We review recent progress and highlight some studies that have begun tracing a functional neuronal circuit diagram that may prove essential in establishing novel treatment strategies in mood disorders. First, we shed light on the complexity of mesocorticolimbic dopamine (DA) responses to stress by discussing two recent studies reporting that optogenetic activation of midbrain DA neurons can induce or reverse depression-related behaviors. Second, we describe the role of the lateral habenula circuitry in the pathophysiology of depression. Finally, we discuss how the prefrontal cortex controls limbic and neuromodulatory circuits in mood disorders. © 2013 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

Interpretation of correlated neural variability from models of feed-forward and recurrent circuits

Science.gov (United States)

2018-01-01

Neural populations respond to the repeated presentations of a sensory stimulus with correlated variability. These correlations have been studied in detail, with respect to their mechanistic origin, as well as their influence on stimulus discrimination and on the performance of population codes. A number of theoretical studies have endeavored to link network architecture to the nature of the correlations in neural activity. Here, we contribute to this effort: in models of circuits of stochastic neurons, we elucidate the implications of various network architectures—recurrent connections, shared feed-forward projections, and shared gain fluctuations—on the stimulus dependence in correlations. Specifically, we derive mathematical relations that specify the dependence of population-averaged covariances on firing rates, for different network architectures. In turn, these relations can be used to analyze data on population activity. We examine recordings from neural populations in mouse auditory cortex. We find that a recurrent network model with random effective connections captures the observed statistics. Furthermore, using our circuit model, we investigate the relation between network parameters, correlations, and how well different stimuli can be discriminated from one another based on the population activity. As such, our approach allows us to relate properties of the neural circuit to information processing. PMID:29408930

Interpretation of correlated neural variability from models of feed-forward and recurrent circuits.

Directory of Open Access Journals (Sweden)

Volker Pernice

2018-02-01

Full Text Available Neural populations respond to the repeated presentations of a sensory stimulus with correlated variability. These correlations have been studied in detail, with respect to their mechanistic origin, as well as their influence on stimulus discrimination and on the performance of population codes. A number of theoretical studies have endeavored to link network architecture to the nature of the correlations in neural activity. Here, we contribute to this effort: in models of circuits of stochastic neurons, we elucidate the implications of various network architectures-recurrent connections, shared feed-forward projections, and shared gain fluctuations-on the stimulus dependence in correlations. Specifically, we derive mathematical relations that specify the dependence of population-averaged covariances on firing rates, for different network architectures. In turn, these relations can be used to analyze data on population activity. We examine recordings from neural populations in mouse auditory cortex. We find that a recurrent network model with random effective connections captures the observed statistics. Furthermore, using our circuit model, we investigate the relation between network parameters, correlations, and how well different stimuli can be discriminated from one another based on the population activity. As such, our approach allows us to relate properties of the neural circuit to information processing.

Seeing the whole picture: A comprehensive imaging approach to functional mapping of circuits in behaving zebrafish.

Science.gov (United States)

Feierstein, C E; Portugues, R; Orger, M B

2015-06-18

In recent years, the zebrafish has emerged as an appealing model system to tackle questions relating to the neural circuit basis of behavior. This can be attributed not just to the growing use of genetically tractable model organisms, but also in large part to the rapid advances in optical techniques for neuroscience, which are ideally suited for application to the small, transparent brain of the larval fish. Many characteristic features of vertebrate brains, from gross anatomy down to particular circuit motifs and cell-types, as well as conserved behaviors, can be found in zebrafish even just a few days post fertilization, and, at this early stage, the physical size of the brain makes it possible to analyze neural activity in a comprehensive fashion. In a recent study, we used a systematic and unbiased imaging method to record the pattern of activity dynamics throughout the whole brain of larval zebrafish during a simple visual behavior, the optokinetic response (OKR). This approach revealed the broadly distributed network of neurons that were active during the behavior and provided insights into the fine-scale functional architecture in the brain, inter-individual variability, and the spatial distribution of behaviorally relevant signals. Combined with mapping anatomical and functional connectivity, targeted electrophysiological recordings, and genetic labeling of specific populations, this comprehensive approach in zebrafish provides an unparalleled opportunity to study complete circuits in a behaving vertebrate animal. Copyright © 2014. Published by Elsevier Ltd.

A central neural circuit for itch sensation.

Science.gov (United States)

Mu, Di; Deng, Juan; Liu, Ke-Fei; Wu, Zhen-Yu; Shi, Yu-Feng; Guo, Wei-Min; Mao, Qun-Quan; Liu, Xing-Jun; Li, Hui; Sun, Yan-Gang

2017-08-18

Although itch sensation is an important protective mechanism for animals, chronic itch remains a challenging clinical problem. Itch processing has been studied extensively at the spinal level. However, how itch information is transmitted to the brain and what central circuits underlie the itch-induced scratching behavior remain largely unknown. We found that the spinoparabrachial pathway was activated during itch processing and that optogenetic suppression of this pathway impaired itch-induced scratching behaviors. Itch-mediating spinal neurons, which express the gastrin-releasing peptide receptor, are disynaptically connected to the parabrachial nucleus via glutamatergic spinal projection neurons. Blockade of synaptic output of glutamatergic neurons in the parabrachial nucleus suppressed pruritogen-induced scratching behavior. Thus, our studies reveal a central neural circuit that is critical for itch signal processing. Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

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

Science.gov (United States)

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

2016-11-01

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

Unbalanced Neuronal Circuits in Addiction

OpenAIRE

Volkow, Nora D.; Wang, Gen-Jack; Tomasi, Dardo; Baler, Ruben D.

2013-01-01

Through sequential waves of drug-induced neurochemical stimulation, addiction co-opts the brain's neuronal circuits that mediate reward, motivation, , to behavioral inflexibility and a severe disruption of self-control and compulsive drug intake. Brain imaging technologies have allowed neuroscientists to map out the neural landscape of addiction in the human brain and to understand how drugs modify it.

Olfactory systems and neural circuits that modulate predator odor fear

Directory of Open Access Journals (Sweden)

Lorey K. Takahashi

2014-03-01

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

Olfactory systems and neural circuits that modulate predator odor fear

Science.gov (United States)

Takahashi, Lorey K.

2014-01-01

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

Response variance in functional maps: neural darwinism revisited.

Directory of Open Access Journals (Sweden)

Hirokazu Takahashi

Full Text Available The mechanisms by which functional maps and map plasticity contribute to cortical computation remain controversial. Recent studies have revisited the theory of neural Darwinism to interpret the learning-induced map plasticity and neuronal heterogeneity observed in the cortex. Here, we hypothesize that the Darwinian principle provides a substrate to explain the relationship between neuron heterogeneity and cortical functional maps. We demonstrate in the rat auditory cortex that the degree of response variance is closely correlated with the size of its representational area. Further, we show that the response variance within a given population is altered through training. These results suggest that larger representational areas may help to accommodate heterogeneous populations of neurons. Thus, functional maps and map plasticity are likely to play essential roles in Darwinian computation, serving as effective, but not absolutely necessary, structures to generate diverse response properties within a neural population.

Response variance in functional maps: neural darwinism revisited.

Science.gov (United States)

Takahashi, Hirokazu; Yokota, Ryo; Kanzaki, Ryohei

2013-01-01

The mechanisms by which functional maps and map plasticity contribute to cortical computation remain controversial. Recent studies have revisited the theory of neural Darwinism to interpret the learning-induced map plasticity and neuronal heterogeneity observed in the cortex. Here, we hypothesize that the Darwinian principle provides a substrate to explain the relationship between neuron heterogeneity and cortical functional maps. We demonstrate in the rat auditory cortex that the degree of response variance is closely correlated with the size of its representational area. Further, we show that the response variance within a given population is altered through training. These results suggest that larger representational areas may help to accommodate heterogeneous populations of neurons. Thus, functional maps and map plasticity are likely to play essential roles in Darwinian computation, serving as effective, but not absolutely necessary, structures to generate diverse response properties within a neural population.

Neural Circuits via Which Single Prolonged Stress Exposure Leads to Fear Extinction Retention Deficits

Science.gov (United States)

Knox, Dayan; Stanfield, Briana R.; Staib, Jennifer M.; David, Nina P.; Keller, Samantha M.; DePietro, Thomas

2016-01-01

Single prolonged stress (SPS) has been used to examine mechanisms via which stress exposure leads to post-traumatic stress disorder symptoms. SPS induces fear extinction retention deficits, but neural circuits critical for mediating these deficits are unknown. To address this gap, we examined the effect of SPS on neural activity in brain regions…

The Maps Inside your Head

CERN Multimedia

CERN. Geneva

2018-01-01

How do our brains make sense of a complex and unpredictable world? In this talk, I will discuss a physicist's approach to the neural topography of information processing in the brain. First I will review the brain's architecture, and how neural circuits map out the sensory and cognitive worlds. Then I will describe how highly complex sensory and cognitive tasks are carried out by the cooperative action of many specialized neurons and circuits, each of which has a simple function. I will illustrate my remarks with one sensory example and one cognitive example. For the sensory examples, I will consider the sense of smell ("olfaction"), whereby humans and other animals distinguish vast arrays of odor mixtures using very limited neural resources. For the cognitive example, I will consider the "sense of place", that is, how animals mentally represent their physical location. Both examples demonstrate that brains have evolved neural circuits that exploit sophisticated principles of mathematics - principles that sci...

Unbalanced neuronal circuits in addiction.

Science.gov (United States)

Volkow, Nora D; Wang, Gen-Jack; Tomasi, Dardo; Baler, Ruben D

2013-08-01

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. Published by Elsevier Ltd.

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

Science.gov (United States)

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

2012-01-01

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

Classical Conditioning with Pulsed Integrated Neural Networks: Circuits and System

DEFF Research Database (Denmark)

Lehmann, Torsten

1998-01-01

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

From circuits to behaviour in the amygdala

Science.gov (United States)

Janak, Patricia H.; Tye, Kay M.

2015-01-01

The amygdala has long been associated with emotion and motivation, playing an essential part in processing both fearful and rewarding environmental stimuli. How can a single structure be crucial for such different functions? With recent technological advances that allow for causal investigations of specific neural circuit elements, we can now begin to map the complex anatomical connections of the amygdala onto behavioural function. Understanding how the amygdala contributes to a wide array of behaviours requires the study of distinct amygdala circuits. PMID:25592533

The Complexity of Dynamics in Small Neural Circuits.

Directory of Open Access Journals (Sweden)

Diego Fasoli

2016-08-01

Full Text Available Mean-field approximations are a powerful tool for studying large neural networks. However, they do not describe well the behavior of networks composed of a small number of neurons. In this case, major differences between the mean-field approximation and the real behavior of the network can arise. Yet, many interesting problems in neuroscience involve the study of mesoscopic networks composed of a few tens of neurons. Nonetheless, mathematical methods that correctly describe networks of small size are still rare, and this prevents us to make progress in understanding neural dynamics at these intermediate scales. Here we develop a novel systematic analysis of the dynamics of arbitrarily small networks composed of homogeneous populations of excitatory and inhibitory firing-rate neurons. We study the local bifurcations of their neural activity with an approach that is largely analytically tractable, and we numerically determine the global bifurcations. We find that for strong inhibition these networks give rise to very complex dynamics, caused by the formation of multiple branching solutions of the neural dynamics equations that emerge through spontaneous symmetry-breaking. This qualitative change of the neural dynamics is a finite-size effect of the network, that reveals qualitative and previously unexplored differences between mesoscopic cortical circuits and their mean-field approximation. The most important consequence of spontaneous symmetry-breaking is the ability of mesoscopic networks to regulate their degree of functional heterogeneity, which is thought to help reducing the detrimental effect of noise correlations on cortical information processing.

Photovoltaic Pixels for Neural Stimulation: Circuit Models and Performance.

Science.gov (United States)

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

2016-02-01

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

Changed Synaptic Plasticity in Neural Circuits of Depressive-Like and Escitalopram-Treated Rats

Science.gov (United States)

Li, Xiao-Li; Yuan, Yong-Gui; Xu, Hua; Wu, Di; Gong, Wei-Gang; Geng, Lei-Yu; Wu, Fang-Fang; Tang, Hao; Xu, Lin

2015-01-01

Background: Although progress has been made in the detection and characterization of neural plasticity in depression, it has not been fully understood in individual synaptic changes in the neural circuits under chronic stress and antidepressant treatment. Methods: Using electron microscopy and Western-blot analyses, the present study quantitatively examined the changes in the Gray’s Type I synaptic ultrastructures and the expression of synapse-associated proteins in the key brain regions of rats’ depressive-related neural circuit after chronic unpredicted mild stress and/or escitalopram administration. Meanwhile, their depressive behaviors were also determined by several tests. Results: The Type I synapses underwent considerable remodeling after chronic unpredicted mild stress, which resulted in the changed width of the synaptic cleft, length of the active zone, postsynaptic density thickness, and/or synaptic curvature in the subregions of medial prefrontal cortex and hippocampus, as well as the basolateral amygdaloid nucleus of the amygdala, accompanied by changed expression of several synapse-associated proteins. Chronic escitalopram administration significantly changed the above alternations in the chronic unpredicted mild stress rats but had little effect on normal controls. Also, there was a positive correlation between the locomotor activity and the maximal synaptic postsynaptic density thickness in the stratum radiatum of the Cornu Ammonis 1 region and a negative correlation between the sucrose preference and the length of the active zone in the basolateral amygdaloid nucleus region in chronic unpredicted mild stress rats. Conclusion: These findings strongly indicate that chronic stress and escitalopram can alter synaptic plasticity in the neural circuits, and the remodeled synaptic ultrastructure was correlated with the rats’ depressive behaviors, suggesting a therapeutic target for further exploration. PMID:25899067

Wireless neural recording with single low-power integrated circuit.

Science.gov (United States)

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

2009-08-01

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

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

Directory of Open Access Journals (Sweden)

Jacob T. Robinson

2013-03-01

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

Emotion and decision making: multiple modulatory neural circuits.

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Phelps, Elizabeth A; Lempert, Karolina M; Sokol-Hessner, Peter

2014-01-01

Although the prevalent view of emotion and decision making is derived from the notion that there are dual systems of emotion and reason, a modulatory relationship more accurately reflects the current research in affective neuroscience and neuroeconomics. Studies show two potential mechanisms for affect's modulation of the computation of subjective value and decisions. Incidental affective states may carry over to the assessment of subjective value and the decision, and emotional reactions to the choice may be incorporated into the value calculation. In addition, this modulatory relationship is reciprocal: Changing emotion can change choices. This research suggests that the neural mechanisms mediating the relation between affect and choice vary depending on which affective component is engaged and which decision variables are assessed. We suggest that a detailed and nuanced understanding of emotion and decision making requires characterizing the multiple modulatory neural circuits underlying the different means by which emotion and affect can influence choices.

Altered topology of neural circuits in congenital prosopagnosia.

Science.gov (United States)

Rosenthal, Gideon; Tanzer, Michal; Simony, Erez; Hasson, Uri; Behrmann, Marlene; Avidan, Galia

2017-08-21

Using a novel, fMRI-based inter-subject functional correlation (ISFC) approach, which isolates stimulus-locked inter-regional correlation patterns, we compared the cortical topology of the neural circuit for face processing in participants with an impairment in face recognition, congenital prosopagnosia (CP), and matched controls. Whereas the anterior temporal lobe served as the major network hub for face processing in controls, this was not the case for the CPs. Instead, this group evinced hyper-connectivity in posterior regions of the visual cortex, mostly associated with the lateral occipital and the inferior temporal cortices. Moreover, the extent of this hyper-connectivity was correlated with the face recognition deficit. These results offer new insights into the perturbed cortical topology in CP, which may serve as the underlying neural basis of the behavioral deficits typical of this disorder. The approach adopted here has the potential to uncover altered topologies in other neurodevelopmental disorders, as well.

Memristor-based neural networks: Synaptic versus neuronal stochasticity

KAUST Repository

Naous, Rawan; Alshedivat, Maruan; Neftci, Emre; Cauwenberghs, Gert; Salama, Khaled N.

2016-01-01

In neuromorphic circuits, stochasticity in the cortex can be mapped into the synaptic or neuronal components. The hardware emulation of these stochastic neural networks are currently being extensively studied using resistive memories or memristors

Wireless Neural Recording With Single Low-Power Integrated Circuit

Science.gov (United States)

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

2010-01-01

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

Metabolic neural mapping in neonatal rats

International Nuclear Information System (INIS)

DiRocco, R.J.; Hall, W.G.

1981-01-01

Functional neural mapping by 14 C-deoxyglucose autoradiography in adult rats has shown that increases in neural metabolic rate that are coupled to increased neurophysiological activity are more evident in axon terminals and dendrites than neuron cell bodies. Regions containing architectonically well-defined concentrations of terminals and dendrites (neuropil) have high metabolic rates when the neuropil is physiologically active. In neonatal rats, however, we find that regions containing well-defined groupings of neuron cell bodies have high metabolic rates in 14 C-deoxyglucose autoradiograms. The striking difference between the morphological appearance of 14 C-deoxyglucose autoradiograms obtained from neonatal and adult rats is probably related to developmental changes in morphometric features of differentiating neurons, as well as associated changes in type and locus of neural work performed

Knowledge synthesis with maps of neural connectivity.

Science.gov (United States)

Tallis, Marcelo; Thompson, Richard; Russ, Thomas A; Burns, Gully A P C

2011-01-01

This paper describes software for neuroanatomical knowledge synthesis based on neural connectivity data. This software supports a mature methodology developed since the early 1990s. Over this time, the Swanson laboratory at USC has generated an account of the neural connectivity of the sub-structures of the hypothalamus, amygdala, septum, hippocampus, and bed nucleus of the stria terminalis. This is based on neuroanatomical data maps drawn into a standard brain atlas by experts. In earlier work, we presented an application for visualizing and comparing anatomical macro connections using the Swanson third edition atlas as a framework for accurate registration. Here we describe major improvements to the NeuARt application based on the incorporation of a knowledge representation of experimental design. We also present improvements in the interface and features of the data mapping components within a unified web-application. As a step toward developing an accurate sub-regional account of neural connectivity, we provide navigational access between the data maps and a semantic representation of area-to-area connections that they support. We do so based on an approach called "Knowledge Engineering from Experimental Design" (KEfED) model that is based on experimental variables. We have extended the underlying KEfED representation of tract-tracing experiments by incorporating the definition of a neuronanatomical data map as a measurement variable in the study design. This paper describes the software design of a web-application that allows anatomical data sets to be described within a standard experimental context and thus indexed by non-spatial experimental design features.

Knowledge synthesis with maps of neural connectivity

Directory of Open Access Journals (Sweden)

Marcelo eTallis

2011-11-01

Full Text Available This paper describes software for neuroanatomical knowledge synthesis based on high-quality neural connectivity data. This software supports a mature neuroanatomical methodology developed since the early 1990s. Over this time, the Swanson laboratory at USC has generated an account of the neural connectivity of the sub-structures of the hypothalamus, amygdala, septum, hippocampus and bed nucleus of the stria terminalis. This is based on neuroanatomical data maps drawn into a standard brain atlas by experts. In earlier work, we presented an application for visualizing and comparing anatomical macroconnections using the Swanson 3rd edition atlas as a framework for accurate registration. Here we describe major improvements to the NeuARt application based on the incorporation of a knowledge representation of experimental design. We also present improvements in the interface and features of the neuroanatomical data mapping components within a unified web-application. As a step towards developing an accurate sub-regional account of neural connectivity, we provide navigational access between the neuroanatomical data maps and a semantic representation of area-to-area connections that they support. We do so based on an approach called ’Knowledge Engineering from Experimental Design’ (KEfED model that is based on experimental variables. We have extended the underlying KEfED representation of tract-tracing experiments by incorporating the definition of a neuronanatomical data map as a measurement variable in the study design. This paper describes the software design of a web application that allows anatomical data sets to be described within a standard experimental context and thus incorporated with non-spatial data sets.

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

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Naina Kurup

2017-06-01

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

Neural Networks Integrated Circuit for Biomimetics MEMS Microrobot

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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.

Predicting the topology of dynamic neural networks for the simulation of electronic circuits

NARCIS (Netherlands)

Schilders, W.H.A.

2009-01-01

In this paper we discuss the use of the state-space modelling MOESP algorithm to generate precise information about the number of neurons and hidden layers in dynamic neural networks developed for the behavioural modelling of electronic circuits. The Bartels–Stewart algorithm is used to transform

A decision-making model based on a spiking neural circuit and synaptic plasticity.

Science.gov (United States)

Wei, Hui; Bu, Yijie; Dai, Dawei

2017-10-01

To adapt to the environment and survive, most animals can control their behaviors by making decisions. The process of decision-making and responding according to cues in the environment is stable, sustainable, and learnable. Understanding how behaviors are regulated by neural circuits and the encoding and decoding mechanisms from stimuli to responses are important goals in neuroscience. From results observed in Drosophila experiments, the underlying decision-making process is discussed, and a neural circuit that implements a two-choice decision-making model is proposed to explain and reproduce the observations. Compared with previous two-choice decision making models, our model uses synaptic plasticity to explain changes in decision output given the same environment. Moreover, biological meanings of parameters of our decision-making model are discussed. In this paper, we explain at the micro-level (i.e., neurons and synapses) how observable decision-making behavior at the macro-level is acquired and achieved.

Activity-regulated genes as mediators of neural circuit plasticity.

Science.gov (United States)

Leslie, Jennifer H; Nedivi, Elly

2011-08-01

Modifications of neuronal circuits allow the brain to adapt and change with experience. This plasticity manifests during development and throughout life, and can be remarkably long lasting. Evidence has linked activity-regulated gene expression to the long-term structural and electrophysiological adaptations that take place during developmental critical periods, learning and memory, and alterations to sensory map representations in the adult. In all these cases, the cellular response to neuronal activity integrates multiple tightly coordinated mechanisms to precisely orchestrate long-lasting, functional and structural changes in brain circuits. Experience-dependent plasticity is triggered when neuronal excitation activates cellular signaling pathways from the synapse to the nucleus that initiate new programs of gene expression. The protein products of activity-regulated genes then work via a diverse array of cellular mechanisms to modify neuronal functional properties. Synaptic strengthening or weakening can reweight existing circuit connections, while structural changes including synapse addition and elimination create new connections. Posttranscriptional regulatory mechanisms, often also dependent on activity, further modulate activity-regulated gene transcript and protein function. Thus, activity-regulated genes implement varied forms of structural and functional plasticity to fine-tune brain circuit wiring. Copyright © 2011 Elsevier Ltd. All rights reserved.

Alteration in neonatal nutrition causes perturbations in hypothalamic neural circuits controlling reproductive function.

Science.gov (United States)

Caron, Emilie; Ciofi, Philippe; Prevot, Vincent; Bouret, Sebastien G

2012-08-15

It is increasingly accepted that alterations of the early life environment may have lasting impacts on physiological functions. In particular, epidemiological and animal studies have indicated that changes in growth and nutrition during childhood and adolescence can impair reproductive function. However, the precise biological mechanisms that underlie these programming effects of neonatal nutrition on reproduction are still poorly understood. Here, we used a mouse model of divergent litter size to investigate the effects of early postnatal overnutrition and undernutrition on the maturation of hypothalamic circuits involved in reproductive function. Neonatally undernourished females display attenuated postnatal growth associated with delayed puberty and defective development of axonal projections from the arcuate nucleus to the preoptic region. These alterations persist into adulthood and specifically affect the organization of neural projections containing kisspeptin, a key neuropeptide involved in pubertal activation and fertility. Neonatal overfeeding also perturbs the development of neural projections from the arcuate nucleus to the preoptic region, but it does not result in alterations in kisspeptin projections. These studies indicate that alterations in the early nutritional environment cause lasting and deleterious effects on the organization of neural circuits involved in the control of reproduction, and that these changes are associated with lifelong functional perturbations.

Developmental and Architectural Principles of the Lateral-line Neural Map

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Hernan eLopez-Schier

2013-03-01

Full Text Available The transmission and central representation of sensory cues through the accurate construction of neural maps is essential for animals to react to environmental stimuli. Structural diversity of sensorineural maps along a continuum between discrete- and continuous-map architectures can influence behavior. The mechanosensory lateral line of fishes and amphibians, for example, detects complex hydrodynamics occurring around the animal body. It. It triggers innate fast escape reactions but also modulates complex navigation behaviors that require constant knowledge about the environment. The aim of this article is to summarize recent work in the zebrafish that has shed light on the development and structure of the lateralis neural map, which is helping to understand how individual sensory modalities generate appropriate behavioral responses to the sensory context.

Design of chaotic analog noise generators with logistic map and MOS QT circuits

International Nuclear Information System (INIS)

Vazquez-Medina, R.; Diaz-Mendez, A.; Rio-Correa, J.L. del; Lopez-Hernandez, J.

2009-01-01

In this paper a method to design chaotic analog noise generators using MOS transistors is presented. Two aspects are considered, the determination of operation regime of the MOS circuit and the statistical distribution of its output signal. The operation regime is related with the transconductance linear (TL: translinear) principle. For MOS transistors this principle was originally formulated in weak inversion regime; but, strong inversion regimen is used because in 1991, Seevinck and Wiegerink made the generalization for this principle. The statistical distribution of the output signal on the circuit, which should be a uniform distribution, is related with the parameter value that rules the transfer function of the circuit, the initial condition (seed) in the circuit and its operation as chaotic generator. To show these concepts, the MOS Quadratic Translinear circuit proposed by Wiegerink in 1993 was selected and it is related with the logistic map and its properties. This circuit will operate as noise generator if it works in strong inversion regime using current-mode approach when the parameter that rules the transfer function is higher than the onset chaos value (3.5699456...) for the logistic map.

Whole-brain activity mapping onto a zebrafish brain atlas

Science.gov (United States)

Randlett, Owen; Wee, Caroline L.; Naumann, Eva A.; Nnaemeka, Onyeka; Schoppik, David; Fitzgerald, James E.; Portugues, Ruben; Lacoste, Alix M.B.; Riegler, Clemens; Engert, Florian; Schier, Alexander F.

2015-01-01

In order to localize the neural circuits involved in generating behaviors, it is necessary to assign activity onto anatomical maps of the nervous system. Using brain registration across hundreds of larval zebrafish, we have built an expandable open source atlas containing molecular labels and anatomical region definitions, the Z-Brain. Using this platform and immunohistochemical detection of phosphorylated-Extracellular signal-regulated kinase (ERK/MAPK) as a readout of neural activity, we have developed a system to create and contextualize whole brain maps of stimulus- and behavior-dependent neural activity. This MAP-Mapping (Mitogen Activated Protein kinase – Mapping) assay is technically simple, fast, inexpensive, and data analysis is completely automated. Since MAP-Mapping is performed on fish that are freely swimming, it is applicable to nearly any stimulus or behavior. We demonstrate the utility of our high-throughput approach using hunting/feeding, pharmacological, visual and noxious stimuli. The resultant maps outline hundreds of areas associated with behaviors. PMID:26778924

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

Directory of Open Access Journals (Sweden)

Chang Hao Chen

2014-01-01

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

Pattern recognition neural-net by spatial mapping of biology visual field

Science.gov (United States)

Lin, Xin; Mori, Masahiko

2000-05-01

The method of spatial mapping in biology vision field is applied to artificial neural networks for pattern recognition. By the coordinate transform that is called the complex-logarithm mapping and Fourier transform, the input images are transformed into scale- rotation- and shift- invariant patterns, and then fed into a multilayer neural network for learning and recognition. The results of computer simulation and an optical experimental system are described.

Whole-brain activity mapping onto a zebrafish brain atlas.

Science.gov (United States)

Randlett, Owen; Wee, Caroline L; Naumann, Eva A; Nnaemeka, Onyeka; Schoppik, David; Fitzgerald, James E; Portugues, Ruben; Lacoste, Alix M B; Riegler, Clemens; Engert, Florian; Schier, Alexander F

2015-11-01

In order to localize the neural circuits involved in generating behaviors, it is necessary to assign activity onto anatomical maps of the nervous system. Using brain registration across hundreds of larval zebrafish, we have built an expandable open-source atlas containing molecular labels and definitions of anatomical regions, the Z-Brain. Using this platform and immunohistochemical detection of phosphorylated extracellular signal–regulated kinase (ERK) as a readout of neural activity, we have developed a system to create and contextualize whole-brain maps of stimulus- and behavior-dependent neural activity. This mitogen-activated protein kinase (MAP)-mapping assay is technically simple, and data analysis is completely automated. Because MAP-mapping is performed on freely swimming fish, it is applicable to studies of nearly any stimulus or behavior. Here we demonstrate our high-throughput approach using pharmacological, visual and noxious stimuli, as well as hunting and feeding. The resultant maps outline hundreds of areas associated with behaviors.

Visualization of neural networks using saliency maps

DEFF Research Database (Denmark)

Mørch, Niels J.S.; Kjems, Ulrik; Hansen, Lars Kai

1995-01-01

The saliency map is proposed as a new method for understanding and visualizing the nonlinearities embedded in feedforward neural networks, with emphasis on the ill-posed case, where the dimensionality of the input-field by far exceeds the number of examples. Several levels of approximations...

The neural circuits of innate fear: detection, integration, action, and memorization

Science.gov (United States)

Silva, Bianca A.; Gross, Cornelius T.

2016-01-01

How fear is represented in the brain has generated a lot of research attention, not only because fear increases the chances for survival when appropriately expressed but also because it can lead to anxiety and stress-related disorders when inadequately processed. In this review, we summarize recent progress in the understanding of the neural circuits processing innate fear in rodents. We propose that these circuits are contained within three main functional units in the brain: a detection unit, responsible for gathering sensory information signaling the presence of a threat; an integration unit, responsible for incorporating the various sensory information and recruiting downstream effectors; and an output unit, in charge of initiating appropriate bodily and behavioral responses to the threatful stimulus. In parallel, the experience of innate fear also instructs a learning process leading to the memorization of the fearful event. Interestingly, while the detection, integration, and output units processing acute fear responses to different threats tend to be harbored in distinct brain circuits, memory encoding of these threats seems to rely on a shared learning system. PMID:27634145

Why we can talk, debate, and change our minds: neural circuits, basal ganglia operations, and transcriptional factors.

Science.gov (United States)

Lieberman, Philip

2014-12-01

Ackermann et al. disregard attested knowledge concerning aphasia, Parkinson disease, cortical-to-striatal circuits, basal ganglia, laryngeal phonation, and other matters. Their dual-pathway model cannot account for "what is special about the human brain." Their human cortical-to-laryngeal neural circuit does not exist. Basal ganglia operations, enhanced by mutations on FOXP2, confer human motor-control, linguistic, and cognitive capabilities.

Refining the Role of 5-HT in Postnatal Development of Brain Circuits

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Anne Teissier

2017-05-01

Full Text Available Changing serotonin (5-hydroxytryptamine, 5-HT brain levels during critical periods in development has long-lasting effects on brain function, particularly on later anxiety/depression-related behaviors in adulthood. A large part of the known developmental effects of 5-HT occur during critical periods of postnatal life, when activity-dependent mechanisms remodel neural circuits. This was first demonstrated for the maturation of sensory brain maps in the barrel cortex and the visual system. More recently this has been extended to the 5-HT raphe circuits themselves and to limbic circuits. Recent studies overviewed here used new genetic models in mice and rats and combined physiological and structural approaches to provide new insights on the cellular and molecular mechanisms controlled by 5-HT during late stages of neural circuit maturation in the raphe projections, the somatosensory cortex and the visual system. Similar mechanisms appear to be also involved in the maturation of limbic circuits such as prefrontal circuits. The latter are of particular relevance to understand the impact of transient 5-HT dysfunction during postnatal life on psychiatric illnesses and emotional disorders in adult life.

Ontology Mapping Neural Network: An Approach to Learning and Inferring Correspondences among Ontologies

Science.gov (United States)

Peng, Yefei

2010-01-01

An ontology mapping neural network (OMNN) is proposed in order to learn and infer correspondences among ontologies. It extends the Identical Elements Neural Network (IENN)'s ability to represent and map complex relationships. The learning dynamics of simultaneous (interlaced) training of similar tasks interact at the shared connections of the…

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

Science.gov (United States)

Mu, Li; Yigang, He

2016-12-01

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

Earthquake-induced landslide-susceptibility mapping using an artificial neural network

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S. Lee

2006-01-01

Full Text Available The purpose of this study was to apply and verify landslide-susceptibility analysis techniques using an artificial neural network and a Geographic Information System (GIS applied to Baguio City, Philippines. The 16 July 1990 earthquake-induced landslides were studied. Landslide locations were identified from interpretation of aerial photographs and field survey, and a spatial database was constructed from topographic maps, geology, land cover and terrain mapping units. Factors that influence landslide occurrence, such as slope, aspect, curvature and distance from drainage were calculated from the topographic database. Lithology and distance from faults were derived from the geology database. Land cover was identified from the topographic database. Terrain map units were interpreted from aerial photographs. These factors were used with an artificial neural network to analyze landslide susceptibility. Each factor weight was determined by a back-propagation exercise. Landslide-susceptibility indices were calculated using the back-propagation weights, and susceptibility maps were constructed from GIS data. The susceptibility map was compared with known landslide locations and verified. The demonstrated prediction accuracy was 93.20%.

SPATIAL DATA MINING TOOLBOX FOR MAPPING SUITABILITY OF LANDFILL SITES USING NEURAL NETWORKS

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S. K. M. Abujayyab

2016-09-01

Full Text Available Mapping the suitability of landfill sites is a complex field and is involved with multidiscipline. The purpose of this research is to create an ArcGIS spatial data mining toolbox for mapping the suitability of landfill sites at a regional scale using neural networks. The toolbox is constructed from six sub-tools to prepare, train, and process data. The employment of the toolbox is straightforward. The multilayer perceptron (MLP neural networks structure with a backpropagation learning algorithm is used. The dataset is mined from the north states in Malaysia. A total of 14 criteria are utilized to build the training dataset. The toolbox provides a platform for decision makers to implement neural networks for mapping the suitability of landfill sites in the ArcGIS environment. The result shows the ability of the toolbox to produce suitability maps for landfill sites.

The Neural Correlates of Moral Thinking: A Meta-Analysis

OpenAIRE

Douglas J. Bryant; Wang F; Kelley Deardeuff; Emily Zoccoli; Chang S. Nam

2016-01-01

We conducted a meta-analysis to evaluate current research that aims to map the neural correlates of two typical conditions of moral judgment: right-wrong moral judgments and decision-making in moral dilemmas. Utilizing the activation likelihood estimation (ALE) method, we conducted a meta-analysis using neuroimaging data obtained from twenty-one previous studies that measured responses in one or the other of these conditions. We found that across the studies (n = 400), distinct neural circuit...

Stretchable Transparent Electrode Arrays for Simultaneous Electrical and Optical Interrogation of Neural Circuits in Vivo.

Science.gov (United States)

Zhang, Jing; Liu, Xiaojun; Xu, Wenjing; Luo, Wenhan; Li, Ming; Chu, Fangbing; Xu, Lu; Cao, Anyuan; Guan, Jisong; Tang, Shiming; Duan, Xiaojie

2018-04-09

Recent developments of transparent electrode arrays provide a unique capability for simultaneous optical and electrical interrogation of neural circuits in the brain. However, none of these electrode arrays possess the stretchability highly desired for interfacing with mechanically active neural systems, such as the brain under injury, the spinal cord, and the peripheral nervous system (PNS). Here, we report a stretchable transparent electrode array from carbon nanotube (CNT) web-like thin films that retains excellent electrochemical performance and broad-band optical transparency under stretching and is highly durable under cyclic stretching deformation. We show that the CNT electrodes record well-defined neuronal response signals with negligible light-induced artifacts from cortical surfaces under optogenetic stimulation. Simultaneous two-photon calcium imaging through the transparent CNT electrodes from cortical surfaces of GCaMP-expressing mice with epilepsy shows individual activated neurons in brain regions from which the concurrent electrical recording is taken, thus providing complementary cellular information in addition to the high-temporal-resolution electrical recording. Notably, the studies on rats show that the CNT electrodes remain operational during and after brain contusion that involves the rapid deformation of both the electrode array and brain tissue. This enables real-time, continuous electrophysiological monitoring of cortical activity under traumatic brain injury. These results highlight the potential application of the stretchable transparent CNT electrode arrays in combining electrical and optical modalities to study neural circuits, especially under mechanically active conditions, which could potentially provide important new insights into the local circuit dynamics of the spinal cord and PNS as well as the mechanism underlying traumatic injuries of the nervous system.

MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning.

Science.gov (United States)

Liu, Yang; Yang, Jie; Huang, Yuan; Xu, Lixiong; Li, Siguang; Qi, Man

2015-01-01

Artificial neural networks (ANNs) have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.

Copper is an endogenous modulator of neural circuit spontaneous activity.

Science.gov (United States)

Dodani, Sheel C; Firl, Alana; Chan, Jefferson; Nam, Christine I; Aron, Allegra T; Onak, Carl S; Ramos-Torres, Karla M; Paek, Jaeho; Webster, Corey M; Feller, Marla B; Chang, Christopher J

2014-11-18

For reasons that remain insufficiently understood, the brain requires among the highest levels of metals in the body for normal function. The traditional paradigm for this organ and others is that fluxes of alkali and alkaline earth metals are required for signaling, but transition metals are maintained in static, tightly bound reservoirs for metabolism and protection against oxidative stress. Here we show that copper is an endogenous modulator of spontaneous activity, a property of functional neural circuitry. Using Copper Fluor-3 (CF3), a new fluorescent Cu(+) sensor for one- and two-photon imaging, we show that neurons and neural tissue maintain basal stores of loosely bound copper that can be attenuated by chelation, which define a labile copper pool. Targeted disruption of these labile copper stores by acute chelation or genetic knockdown of the CTR1 (copper transporter 1) copper channel alters the spatiotemporal properties of spontaneous activity in developing hippocampal and retinal circuits. The data identify an essential role for copper neuronal function and suggest broader contributions of this transition metal to cell signaling.

MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning

Directory of Open Access Journals (Sweden)

Yang Liu

2015-01-01

Full Text Available Artificial neural networks (ANNs have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.

Neural circuit mechanisms of short-term memory

Science.gov (United States)

Goldman, Mark

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

Acute Stress Influences Neural Circuits of Reward Processing

Directory of Open Access Journals (Sweden)

Anthony John Porcelli

2012-11-01

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

The neural circuit basis of learning

Science.gov (United States)

Patrick, Kaifosh William John

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

The road to restoring neural circuits for the treatment of Alzheimer's disease.

Science.gov (United States)

Canter, Rebecca G; Penney, Jay; Tsai, Li-Huei

2016-11-10

Alzheimer's disease is a progressive loss of memory and cognition, for which there is no cure. Although genetic studies initially suggested a primary role for amyloid-in Alzheimer's disease, treatment strategies targeted at reducing amyloid-have failed to reverse cognitive symptoms. These clinical findings suggest that cognitive decline is the result of a complex pathophysiology and that targeting amyloid-alone may not be sufficient to treat Alzheimer's disease. Instead, a broad outlook on neural-circuit-damaging processes may yield insights into new therapeutic strategies for curing memory loss in the disease.

Neural Network for Sparse Reconstruction

Directory of Open Access Journals (Sweden)

Qingfa Li

2014-01-01

Full Text Available We construct a neural network based on smoothing approximation techniques and projected gradient method to solve a kind of sparse reconstruction problems. Neural network can be implemented by circuits and can be seen as an important method for solving optimization problems, especially large scale problems. Smoothing approximation is an efficient technique for solving nonsmooth optimization problems. We combine these two techniques to overcome the difficulties of the choices of the step size in discrete algorithms and the item in the set-valued map of differential inclusion. In theory, the proposed network can converge to the optimal solution set of the given problem. Furthermore, some numerical experiments show the effectiveness of the proposed network in this paper.

Real-time emulation of neural images in the outer retinal circuit.

Science.gov (United States)

Hasegawa, Jun; Yagi, Tetsuya

2008-12-01

We describe a novel real-time system that emulates the architecture and functionality of the vertebrate retina. This system reconstructs the neural images formed by the retinal neurons in real time by using a combination of analog and digital systems consisting of a neuromorphic silicon retina chip, a field-programmable gate array, and a digital computer. While the silicon retina carries out the spatial filtering of input images instantaneously, using the embedded resistive networks that emulate the receptive field structure of the outer retinal neurons, the digital computer carries out the temporal filtering of the spatially filtered images to emulate the dynamical properties of the outer retinal circuits. The emulations of the neural image, including 128 x 128 bipolar cells, are carried out at a frame rate of 62.5 Hz. The emulation of the response to the Hermann grid and a spot of light and an annulus of lights has demonstrated that the system responds as expected by previous physiological and psychophysical observations. Furthermore, the emulated dynamics of neural images in response to natural scenes revealed the complex nature of retinal neuron activity. We have concluded that the system reflects the spatiotemporal responses of bipolar cells in the vertebrate retina. The proposed emulation system is expected to aid in understanding the visual computation in the retina and the brain.

Reactivating Neural Circuits with Clinically Accessible Stimulation to Restore Hand Function in Persons with Tetraplegia

Science.gov (United States)

2017-09-01

AWARD NUMBER: W81XWH-16-1-0395 TITLE: Reactivating Neural Circuits with Clinically Accessible Stimulation to Restore Hand Function in...estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data...Clinically Accessible Stimulation to Restore Hand Function in Persons with Tetraplegia 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S

An Integrated Circuit for Simultaneous Extracellular Electrophysiology Recording and Optogenetic Neural Manipulation.

Science.gov (United States)

Chen, Chang Hao; McCullagh, Elizabeth A; Pun, Sio Hang; Mak, Peng Un; Vai, Mang I; Mak, Pui In; Klug, Achim; Lei, Tim C

2017-03-01

The ability to record and to control action potential firing in neuronal circuits is critical to understand how the brain functions. The objective of this study is to develop a monolithic integrated circuit (IC) to record action potentials and simultaneously control action potential firing using optogenetics. A low-noise and high input impedance (or low input capacitance) neural recording amplifier is combined with a high current laser/light-emitting diode (LED) driver in a single IC. The low input capacitance of the amplifier (9.7 pF) was achieved by adding a dedicated unity gain stage optimized for high impedance metal electrodes. The input referred noise of the amplifier is [Formula: see text], which is lower than the estimated thermal noise of the metal electrode. Thus, the action potentials originating from a single neuron can be recorded with a signal-to-noise ratio of at least 6.6. The LED/laser current driver delivers a maximum current of 330 mA, which is adequate for optogenetic control. The functionality of the IC was tested with an anesthetized Mongolian gerbil and auditory stimulated action potentials were recorded from the inferior colliculus. Spontaneous firings of fifth (trigeminal) nerve fibers were also inhibited using the optogenetic protein Halorhodopsin. Moreover, a noise model of the system was derived to guide the design. A single IC to measure and control action potentials using optogenetic proteins is realized so that more complicated behavioral neuroscience research and the translational neural disorder treatments become possible in the future.

Foreground removal from Planck Sky Model temperature maps using a MLP neural network

DEFF Research Database (Denmark)

Nørgaard-Nielsen, Hans Ulrik; Hebert, K.

2009-01-01

with no systematic errors. To demonstrate the feasibility of a simple multilayer perceptron (MLP) neural network for extracting the CMB temperature signal, we have analyzed a specific data set, namely the Planck Sky Model maps, developed for evaluation of different component separation methods before including them...... in the Planck data analysis pipeline. It is found that a MLP neural network can provide a CMB map of about 80% of the sky to a very high degree uncorrelated with the foreground components. Also the derived power spectrum shows little evidence for systematic errors....

Terrain Mapping and Classification in Outdoor Environments Using Neural Networks

OpenAIRE

Alberto Yukinobu Hata; Denis Fernando Wolf; Gustavo Pessin; Fernando Osório

2009-01-01

This paper describes a three-dimensional terrain mapping and classification technique to allow the operation of mobile robots in outdoor environments using laser range finders. We propose the use of a multi-layer perceptron neural network to classify the terrain into navigable, partially navigable, and non-navigable. The maps generated by our approach can be used for path planning, navigation, and local obstacle avoidance. Experimental tests using an outdoor robot and a laser sensor demonstra...

Neural network representation and learning of mappings and their derivatives

Science.gov (United States)

White, Halbert; Hornik, Kurt; Stinchcombe, Maxwell; Gallant, A. Ronald

1991-01-01

Discussed here are recent theorems proving that artificial neural networks are capable of approximating an arbitrary mapping and its derivatives as accurately as desired. This fact forms the basis for further results establishing the learnability of the desired approximations, using results from non-parametric statistics. These results have potential applications in robotics, chaotic dynamics, control, and sensitivity analysis. An example involving learning the transfer function and its derivatives for a chaotic map is discussed.

Spiking Neural Networks with Unsupervised Learning Based on STDP Using Resistive Synaptic Devices and Analog CMOS Neuron Circuit.

Science.gov (United States)

Kwon, Min-Woo; Baek, Myung-Hyun; Hwang, Sungmin; Kim, Sungjun; Park, Byung-Gook

2018-09-01

We designed the CMOS analog integrate and fire (I&F) neuron circuit can drive resistive synaptic device. The neuron circuit consists of a current mirror for spatial integration, a capacitor for temporal integration, asymmetric negative and positive pulse generation part, a refractory part, and finally a back-propagation pulse generation part for learning of the synaptic devices. The resistive synaptic devices were fabricated using HfOx switching layer by atomic layer deposition (ALD). The resistive synaptic device had gradual set and reset characteristics and the conductance was adjusted by spike-timing-dependent-plasticity (STDP) learning rule. We carried out circuit simulation of synaptic device and CMOS neuron circuit. And we have developed an unsupervised spiking neural networks (SNNs) for 5 × 5 pattern recognition and classification using the neuron circuit and synaptic devices. The hardware-based SNNs can autonomously and efficiently control the weight updates of the synapses between neurons, without the aid of software calculations.

Using a Conflict Map as an Instructional Tool To Change Student Conceptions in Simple Series Electric-Circuits.

Science.gov (United States)

Tsai, Chin-Chung

2003-01-01

Examines the effects of using a conflict map on 8th grade students' conceptual change and ideational networks about simple series electric circuits. Analyzes student interview data through a flow map method. Shows that the use of conflict maps could help students construct greater, richer, and more integrated ideational networks about electric…

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

Science.gov (United States)

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

2013-04-01

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

Multi-Connection Pattern Analysis: Decoding the representational content of neural communication.

Science.gov (United States)

Li, Yuanning; Richardson, Robert Mark; Ghuman, Avniel Singh

2017-11-15

The lack of multivariate methods for decoding the representational content of interregional neural communication has left it difficult to know what information is represented in distributed brain circuit interactions. Here we present Multi-Connection Pattern Analysis (MCPA), which works by learning mappings between the activity patterns of the populations as a factor of the information being processed. These maps are used to predict the activity from one neural population based on the activity from the other population. Successful MCPA-based decoding indicates the involvement of distributed computational processing and provides a framework for probing the representational structure of the interaction. Simulations demonstrate the efficacy of MCPA in realistic circumstances. In addition, we demonstrate that MCPA can be applied to different signal modalities to evaluate a variety of hypothesis associated with information coding in neural communications. We apply MCPA to fMRI and human intracranial electrophysiological data to provide a proof-of-concept of the utility of this method for decoding individual natural images and faces in functional connectivity data. We further use a MCPA-based representational similarity analysis to illustrate how MCPA may be used to test computational models of information transfer among regions of the visual processing stream. Thus, MCPA can be used to assess the information represented in the coupled activity of interacting neural circuits and probe the underlying principles of information transformation between regions. Copyright © 2017 Elsevier Inc. All rights reserved.

A Voltage Mode Memristor Bridge Synaptic Circuit with Memristor Emulators

Directory of Open Access Journals (Sweden)

Leon Chua

2012-03-01

Full Text Available A memristor bridge neural circuit which is able to perform signed synaptic weighting was proposed in our previous study, where the synaptic operation was verified via software simulation of the mathematical model of the HP memristor. This study is an extension of the previous work advancing toward the circuit implementation where the architecture of the memristor bridge synapse is built with memristor emulator circuits. In addition, a simple neural network which performs both synaptic weighting and summation is built by combining memristor emulators-based synapses and differential amplifier circuits. The feasibility of the memristor bridge neural circuit is verified via SPICE simulations.

Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce.

Science.gov (United States)

Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan

2016-01-01

A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network's initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data.

Neural dynamics of the cognitive map in the hippocampus.

Science.gov (United States)

Wagatsuma, Hiroaki; Yamaguchi, Yoko

2007-06-01

The rodent hippocampus has been thought to represent the spatial environment as a cognitive map. In the classical theory, the cognitive map has been explained as a consequence of the fact that different spatial regions are assigned to different cell populations in the framework of rate coding. Recently, the relation between place cell firing and local field oscillation theta in terms of theta phase precession was experimentally discovered and suggested as a temporal coding mechanism leading to memory formation of behavioral sequences accompanied with asymmetric Hebbian plasticity. The cognitive map theory is apparently outside of the sequence memory view. Therefore, theoretical analysis is necessary to consider the biological neural dynamics for the sequence encoding of the memory of behavioral sequences, providing the cognitive map formation. In this article, we summarize the theoretical neural dynamics of the real-time sequence encoding by theta phase precession, called theta phase coding, and review a series of theoretical models with the theta phase coding that we previously reported. With respect to memory encoding functions, instantaneous memory formation of one-time experience was first demonstrated, and then the ability of integration of memories of behavioral sequences into a network of the cognitive map was shown. In terms of memory retrieval functions, theta phase coding enables the hippocampus to represent the spatial location in the current behavioral context even with ambiguous sensory input when multiple sequences were coded. Finally, for utilization, retrieved temporal sequences in the hippocampus can be available for action selection, through the process of reverting theta rhythm-dependent activities to information in the behavioral time scale. This theoretical approach allows us to investigate how the behavioral sequences are encoded, updated, retrieved and used in the hippocampus, as the real-time interaction with the external environment. It may

A Neural Circuit for Auditory Dominance over Visual Perception.

Science.gov (United States)

Song, You-Hyang; Kim, Jae-Hyun; Jeong, Hye-Won; Choi, Ilsong; Jeong, Daun; Kim, Kwansoo; Lee, Seung-Hee

2017-02-22

When conflicts occur during integration of visual and auditory information, one modality often dominates the other, but the underlying neural circuit mechanism remains unclear. Using auditory-visual discrimination tasks for head-fixed mice, we found that audition dominates vision in a process mediated by interaction between inputs from the primary visual (VC) and auditory (AC) cortices in the posterior parietal cortex (PTLp). Co-activation of the VC and AC suppresses VC-induced PTLp responses, leaving AC-induced responses. Furthermore, parvalbumin-positive (PV+) interneurons in the PTLp mainly receive AC inputs, and muscimol inactivation of the PTLp or optogenetic inhibition of its PV+ neurons abolishes auditory dominance in the resolution of cross-modal sensory conflicts without affecting either sensory perception. Conversely, optogenetic activation of PV+ neurons in the PTLp enhances the auditory dominance. Thus, our results demonstrate that AC input-specific feedforward inhibition of VC inputs in the PTLp is responsible for the auditory dominance during cross-modal integration. Copyright © 2017 Elsevier Inc. All rights reserved.

A neural circuit for angular velocity computation

Directory of Open Access Journals (Sweden)

Samuel B Snider

2010-12-01

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

Logarithmic r-θ mapping for hybrid optical neural network filter for multiple objects recognition within cluttered scenes

Science.gov (United States)

Kypraios, Ioannis; Young, Rupert C. D.; Chatwin, Chris R.; Birch, Phil M.

2009-04-01

θThe window unit in the design of the complex logarithmic r-θ mapping for hybrid optical neural network filter can allow multiple objects of the same class to be detected within the input image. Additionally, the architecture of the neural network unit of the complex logarithmic r-θ mapping for hybrid optical neural network filter becomes attractive for accommodating the recognition of multiple objects of different classes within the input image by modifying the output layer of the unit. We test the overall filter for multiple objects of the same and of different classes' recognition within cluttered input images and video sequences of cluttered scenes. Logarithmic r-θ mapping for hybrid optical neural network filter is shown to exhibit with a single pass over the input data simultaneously in-plane rotation, out-of-plane rotation, scale, log r-θ map translation and shift invariance, and good clutter tolerance by recognizing correctly the different objects within the cluttered scenes. We record in our results additional extracted information from the cluttered scenes about the objects' relative position, scale and in-plane rotation.

A computational framework for ultrastructural mapping of neural circuitry.

Directory of Open Access Journals (Sweden)

James R Anderson

2009-03-01

Full Text Available Circuitry mapping of metazoan neural systems is difficult because canonical neural regions (regions containing one or more copies of all components are large, regional borders are uncertain, neuronal diversity is high, and potential network topologies so numerous that only anatomical ground truth can resolve them. Complete mapping of a specific network requires synaptic resolution, canonical region coverage, and robust neuronal classification. Though transmission electron microscopy (TEM remains the optimal tool for network mapping, the process of building large serial section TEM (ssTEM image volumes is rendered difficult by the need to precisely mosaic distorted image tiles and register distorted mosaics. Moreover, most molecular neuronal class markers are poorly compatible with optimal TEM imaging. Our objective was to build a complete framework for ultrastructural circuitry mapping. This framework combines strong TEM-compliant small molecule profiling with automated image tile mosaicking, automated slice-to-slice image registration, and gigabyte-scale image browsing for volume annotation. Specifically we show how ultrathin molecular profiling datasets and their resultant classification maps can be embedded into ssTEM datasets and how scripted acquisition tools (SerialEM, mosaicking and registration (ir-tools, and large slice viewers (MosaicBuilder, Viking can be used to manage terabyte-scale volumes. These methods enable large-scale connectivity analyses of new and legacy data. In well-posed tasks (e.g., complete network mapping in retina, terabyte-scale image volumes that previously would require decades of assembly can now be completed in months. Perhaps more importantly, the fusion of molecular profiling, image acquisition by SerialEM, ir-tools volume assembly, and data viewers/annotators also allow ssTEM to be used as a prospective tool for discovery in nonneural systems and a practical screening methodology for neurogenetics. Finally

An integrated multichannel neural recording analog front-end ASIC with area-efficient driven right leg circuit.

Science.gov (United States)

Tao Tang; Wang Ling Goh; Lei Yao; Jia Hao Cheong; Yuan Gao

2017-07-01

This paper describes an integrated multichannel neural recording analog front end (AFE) with a novel area-efficient driven right leg (DRL) circuit to improve the system common mode rejection ratio (CMRR). The proposed AFE consists of an AC-coupled low-noise programmable-gain amplifier, an area-efficient DRL block and a 10-bit SAR ADC. Compared to conventional DRL circuit, the proposed capacitor-less DRL design achieves 90% chip area reduction with enhanced CMRR performance, making it ideal for multichannel biomedical recording applications. The AFE circuit has been designed in a standard 0.18-μm CMOS process. Post-layout simulation results show that the AFE provides two gain settings of 54dB/60dB while consuming 1 μA per channel under a supply voltage of 1 V. The input-referred noise of the AFE integrated from 1 Hz to 10k Hz is only 4 μVrms and the CMRR is 110 dB.

Morphological self-organizing feature map neural network with applications to automatic target recognition

Science.gov (United States)

Zhang, Shijun; Jing, Zhongliang; Li, Jianxun

2005-01-01

The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and real-world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.

High-throughput mapping of brain-wide activity in awake and drug-responsive vertebrates.

Science.gov (United States)

Lin, Xudong; Wang, Shiqi; Yu, Xudong; Liu, Zhuguo; Wang, Fei; Li, Wai Tsun; Cheng, Shuk Han; Dai, Qiuyun; Shi, Peng

2015-02-07

The reconstruction of neural activity across complete neural circuits, or brain activity mapping, has great potential in both fundamental and translational neuroscience research. Larval zebrafish, a vertebrate model, has recently been demonstrated to be amenable to whole brain activity mapping in behaving animals. Here we demonstrate a microfluidic array system ("Fish-Trap") that enables high-throughput mapping of brain-wide activity in awake larval zebrafish. Unlike the commonly practiced larva-processing methods using a rigid gel or a capillary tube, which are laborious and time-consuming, the hydrodynamic design of our microfluidic chip allows automatic, gel-free, and anesthetic-free processing of tens of larvae for microscopic imaging with single-cell resolution. Notably, this system provides the capability to directly couple pharmaceutical stimuli with real-time recording of neural activity in a large number of animals, and the local and global effects of pharmacoactive drugs on the nervous system can be directly visualized and evaluated by analyzing drug-induced functional perturbation within or across different brain regions. Using this technology, we tested a set of neurotoxin peptides and obtained new insights into how to exploit neurotoxin derivatives as therapeutic agents. The novel and versatile "Fish-Trap" technology can be readily unitized to study other stimulus (optical, acoustic, or physical) associated functional brain circuits using similar experimental strategies.

NeuroMap: A spline-based interactive open-source software for spatiotemporal mapping of 2D and 3D MEA data

Directory of Open Access Journals (Sweden)

Oussama eAbdoun

2011-01-01

Full Text Available A major characteristic of neural networks is the complexity of their organization at various spatial scales, from microscopic local circuits to macroscopic brain-scale areas. Understanding how neural information is processed thus entails the ability to study them at multiple scales simultaneously. This is made possible using microelectrodes array (MEA technology. Indeed, high-density MEAs provide large-scale covering (several mm² of whole neural structures combined with microscopic resolution (about 50µm of unit activity. Yet, current options for spatiotemporal representation of MEA-collected data remain limited. Here we present NeuroMap, a new interactive Matlab-based software for spatiotemporal mapping of MEA data. NeuroMap uses thin plate spline interpolation, which provides several assets with respect to conventional mapping methods used currently. First, any MEA design can be considered, including 2D or 3D, regular or irregular, arrangements of electrodes. Second, spline interpolation allows the estimation of activity across the tissue with local extrema not necessarily at recording sites. Finally, this interpolation approach provides a straightforward analytical estimation of the spatial Laplacian for better current sources localization. In this software, coregistration of 2D MEA data on the anatomy of the neural tissue is made possible by fine matching of anatomical data with electrode positions using rigid deformation based correction of anatomical pictures. Overall, NeuroMap provides substantial material for detailed spatiotemporal analysis of MEA data. The package is distributed under GNU General Public License (GPL and available at http://sites.google.com/site/neuromapsoftware.

NeuroMap: A Spline-Based Interactive Open-Source Software for Spatiotemporal Mapping of 2D and 3D MEA Data.

Science.gov (United States)

Abdoun, Oussama; Joucla, Sébastien; Mazzocco, Claire; Yvert, Blaise

2011-01-01

A major characteristic of neural networks is the complexity of their organization at various spatial scales, from microscopic local circuits to macroscopic brain-scale areas. Understanding how neural information is processed thus entails the ability to study them at multiple scales simultaneously. This is made possible using microelectrodes array (MEA) technology. Indeed, high-density MEAs provide large-scale coverage (several square millimeters) of whole neural structures combined with microscopic resolution (about 50 μm) of unit activity. Yet, current options for spatiotemporal representation of MEA-collected data remain limited. Here we present NeuroMap, a new interactive Matlab-based software for spatiotemporal mapping of MEA data. NeuroMap uses thin plate spline interpolation, which provides several assets with respect to conventional mapping methods used currently. First, any MEA design can be considered, including 2D or 3D, regular or irregular, arrangements of electrodes. Second, spline interpolation allows the estimation of activity across the tissue with local extrema not necessarily at recording sites. Finally, this interpolation approach provides a straightforward analytical estimation of the spatial Laplacian for better current sources localization. In this software, coregistration of 2D MEA data on the anatomy of the neural tissue is made possible by fine matching of anatomical data with electrode positions using rigid-deformation-based correction of anatomical pictures. Overall, NeuroMap provides substantial material for detailed spatiotemporal analysis of MEA data. The package is distributed under GNU General Public License and available at http://sites.google.com/site/neuromapsoftware.

Two multichannel integrated circuits for neural recording and signal processing.

Science.gov (United States)

Obeid, Iyad; Morizio, James C; Moxon, Karen A; Nicolelis, Miguel A L; Wolf, Patrick D

2003-02-01

We have developed, manufactured, and tested two analog CMOS integrated circuit "neurochips" for recording from arrays of densely packed neural electrodes. Device A is a 16-channel buffer consisting of parallel noninverting amplifiers with a gain of 2 V/V. Device B is a 16-channel two-stage analog signal processor with differential amplification and high-pass filtering. It features selectable gains of 250 and 500 V/V as well as reference channel selection. The resulting amplifiers on Device A had a mean gain of 1.99 V/V with an equivalent input noise of 10 microV(rms). Those on Device B had mean gains of 53.4 and 47.4 dB with a high-pass filter pole at 211 Hz and an equivalent input noise of 4.4 microV(rms). Both devices were tested in vivo with electrode arrays implanted in the somatosensory cortex.

Invertebrate diversity classification using self-organizing map neural network: with some special topological functions

Directory of Open Access Journals (Sweden)

WenJun Zhang

2014-06-01

Full Text Available In present study we used self-organizing map (SOM neural network to conduct the non-supervisory clustering of invertebrate orders in rice field. Four topological functions, i.e., cossintopf, sincostopf, acossintopf, and expsintopf, established on the template in toolbox of Matlab, were used in SOM neural network learning. Results showed that clusters were different when using different topological functions because different topological functions will generate different spatial structure of neurons in neural network. We may chose these functions and results based on comparison with the practical situation.

A neural circuit covarying with social hierarchy in macaques.

Science.gov (United States)

Noonan, MaryAnn P; Sallet, Jerome; Mars, Rogier B; Neubert, Franz X; O'Reilly, Jill X; Andersson, Jesper L; Mitchell, Anna S; Bell, Andrew H; Miller, Karla L; Rushworth, Matthew F S

2014-09-01

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.

A neural circuit covarying with social hierarchy in macaques.

Directory of Open Access Journals (Sweden)

MaryAnn P Noonan

2014-09-01

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

A Neural Circuit Covarying with Social Hierarchy in Macaques

Science.gov (United States)

Neubert, Franz X.; O'Reilly, Jill X.; Andersson, Jesper L.; Mitchell, Anna S.; Bell, Andrew H.; Miller, Karla L.; Rushworth, Matthew F. S.

2014-01-01

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. PMID:25180883

Distributed neural system for emotional intelligence revealed by lesion mapping.

Science.gov (United States)

Barbey, Aron K; Colom, Roberto; Grafman, Jordan

2014-03-01

Cognitive neuroscience has made considerable progress in understanding the neural architecture of human intelligence, identifying a broadly distributed network of frontal and parietal regions that support goal-directed, intelligent behavior. However, the contributions of this network to social and emotional aspects of intellectual function remain to be well characterized. Here we investigated the neural basis of emotional intelligence in 152 patients with focal brain injuries using voxel-based lesion-symptom mapping. Latent variable modeling was applied to obtain measures of emotional intelligence, general intelligence and personality from the Mayer, Salovey, Caruso Emotional Intelligence Test (MSCEIT), the Wechsler Adult Intelligence Scale and the Neuroticism-Extroversion-Openness Inventory, respectively. Regression analyses revealed that latent scores for measures of general intelligence and personality reliably predicted latent scores for emotional intelligence. Lesion mapping results further indicated that these convergent processes depend on a shared network of frontal, temporal and parietal brain regions. The results support an integrative framework for understanding the architecture of executive, social and emotional processes and make specific recommendations for the interpretation and application of the MSCEIT to the study of emotional intelligence in health and disease.

Distributed neural system for emotional intelligence revealed by lesion mapping

Science.gov (United States)

Colom, Roberto; Grafman, Jordan

2014-01-01

Cognitive neuroscience has made considerable progress in understanding the neural architecture of human intelligence, identifying a broadly distributed network of frontal and parietal regions that support goal-directed, intelligent behavior. However, the contributions of this network to social and emotional aspects of intellectual function remain to be well characterized. Here we investigated the neural basis of emotional intelligence in 152 patients with focal brain injuries using voxel-based lesion-symptom mapping. Latent variable modeling was applied to obtain measures of emotional intelligence, general intelligence and personality from the Mayer, Salovey, Caruso Emotional Intelligence Test (MSCEIT), the Wechsler Adult Intelligence Scale and the Neuroticism-Extroversion-Openness Inventory, respectively. Regression analyses revealed that latent scores for measures of general intelligence and personality reliably predicted latent scores for emotional intelligence. Lesion mapping results further indicated that these convergent processes depend on a shared network of frontal, temporal and parietal brain regions. The results support an integrative framework for understanding the architecture of executive, social and emotional processes and make specific recommendations for the interpretation and application of the MSCEIT to the study of emotional intelligence in health and disease. PMID:23171618

Deep neural mapping support vector machines.

Science.gov (United States)

Li, Yujian; Zhang, Ting

2017-09-01

The choice of kernel has an important effect on the performance of a support vector machine (SVM). The effect could be reduced by NEUROSVM, an architecture using multilayer perceptron for feature extraction and SVM for classification. In binary classification, a general linear kernel NEUROSVM can be theoretically simplified as an input layer, many hidden layers, and an SVM output layer. As a feature extractor, the sub-network composed of the input and hidden layers is first trained together with a virtual ordinary output layer by backpropagation, then with the output of its last hidden layer taken as input of the SVM classifier for further training separately. By taking the sub-network as a kernel mapping from the original input space into a feature space, we present a novel model, called deep neural mapping support vector machine (DNMSVM), from the viewpoint of deep learning. This model is also a new and general kernel learning method, where the kernel mapping is indeed an explicit function expressed as a sub-network, different from an implicit function induced by a kernel function traditionally. Moreover, we exploit a two-stage procedure of contrastive divergence learning and gradient descent for DNMSVM to jointly training an adaptive kernel mapping instead of a kernel function, without requirement of kernel tricks. As a whole of the sub-network and the SVM classifier, the joint training of DNMSVM is done by using gradient descent to optimize the objective function with the sub-network layer-wise pre-trained via contrastive divergence learning of restricted Boltzmann machines. Compared to the separate training of NEUROSVM, the joint training is a new algorithm for DNMSVM to have advantages over NEUROSVM. Experimental results show that DNMSVM can outperform NEUROSVM and RBFSVM (i.e., SVM with the kernel of radial basis function), demonstrating its effectiveness. Copyright © 2017 Elsevier Ltd. All rights reserved.

Foreground removal from Planck Sky Model temperature maps using a MLP neural network

Science.gov (United States)

Nørgaard-Nielsen, H. U.; Hebert, K.

2009-08-01

Unfortunately, the Cosmic Microwave Background (CMB) radiation is contaminated by emission originating in the Milky Way (synchrotron, free-free and dust emission). Since the cosmological information is statistically in nature, it is essential to remove this foreground emission and leave the CMB with no systematic errors. To demonstrate the feasibility of a simple multilayer perceptron (MLP) neural network for extracting the CMB temperature signal, we have analyzed a specific data set, namely the Planck Sky Model maps, developed for evaluation of different component separation methods before including them in the Planck data analysis pipeline. It is found that a MLP neural network can provide a CMB map of about 80 % of the sky to a very high degree uncorrelated with the foreground components. Also the derived power spectrum shows little evidence for systematic errors.

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

Directory of Open Access Journals (Sweden)

Julie Ann Harris

2014-07-01

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

Anatomical characterization of Cre driver mice for neural circuit mapping and manipulation

Science.gov (United States)

Harris, Julie A.; Hirokawa, Karla E.; Sorensen, Staci A.; Gu, Hong; Mills, Maya; Ng, Lydia L.; Bohn, Phillip; Mortrud, Marty; Ouellette, Benjamin; Kidney, Jolene; Smith, Kimberly A.; Dang, Chinh; Sunkin, Susan; Bernard, Amy; Oh, Seung Wook; Madisen, Linda; Zeng, Hongkui

2014-01-01

Significant advances in circuit-level analyses of the brain require tools that allow for labeling, modulation of gene expression, and monitoring and manipulation of cellular activity in specific cell types and/or anatomical regions. Large-scale projects and individual laboratories have produced hundreds of gene-specific promoter-driven Cre mouse lines invaluable for enabling genetic access to subpopulations of cells in the brain. However, the potential utility of each line may not be fully realized without systematic whole brain characterization of transgene expression patterns. We established a high-throughput in situ hybridization (ISH), imaging and data processing pipeline to describe whole brain gene expression patterns in Cre driver mice. Currently, anatomical data from over 100 Cre driver lines are publicly available via the Allen Institute's Transgenic Characterization database, which can be used to assist researchers in choosing the appropriate Cre drivers for functional, molecular, or connectional studies of different regions and/or cell types in the brain. PMID:25071457

Memristor-based neural networks: Synaptic versus neuronal stochasticity

KAUST Repository

Naous, Rawan

2016-11-02

In neuromorphic circuits, stochasticity in the cortex can be mapped into the synaptic or neuronal components. The hardware emulation of these stochastic neural networks are currently being extensively studied using resistive memories or memristors. The ionic process involved in the underlying switching behavior of the memristive elements is considered as the main source of stochasticity of its operation. Building on its inherent variability, the memristor is incorporated into abstract models of stochastic neurons and synapses. Two approaches of stochastic neural networks are investigated. Aside from the size and area perspective, the impact on the system performance, in terms of accuracy, recognition rates, and learning, among these two approaches and where the memristor would fall into place are the main comparison points to be considered.

Evolutionary mechanisms that generate morphology and neural-circuit diversity of the cerebellum.

Science.gov (United States)

Hibi, Masahiko; Matsuda, Koji; Takeuchi, Miki; Shimizu, Takashi; Murakami, Yasunori

2017-05-01

The cerebellum is derived from the dorsal part of the anterior-most hindbrain. The vertebrate cerebellum contains glutamatergic granule cells (GCs) and gamma-aminobutyric acid (GABA)ergic Purkinje cells (PCs). These cerebellar neurons are generated from neuronal progenitors or neural stem cells by mechanisms that are conserved among vertebrates. However, vertebrate cerebella are widely diverse with respect to their gross morphology and neural circuits. The cerebellum of cyclostomes, the basal vertebrates, has a negligible structure. Cartilaginous fishes have a cerebellum containing GCs, PCs, and deep cerebellar nuclei (DCNs), which include projection neurons. Ray-finned fish lack DCNs but have projection neurons termed eurydendroid cells (ECs) in the vicinity of the PCs. Among ray-finned fishes, the cerebellum of teleost zebrafish has a simple lobular structure, whereas that of weakly electric mormyrid fish is large and foliated. Amniotes, which include mammals, independently evolved a large, foliated cerebellum, which contains massive numbers of GCs and has functional connections with the dorsal telencephalon (neocortex). Recent studies of cyclostomes and cartilaginous fish suggest that the genetic program for cerebellum development was already encoded in the genome of ancestral vertebrates. In this review, we discuss how alterations of the genetic and cellular programs generated diversity of the cerebellum during evolution. © 2017 Japanese Society of Developmental Biologists.

Image Fusion Based on the Self-Organizing Feature Map Neural Networks

Institute of Scientific and Technical Information of China (English)

ZHANG Zhaoli; SUN Shenghe

2001-01-01

This paper presents a new image datafusion scheme based on the self-organizing featuremap (SOFM) neural networks.The scheme consists ofthree steps:(1) pre-processing of the images,whereweighted median filtering removes part of the noisecomponents corrupting the image,(2) pixel clusteringfor each image using two-dimensional self-organizingfeature map neural networks,and (3) fusion of the im-ages obtained in Step (2) utilizing fuzzy logic,whichsuppresses the residual noise components and thusfurther improves the image quality.It proves thatsuch a three-step combination offers an impressive ef-fectiveness and performance improvement,which isconfirmed by simulations involving three image sen-sors (each of which has a different noise structure).

Fast non-linear extraction of plasma equilibrium parameters using a neural network mapping

International Nuclear Information System (INIS)

Lister, J.B.; Schnurrenberger, H.

1990-07-01

The shaping of non-circular plasmas requires a non-linear mapping between the measured diagnostic signals and selected equilibrium parameters. The particular configuration of Neural Network known as the multi-layer perceptron provides a powerful and general technique for formulating an arbitrary continuous non-linear multi-dimensional mapping. This technique has been successfully applied to the extraction of equilibrium parameters from measurements of single-null diverted plasmas in the DIII-D tokamak; the results are compared with a purely linear mapping. The method is promising, and hardware implementation is straightforward. (author) 15 refs., 7 figs

Cross-talk between the epigenome and neural circuits in drug addiction.

Science.gov (United States)

Mews, Philipp; Calipari, Erin S

2017-01-01

Drug addiction is a behavioral disorder characterized by dysregulated learning about drugs and associated cues that result in compulsive drug seeking and relapse. Learning about drug rewards and predictive cues is a complex process controlled by a computational network of neural connections interacting with transcriptional and molecular mechanisms within each cell to precisely guide behavior. The interplay between rapid, temporally specific neuronal activation, and longer-term changes in transcription is of critical importance in the expression of appropriate, or in the case of drug addiction, inappropriate behaviors. Thus, these factors and their interactions must be considered together, especially in the context of treatment. Understanding the complex interplay between epigenetic gene regulation and circuit connectivity will allow us to formulate novel therapies to normalize maladaptive reward behaviors, with a goal of modulating addictive behaviors, while leaving natural reward-associated behavior unaffected. © 2017 Elsevier B.V. All rights reserved.

A non-penalty recurrent neural network for solving a class of constrained optimization problems.

Science.gov (United States)

Hosseini, Alireza

2016-01-01

In this paper, we explain a methodology to analyze convergence of some differential inclusion-based neural networks for solving nonsmooth optimization problems. For a general differential inclusion, we show that if its right hand-side set valued map satisfies some conditions, then solution trajectory of the differential inclusion converges to optimal solution set of its corresponding in optimization problem. Based on the obtained methodology, we introduce a new recurrent neural network for solving nonsmooth optimization problems. Objective function does not need to be convex on R(n) nor does the new neural network model require any penalty parameter. We compare our new method with some penalty-based and non-penalty based models. Moreover for differentiable cases, we implement circuit diagram of the new neural network. Copyright © 2015 Elsevier Ltd. All rights reserved.

Optimal neural computations require analog processors

Energy Technology Data Exchange (ETDEWEB)

Beiu, V.

1998-12-31

This paper discusses some of the limitations of hardware implementations of neural networks. The authors start by presenting neural structures and their biological inspirations, while mentioning the simplifications leading to artificial neural networks. Further, the focus will be on hardware imposed constraints. They will present recent results for three different alternatives of parallel implementations of neural networks: digital circuits, threshold gate circuits, and analog circuits. The area and the delay will be related to the neurons` fan-in and to the precision of their synaptic weights. The main conclusion is that hardware-efficient solutions require analog computations, and suggests the following two alternatives: (i) cope with the limitations imposed by silicon, by speeding up the computation of the elementary silicon neurons; (2) investigate solutions which would allow the use of the third dimension (e.g. using optical interconnections).

Fast non-linear extraction of plasma equilibrium parameters using a neural network mapping

International Nuclear Information System (INIS)

Lister, J.B.; Schnurrenberger, H.

1991-01-01

The shaping of non-circular plasmas requires a non-linear mapping between the measured diagnostic signals and selected equilibrium parameters. The particular configuration of neural network known as the multilayer perceptron provides a powerful and general technique for formulating an arbitrary continuous non-linear multi-dimensional mapping. This technique has been successfully applied to the extraction of equilibrium parameters from measurements of single-null diverted plasmas in the DIII-D tokamak; the results are compared with a purely linear mapping. The method is promising, and hardware implementation is straightforward. (author). 17 refs, 8 figs, 2 tab

A tweaking principle for executive control: neuronal circuit mechanism for rule-based task switching and conflict resolution.

Science.gov (United States)

Ardid, Salva; Wang, Xiao-Jing

2013-12-11

A hallmark of executive control is the brain's agility to shift between different tasks depending on the behavioral rule currently in play. In this work, we propose a "tweaking hypothesis" for task switching: a weak rule signal provides a small bias that is dramatically amplified by reverberating attractor dynamics in neural circuits for stimulus categorization and action selection, leading to an all-or-none reconfiguration of sensory-motor mapping. Based on this principle, we developed a biologically realistic model with multiple modules for task switching. We found that the model quantitatively accounts for complex task switching behavior: switch cost, congruency effect, and task-response interaction; as well as monkey's single-neuron activity associated with task switching. The model yields several testable predictions, in particular, that category-selective neurons play a key role in resolving sensory-motor conflict. This work represents a neural circuit model for task switching and sheds insights in the brain mechanism of a fundamental cognitive capability.

High-throughput dual-color precision imaging for brain-wide mapping of the connectome with cytoarchitectonic landmarks at the cellular level (Conference Presentation)

Science.gov (United States)

Luo, Qingming; Gong, Hui; Yuan, Jing; Li, Xiangning; Li, Anan; Xu, Tonghui

2017-02-01

Deciphering the fine morphology and precise location of neurons and neural circuits are crucial to enhance our understanding of brain function and diseases. Traditionally, we have to map brain images to coarse axial-sampling planar reference atlases to orient neural structures. However, this means might fail to orient neural projections at single-cell resolution due to position errors resulting from individual differences at the cellular level. Here, we present a high-throughput imaging method that can automatically obtain the fine morphologies and precise locations of both neurons and circuits, employing wide-field large-volume tomography to acquire three-dimensional images of thick tissue and implementing real-time soma counterstaining to obtain cytoarchitectonic landmarks during the imaging process. The reconstruction and orientation of brain-wide neural circuits at single-neuron resolution can be accomplished for the same mouse brain without additional counterstains or image registration. Using our method, mouse brain imaging datasets of multiple type-specific neurons and circuits were successfully acquired, demonstrating the versatility. The results show that the simultaneous acquisition of labeled neural structures and cytoarchitecture reference at single-neuron resolution in the same brain greatly facilitates precise tracing of long-range projections and accurate locating of nuclei. Our method provides a novel and effective tool for application in studies on genetic dissection, brain function and the pathology of the nervous system.

Computational aspects of feedback in neural circuits.

Directory of Open Access Journals (Sweden)

Wolfgang Maass

2007-01-01

Full Text Available 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 trained readout neurons is fed back into the circuit. We show that this new model overcomes the limitation of a rapidly fading memory. In fact, we prove that in the idealized case without noise it can carry out any conceivable digital or analog computation on time-varying inputs. But even with noise, the resulting computational model can perform a large class of biologically relevant real-time computations that require a nonfading memory. We demonstrate these computational implications of feedback both theoretically, and through computer simulations of detailed cortical microcircuit models that are subject to noise and have complex inherent dynamics. We show that the application of simple learning procedures (such as linear regression or perceptron learning to a few neurons enables such circuits to represent time over behaviorally relevant long time spans, to integrate evidence from incoming spike trains over longer periods of time, and to process new information contained in such spike trains in diverse ways according to the current internal state of the circuit. In particular we show that such generic cortical microcircuits with feedback provide a new model for working memory that is consistent with a large set of biological constraints. Although this article examines primarily the computational role of feedback in circuits of neurons, the mathematical principles on which its analysis is based apply to a variety of dynamical systems. Hence they may also

A wireless integrated circuit for 100-channel charge-balanced neural stimulation.

Science.gov (United States)

Thurgood, B K; Warren, D J; Ledbetter, N M; Clark, G A; Harrison, R R

2009-12-01

The authors present the design of an integrated circuit for wireless neural stimulation, along with benchtop and in - vivo experimental results. The chip has the ability to drive 100 individual stimulation electrodes with constant-current pulses of varying amplitude, duration, interphasic delay, and repetition rate. The stimulation is performed by using a biphasic (cathodic and anodic) current source, injecting and retracting charge from the nervous system. Wireless communication and power are delivered over a 2.765-MHz inductive link. Only three off-chip components are needed to operate the stimulator: a 10-nF capacitor to aid in power-supply regulation, a small capacitor (power and command reception. The chip was fabricated in a commercially available 0.6- mum 2P3M BiCMOS process. The chip was able to activate motor fibers to produce muscle twitches via a Utah Slanted Electrode Array implanted in cat sciatic nerve, and to activate sensory fibers to recruit evoked potentials in somatosensory cortex.

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices.

Science.gov (United States)

Gokmen, Tayfun; Onen, Murat; Haensch, Wilfried

2017-01-01

In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN) in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU) devices to convolutional neural networks (CNNs). We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures.

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices

Science.gov (United States)

Gokmen, Tayfun; Onen, Murat; Haensch, Wilfried

2017-01-01

In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN) in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU) devices to convolutional neural networks (CNNs). We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures. PMID:29066942

Butyrate reduces appetite and activates brown adipose tissue via the gut-brain neural circuit.

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Li, Zhuang; Yi, Chun-Xia; Katiraei, Saeed; Kooijman, Sander; Zhou, Enchen; Chung, Chih Kit; Gao, Yuanqing; van den Heuvel, José K; Meijer, Onno C; Berbée, Jimmy F P; Heijink, Marieke; Giera, Martin; Willems van Dijk, Ko; Groen, Albert K; Rensen, Patrick C N; Wang, Yanan

2017-11-03

Butyrate exerts metabolic benefits in mice and humans, the underlying mechanisms being still unclear. We aimed to investigate the effect of butyrate on appetite and energy expenditure, and to what extent these two components contribute to the beneficial metabolic effects of butyrate. Acute effects of butyrate on appetite and its method of action were investigated in mice following an intragastric gavage or intravenous injection of butyrate. To study the contribution of satiety to the metabolic benefits of butyrate, mice were fed a high-fat diet with butyrate, and an additional pair-fed group was included. Mechanistic involvement of the gut-brain neural circuit was investigated in vagotomised mice. Acute oral, but not intravenous, butyrate administration decreased food intake, suppressed the activity of orexigenic neurons that express neuropeptide Y in the hypothalamus, and decreased neuronal activity within the nucleus tractus solitarius and dorsal vagal complex in the brainstem. Chronic butyrate supplementation prevented diet-induced obesity, hyperinsulinaemia, hypertriglyceridaemia and hepatic steatosis, largely attributed to a reduction in food intake. Butyrate also modestly promoted fat oxidation and activated brown adipose tissue (BAT), evident from increased utilisation of plasma triglyceride-derived fatty acids. This effect was not due to the reduced food intake, but explained by an increased sympathetic outflow to BAT. Subdiaphragmatic vagotomy abolished the effects of butyrate on food intake as well as the stimulation of metabolic activity in BAT. Butyrate acts on the gut-brain neural circuit to improve energy metabolism via reducing energy intake and enhancing fat oxidation by activating BAT. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Deep Neural Architectures for Mapping Scalp to Intracranial EEG.

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Antoniades, Andreas; Spyrou, Loukianos; Martin-Lopez, David; Valentin, Antonio; Alarcon, Gonzalo; Sanei, Saeid; Took, Clive Cheong

2018-03-19

Data is often plagued by noise which encumbers machine learning of clinically useful biomarkers and electroencephalogram (EEG) data is no exemption. Intracranial EEG (iEEG) data enhances the training of deep learning models of the human brain, yet is often prohibitive due to the invasive recording process. A more convenient alternative is to record brain activity using scalp electrodes. However, the inherent noise associated with scalp EEG data often impedes the learning process of neural models, achieving substandard performance. Here, an ensemble deep learning architecture for nonlinearly mapping scalp to iEEG data is proposed. The proposed architecture exploits the information from a limited number of joint scalp-intracranial recording to establish a novel methodology for detecting the epileptic discharges from the sEEG of a general population of subjects. Statistical tests and qualitative analysis have revealed that the generated pseudo-intracranial data are highly correlated with the true intracranial data. This facilitated the detection of IEDs from the scalp recordings where such waveforms are not often visible. As a real-world clinical application, these pseudo-iEEGs are then used by a convolutional neural network for the automated classification of intracranial epileptic discharges (IEDs) and non-IED of trials in the context of epilepsy analysis. Although the aim of this work was to circumvent the unavailability of iEEG and the limitations of sEEG, we have achieved a classification accuracy of 68% an increase of 6% over the previously proposed linear regression mapping.

Neural mechanisms underlying sensitivity to reverse-phi motion in the fly.

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Leonhardt, Aljoscha; Meier, Matthias; Serbe, Etienne; Eichner, Hubert; Borst, Alexander

2017-01-01

Optical illusions provide powerful tools for mapping the algorithms and circuits that underlie visual processing, revealing structure through atypical function. Of particular note in the study of motion detection has been the reverse-phi illusion. When contrast reversals accompany discrete movement, detected direction tends to invert. This occurs across a wide range of organisms, spanning humans and invertebrates. Here, we map an algorithmic account of the phenomenon onto neural circuitry in the fruit fly Drosophila melanogaster. Through targeted silencing experiments in tethered walking flies as well as electrophysiology and calcium imaging, we demonstrate that ON- or OFF-selective local motion detector cells T4 and T5 are sensitive to certain interactions between ON and OFF. A biologically plausible detector model accounts for subtle features of this particular form of illusory motion reversal, like the re-inversion of turning responses occurring at extreme stimulus velocities. In light of comparable circuit architecture in the mammalian retina, we suggest that similar mechanisms may apply even to human psychophysics.

Collapse susceptibility mapping in karstified gypsum terrain (Sivas basin - Turkey) by conditional probability, logistic regression, artificial neural network models

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Yilmaz, Isik; Keskin, Inan; Marschalko, Marian; Bednarik, Martin

2010-05-01

This study compares the GIS based collapse susceptibility mapping methods such as; conditional probability (CP), logistic regression (LR) and artificial neural networks (ANN) applied in gypsum rock masses in Sivas basin (Turkey). Digital Elevation Model (DEM) was first constructed using GIS software. Collapse-related factors, directly or indirectly related to the causes of collapse occurrence, such as distance from faults, slope angle and aspect, topographical elevation, distance from drainage, topographic wetness index- TWI, stream power index- SPI, Normalized Difference Vegetation Index (NDVI) by means of vegetation cover, distance from roads and settlements were used in the collapse susceptibility analyses. In the last stage of the analyses, collapse susceptibility maps were produced from CP, LR and ANN models, and they were then compared by means of their validations. Area Under Curve (AUC) values obtained from all three methodologies showed that the map obtained from ANN model looks like more accurate than the other models, and the results also showed that the artificial neural networks is a usefull tool in preparation of collapse susceptibility map and highly compatible with GIS operating features. Key words: Collapse; doline; susceptibility map; gypsum; GIS; conditional probability; logistic regression; artificial neural networks.

Bidirectional global spontaneous network activity precedes the canonical unidirectional circuit organization in the developing hippocampus.

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Shi, Yulin; Ikrar, Taruna; Olivas, Nicholas D; Xu, Xiangmin

2014-06-15

Spontaneous network activity is believed to sculpt developing neural circuits. Spontaneous giant depolarizing potentials (GDPs) were first identified with single-cell recordings from rat CA3 pyramidal neurons, but here we identify and characterize a large-scale spontaneous network activity we term global network activation (GNA) in the developing mouse hippocampal slices, which is measured macroscopically by fast voltage-sensitive dye imaging. The initiation and propagation of GNA in the mouse is largely GABA-independent and dominated by glutamatergic transmission via AMPA receptors. Despite the fact that signal propagation in the adult hippocampus is strongly unidirectional through the canonical trisynaptic circuit (dentate gyrus [DG] to CA3 to CA1), spontaneous GNA in the developing hippocampus originates in distal CA3 and propagates both forward to CA1 and backward to DG. Photostimulation-evoked GNA also shows prominent backward propagation in the developing hippocampus from CA3 to DG. Mouse GNA is strongly correlated to electrophysiological recordings of highly localized single-cell and local field potential events. Photostimulation mapping of neural circuitry demonstrates that the enhancement of local circuit connections to excitatory pyramidal neurons occurs over the same time course as GNA and reveals the underlying pathways accounting for GNA backward propagation from CA3 to DG. The disappearance of GNA coincides with a transition to the adult-like unidirectional circuit organization at about 2 weeks of age. Taken together, our findings strongly suggest a critical link between GNA activity and maturation of functional circuit connections in the developing hippocampus. Copyright © 2013 Wiley Periodicals, Inc.

Unraveling the central proopiomelanocortin neural circuits

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Aaron J. Mercer

2013-02-01

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

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

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Marcie ePenner-Wilger

2013-12-01

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

A feed-forward Hopfield neural network algorithm (FHNNA) with a colour satellite image for water quality mapping

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Asal Kzar, Ahmed; Mat Jafri, M. Z.; Hwee San, Lim; Al-Zuky, Ali A.; Mutter, Kussay N.; Hassan Al-Saleh, Anwar

2016-06-01

There are many techniques that have been given for water quality problem, but the remote sensing techniques have proven their success, especially when the artificial neural networks are used as mathematical models with these techniques. Hopfield neural network is one type of artificial neural networks which is common, fast, simple, and efficient, but it when it deals with images that have more than two colours such as remote sensing images. This work has attempted to solve this problem via modifying the network that deals with colour remote sensing images for water quality mapping. A Feed-forward Hopfield Neural Network Algorithm (FHNNA) was modified and used with a satellite colour image from type of Thailand earth observation system (THEOS) for TSS mapping in the Penang strait, Malaysia, through the classification of TSS concentrations. The new algorithm is based essentially on three modifications: using HNN as feed-forward network, considering the weights of bitplanes, and non-self-architecture or zero diagonal of weight matrix, in addition, it depends on a validation data. The achieved map was colour-coded for visual interpretation. The efficiency of the new algorithm has found out by the higher correlation coefficient (R=0.979) and the lower root mean square error (RMSE=4.301) between the validation data that were divided into two groups. One used for the algorithm and the other used for validating the results. The comparison was with the minimum distance classifier. Therefore, TSS mapping of polluted water in Penang strait, Malaysia, can be performed using FHNNA with remote sensing technique (THEOS). It is a new and useful application of HNN, so it is a new model with remote sensing techniques for water quality mapping which is considered important environmental problem.

A neural command circuit for grooming movement control.

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

2015-09-07

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

Ontogeny of neural circuits underlying spatial memory in the rat

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James Alexander Ainge

2012-03-01

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

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

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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

Differential receptive field organizations give rise to nearly identical neural correlations across three parallel sensory maps in weakly electric fish.

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Hofmann, Volker; Chacron, Maurice J

2017-09-01

Understanding how neural populations encode sensory information thereby leading to perception and behavior (i.e., the neural code) remains an important problem in neuroscience. When investigating the neural code, one must take into account the fact that neural activities are not independent but are actually correlated with one another. Such correlations are seen ubiquitously and have a strong impact on neural coding. Here we investigated how differences in the antagonistic center-surround receptive field (RF) organization across three parallel sensory maps influence correlations between the activities of electrosensory pyramidal neurons. Using a model based on known anatomical differences in receptive field center size and overlap, we initially predicted large differences in correlated activity across the maps. However, in vivo electrophysiological recordings showed that, contrary to modeling predictions, electrosensory pyramidal neurons across all three segments displayed nearly identical correlations. To explain this surprising result, we incorporated the effects of RF surround in our model. By systematically varying both the RF surround gain and size relative to that of the RF center, we found that multiple RF structures gave rise to similar levels of correlation. In particular, incorporating known physiological differences in RF structure between the three maps in our model gave rise to similar levels of correlation. Our results show that RF center overlap alone does not determine correlations which has important implications for understanding how RF structure influences correlated neural activity.

Neural imaginaries and clinical epistemology: Rhetorically mapping the adolescent brain in the clinical encounter.

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Buchbinder, Mara

2015-10-01

The social work of brain images has taken center stage in recent theorizing of the intersections between neuroscience and society. However, neuroimaging is only one of the discursive modes through which public representations of neurobiology travel. This article adopts an expanded view toward the social implications of neuroscientific thinking to examine how neural imaginaries are constructed in the absence of visual evidence. Drawing on ethnographic fieldwork conducted over 18 months (2008-2009) in a United States multidisciplinary pediatric pain clinic, I examine the pragmatic clinical work undertaken to represent ambiguous symptoms in neurobiological form. Focusing on one physician, I illustrate how, by rhetorically mapping the brain as a therapeutic tool, she engaged in a distinctive form of representation that I call neural imagining. In shifting my focus away from the purely material dimensions of brain images, I juxtapose the cultural work of brain scanning technologies with clinical neural imaginaries in which the teenage brain becomes a space of possibility, not to map things as they are, but rather, things as we hope they might be. These neural imaginaries rely upon a distinctive clinical epistemology that privileges the creative work of the imagination over visualization technologies in revealing the truths of the body. By creating a therapeutic space for adolescents to exercise their imaginative faculties and a discursive template for doing so, neural imagining relocates adolescents' agency with respect to epistemologies of bodily knowledge and the role of visualization practices therein. In doing so, it provides a more hopeful alternative to the dominant popular and scientific representations of the teenage brain that view it primarily through the lens of pathology. Copyright © 2014 Elsevier Ltd. All rights reserved.

Recurrent Neural Networks to Correct Satellite Image Classification Maps

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Maggiori, Emmanuel; Charpiat, Guillaume; Tarabalka, Yuliya; Alliez, Pierre

2017-09-01

While initially devised for image categorization, convolutional neural networks (CNNs) are being increasingly used for the pixelwise semantic labeling of images. However, the proper nature of the most common CNN architectures makes them good at recognizing but poor at localizing objects precisely. This problem is magnified in the context of aerial and satellite image labeling, where a spatially fine object outlining is of paramount importance. Different iterative enhancement algorithms have been presented in the literature to progressively improve the coarse CNN outputs, seeking to sharpen object boundaries around real image edges. However, one must carefully design, choose and tune such algorithms. Instead, our goal is to directly learn the iterative process itself. For this, we formulate a generic iterative enhancement process inspired from partial differential equations, and observe that it can be expressed as a recurrent neural network (RNN). Consequently, we train such a network from manually labeled data for our enhancement task. In a series of experiments we show that our RNN effectively learns an iterative process that significantly improves the quality of satellite image classification maps.

Pattern Classification with Memristive Crossbar Circuits

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2016-03-31

Pattern Classification with Memristive Crossbar Circuits Dmitri B. Strukov Department of Electrical and Computer Engineering Department UC Santa...pattern classification ; deep learning; convolutional neural network networks. Introduction Deep-learning convolutional neural networks (DLCNN), which...the best classification performances on a variety of benchmark tasks [1]. The major challenge in building fast and energy- efficient networks of this

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices

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Tayfun Gokmen

2017-10-01

Full Text Available In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU devices to convolutional neural networks (CNNs. We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures.

Fractional Snow Cover Mapping by Artificial Neural Networks and Support Vector Machines

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Çiftçi, B. B.; Kuter, S.; Akyürek, Z.; Weber, G.-W.

2017-11-01

Snow is an important land cover whose distribution over space and time plays a significant role in various environmental processes. Hence, snow cover mapping with high accuracy is necessary to have a real understanding for present and future climate, water cycle, and ecological changes. This study aims to investigate and compare the design and use of artificial neural networks (ANNs) and support vector machines (SVMs) algorithms for fractional snow cover (FSC) mapping from satellite data. ANN and SVM models with different model building settings are trained by using Moderate Resolution Imaging Spectroradiometer surface reflectance values of bands 1-7, normalized difference snow index and normalized difference vegetation index as predictor variables. Reference FSC maps are generated from higher spatial resolution Landsat ETM+ binary snow cover maps. Results on the independent test data set indicate that the developed ANN model with hyperbolic tangent transfer function in the output layer and the SVM model with radial basis function kernel produce high FSC mapping accuracies with the corresponding values of R = 0.93 and R = 0.92, respectively.

High level cognitive information processing in neural networks

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Barnden, John A.; Fields, Christopher A.

1992-01-01

Two related research efforts were addressed: (1) high-level connectionist cognitive modeling; and (2) local neural circuit modeling. The goals of the first effort were to develop connectionist models of high-level cognitive processes such as problem solving or natural language understanding, and to understand the computational requirements of such models. The goals of the second effort were to develop biologically-realistic model of local neural circuits, and to understand the computational behavior of such models. In keeping with the nature of NASA's Innovative Research Program, all the work conducted under the grant was highly innovative. For instance, the following ideas, all summarized, are contributions to the study of connectionist/neural networks: (1) the temporal-winner-take-all, relative-position encoding, and pattern-similarity association techniques; (2) the importation of logical combinators into connection; (3) the use of analogy-based reasoning as a bridge across the gap between the traditional symbolic paradigm and the connectionist paradigm; and (4) the application of connectionism to the domain of belief representation/reasoning. The work on local neural circuit modeling also departs significantly from the work of related researchers. In particular, its concentration on low-level neural phenomena that could support high-level cognitive processing is unusual within the area of biological local circuit modeling, and also serves to expand the horizons of the artificial neural net field.

A fuzzy neural network model to forecast the percent cloud coverage and cloud top temperature maps

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Y. Tulunay

2008-12-01

Full Text Available Atmospheric processes are highly nonlinear. A small group at the METU in Ankara has been working on a fuzzy data driven generic model of nonlinear processes. The model developed is called the Middle East Technical University Fuzzy Neural Network Model (METU-FNN-M. The METU-FNN-M consists of a Fuzzy Inference System (METU-FIS, a data driven Neural Network module (METU-FNN of one hidden layer and several neurons, and a mapping module, which employs the Bezier Surface Mapping technique. In this paper, the percent cloud coverage (%CC and cloud top temperatures (CTT are forecast one month ahead of time at 96 grid locations. The probable influence of cosmic rays and sunspot numbers on cloudiness is considered by using the METU-FNN-M.

Frank Beach Award Winner: Steroids as Neuromodulators of Brain Circuits and Behavior

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Remage-Healey, Luke

2014-01-01

Neurons communicate primarily via action potentials that transmit information on the timescale of milliseconds. Neurons also integrate information via alterations in gene transcription and protein translation that are sustained for hours to days after initiation. Positioned between these two signaling timescales are the minute-by-minute actions of neuromodulators. Over the course of minutes, the classical neuromodulators (such as serotonin, dopamine, octopamine, and norepinephrine) can alter and/or stabilize neural circuit patterning as well as behavioral states. Neuromodulators allow many flexible outputs from neural circuits and can encode information content into the firing state of neural networks. The idea that steroid molecules can operate as genuine behavioral neuromodulators - synthesized by and acting within brain circuits on a minute-by-minute timescale - has gained traction in recent years. Evidence for brain steroid synthesis at synaptic terminals has converged with evidence for the rapid actions of brain-derived steroids on neural circuits and behavior. The general principle emerging from this work is that the production of steroid hormones within brain circuits can alter their functional connectivity and shift sensory representations by enhancing their information coding. Steroids produced in the brain can therefore change the information content of neuronal networks to rapidly modulate sensory experience and sensorimotor functions. PMID:25110187

The Pleiotropic MET Receptor Network: Circuit Development and the Neural-Medical Interface of Autism.

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Eagleson, Kathie L; Xie, Zhihui; Levitt, Pat

2017-03-01

People with autism spectrum disorder and other neurodevelopmental disorders (NDDs) are behaviorally and medically heterogeneous. The combination of polygenicity and gene pleiotropy-the influence of one gene on distinct phenotypes-raises questions of how specific genes and their protein products interact to contribute to NDDs. A preponderance of evidence supports developmental and pathophysiological roles for the MET receptor tyrosine kinase, a multifunctional receptor that mediates distinct biological responses depending upon cell context. MET influences neuron architecture and synapse maturation in the forebrain and regulates homeostasis in gastrointestinal and immune systems, both commonly disrupted in NDDs. Peak expression of synapse-enriched MET is conserved across rodent and primate forebrain, yet regional differences in primate neocortex are pronounced, with enrichment in circuits that participate in social information processing. A functional risk allele in the MET promoter, enriched in subgroups of children with autism spectrum disorder, reduces transcription and disrupts socially relevant neural circuits structurally and functionally. In mice, circuit-specific deletion of Met causes distinct atypical behaviors. MET activation increases dendritic complexity and nascent synapse number, but synapse maturation requires reductions in MET. MET mediates its specific biological effects through different intracellular signaling pathways and has a complex protein interactome that is enriched in autism spectrum disorder and other NDD candidates. The interactome is coregulated in developing human neocortex. We suggest that a gene as pleiotropic and highly regulated as MET, together with its interactome, is biologically relevant in normal and pathophysiological contexts, affecting central and peripheral phenotypes that contribute to NDD risk and clinical symptoms. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

The role of zebrafish (Danio rerio in dissecting the genetics and neural circuits of executive function

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Matthew O Parker

2013-04-01

Full Text Available Zebrafish have great potential to contribute to our understanding of behavioural genetics and thus to contribute to our understanding of the aetiology of psychiatric disease. However, progress is dependent upon the rate at which behavioural assays addressing complex behavioural phenotypes are designed, reported and validated. Here we critically review existing behavioural assays with particular focus on the use of adult zebrafish to explore executive processes and phenotypes associated with human psychiatric disease. We outline the case for using zebrafish as models to study impulse control and attention, discussing the validity of applying extant rodent assays to zebrafish and evidence for the conservation of relevant neural circuits.

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

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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 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 toward a likely dominant role of short-term synaptic plasticity alterations in schizophrenia while dysfunction in autism spectrum disorders (ASDs) may be due to a combination of both short-term and long-term synaptic plasticity alterations. PMID:25505409

Self-Organizing Maps Neural Networks Applied to the Classification of Ethanol Samples According to the Region of Commercialization

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Aline Regina Walkoff

2017-10-01

Full Text Available Physical-chemical analysis data were collected, from 998 ethanol samples of automotive ethanol commercialized in the northern, midwestern and eastern regions of the state of Paraná. The data presented self-organizing maps (SOM neural networks, which classified them according to those regions. The self-organizing maps best configuration had a 45 x 45 topology and 5000 training epochs, with a final learning rate of 6.7x10-4, a final neighborhood relationship of 3x10-2 and a mean quantization error of 2x10-2. This neural network provided a topological map depicting three separated groups, each one corresponding to samples of a same region of commercialization. Four maps of weights, one for each parameter, were presented. The network established the pH was the most important variable for classification and electrical conductivity the least one. The self-organizing maps application allowed the segmentation of alcohol samples, therefore identifying them according to the region of commercialization. DOI: http://dx.doi.org/10.17807/orbital.v9i4.982

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

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

2011-11-01

Few studies demonstrated neural circuits related to disgust were influenced by internal sexual orientation in male. Here we used fMRI to study the neural responses to disgust in homosexual and heterosexual men to investigate that issue. Thirty-two healthy male volunteers (sixteen homosexual and sixteen heterosexual) were scanned while viewing alternating blocks of three types of erotic film: heterosexual couples (F-M), male homosexual couples (M-M), and female homosexual couples (F-F) engaged in sexual activity. All the participants rated their level of disgust and sexual arousal as well. The F-F and M-M stimuli induced disgust in homosexual and heterosexual men, respectively. The common activations related to disgusting stimuli included: bilateral frontal gyrus and occipital gyrus, right middle temporal gyrus, left superior temporal gyrus, right cerebellum, and right thalamus. Homosexual men had greater neural responses in the left medial frontal gyrus than did heterosexual men to the sexual disgusting stimuli; in contrast, heterosexual men showed significantly greater activation than homosexual men in the left cuneus. ROI analysis showed that negative correlation were found between the magnitude of MRI signals in the left medial frontal gyrus and scores of disgust in homosexual subjects (pmen. Crown Copyright © 2010. Published by Elsevier Ireland Ltd. All rights reserved.

Dissociable attentional and affective circuits in medication-naïve children with attention-deficit/hyperactivity disorder.

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Posner, Jonathan; Rauh, Virginia; Gruber, Allison; Gat, Inbal; Wang, Zhishun; Peterson, Bradley S

2013-07-30

Current neurocognitive models of attention-deficit/hyperactivity disorder (ADHD) suggest that neural circuits involving both attentional and affective processing make independent contributions to the phenomenology of the disorder. However, a clear dissociation of attentional and affective circuits and their behavioral correlates has yet to be shown in medication-naïve children with ADHD. Using resting-state functional connectivity MRI (rs-fcMRI) in a cohort of medication naïve children with (N=22) and without (N=20) ADHD, we demonstrate that children with ADHD have reduced connectivity in two neural circuits: one underlying executive attention (EA) and the other emotional regulation (ER). We also demonstrate a double dissociation between these two neural circuits and their behavioral correlates such that reduced connectivity in the EA circuit correlates with executive attention deficits but not with emotional lability, while on the other hand, reduced connectivity in the ER circuit correlates with emotional lability but not with executive attention deficits. These findings suggest potential avenues for future research such as examining treatment effects on these two neural circuits as well as the potential prognostic and developmental significance of disturbances in one circuit vs the other. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

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

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Sengupta, Abhronil; Shim, Yong; Roy, Kaushik

2016-12-01

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

Neural mechanisms underlying sensitivity to reverse-phi motion in the fly

Science.gov (United States)

Meier, Matthias; Serbe, Etienne; Eichner, Hubert; Borst, Alexander

2017-01-01

Optical illusions provide powerful tools for mapping the algorithms and circuits that underlie visual processing, revealing structure through atypical function. Of particular note in the study of motion detection has been the reverse-phi illusion. When contrast reversals accompany discrete movement, detected direction tends to invert. This occurs across a wide range of organisms, spanning humans and invertebrates. Here, we map an algorithmic account of the phenomenon onto neural circuitry in the fruit fly Drosophila melanogaster. Through targeted silencing experiments in tethered walking flies as well as electrophysiology and calcium imaging, we demonstrate that ON- or OFF-selective local motion detector cells T4 and T5 are sensitive to certain interactions between ON and OFF. A biologically plausible detector model accounts for subtle features of this particular form of illusory motion reversal, like the re-inversion of turning responses occurring at extreme stimulus velocities. In light of comparable circuit architecture in the mammalian retina, we suggest that similar mechanisms may apply even to human psychophysics. PMID:29261684

Mapping synaptic pathology within cerebral cortical circuits in subjects with schizophrenia

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Robert Sweet

2010-06-01

Full Text Available Converging lines of evidence indicate that schizophrenia is characterized by impairments of synaptic machinery within cerebral cortical circuits. Efforts to localize these alterations in brain tissue from subjects with schizophrenia have frequently been limited to the quantification of structures that are non-selectively identified (e.g. dendritic spines labeled in Golgi preparations, axon boutons labeled with synaptophysin, or to quantification of proteins using methods unable to resolve relevant cellular compartments. Multiple label fluorescence confocal microscopy represents a means to circumvent many of these limitations, by concurrently extracting information regarding the number, morphology, and relative protein content of synaptic structures. An important adaptation required for studies of human disease is coupling this approach to stereologic methods for systematic random sampling of relevant brain regions. In this review article we consider the application of multiple label fluorescence confocal microscopy to the mapping of synaptic alterations in subjects with schizophrenia and describe the application of a novel, readily automated, iterative intensity/morphological segmentation algorithm for the extraction of information regarding synaptic structure number, size, and relative protein level from tissue sections obtained using unbiased stereological principles of sampling. In this context, we provide examples of the examination of pre- and post-synaptic structures within excitatory and inhibitory circuits of the cerebral cortex.

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

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Rita Moretti

2016-12-01

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

SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo.

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Jimenez-Romero, Cristian; Johnson, Jeffrey

2017-01-01

The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the development of tools for the simulation and study of neuronal dynamics ranging from phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley-based and multi-compartmental models. However, despite the multiple features offered by neural modelling tools, their integration with environments for the simulation of robots and agents can be challenging and time consuming. The implementation of artificial neural circuits to control robots generally involves the following tasks: (1) understanding the simulation tools, (2) creating the neural circuit in the neural simulator, (3) linking the simulated neural circuit with the environment of the agent and (4) programming the appropriate interface in the robot or agent to use the neural controller. The accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate students or novice researchers. This paper presents an alternative tool which facilitates the simulation of simple SNN circuits using the multi-agent simulation and the programming environment Netlogo (educational software that simplifies the study and experimentation of complex systems). The engine proposed and implemented in Netlogo for the simulation of a functional model of SNN is a simplification of integrate and fire (I&F) models. The characteristics of the engine (including neuronal dynamics, STDP learning and synaptic delay) are demonstrated through the implementation of an agent representing an artificial insect controlled by a simple neural circuit. The setup of the experiment and its outcomes are described in this work.

Integrating Neural Circuits Controlling Female Sexual Behavior.

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Micevych, Paul E; Meisel, Robert L

2017-01-01

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

Integrating Neural Circuits Controlling Female Sexual Behavior

Directory of Open Access Journals (Sweden)

Paul E. Micevych

2017-06-01

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

Transsynaptic Mapping of Second-Order Taste Neurons in Flies by trans-Tango.

Science.gov (United States)

Talay, Mustafa; Richman, Ethan B; Snell, Nathaniel J; Hartmann, Griffin G; Fisher, John D; Sorkaç, Altar; Santoyo, Juan F; Chou-Freed, Cambria; Nair, Nived; Johnson, Mark; Szymanski, John R; Barnea, Gilad

2017-11-15

Mapping neural circuits across defined synapses is essential for understanding brain function. Here we describe trans-Tango, a technique for anterograde transsynaptic circuit tracing and manipulation. At the core of trans-Tango is a synthetic signaling pathway that is introduced into all neurons in the animal. This pathway converts receptor activation at the cell surface into reporter expression through site-specific proteolysis. Specific labeling is achieved by presenting a tethered ligand at the synapses of genetically defined neurons, thereby activating the pathway in their postsynaptic partners and providing genetic access to these neurons. We first validated trans-Tango in the Drosophila olfactory system and then implemented it in the gustatory system, where projections beyond the first-order receptor neurons are not fully characterized. We identified putative second-order neurons within the sweet circuit that include projection neurons targeting known neuromodulation centers in the brain. These experiments establish trans-Tango as a flexible platform for transsynaptic circuit analysis. Copyright © 2017 Elsevier Inc. All rights reserved.

Forward and reverse mapping for milling process using artificial neural networks

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Rashmi L. Malghan

2018-02-01

Full Text Available The data set presented is related to the milling process of AA6061-4.5%Cu-5%SiCp composite. The data primarily concentrates on predicting values of some machining responses, such as cutting force, surface finish and power utilization utilizing using forward back propagation neural network based approach, i.e. ANN based on three process parameters, such as spindle speed, feed rate and depth of cut.The comparing reverse model is likewise created to prescribe the ideal settings of processing parameters for accomplishing the desired responses as indicated by the necessities of the end clients. These modelling approaches are very proficient to foresee the benefits of machining responses and also process parameter settings in light of the experimental technique. Keywords: ANN, Forward mapping, Reverse mapping, Milling process

Bridging the Gap: Towards a Cell-Type Specific Understanding of Neural Circuits Underlying Fear Behaviors

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McCullough, KM; Morrison, FG; Ressler, KJ

2016-01-01

Fear and anxiety-related disorders are remarkably common and debilitating, and are often characterized by dysregulated fear responses. Rodent models of fear learning and memory have taken great strides towards elucidating the specific neuronal circuitries underlying the learning of fear responses. The present review addresses recent research utilizing optogenetic approaches to parse circuitries underlying fear behaviors. It also highlights the powerful advances made when optogenetic techniques are utilized in a genetically defined, cell-type specific, manner. The application of next-generation genetic and sequencing approaches in a cell-type specific context will be essential for a mechanistic understanding of the neural circuitry underlying fear behavior and for the rational design of targeted, circuit specific, pharmacologic interventions for the treatment and prevention of fear-related disorders. PMID:27470092

Emergence of task-dependent representations in working memory circuits

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Cristina eSavin

2014-05-01

Full Text Available A wealth of experimental evidence suggests that working memory circuits preferentially represent information that is behaviorally relevant. Still, we are missing a mechanistic account of how these representations come about. Here we provide a simple explanation for a range of experimental findings, in light of prefrontal circuits adapting to task constraints by reward-dependent learning. In particular, we model a neural network shaped by reward-modulated spike-timing dependent plasticity (r-STDP and homeostatic plasticity (intrinsic excitability and synaptic scaling. We show that the experimentally-observed neural representations naturally emerge in an initially unstructured circuit as it learns to solve several working memory tasks. These results point to a critical, and previously unappreciated, role for reward-dependent learning in shaping prefrontal cortex activity.

On optical detection of densely labeled synapses in neuropil and mapping connectivity with combinatorially multiplexed fluorescent synaptic markers.

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Yuriy Mishchenko

Full Text Available We propose a new method for mapping neural connectivity optically, by utilizing Cre/Lox system Brainbow to tag synapses of different neurons with random mixtures of different fluorophores, such as GFP, YFP, etc., and then detecting patterns of fluorophores at different synapses using light microscopy (LM. Such patterns will immediately report the pre- and post-synaptic cells at each synaptic connection, without tracing neural projections from individual synapses to corresponding cell bodies. We simulate fluorescence from a population of densely labeled synapses in a block of hippocampal neuropil, completely reconstructed from electron microscopy data, and show that high-end LM is able to detect such patterns with over 95% accuracy. We conclude, therefore, that with the described approach neural connectivity in macroscopically large neural circuits can be mapped with great accuracy, in scalable manner, using fast optical tools, and straightforward image processing. Relying on an electron microscopy dataset, we also derive and explicitly enumerate the conditions that should be met to allow synaptic connectivity studies with high-resolution optical tools.

Artificial immune system algorithm in VLSI circuit configuration

Science.gov (United States)

Mansor, Mohd. Asyraf; Sathasivam, Saratha; Kasihmuddin, Mohd Shareduwan Mohd

2017-08-01

In artificial intelligence, the artificial immune system is a robust bio-inspired heuristic method, extensively used in solving many constraint optimization problems, anomaly detection, and pattern recognition. This paper discusses the implementation and performance of artificial immune system (AIS) algorithm integrated with Hopfield neural networks for VLSI circuit configuration based on 3-Satisfiability problems. Specifically, we emphasized on the clonal selection technique in our binary artificial immune system algorithm. We restrict our logic construction to 3-Satisfiability (3-SAT) clauses in order to outfit with the transistor configuration in VLSI circuit. The core impetus of this research is to find an ideal hybrid model to assist in the VLSI circuit configuration. In this paper, we compared the artificial immune system (AIS) algorithm (HNN-3SATAIS) with the brute force algorithm incorporated with Hopfield neural network (HNN-3SATBF). Microsoft Visual C++ 2013 was used as a platform for training, simulating and validating the performances of the proposed network. The results depict that the HNN-3SATAIS outperformed HNN-3SATBF in terms of circuit accuracy and CPU time. Thus, HNN-3SATAIS can be used to detect an early error in the VLSI circuit design.

Role of motoneuron-derived neurotrophin 3 in survival and axonal projection of sensory neurons during neural circuit formation.

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Usui, Noriyoshi; Watanabe, Keisuke; Ono, Katsuhiko; Tomita, Koichi; Tamamaki, Nobuaki; Ikenaka, Kazuhiro; Takebayashi, Hirohide

2012-03-01

Sensory neurons possess the central and peripheral branches and they form unique spinal neural circuits with motoneurons during development. Peripheral branches of sensory axons fasciculate with the motor axons that extend toward the peripheral muscles from the central nervous system (CNS), whereas the central branches of proprioceptive sensory neurons directly innervate motoneurons. Although anatomically well documented, the molecular mechanism underlying sensory-motor interaction during neural circuit formation is not fully understood. To investigate the role of motoneuron on sensory neuron development, we analyzed sensory neuron phenotypes in the dorsal root ganglia (DRG) of Olig2 knockout (KO) mouse embryos, which lack motoneurons. We found an increased number of apoptotic cells in the DRG of Olig2 KO embryos at embryonic day (E) 10.5. Furthermore, abnormal axonal projections of sensory neurons were observed in both the peripheral branches at E10.5 and central branches at E15.5. To understand the motoneuron-derived factor that regulates sensory neuron development, we focused on neurotrophin 3 (Ntf3; NT-3), because Ntf3 and its receptors (Trk) are strongly expressed in motoneurons and sensory neurons, respectively. The significance of motoneuron-derived Ntf3 was analyzed using Ntf3 conditional knockout (cKO) embryos, in which we observed increased apoptosis and abnormal projection of the central branch innervating motoneuron, the phenotypes being apparently comparable with that of Olig2 KO embryos. Taken together, we show that the motoneuron is a functional source of Ntf3 and motoneuron-derived Ntf3 is an essential pre-target neurotrophin for survival and axonal projection of sensory neurons.

Shaping vulnerability to addiction - the contribution of behavior, neural circuits and molecular mechanisms.

Science.gov (United States)

Egervari, Gabor; Ciccocioppo, Roberto; Jentsch, J David; Hurd, Yasmin L

2018-02-01

Substance use disorders continue to impose increasing medical, financial and emotional burdens on society in the form of morbidity and overdose, family disintegration, loss of employment and crime, while advances in prevention and treatment options remain limited. Importantly, not all individuals exposed to abused substances effectively develop the disease. Genetic factors play a significant role in determining addiction vulnerability and interactions between innate predisposition, environmental factors and personal experiences are also critical. Thus, understanding individual differences that contribute to the initiation of substance use as well as on long-term maladaptations driving compulsive drug use and relapse propensity is of critical importance to reduce this devastating disorder. In this paper, we discuss current topics in the field of addiction regarding individual vulnerability related to behavioral endophenotypes, neural circuits, as well as genetics and epigenetic mechanisms. Expanded knowledge of these factors is of importance to improve and personalize prevention and treatment interventions in the future. Copyright © 2017 Elsevier Ltd. All rights reserved.

Inherently stochastic spiking neurons for probabilistic neural computation

KAUST Repository

Al-Shedivat, Maruan

2015-04-01

Neuromorphic engineering aims to design hardware that efficiently mimics neural circuitry and provides the means for emulating and studying neural systems. In this paper, we propose a new memristor-based neuron circuit that uniquely complements the scope of neuron implementations and follows the stochastic spike response model (SRM), which plays a cornerstone role in spike-based probabilistic algorithms. We demonstrate that the switching of the memristor is akin to the stochastic firing of the SRM. Our analysis and simulations show that the proposed neuron circuit satisfies a neural computability condition that enables probabilistic neural sampling and spike-based Bayesian learning and inference. Our findings constitute an important step towards memristive, scalable and efficient stochastic neuromorphic platforms. © 2015 IEEE.

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

International Nuclear Information System (INIS)

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

2011-01-01

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

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

Energy Technology Data Exchange (ETDEWEB)

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

2011-11-15

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

Delineating the Diversity of Spinal Interneurons in Locomotor Circuits.

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Gosgnach, Simon; Bikoff, Jay B; Dougherty, Kimberly J; El Manira, Abdeljabbar; Lanuza, Guillermo M; Zhang, Ying

2017-11-08

Locomotion is common to all animals and is essential for survival. Neural circuits located in the spinal cord have been shown to be necessary and sufficient for the generation and control of the basic locomotor rhythm by activating muscles on either side of the body in a specific sequence. Activity in these neural circuits determines the speed, gait pattern, and direction of movement, so the specific locomotor pattern generated relies on the diversity of the neurons within spinal locomotor circuits. Here, we review findings demonstrating that developmental genetics can be used to identify populations of neurons that comprise these circuits and focus on recent work indicating that many of these populations can be further subdivided into distinct subtypes, with each likely to play complementary functions during locomotion. Finally, we discuss data describing the manner in which these populations interact with each other to produce efficient, task-dependent locomotion. Copyright © 2017 the authors 0270-6474/17/3710835-07$15.00/0.

An Implantable Mixed Analog/Digital Neural Stimulator Circuit

DEFF Research Database (Denmark)

Gudnason, Gunnar; Bruun, Erik; Haugland, Morten

1999-01-01

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

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

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César da Silva Chagas

2013-04-01

Full Text Available Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI, derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs was greater than of the classic Maximum Likelihood Classifier (MLC. Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 % was superior to the MLC map (57.94 %. The main errors when using the two classifiers were caused by: a the geological heterogeneity of the area coupled with problems related to the geological map; b the depth of lithic contact and/or rock exposure, and c problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

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

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Chiara eBegliomini

2014-09-01

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

Single-shot T2 mapping using overlapping-echo detachment planar imaging and a deep convolutional neural network.

Science.gov (United States)

Cai, Congbo; Wang, Chao; Zeng, Yiqing; Cai, Shuhui; Liang, Dong; Wu, Yawen; Chen, Zhong; Ding, Xinghao; Zhong, Jianhui

2018-04-24

An end-to-end deep convolutional neural network (CNN) based on deep residual network (ResNet) was proposed to efficiently reconstruct reliable T 2 mapping from single-shot overlapping-echo detachment (OLED) planar imaging. The training dataset was obtained from simulations that were carried out on SPROM (Simulation with PRoduct Operator Matrix) software developed by our group. The relationship between the original OLED image containing two echo signals and the corresponding T 2 mapping was learned by ResNet training. After the ResNet was trained, it was applied to reconstruct the T 2 mapping from simulation and in vivo human brain data. Although the ResNet was trained entirely on simulated data, the trained network was generalized well to real human brain data. The results from simulation and in vivo human brain experiments show that the proposed method significantly outperforms the echo-detachment-based method. Reliable T 2 mapping with higher accuracy is achieved within 30 ms after the network has been trained, while the echo-detachment-based OLED reconstruction method took approximately 2 min. The proposed method will facilitate real-time dynamic and quantitative MR imaging via OLED sequence, and deep convolutional neural network has the potential to reconstruct maps from complex MRI sequences efficiently. © 2018 International Society for Magnetic Resonance in Medicine.

Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey)

Science.gov (United States)

Yilmaz, Işık

2009-06-01

The purpose of this study is to compare the landslide susceptibility mapping methods of frequency ratio (FR), logistic regression and artificial neural networks (ANN) applied in the Kat County (Tokat—Turkey). Digital elevation model (DEM) was first constructed using GIS software. Landslide-related factors such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index (TWI) and stream power index (SPI) were used in the landslide susceptibility analyses. Landslide susceptibility maps were produced from the frequency ratio, logistic regression and neural networks models, and they were then compared by means of their validations. The higher accuracies of the susceptibility maps for all three models were obtained from the comparison of the landslide susceptibility maps with the known landslide locations. However, respective area under curve (AUC) values of 0.826, 0.842 and 0.852 for frequency ratio, logistic regression and artificial neural networks showed that the map obtained from ANN model is more accurate than the other models, accuracies of all models can be evaluated relatively similar. The results obtained in this study also showed that the frequency ratio model can be used as a simple tool in assessment of landslide susceptibility when a sufficient number of data were obtained. Input process, calculations and output process are very simple and can be readily understood in the frequency ratio model, however logistic regression and neural networks require the conversion of data to ASCII or other formats. Moreover, it is also very hard to process the large amount of data in the statistical package.

Fractional Hopfield Neural Networks: Fractional Dynamic Associative Recurrent Neural Networks.

Science.gov (United States)

Pu, Yi-Fei; Yi, Zhang; Zhou, Ji-Liu

2017-10-01

This paper mainly discusses a novel conceptual framework: fractional Hopfield neural networks (FHNN). As is commonly known, fractional calculus has been incorporated into artificial neural networks, mainly because of its long-term memory and nonlocality. Some researchers have made interesting attempts at fractional neural networks and gained competitive advantages over integer-order neural networks. Therefore, it is naturally makes one ponder how to generalize the first-order Hopfield neural networks to the fractional-order ones, and how to implement FHNN by means of fractional calculus. We propose to introduce a novel mathematical method: fractional calculus to implement FHNN. First, we implement fractor in the form of an analog circuit. Second, we implement FHNN by utilizing fractor and the fractional steepest descent approach, construct its Lyapunov function, and further analyze its attractors. Third, we perform experiments to analyze the stability and convergence of FHNN, and further discuss its applications to the defense against chip cloning attacks for anticounterfeiting. The main contribution of our work is to propose FHNN in the form of an analog circuit by utilizing a fractor and the fractional steepest descent approach, construct its Lyapunov function, prove its Lyapunov stability, analyze its attractors, and apply FHNN to the defense against chip cloning attacks for anticounterfeiting. A significant advantage of FHNN is that its attractors essentially relate to the neuron's fractional order. FHNN possesses the fractional-order-stability and fractional-order-sensitivity characteristics.

[Lower urinary tract dysfunction and neuropathological findings of the neural circuits controlling micturition in familial amyotrophic lateral sclerosis with L106V mutation in the SOD1 gene].

Science.gov (United States)

Hineno, Akiyo; Oyanagi, Kiyomitsu; Nakamura, Akinori; Shimojima, Yoshio; Yoshida, Kunihiro; Ikeda, Shu-Ichi

2016-01-01

We report lower urinary tract dysfunction and neuropathological findings of the neural circuits controlling micturition in the patients with familial amyotrophic lateral sclerosis having L106V mutation in the SOD1 gene. Ten of 20 patients showed lower urinary tract dysfunction and 5 patients developed within 1 year after the onset of weakness. In 8 patients with an artificial respirator, 6 patients showed lower urinary tract dysfunction. Lower urinary tract dysfunction and respiratory failure requiring an artificial respirator occurred simultaneously in 3 patients. Neuronal loss and gliosis were observed in the neural circuits controlling micturition, such as frontal lobe, thalamus, hypothalamus, striatum, periaqueductal gray, ascending spinal tract, lateral corticospinal tract, intermediolateral nucleus and Onufrowicz' nucleus. Lower urinary tract dysfunction, especially storage symptoms, developed about 1 year after the onset of weakness, and the dysfunction occurred simultaneously with artificial respirator use in the patients.

A Genetic Toolkit for Dissecting Dopamine Circuit Function in Drosophila

Directory of Open Access Journals (Sweden)

Tingting Xie

2018-04-01

Full Text Available Summary: The neuromodulator dopamine (DA plays a key role in motor control, motivated behaviors, and higher-order cognitive processes. Dissecting how these DA neural networks tune the activity of local neural circuits to regulate behavior requires tools for manipulating small groups of DA neurons. To address this need, we assembled a genetic toolkit that allows for an exquisite level of control over the DA neural network in Drosophila. To further refine targeting of specific DA neurons, we also created reagents that allow for the conversion of any existing GAL4 line into Split GAL4 or GAL80 lines. We demonstrated how this toolkit can be used with recently developed computational methods to rapidly generate additional reagents for manipulating small subsets or individual DA neurons. Finally, we used the toolkit to reveal a dynamic interaction between a small subset of DA neurons and rearing conditions in a social space behavioral assay. : The rapid analysis of how dopaminergic circuits regulate behavior is limited by the genetic tools available to target and manipulate small numbers of these neurons. Xie et al. present genetic tools in Drosophila that allow rational targeting of sparse dopaminergic neuronal subsets and selective knockdown of dopamine signaling. Keywords: dopamine, genetics, behavior, neural circuits, neuromodulation, Drosophila

Sex-specific neural circuits of emotion regulation in the centromedial amygdala.

Science.gov (United States)

Wu, Yan; Li, Huandong; Zhou, Yuan; Yu, Jian; Zhang, Yuanchao; Song, Ming; Qin, Wen; Yu, Chunshui; Jiang, Tianzi

2016-03-23

Sex-related differences in emotion regulation (ER) in the frequency power distribution within the human amygdala, a brain region involved in emotion processing, have been reported. However, how sex differences in ER are manifested in the brain networks which are seeded on the amygdala subregions is unclear. The goal of this study was to investigate this issue from a brain network perspective. Utilizing resting-state functional connectivity (RSFC) analysis, we found that the sex-specific functional connectivity patterns associated with ER trait level were only seeded in the centromedial amygdala (CM). Women with a higher trait-level ER had a stronger negative RSFC between the right CM and the medial superior frontal gyrus (mSFG), and stronger positive RSFC between the right CM and the anterior insula (AI) and the superior temporal gyrus (STG). But men with a higher trait-level ER was associated with weaker negative RSFC of the right CM-mSFG and positive RSFCs of the right CM-left AI, right CM-right AI/STG, and right CM-left STG. These results provide evidence for the sex-related effects in ER based on CM and indicate that men and women may differ in the neural circuits associated with emotion representation and integration.

High speed digital interfacing for a neural data acquisition system

Directory of Open Access Journals (Sweden)

Bahr Andreas

2016-09-01

Full Text Available Diseases like schizophrenia and genetic epilepsy are supposed to be caused by disorders in the early development of the brain. For the further investigation of these relationships a custom designed application specific integrated circuit (ASIC was developed that is optimized for the recording from neonatal mice [Bahr A, Abu-Saleh L, Schroeder D, Krautschneider W. 16 Channel Neural Recording Integrated Circuit with SPI Interface and Error Correction Coding. Proc. 9th BIOSTEC 2016. Biodevices: Rome, Italy, 2016; 1: 263; Bahr A, Abu-Saleh L, Schroeder D, Krautschneider W. Development of a neural recording mixed signal integrated circuit for biomedical signal acquisition. Biomed Eng Biomed Tech Abstracts 2015; 60(S1: 298–299; Bahr A, Abu-Saleh L, Schroeder D, Krautschneider WH. 16 Channel Neural Recording Mixed Signal ASIC. CDNLive EMEA 2015 Conference Proceedings, 2015.]. To enable the live display of the neural signals a multichannel neural data acquisition system with live display functionality is presented. It implements a high speed data transmission from the ASIC to a computer with a live display functionality. The system has been successfully implemented and was used in a neural recording of a head-fixed mouse.

A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning.

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Kappel, David; Legenstein, Robert; Habenschuss, Stefan; Hsieh, Michael; Maass, Wolfgang

2018-01-01

Synaptic connections between neurons in the brain are dynamic because of continuously ongoing spine dynamics, axonal sprouting, and other processes. In fact, it was recently shown that the spontaneous synapse-autonomous component of spine dynamics is at least as large as the component that depends on the history of pre- and postsynaptic neural activity. These data are inconsistent with common models for network plasticity and raise the following questions: how can neural circuits maintain a stable computational function in spite of these continuously ongoing processes, and what could be functional uses of these ongoing processes? Here, we present a rigorous theoretical framework for these seemingly stochastic spine dynamics and rewiring processes in the context of reward-based learning tasks. We show that spontaneous synapse-autonomous processes, in combination with reward signals such as dopamine, can explain the capability of networks of neurons in the brain to configure themselves for specific computational tasks, and to compensate automatically for later changes in the network or task. Furthermore, we show theoretically and through computer simulations that stable computational performance is compatible with continuously ongoing synapse-autonomous changes. After reaching good computational performance it causes primarily a slow drift of network architecture and dynamics in task-irrelevant dimensions, as observed for neural activity in motor cortex and other areas. On the more abstract level of reinforcement learning the resulting model gives rise to an understanding of reward-driven network plasticity as continuous sampling of network configurations.

Inherently stochastic spiking neurons for probabilistic neural computation

KAUST Repository

Al-Shedivat, Maruan; Naous, Rawan; Neftci, Emre; Cauwenberghs, Gert; Salama, Khaled N.

2015-01-01

. Our analysis and simulations show that the proposed neuron circuit satisfies a neural computability condition that enables probabilistic neural sampling and spike-based Bayesian learning and inference. Our findings constitute an important step towards

Imaging the neural circuitry and chemical control of aggressive motivation

Directory of Open Access Journals (Sweden)

Blanchard D Caroline

2008-11-01

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

Three Pillars for the Neural Control of Appetite.

Science.gov (United States)

Sternson, Scott M; Eiselt, Anne-Kathrin

2017-02-10

The neural control of appetite is important for understanding motivated behavior as well as the present rising prevalence of obesity. Over the past several years, new tools for cell type-specific neuron activity monitoring and perturbation have enabled increasingly detailed analyses of the mechanisms underlying appetite-control systems. Three major neural circuits strongly and acutely influence appetite but with notably different characteristics. Although these circuits interact, they have distinct properties and thus appear to contribute to separate but interlinked processes influencing appetite, thereby forming three pillars of appetite control. Here, we summarize some of the key characteristics of appetite circuits that are emerging from recent work and synthesize the findings into a provisional framework that can guide future studies.

Spatial interpolation and radiological mapping of ambient gamma dose rate by using artificial neural networks and fuzzy logic methods.

Science.gov (United States)

Yeşilkanat, Cafer Mert; Kobya, Yaşar; Taşkın, Halim; Çevik, Uğur

2017-09-01

The aim of this study was to determine spatial risk dispersion of ambient gamma dose rate (AGDR) by using both artificial neural network (ANN) and fuzzy logic (FL) methods, compare the performances of methods, make dose estimations for intermediate stations with no previous measurements and create dose rate risk maps of the study area. In order to determine the dose distribution by using artificial neural networks, two main networks and five different network structures were used; feed forward ANN; Multi-layer perceptron (MLP), Radial basis functional neural network (RBFNN), Quantile regression neural network (QRNN) and recurrent ANN; Jordan networks (JN), Elman networks (EN). In the evaluation of estimation performance obtained for the test data, all models appear to give similar results. According to the cross-validation results obtained for explaining AGDR distribution, Pearson's r coefficients were calculated as 0.94, 0.91, 0.89, 0.91, 0.91 and 0.92 and RMSE values were calculated as 34.78, 43.28, 63.92, 44.86, 46.77 and 37.92 for MLP, RBFNN, QRNN, JN, EN and FL, respectively. In addition, spatial risk maps showing distributions of AGDR of the study area were created by all models and results were compared with geological, topological and soil structure. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

Science.gov (United States)

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

2016-01-01

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

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

Science.gov (United States)

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

2016-09-01

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

Deciphering the Cognitive and Neural Mechanisms Underlying ...

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

Deciphering the Cognitive and Neural Mechanisms Underlying Auditory Learning. This project seeks to understand the brain mechanisms necessary for people to learn to perceive sounds. Neural circuits and learning. The research team will test people with and without musical training to evaluate their capacity to learn ...

Mapping Speech Spectra from Throat Microphone to Close-Speaking Microphone: A Neural Network Approach

Directory of Open Access Journals (Sweden)

B. Yegnanarayana

2007-01-01

Full Text Available Speech recorded from a throat microphone is robust to the surrounding noise, but sounds unnatural unlike the speech recorded from a close-speaking microphone. This paper addresses the issue of improving the perceptual quality of the throat microphone speech by mapping the speech spectra from the throat microphone to the close-speaking microphone. A neural network model is used to capture the speaker-dependent functional relationship between the feature vectors (cepstral coefficients of the two speech signals. A method is proposed to ensure the stability of the all-pole synthesis filter. Objective evaluations indicate the effectiveness of the proposed mapping scheme. The advantage of this method is that the model gives a smooth estimate of the spectra of the close-speaking microphone speech. No distortions are perceived in the reconstructed speech. This mapping technique is also used for bandwidth extension of telephone speech.

Rodent Zic Genes in Neural Network Wiring.

Science.gov (United States)

Herrera, Eloísa

2018-01-01

The formation of the nervous system is a multistep process that yields a mature brain. Failure in any of the steps of this process may cause brain malfunction. In the early stages of embryonic development, neural progenitors quickly proliferate and then, at a specific moment, differentiate into neurons or glia. Once they become postmitotic neurons, they migrate to their final destinations and begin to extend their axons to connect with other neurons, sometimes located in quite distant regions, to establish different neural circuits. During the last decade, it has become evident that Zic genes, in addition to playing important roles in early development (e.g., gastrulation and neural tube closure), are involved in different processes of late brain development, such as neuronal migration, axon guidance, and refinement of axon terminals. ZIC proteins are therefore essential for the proper wiring and connectivity of the brain. In this chapter, we review our current knowledge of the role of Zic genes in the late stages of neural circuit formation.

Hardware implementation of an adaptive resonance theory (ART) neural network using compensated operational amplifiers

Science.gov (United States)

Ho, Ching S.; Liou, Juin J.; Georgiopoulos, Michael; Christodoulou, Christos G.

1994-03-01

This paper presents an analog circuit design and implementation for an adaptive resonance theory neural network architecture called the augmented ART1 neural network (AART1-NN). Practical monolithic operational amplifiers (Op-Amps) LM741 and LM318 are selected to implement the circuit, and a simple compensation scheme is developed to adjust the Op-Amp electrical characteristics to meet the design requirement. A 7-node prototype circuit has been designed and verified using the Pspice circuit simulator run on a Sun workstation. Results simulated from the AART1-NN circuit using the LM741, LM318, and ideal Op-Amps are presented and compared.

Sensor agnostic object recognition using a map seeking circuit

Science.gov (United States)

Overman, Timothy L.; Hart, Michael

2012-05-01

Automatic object recognition capabilities are traditionally tuned to exploit the specific sensing modality they were designed to. Their successes (and shortcomings) are tied to object segmentation from the background, they typically require highly skilled personnel to train them, and they become cumbersome with the introduction of new objects. In this paper we describe a sensor independent algorithm based on the biologically inspired technology of map seeking circuits (MSC) which overcomes many of these obstacles. In particular, the MSC concept offers transparency in object recognition from a common interface to all sensor types, analogous to a USB device. It also provides a common core framework that is independent of the sensor and expandable to support high dimensionality decision spaces. Ease in training is assured by using commercially available 3D models from the video game community. The search time remains linear no matter how many objects are introduced, ensuring rapid object recognition. Here, we report results of an MSC algorithm applied to object recognition and pose estimation from high range resolution radar (1D), electrooptical imagery (2D), and LIDAR point clouds (3D) separately. By abstracting the sensor phenomenology from the underlying a prior knowledge base, MSC shows promise as an easily adaptable tool for incorporating additional sensor inputs.

Large-scale multielectrode recording and stimulation of neural activity

International Nuclear Information System (INIS)

Sher, A.; Chichilnisky, E.J.; Dabrowski, W.; Grillo, A.A.; Grivich, M.; Gunning, D.; Hottowy, P.; Kachiguine, S.; Litke, A.M.; Mathieson, K.; Petrusca, D.

2007-01-01

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

Calcium Imaging of Neuronal Circuits In Vivo Using a Circuit-Tracing Pseudorabies Virus

OpenAIRE

sprotocols

2014-01-01

Authors: Andrea E. Granstedt, Bernd Kuhn, Samuel S.-H. Wang and Lynn W. Enquist Corresponding author ([]()). ### INTRODUCTION Pseudorabies virus (PRV) is a neuroinvasive virus of the herpes family that has a broad host range but does not infect higher-order primates. PRV characteristically travels along chains of synaptically connected neurons and has been used extensively for elucidating neural circuits in the peripheral and central ner...

Wide-field optical mapping of neural activity and brain haemodynamics: considerations and novel approaches

Science.gov (United States)

Ma, Ying; Shaik, Mohammed A.; Kozberg, Mariel G.; Thibodeaux, David N.; Zhao, Hanzhi T.; Yu, Hang

2016-01-01

Although modern techniques such as two-photon microscopy can now provide cellular-level three-dimensional imaging of the intact living brain, the speed and fields of view of these techniques remain limited. Conversely, two-dimensional wide-field optical mapping (WFOM), a simpler technique that uses a camera to observe large areas of the exposed cortex under visible light, can detect changes in both neural activity and haemodynamics at very high speeds. Although WFOM may not provide single-neuron or capillary-level resolution, it is an attractive and accessible approach to imaging large areas of the brain in awake, behaving mammals at speeds fast enough to observe widespread neural firing events, as well as their dynamic coupling to haemodynamics. Although such wide-field optical imaging techniques have a long history, the advent of genetically encoded fluorophores that can report neural activity with high sensitivity, as well as modern technologies such as light emitting diodes and sensitive and high-speed digital cameras have driven renewed interest in WFOM. To facilitate the wider adoption and standardization of WFOM approaches for neuroscience and neurovascular coupling research, we provide here an overview of the basic principles of WFOM, considerations for implementation of wide-field fluorescence imaging of neural activity, spectroscopic analysis and interpretation of results. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’. PMID:27574312

Wide-field optical mapping of neural activity and brain haemodynamics: considerations and novel approaches.

Science.gov (United States)

Ma, Ying; Shaik, Mohammed A; Kim, Sharon H; Kozberg, Mariel G; Thibodeaux, David N; Zhao, Hanzhi T; Yu, Hang; Hillman, Elizabeth M C

2016-10-05

Although modern techniques such as two-photon microscopy can now provide cellular-level three-dimensional imaging of the intact living brain, the speed and fields of view of these techniques remain limited. Conversely, two-dimensional wide-field optical mapping (WFOM), a simpler technique that uses a camera to observe large areas of the exposed cortex under visible light, can detect changes in both neural activity and haemodynamics at very high speeds. Although WFOM may not provide single-neuron or capillary-level resolution, it is an attractive and accessible approach to imaging large areas of the brain in awake, behaving mammals at speeds fast enough to observe widespread neural firing events, as well as their dynamic coupling to haemodynamics. Although such wide-field optical imaging techniques have a long history, the advent of genetically encoded fluorophores that can report neural activity with high sensitivity, as well as modern technologies such as light emitting diodes and sensitive and high-speed digital cameras have driven renewed interest in WFOM. To facilitate the wider adoption and standardization of WFOM approaches for neuroscience and neurovascular coupling research, we provide here an overview of the basic principles of WFOM, considerations for implementation of wide-field fluorescence imaging of neural activity, spectroscopic analysis and interpretation of results.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'. © 2016 The Authors.

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

Science.gov (United States)

Sengupta, Abhronil; Banerjee, Aparajita; Roy, Kaushik

2016-12-01

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

Impact of adolescent social experiences on behavior and neural circuits implicated in mental illnesses.

Science.gov (United States)

Burke, Andrew R; McCormick, Cheryl M; Pellis, Sergio M; Lukkes, Jodi L

2017-05-01

Negative social experiences during adolescence are central features for several stress-related mental illnesses. Social play fighting behavior in rats peaks during early adolescence and is essential for the final maturation of brain and behavior. Manipulation of the rat adolescent social experience alters many neurobehavioral measurements implicated in anxiety, depression, and substance abuse. In this review, we will highlight the importance of social play and the use of three separate social stress models (isolation-rearing, social defeat, and social instability stress) to disrupt the acquisition of this adaptive behavior. Social stress during adolescence leads to the development of anxiety and depressive behavior as well as escalated drug use in adulthood. Furthermore, sex- and age-dependent effects on the hormonal stress response following adolescent social stress are also observed. Finally, manipulation of the social experience during adolescence alters stress-related neural circuits and monoaminergic systems. Overall, positive social experiences among age-matched conspecifics during rat adolescence are critical for healthy neurobehavioral maturation. Copyright © 2017 Elsevier Ltd. All rights reserved.

The neural basis of financial risk taking.

Science.gov (United States)

Kuhnen, Camelia M; Knutson, Brian

2005-09-01

Investors systematically deviate from rationality when making financial decisions, yet the mechanisms responsible for these deviations have not been identified. Using event-related fMRI, we examined whether anticipatory neural activity would predict optimal and suboptimal choices in a financial decision-making task. We characterized two types of deviations from the optimal investment strategy of a rational risk-neutral agent as risk-seeking mistakes and risk-aversion mistakes. Nucleus accumbens activation preceded risky choices as well as risk-seeking mistakes, while anterior insula activation preceded riskless choices as well as risk-aversion mistakes. These findings suggest that distinct neural circuits linked to anticipatory affect promote different types of financial choices and indicate that excessive activation of these circuits may lead to investing mistakes. Thus, consideration of anticipatory neural mechanisms may add predictive power to the rational actor model of economic decision making.

Bootstrapped neural nets versus regression kriging in the digital mapping of pedological attributes: the automatic and time-consuming perspectives

Science.gov (United States)

Langella, Giuliano; Basile, Angelo; Bonfante, Antonello; Manna, Piero; Terribile, Fabio

2013-04-01

Digital soil mapping procedures are widespread used to build two-dimensional continuous maps about several pedological attributes. Our work addressed a regression kriging (RK) technique and a bootstrapped artificial neural network approach in order to evaluate and compare (i) the accuracy of prediction, (ii) the susceptibility of being included in automatic engines (e.g. to constitute web processing services), and (iii) the time cost needed for calibrating models and for making predictions. Regression kriging is maybe the most widely used geostatistical technique in the digital soil mapping literature. Here we tried to apply the EBLUP regression kriging as it is deemed to be the most statistically sound RK flavor by pedometricians. An unusual multi-parametric and nonlinear machine learning approach was accomplished, called BAGAP (Bootstrap aggregating Artificial neural networks with Genetic Algorithms and Principal component regression). BAGAP combines a selected set of weighted neural nets having specified characteristics to yield an ensemble response. The purpose of applying these two particular models is to ascertain whether and how much a more cumbersome machine learning method could be much promising in making more accurate/precise predictions. Being aware of the difficulty to handle objects based on EBLUP-RK as well as BAGAP when they are embedded in environmental applications, we explore the susceptibility of them in being wrapped within Web Processing Services. Two further kinds of aspects are faced for an exhaustive evaluation and comparison: automaticity and time of calculation with/without high performance computing leverage.

Modulation of anxiety circuits by serotonergic systems

DEFF Research Database (Denmark)

Lowry, Christopher A; Johnson, Philip L; Hay-Schmidt, Anders

2005-01-01

of emotionally salient events, often when both rewarding and aversive outcomes are possible. In this review, we highlight recent advances in our understanding of the neural circuits regulating anxiety states and anxiety-related behavior with an emphasis on the role of brainstem serotonergic systems in modulating...... anxiety-related circuits. In particular, we explore the possibility that the regulation of anxiety states and anxiety-related behavior by serotonergic systems is dependent on a specific, topographically organized mesolimbocortical serotonergic system that originates in the mid-rostrocaudal and caudal...

Focusing on neuronal cell-type specific mechanisms for brain circuit organization, function and dysfunction

Institute of Scientific and Technical Information of China (English)

Lu Li

2017-01-01

Mammalian brain circuits consist of dynamically interconnected neurons with characteristic morphology, physiology, connectivity and genetics which are often called neuronal cell types. Neuronal cell types have been considered as building blocks of brain circuits, but knowledge of how neuron types or subtypes connect to and interact with each other to perform neural computation is still lacking. Such mechanistic insights are critical not only to our understanding of normal brain functions, such as perception, motion and cognition, but also to brain disorders including Alzheimer's disease, Schizophrenia and epilepsy, to name a few. Thus it is necessary to carry out systematic and standardized studies on neuronal cell-type specific mechanisms for brain circuit organization and function, which will provide good opportunities to bridge basic and clinical research. Here based on recent technology advancements, we discuss the strategy to target and manipulate specific populations of neuronsin vivo to provide unique insights on how neuron types or subtypes behave, interact, and generate emergent properties in a fully connected brain network. Our approach is highlighted by combining transgenic animal models, targeted electrophysiology and imaging with robotics, thus complete and standardized mapping ofin vivo properties of genetically defined neuron populations can be achieved in transgenic mouse models, which will facilitate the development of novel therapeutic strategies for brain disorders.

Chaotic diagonal recurrent neural network

International Nuclear Information System (INIS)

Wang Xing-Yuan; Zhang Yi

2012-01-01

We propose a novel neural network based on a diagonal recurrent neural network and chaos, and its structure and learning algorithm are designed. The multilayer feedforward neural network, diagonal recurrent neural network, and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map. The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks. (interdisciplinary physics and related areas of science and technology)

Cognitive Mapping Based on Conjunctive Representations of Space and Movement

Directory of Open Access Journals (Sweden)

Taiping Zeng

2017-11-01

Full Text Available It is a challenge to build robust simultaneous localization and mapping (SLAM system in dynamical large-scale environments. Inspired by recent findings in the entorhinal–hippocampal neuronal circuits, we propose a cognitive mapping model that includes continuous attractor networks of head-direction cells and conjunctive grid cells to integrate velocity information by conjunctive encodings of space and movement. Visual inputs from the local view cells in the model provide feedback cues to correct drifting errors of the attractors caused by the noisy velocity inputs. We demonstrate the mapping performance of the proposed cognitive mapping model on an open-source dataset of 66 km car journey in a 3 km × 1.6 km urban area. Experimental results show that the proposed model is robust in building a coherent semi-metric topological map of the entire urban area using a monocular camera, even though the image inputs contain various changes caused by different light conditions and terrains. The results in this study could inspire both neuroscience and robotic research to better understand the neural computational mechanisms of spatial cognition and to build robust robotic navigation systems in large-scale environments.

Deep learning with coherent nanophotonic circuits

Science.gov (United States)

Shen, Yichen; Harris, Nicholas C.; Skirlo, Scott; Prabhu, Mihika; Baehr-Jones, Tom; Hochberg, Michael; Sun, Xin; Zhao, Shijie; Larochelle, Hugo; Englund, Dirk; Soljačić, Marin

2017-07-01

Artificial neural networks are computational network models inspired by signal processing in the brain. These models have dramatically improved performance for many machine-learning tasks, including speech and image recognition. However, today's computing hardware is inefficient at implementing neural networks, in large part because much of it was designed for von Neumann computing schemes. Significant effort has been made towards developing electronic architectures tuned to implement artificial neural networks that exhibit improved computational speed and accuracy. Here, we propose a new architecture for a fully optical neural network that, in principle, could offer an enhancement in computational speed and power efficiency over state-of-the-art electronics for conventional inference tasks. We experimentally demonstrate the essential part of the concept using a programmable nanophotonic processor featuring a cascaded array of 56 programmable Mach-Zehnder interferometers in a silicon photonic integrated circuit and show its utility for vowel recognition.

Central neural pathways for thermoregulation

Science.gov (United States)

Morrison, Shaun F.; Nakamura, Kazuhiro

2010-01-01

Central neural circuits orchestrate a homeostatic repertoire to maintain body temperature during environmental temperature challenges and to alter body temperature during the inflammatory response. This review summarizes the functional organization of the neural pathways through which cutaneous thermal receptors alter thermoregulatory effectors: the cutaneous circulation for heat loss, the brown adipose tissue, skeletal muscle and heart for thermogenesis and species-dependent mechanisms (sweating, panting and saliva spreading) for evaporative heat loss. These effectors are regulated by parallel but distinct, effector-specific neural pathways that share a common peripheral thermal sensory input. The thermal afferent circuits include cutaneous thermal receptors, spinal dorsal horn neurons and lateral parabrachial nucleus neurons projecting to the preoptic area to influence warm-sensitive, inhibitory output neurons which control thermogenesis-promoting neurons in the dorsomedial hypothalamus that project to premotor neurons in the rostral ventromedial medulla, including the raphe pallidus, that descend to provide the excitation necessary to drive thermogenic thermal effectors. A distinct population of warm-sensitive preoptic neurons controls heat loss through an inhibitory input to raphe pallidus neurons controlling cutaneous vasoconstriction. PMID:21196160

Neural electrical activity and neural network growth.

Science.gov (United States)

Gafarov, F M

2018-05-01

The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interneuronal connections. Nevertheless, these processes are rather difficult to study experimentally, due to the absence of theoretical description and quantitative parameters for estimation of the neuronal activity influence on growth in neural networks. In this work we propose a general framework for a theoretical description of the activity-dependent neural network growth. The theoretical description incorporates a closed-loop growth model in which the neural activity can affect neurite outgrowth, which in turn can affect neural activity. We carried out the detailed quantitative analysis of spatiotemporal activity patterns and studied the relationship between individual cells and the network as a whole to explore the relationship between developing connectivity and activity patterns. The model, developed in this work will allow us to develop new experimental techniques for studying and quantifying the influence of the neuronal activity on growth processes in neural networks and may lead to a novel techniques for constructing large-scale neural networks by self-organization. Copyright © 2018 Elsevier Ltd. All rights reserved.

Pulsed neural networks consisting of single-flux-quantum spiking neurons

International Nuclear Information System (INIS)

Hirose, T.; Asai, T.; Amemiya, Y.

2007-01-01

An inhibitory pulsed neural network was developed for brain-like information processing, by using single-flux-quantum (SFQ) circuits. It consists of spiking neuron devices that are coupled to each other through all-to-all inhibitory connections. The network selects neural activity. The operation of the neural network was confirmed by computer simulation. SFQ neuron devices can imitate the operation of the inhibition phenomenon of neural networks

Neural correlates and neural computations in posterior parietal cortex during perceptual decision-making

Directory of Open Access Journals (Sweden)

Alexander eHuk

2012-10-01

Full Text Available A recent line of work has found remarkable success in relating perceptual decision-making and the spiking activity in the macaque lateral intraparietal area (LIP. In this review, we focus on questions about the neural computations in LIP that are not answered by demonstrations of neural correlates of psychological processes. We highlight three areas of limitations in our current understanding of the precise neural computations that might underlie neural correlates of decisions: (1 empirical questions not yet answered by existing data; (2 implementation issues related to how neural circuits could actually implement the mechanisms suggested by both physiology and psychology; and (3 ecological constraints related to the use of well-controlled laboratory tasks and whether they provide an accurate window on sensorimotor computation. These issues motivate the adoption of a more general encoding-decoding framework that will be fruitful for more detailed contemplation of how neural computations in LIP relate to the formation of perceptual decisions.

Functional neural networks underlying response inhibition in adolescents and adults.

Science.gov (United States)

Stevens, Michael C; Kiehl, Kent A; Pearlson, Godfrey D; Calhoun, Vince D

2007-07-19

This study provides the first description of neural network dynamics associated with response inhibition in healthy adolescents and adults. Functional and effective connectivity analyses of whole brain hemodynamic activity elicited during performance of a Go/No-Go task were used to identify functionally integrated neural networks and characterize their causal interactions. Three response inhibition circuits formed a hierarchical, inter-dependent system wherein thalamic modulation of input to premotor cortex by fronto-striatal regions led to response suppression. Adolescents differed from adults in the degree of network engagement, regional fronto-striatal-thalamic connectivity, and network dynamics. We identify and characterize several age-related differences in the function of neural circuits that are associated with behavioral performance changes across adolescent development.

Development switch in neural circuitry underlying odor-malaise learning.

Science.gov (United States)

Shionoya, Kiseko; Moriceau, Stephanie; Lunday, Lauren; Miner, Cathrine; Roth, Tania L; Sullivan, Regina M

2006-01-01

Fetal and infant rats can learn to avoid odors paired with illness before development of brain areas supporting this learning in adults, suggesting an alternate learning circuit. Here we begin to document the transition from the infant to adult neural circuit underlying odor-malaise avoidance learning using LiCl (0.3 M; 1% of body weight, ip) and a 30-min peppermint-odor exposure. Conditioning groups included: Paired odor-LiCl, Paired odor-LiCl-Nursing, LiCl, and odor-saline. Results showed that Paired LiCl-odor conditioning induced a learned odor aversion in postnatal day (PN) 7, 12, and 23 pups. Odor-LiCl Paired Nursing induced a learned odor preference in PN7 and PN12 pups but blocked learning in PN23 pups. 14C 2-deoxyglucose (2-DG) autoradiography indicated enhanced olfactory bulb activity in PN7 and PN12 pups with odor preference and avoidance learning. The odor aversion in weanling aged (PN23) pups resulted in enhanced amygdala activity in Paired odor-LiCl pups, but not if they were nursing. Thus, the neural circuit supporting malaise-induced aversions changes over development, indicating that similar infant and adult-learned behaviors may have distinct neural circuits.

Fuel Cell Equivalent Electric Circuit Parameter Mapping

DEFF Research Database (Denmark)

Jeppesen, Christian; Zhou, Fan; Andreasen, Søren Juhl

In this work a simple model for a fuel cell is investigated for diagnostic purpose. The fuel cell is characterized, with respect to the electrical impedance of the fuel cell at non-faulty conditions and under variations in load current. Based on this the equivalent electrical circuit parameters can...

Circuit motifs for contrast-adaptive differentiation in early sensory systems: the role of presynaptic inhibition and short-term plasticity.

Science.gov (United States)

Zhang, Danke; Wu, Si; Rasch, Malte J

2015-01-01

In natural signals, such as the luminance value across of a visual scene, abrupt changes in intensity value are often more relevant to an organism than intensity values at other positions and times. Thus to reduce redundancy, sensory systems are specialized to detect the times and amplitudes of informative abrupt changes in the input stream rather than coding the intensity values at all times. In theory, a system that responds transiently to fast changes is called a differentiator. In principle, several different neural circuit mechanisms exist that are capable of responding transiently to abrupt input changes. However, it is unclear which circuit would be best suited for early sensory systems, where the dynamic range of the natural input signals can be very wide. We here compare the properties of different simple neural circuit motifs for implementing signal differentiation. We found that a circuit motif based on presynaptic inhibition (PI) is unique in a sense that the vesicle resources in the presynaptic site can be stably maintained over a wide range of stimulus intensities, making PI a biophysically plausible mechanism to implement a differentiator with a very wide dynamical range. Moreover, by additionally considering short-term plasticity (STP), differentiation becomes contrast adaptive in the PI-circuit but not in other potential neural circuit motifs. Numerical simulations show that the behavior of the adaptive PI-circuit is consistent with experimental observations suggesting that adaptive presynaptic inhibition might be a good candidate neural mechanism to achieve differentiation in early sensory systems.

Orientation-Selective Retinal Circuits in Vertebrates.

Science.gov (United States)

Antinucci, Paride; Hindges, Robert

2018-01-01

Visual information is already processed in the retina before it is transmitted to higher visual centers in the brain. This includes the extraction of salient features from visual scenes, such as motion directionality or contrast, through neurons belonging to distinct neural circuits. Some retinal neurons are tuned to the orientation of elongated visual stimuli. Such 'orientation-selective' neurons are present in the retinae of most, if not all, vertebrate species analyzed to date, with species-specific differences in frequency and degree of tuning. In some cases, orientation-selective neurons have very stereotyped functional and morphological properties suggesting that they represent distinct cell types. In this review, we describe the retinal cell types underlying orientation selectivity found in various vertebrate species, and highlight their commonalities and differences. In addition, we discuss recent studies that revealed the cellular, synaptic and circuit mechanisms at the basis of retinal orientation selectivity. Finally, we outline the significance of these findings in shaping our current understanding of how this fundamental neural computation is implemented in the visual systems of vertebrates.

Recent Advances in Neural Recording Microsystems

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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.

Model-based evaluation of the short-circuited tripolar cuff configuration.

Science.gov (United States)

Andreasen, Lotte N S; Struijk, Johannes J

2006-05-01

Recordings of neural information for use as feedback in functional electrical stimulation are often contaminated with interfering signals from muscles and from stimulus pulses. The cuff electrode used for the neural recording can be optimized to improve the S/I ratio. In this work, we evaluate a model of both the nerve signal and the interfering signals recorded by a cuff, and subsequently use this model to study the signal to interference ratio of different cuff designs and to evaluate a recently introduced short-circuited tripolar cuff configuration. The results of the model showed good agreement with results from measurements in rabbits and confirmed the superior performance of the short-circuited tripolar configuration as compared with the traditionally used tripolar configuration.

Digi Island: A Serious Game for Teaching and Learning Digital Circuit Optimization

Science.gov (United States)

Harper, Michael; Miller, Joseph; Shen, Yuzhong

2011-01-01

Karnaugh maps, also known as K-maps, are a tool used to optimize or simplify digital logic circuits. A K-map is a graphical display of a logic circuit. K-map optimization is essentially the process of finding a minimum number of maximal aggregations of K-map cells. with values of 1 according to a set of rules. The Digi Island is a serious game designed for aiding students to learn K-map optimization. The game takes place on an exotic island (called Digi Island) in the Pacific Ocean . The player is an adventurer to the Digi Island and will transform it into a tourist attraction by developing real estates, such as amusement parks.and hotels. The Digi Island game elegantly converts boring 1s and Os in digital circuits into usable and unusable spaces on a beautiful island and transforms K-map optimization into real estate development, an activity with which many students are familiar and also interested in. This paper discusses the design, development, and some preliminary results of the Digi Island game.

Neuromorphic Silicon Neuron Circuits

Science.gov (United States)

Indiveri, Giacomo; Linares-Barranco, Bernabé; Hamilton, Tara Julia; van Schaik, André; Etienne-Cummings, Ralph; Delbruck, Tobi; Liu, Shih-Chii; Dudek, Piotr; Häfliger, Philipp; Renaud, Sylvie; Schemmel, Johannes; Cauwenberghs, Gert; Arthur, John; Hynna, Kai; Folowosele, Fopefolu; Saighi, Sylvain; Serrano-Gotarredona, Teresa; Wijekoon, Jayawan; Wang, Yingxue; Boahen, Kwabena

2011-01-01

Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain–machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin–Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips. PMID:21747754

Neuromorphic silicon neuron circuits

Directory of Open Access Journals (Sweden)

Giacomo eIndiveri

2011-05-01

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

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

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Sara L Gonzalez Andino

2011-07-01

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

Identification of discrete chaotic maps with singular points

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P. G. Akishin

2001-01-01

Full Text Available We investigate the ability of artificial neural networks to reconstruct discrete chaotic maps with singular points. We use as a simple test model the Cusp map. We compare the traditional Multilayer Perceptron, the Chebyshev Neural Network and the Wavelet Neural Network. The numerical scheme for the accurate determination of a singular point is also developed. We show that combining a neural network with the numerical algorithm for the determination of the singular point we are able to accurately approximate discrete chaotic maps with singularities.

The malleable brain: plasticity of neural circuits and behavior - a review from students to students.

Science.gov (United States)

Schaefer, Natascha; Rotermund, Carola; Blumrich, Eva-Maria; Lourenco, Mychael V; Joshi, Pooja; Hegemann, Regina U; Jamwal, Sumit; Ali, Nilufar; García Romero, Ezra Michelet; Sharma, Sorabh; Ghosh, Shampa; Sinha, Jitendra K; Loke, Hannah; Jain, Vishal; Lepeta, Katarzyna; Salamian, Ahmad; Sharma, Mahima; Golpich, Mojtaba; Nawrotek, Katarzyna; Paidi, Ramesh K; Shahidzadeh, Sheila M; Piermartiri, Tetsade; Amini, Elham; Pastor, Veronica; Wilson, Yvette; Adeniyi, Philip A; Datusalia, Ashok K; Vafadari, Benham; Saini, Vedangana; Suárez-Pozos, Edna; Kushwah, Neetu; Fontanet, Paula; Turner, Anthony J

2017-06-20

One of the most intriguing features of the brain is its ability to be malleable, allowing it to adapt continually to changes in the environment. Specific neuronal activity patterns drive long-lasting increases or decreases in the strength of synaptic connections, referred to as long-term potentiation and long-term depression, respectively. Such phenomena have been described in a variety of model organisms, which are used to study molecular, structural, and functional aspects of synaptic plasticity. This review originated from the first International Society for Neurochemistry (ISN) and Journal of Neurochemistry (JNC) Flagship School held in Alpbach, Austria (Sep 2016), and will use its curriculum and discussions as a framework to review some of the current knowledge in the field of synaptic plasticity. First, we describe the role of plasticity during development and the persistent changes of neural circuitry occurring when sensory input is altered during critical developmental stages. We then outline the signaling cascades resulting in the synthesis of new plasticity-related proteins, which ultimately enable sustained changes in synaptic strength. Going beyond the traditional understanding of synaptic plasticity conceptualized by long-term potentiation and long-term depression, we discuss system-wide modifications and recently unveiled homeostatic mechanisms, such as synaptic scaling. Finally, we describe the neural circuits and synaptic plasticity mechanisms driving associative memory and motor learning. Evidence summarized in this review provides a current view of synaptic plasticity in its various forms, offers new insights into the underlying mechanisms and behavioral relevance, and provides directions for future research in the field of synaptic plasticity. Read the Editorial Highlight for this article on doi: 10.1111/jnc.14102. © 2017 International Society for Neurochemistry.

Commentary: Elucidating the Neural Correlates of Early Childhood Memory

Science.gov (United States)

Mullally, Sinead L.

2015-01-01

Both episodic memory and the key neural structure believed to support it, namely the hippocampus, are believed to undergo protracted periods of postnatal developmental. Critically however, the hippocampus is comprised of distinct subfields and circuits, and these circuits appear to mature at different rates (Lavenex and Banta Lavenex, 2013).…

Track Circuit Fault Diagnosis Method based on Least Squares Support Vector

Science.gov (United States)

Cao, Yan; Sun, Fengru

2018-01-01

In order to improve the troubleshooting efficiency and accuracy of the track circuit, track circuit fault diagnosis method was researched. Firstly, the least squares support vector machine was applied to design the multi-fault classifier of the track circuit, and then the measured track data as training samples was used to verify the feasibility of the methods. Finally, the results based on BP neural network fault diagnosis methods and the methods used in this paper were compared. Results shows that the track fault classifier based on least squares support vector machine can effectively achieve the five track circuit fault diagnosis with less computing time.

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

CERN Document Server

Rigatos, Gerasimos G

2015-01-01

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

Neural correlates underlying micrographia in Parkinson’s disease

Science.gov (United States)

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

Activation in mesolimbic and visuospatial neural circuits elicited by smoking cues: evidence from functional magnetic resonance imaging.

Science.gov (United States)

Due, Deborah L; Huettel, Scott A; Hall, Warren G; Rubin, David C

2002-06-01

The authors sought to increase understanding of the brain mechanisms involved in cigarette addiction by identifying neural substrates modulated by visual smoking cues in nicotine-deprived smokers. Event-related functional magnetic resonance imaging (fMRI) was used to detect brain activation after exposure to smoking-related images in a group of nicotine-deprived smokers and a nonsmoking comparison group. Subjects viewed a pseudo-random sequence of smoking images, neutral nonsmoking images, and rare targets (photographs of animals). Subjects pressed a button whenever a rare target appeared. In smokers, the fMRI signal was greater after exposure to smoking-related images than after exposure to neutral images in mesolimbic dopamine reward circuits known to be activated by addictive drugs (right posterior amygdala, posterior hippocampus, ventral tegmental area, and medial thalamus) as well as in areas related to visuospatial attention (bilateral prefrontal and parietal cortex and right fusiform gyrus). In nonsmokers, no significant differences in fMRI signal following exposure to smoking-related and neutral images were detected. In most regions studied, both subject groups showed greater activation following presentation of rare target images than after exposure to neutral images. In nicotine-deprived smokers, both reward and attention circuits were activated by exposure to smoking-related images. Smoking cues are processed like rare targets in that they activate attentional regions. These cues are also processed like addictive drugs in that they activate mesolimbic reward regions.

Circuit elements at optical frequencies: nanoinductors, nanocapacitors, and nanoresistors.

Science.gov (United States)

Engheta, Nader; Salandrino, Alessandro; Alù, Andrea

2005-08-26

We present the concept of circuit nanoelements in the optical domain using plasmonic and nonplasmonic nanoparticles. Three basic circuit elements, i.e., nanoinductors, nanocapacitors, and nanoresistors, are discussed in terms of small nanostructures with different material properties. Coupled nanoscale circuits and parallel and series combinations are also envisioned, which may provide road maps for the synthesis of more complex circuits in the IR and visible bands. Ideas for the optical implementation of right-handed and left-handed nanotransmission lines are also forecasted.

Neural Control of the Lower Urinary Tract

Science.gov (United States)

de Groat, William C.; Griffiths, Derek; Yoshimura, Naoki

2015-01-01

This article summarizes anatomical, neurophysiological, pharmacological, and brain imaging studies in humans and animals that have provided insights into the neural circuitry and neurotransmitter mechanisms controlling the lower urinary tract. The functions of the lower urinary tract to store and periodically eliminate urine are regulated by a complex neural control system in the brain, spinal cord, and peripheral autonomic ganglia that coordinates the activity of smooth and striated muscles of the bladder and urethral outlet. The neural control of micturition is organized as a hierarchical system in which spinal storage mechanisms are in turn regulated by circuitry in the rostral brain stem that initiates reflex voiding. Input from the forebrain triggers voluntary voiding by modulating the brain stem circuitry. Many neural circuits controlling the lower urinary tract exhibit switch-like patterns of activity that turn on and off in an all-or-none manner. The major component of the micturition switching circuit is a spinobulbospinal parasympathetic reflex pathway that has essential connections in the periaqueductal gray and pontine micturition center. A computer model of this circuit that mimics the switching functions of the bladder and urethra at the onset of micturition is described. Micturition occurs involuntarily in infants and young children until the age of 3 to 5 years, after which it is regulated voluntarily. Diseases or injuries of the nervous system in adults can cause the re-emergence of involuntary micturition, leading to urinary incontinence. Neuroplasticity underlying these developmental and pathological changes in voiding function is discussed. PMID:25589273

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

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Zhen-Zhen eKou

2014-01-01

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

Integrated neuron circuit for implementing neuromorphic system with synaptic device

Science.gov (United States)

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

2018-02-01

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

Neural correlates of consciousness

African Journals Online (AJOL)

neural cells.1 Under this approach, consciousness is believed to be a product of the ... possible only when the 40 Hz electrical hum is sustained among the brain circuits, ... expect the brain stem ascending reticular activating system. (ARAS) and the ... related synchrony of cortical neurons.11 Indeed, stimulation of brainstem ...

Assessment of Convolution Neural Networks for Surficial Geology Mapping in the South Rae Geological Region, Northwest Territories, Canada

Directory of Open Access Journals (Sweden)

Rasim Latifovic

2018-02-01

Full Text Available Mapping of surficial geology is an important requirement for broadening the geoscience database of northern Canada. Surficial geology maps are an integral data source for mineral and energy exploration. Moreover, they provide information such as the location of gravels and sands, which are important for infrastructure development. Currently, surficial geology maps are produced through expert interpretation of aerial photography and field data. However, interpretation is known to be subjective, labour-intensive and difficult to repeat. The expert knowledge required for interpretation can be challenging to maintain and transfer. In this research, we seek to assess the potential of deep neural networks to aid surficial geology mapping by providing an objective surficial materials initial layer that experts can modify to speed map development and improve consistency between mapped areas. Such an approach may also harness expert knowledge in a way that is transferable to unmapped areas. For this purpose, we assess the ability of convolution neural networks (CNN to predict surficial geology classes under two sampling scenarios. In the first scenario, a CNN uses samples collected over the area to be mapped. In the second, a CNN trained over one area is then applied to locations where the available samples were not used in training the network. The latter case is important, as a collection of in situ training data can be costly. The evaluation of the CNN was carried out using aerial photos, Landsat reflectance, and high-resolution digital elevation data over five areas within the South Rae geological region of Northwest Territories, Canada. The results are encouraging, with the CNN generating average accuracy of 76% when locally trained. For independent test areas (i.e., trained over one area and applied over other, accuracy dropped to 59–70% depending on the classes selected for mapping. In the South Rae region, significant confusion was found

Ab initio and template-based prediction of multi-class distance maps by two-dimensional recursive neural networks

Directory of Open Access Journals (Sweden)

Martin Alberto JM

2009-01-01

Full Text Available Abstract Background Prediction of protein structures from their sequences is still one of the open grand challenges of computational biology. Some approaches to protein structure prediction, especially ab initio ones, rely to some extent on the prediction of residue contact maps. Residue contact map predictions have been assessed at the CASP competition for several years now. Although it has been shown that exact contact maps generally yield correct three-dimensional structures, this is true only at a relatively low resolution (3–4 Å from the native structure. Another known weakness of contact maps is that they are generally predicted ab initio, that is not exploiting information about potential homologues of known structure. Results We introduce a new class of distance restraints for protein structures: multi-class distance maps. We show that Cα trace reconstructions based on 4-class native maps are significantly better than those from residue contact maps. We then build two predictors of 4-class maps based on recursive neural networks: one ab initio, or relying on the sequence and on evolutionary information; one template-based, or in which homology information to known structures is provided as a further input. We show that virtually any level of sequence similarity to structural templates (down to less than 10% yields more accurate 4-class maps than the ab initio predictor. We show that template-based predictions by recursive neural networks are consistently better than the best template and than a number of combinations of the best available templates. We also extract binary residue contact maps at an 8 Å threshold (as per CASP assessment from the 4-class predictors and show that the template-based version is also more accurate than the best template and consistently better than the ab initio one, down to very low levels of sequence identity to structural templates. Furthermore, we test both ab-initio and template-based 8 �

Decision making in the ageing brain: changes in affective and motivational circuits.

Science.gov (United States)

Samanez-Larkin, Gregory R; Knutson, Brian

2015-05-01

As the global population ages, older decision makers will be required to take greater responsibility for their own physical, psychological and financial well-being. With this in mind, researchers have begun to examine the effects of ageing on decision making and associated neural circuits. A new 'affect-integration-motivation' (AIM) framework may help to clarify how affective and motivational circuits support decision making. Recent research has shed light on whether and how ageing influences these circuits, providing an interdisciplinary account of how ageing can alter decision making.

Neural Networks through Shared Maps in Mobile Devices

Directory of Open Access Journals (Sweden)

William Raveane

2014-12-01

Full Text Available We introduce a hybrid system composed of a convolutional neural network and a discrete graphical model for image recognition. This system improves upon traditional sliding window techniques for analysis of an image larger than the training data by effectively processing the full input scene through the neural network in less time. The final result is then inferred from the neural network output through energy minimization to reach a more precize localization than what traditional maximum value class comparisons yield. These results are apt for applying this process in a mobile device for real time image recognition.

Memristor-based neural networks

International Nuclear Information System (INIS)

Thomas, Andy

2013-01-01

The synapse is a crucial element in biological neural networks, but a simple electronic equivalent has been absent. This complicates the development of hardware that imitates biological architectures in the nervous system. Now, the recent progress in the experimental realization of memristive devices has renewed interest in artificial neural networks. The resistance of a memristive system depends on its past states and exactly this functionality can be used to mimic the synaptic connections in a (human) brain. After a short introduction to memristors, we present and explain the relevant mechanisms in a biological neural network, such as long-term potentiation and spike time-dependent plasticity, and determine the minimal requirements for an artificial neural network. We review the implementations of these processes using basic electric circuits and more complex mechanisms that either imitate biological systems or could act as a model system for them. (topical review)

Computer model of a reverberant and parallel circuit coupling

Science.gov (United States)

Kalil, Camila de Andrade; de Castro, Maria Clícia Stelling; Cortez, Célia Martins

2017-11-01

The objective of the present study was to deepen the knowledge about the functioning of the neural circuits by implementing a signal transmission model using the Graph Theory in a small network of neurons composed of an interconnected reverberant and parallel circuit, in order to investigate the processing of the signals in each of them and the effects on the output of the network. For this, a program was developed in C language and simulations were done using neurophysiological data obtained in the literature.

Neural correlates underlying micrographia in Parkinson's disease.

Science.gov (United States)

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

2016-01-01

Micrographia is a common symptom in Parkinson's disease, which manifests as either a consistent or progressive reduction in the size of handwriting or both. Neural correlates underlying micrographia remain unclear. We used functional magnetic resonance imaging to investigate micrographia-related neural activity and connectivity modulations. In addition, the effect of attention and dopaminergic administration on micrographia was examined. We found that consistent micrographia was associated with decreased activity and connectivity in the basal ganglia motor circuit; while progressive micrographia was related to the dysfunction of basal ganglia motor circuit together with disconnections between the rostral supplementary motor area, rostral cingulate motor area and cerebellum. Attention significantly improved both consistent and progressive micrographia, accompanied by recruitment of anterior putamen and dorsolateral prefrontal cortex. Levodopa improved consistent micrographia accompanied by increased activity and connectivity in the basal ganglia motor circuit, but had no effect on progressive micrographia. Our findings suggest that consistent micrographia is related to dysfunction of the basal ganglia motor circuit; while dysfunction of the basal ganglia motor circuit and disconnection between the rostral supplementary motor area, rostral cingulate motor area and cerebellum likely contributes to progressive micrographia. Attention improves both types of micrographia by recruiting additional brain networks. Levodopa improves consistent micrographia by restoring the function of the basal ganglia motor circuit, but does not improve progressive micrographia, probably because of failure to repair the disconnected networks. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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

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Alexander G. Ophir

2017-07-01

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

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

Science.gov (United States)

Ophir, Alexander G.

2017-01-01

The role of memory in mating systems is often neglected despite the fact that most mating systems are defined in part by how animals use space. Monogamy, for example, is usually characterized by affiliative (e.g., pairbonding) and defensive (e.g., mate guarding) behaviors, but a high degree of spatial overlap in home range use is the easiest defining feature of monogamous animals in the wild. The nonapeptides vasopressin and oxytocin have been the focus of much attention for their importance in modulating social behavior, however this work has largely overshadowed their roles in learning and memory. To date, the understanding of memory systems and mechanisms governing social behavior have progressed relatively independently. Bridging these two areas will provide a deeper appreciation for understanding behavior, and in particular the mechanisms that mediate reproductive decision-making. Here, I argue that the ability to mate effectively as monogamous individuals is linked to the ability to track conspecifics in space. I discuss the connectivity across some well-known social and spatial memory nuclei, and propose that the nonapeptide receptors within these structures form a putative “socio-spatial memory neural circuit.” This purported circuit may function to integrate social and spatial information to shape mating decisions in a context-dependent fashion. The lateral septum and/or the nucleus accumbens, and neuromodulation therein, may act as an intermediary to relate socio-spatial information with social behavior. Identifying mechanisms responsible for relating information about the social world with mechanisms mediating mating tactics is crucial to fully appreciate the suite of factors driving reproductive decisions and social decision-making. PMID:28744194

Fermionic models with superconducting circuits

Energy Technology Data Exchange (ETDEWEB)

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

2015-12-01

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

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

Directory of Open Access Journals (Sweden)

David Eriksson

2016-08-01

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

Artificial neural network with self-organizing mapping for reactor stability monitoring

International Nuclear Information System (INIS)

Okumura, Motofumi; Tsuji, Masashi; Shimazu, Yoichiro; Narabayashi, Tadashi

2008-01-01

In BWR stability monitoring damping ratio has been used as a stability index. A method for estimating the damping ratio by applying Principal Component Analysis (PCA) to neutron detector signals measured with local power range monitors (LPRMs) had been developed; In this method, measured fluctuating signal is decomposed into some independent components and the signal component directly related to stability is extracted among them to determine the damping ratio. For online monitoring, it is necessary to select stability related signal component efficiently. The self-organizing map (SOM) is one of the artificial neural networks and has the characteristics such that online learning is possible without supervised learning within a relatively short time. In the present study, the SOM was applied to extract the relevant signal component more quickly and more accurately, and the availability was confirmed through the feasibility study. (author)

Neural network feedforward control of a closed-circuit wind tunnel

Science.gov (United States)

Sutcliffe, Peter

Accurate control of wind-tunnel test conditions can be dramatically enhanced using feedforward control architectures which allow operating conditions to be maintained at a desired setpoint through the use of mathematical models as the primary source of prediction. However, as the desired accuracy of the feedforward prediction increases, the model complexity also increases, so that an ever increasing computational load is incurred. This drawback can be avoided by employing a neural network that is trained offline using the output of a high fidelity wind-tunnel mathematical model, so that the neural network can rapidly reproduce the predictions of the model with a greatly reduced computational overhead. A novel neural network database generation method, developed through the use of fractional factorial arrays, was employed such that a neural network can accurately predict wind-tunnel parameters across a wide range of operating conditions whilst trained upon a highly efficient database. The subsequent network was incorporated into a Neural Network Model Predictive Control (NNMPC) framework to allow an optimised output schedule capable of providing accurate control of the wind-tunnel operating parameters. Facilitation of an optimised path through the solution space is achieved through the use of a chaos optimisation algorithm such that a more globally optimum solution is likely to be found with less computational expense than the gradient descent method. The parameters associated with the NNMPC such as the control horizon are determined through the use of a Taguchi methodology enabling the minimum number of experiments to be carried out to determine the optimal combination. The resultant NNMPC scheme was employed upon the Hessert Low Speed Wind Tunnel at the University of Notre Dame to control the test-section temperature such that it follows a pre-determined reference trajectory during changes in the test-section velocity. Experimental testing revealed that the

Decision making in the ageing brain: Changes in affective and motivational circuits

Science.gov (United States)

Samanez-Larkin, Gregory R.; Knutson, Brian

2017-01-01

As the global population ages, older decision makers will be required to take greater responsibility for their own physical, psychological and financial well-being. With this in mind, researchers have begun to examine the effects of ageing on decision making and associated neural circuits. A new “affect, integration, motivation” (or AIM) framework may help clarify how affective and motivational circuits support decision making. Recent research has shed light on whether and how ageing influences these circuits, providing an interdisciplinary account of how ageing can alter decision making. PMID:25873038

A canonical neural mechanism for behavioral variability

Science.gov (United States)

Darshan, Ran; Wood, William E.; Peters, Susan; Leblois, Arthur; Hansel, David

2017-05-01

The ability to generate variable movements is essential for learning and adjusting complex behaviours. This variability has been linked to the temporal irregularity of neuronal activity in the central nervous system. However, how neuronal irregularity actually translates into behavioural variability is unclear. Here we combine modelling, electrophysiological and behavioural studies to address this issue. We demonstrate that a model circuit comprising topographically organized and strongly recurrent neural networks can autonomously generate irregular motor behaviours. Simultaneous recordings of neurons in singing finches reveal that neural correlations increase across the circuit driving song variability, in agreement with the model predictions. Analysing behavioural data, we find remarkable similarities in the babbling statistics of 5-6-month-old human infants and juveniles from three songbird species and show that our model naturally accounts for these `universal' statistics.

Gene regulation in adult neural stem cells : Current challenges and possible applications

NARCIS (Netherlands)

Encinas, J.M.; Fitzsimons, C.P.

2017-01-01

Adult neural stem and progenitor cells (NSPCs) offer a unique opportunity for neural regeneration and niche modification in physiopathological conditions, harnessing the capability to modify from neuronal circuits to glial scar. Findings exposing the vast plasticity and potential of NSPCs have

K-maps: a vehicle to an optimal solution in combinational logic ...

African Journals Online (AJOL)

K-maps: a vehicle to an optimal solution in combinational logic design problems using digital multiplexers. ... Abstract. Application of Karnaugh maps (K-Maps) for the design of combinational logic circuits and sequential logic circuits is a subject that has been widely discussed. However, the use of K-Maps in the design of ...

An Efficient Hardware Circuit for Spike Sorting Based on Competitive Learning Networks

Directory of Open Access Journals (Sweden)

Huan-Yuan Chen

2017-09-01

Full Text Available This study aims to present an effective VLSI circuit for multi-channel spike sorting. The circuit supports the spike detection, feature extraction and classification operations. The detection circuit is implemented in accordance with the nonlinear energy operator algorithm. Both the peak detection and area computation operations are adopted for the realization of the hardware architecture for feature extraction. The resulting feature vectors are classified by a circuit for competitive learning (CL neural networks. The CL circuit supports both online training and classification. In the proposed architecture, all the channels share the same detection, feature extraction, learning and classification circuits for a low area cost hardware implementation. The clock-gating technique is also employed for reducing the power dissipation. To evaluate the performance of the architecture, an application-specific integrated circuit (ASIC implementation is presented. Experimental results demonstrate that the proposed circuit exhibits the advantages of a low chip area, a low power dissipation and a high classification success rate for spike sorting.

An Efficient Hardware Circuit for Spike Sorting Based on Competitive Learning Networks

Science.gov (United States)

Chen, Huan-Yuan; Chen, Chih-Chang

2017-01-01

This study aims to present an effective VLSI circuit for multi-channel spike sorting. The circuit supports the spike detection, feature extraction and classification operations. The detection circuit is implemented in accordance with the nonlinear energy operator algorithm. Both the peak detection and area computation operations are adopted for the realization of the hardware architecture for feature extraction. The resulting feature vectors are classified by a circuit for competitive learning (CL) neural networks. The CL circuit supports both online training and classification. In the proposed architecture, all the channels share the same detection, feature extraction, learning and classification circuits for a low area cost hardware implementation. The clock-gating technique is also employed for reducing the power dissipation. To evaluate the performance of the architecture, an application-specific integrated circuit (ASIC) implementation is presented. Experimental results demonstrate that the proposed circuit exhibits the advantages of a low chip area, a low power dissipation and a high classification success rate for spike sorting. PMID:28956859

Detection of inter-turn short-circuit at start-up of induction machine based on torque analysis

Directory of Open Access Journals (Sweden)

Pietrowski Wojciech

2017-12-01

Full Text Available Recently, interest in new diagnostics methods in a field of induction machines was observed. Research presented in the paper shows the diagnostics of induction machine based on torque pulsation, under inter-turn short-circuit, during start-up of a machine. In the paper three numerical techniques were used: finite element analysis, signal analysis and artificial neural networks (ANN. The elaborated numerical model of faulty machine consists of field, circuit and motion equations. Voltage excited supply allowed to determine the torque waveform during start-up. The inter-turn short-circuit was treated as a galvanic connection between two points of the stator winding. The waveforms were calculated for different amounts of shorted-turns from 0 to 55. Due to the non-stationary waveforms a wavelet packet decomposition was used to perform an analysis of the torque. The obtained results of analysis were used as input vector for ANN. The response of the neural network was the number of shorted-turns in the stator winding. Special attention was paid to compare response of general regression neural network (GRNN and multi-layer perceptron neural network (MLP. Based on the results of the research, the efficiency of the developed algorithm can be inferred.

All-optical functional synaptic connectivity mapping in acute brain slices using the calcium integrator CaMPARI.

Science.gov (United States)

Zolnik, Timothy A; Sha, Fern; Johenning, Friedrich W; Schreiter, Eric R; Looger, Loren L; Larkum, Matthew E; Sachdev, Robert N S

2017-03-01

The genetically encoded fluorescent calcium integrator calcium-modulated photoactivatable ratiobetric integrator (CaMPARI) reports calcium influx induced by synaptic and neural activity. Its fluorescence is converted from green to red in the presence of violet light and calcium. The rate of conversion - the sensitivity to activity - is tunable and depends on the intensity of violet light. Synaptic activity and action potentials can independently initiate significant CaMPARI conversion. The level of conversion by subthreshold synaptic inputs is correlated to the strength of input, enabling optical readout of relative synaptic strength. When combined with optogenetic activation of defined presynaptic neurons, CaMPARI provides an all-optical method to map synaptic connectivity. The calcium-modulated photoactivatable ratiometric integrator (CaMPARI) is a genetically encoded calcium integrator that facilitates the study of neural circuits by permanently marking cells active during user-specified temporal windows. Permanent marking enables measurement of signals from large swathes of tissue and easy correlation of activity with other structural or functional labels. One potential application of CaMPARI is labelling neurons postsynaptic to specific populations targeted for optogenetic stimulation, giving rise to all-optical functional connectivity mapping. Here, we characterized the response of CaMPARI to several common types of neuronal calcium signals in mouse acute cortical brain slices. Our experiments show that CaMPARI is effectively converted by both action potentials and subthreshold synaptic inputs, and that conversion level is correlated to synaptic strength. Importantly, we found that conversion rate can be tuned: it is linearly related to light intensity. At low photoconversion light levels CaMPARI offers a wide dynamic range due to slower conversion rate; at high light levels conversion is more rapid and more sensitive to activity. Finally, we employed Ca

Disrupted reward circuits is associated with cognitive deficits and depression severity in major depressive disorder.

Science.gov (United States)

Gong, Liang; Yin, Yingying; He, Cancan; Ye, Qing; Bai, Feng; Yuan, Yonggui; Zhang, Haisan; Lv, Luxian; Zhang, Hongxing; Xie, Chunming; Zhang, Zhijun

2017-01-01

Neuroimaging studies have demonstrated that major depressive disorder (MDD) patients show blunted activity responses to reward-related tasks. However, whether abnormal reward circuits affect cognition and depression in MDD patients remains unclear. Seventy-five drug-naive MDD patients and 42 cognitively normal (CN) subjects underwent a resting-state functional magnetic resonance imaging scan. The bilateral nucleus accumbens (NAc) were selected as seeds to construct reward circuits across all subjects. A multivariate linear regression analysis was employed to investigate the neural substrates of cognitive function and depression severity on the reward circuits in MDD patients. The common pathway underlying cognitive deficits and depression was identified with conjunction analysis. Compared with CN subjects, MDD patients showed decreased reward network connectivity that was primarily located in the prefrontal-striatal regions. Importantly, distinct and common neural pathways underlying cognition and depression were identified, implying the independent and synergistic effects of cognitive deficits and depression severity on reward circuits. This study demonstrated that disrupted topological organization within reward circuits was significantly associated with cognitive deficits and depression severity in MDD patients. These findings suggest that in addition to antidepressant treatment, normalized reward circuits should be a focus and a target for improving depression and cognitive deficits in MDD patients. Copyright © 2016 Elsevier Ltd. All rights reserved.

Larger bases and mixed analog/digital neural nets

Energy Technology Data Exchange (ETDEWEB)

Beiu, V.

1998-12-31

The paper overviews results dealing with the approximation capabilities of neural networks, and bounds on the size of threshold gate circuits. Based on an explicit numerical algorithm for Kolmogorov`s superpositions the authors show that minimum size neural networks--for implementing any Boolean function--have the identity function as the activation function. Conclusions and several comments on the required precision are ending the paper.

Standard cell-based implementation of a digital optoelectronic neural-network hardware.

Science.gov (United States)

Maier, K D; Beckstein, C; Blickhan, R; Erhard, W

2001-03-10

A standard cell-based implementation of a digital optoelectronic neural-network architecture is presented. The overall structure of the multilayer perceptron network that was used, the optoelectronic interconnection system between the layers, and all components required in each layer are defined. The design process from VHDL-based modeling from synthesis and partly automatic placing and routing to the final editing of one layer of the circuit of the multilayer perceptrons are described. A suitable approach for the standard cell-based design of optoelectronic systems is presented, and shortcomings of the design tool that was used are pointed out. The layout for the microelectronic circuit of one layer in a multilayer perceptron neural network with a performance potential 1 magnitude higher than neural networks that are purely electronic based has been successfully designed.

Integrated biocircuits: engineering functional multicellular circuits and devices

Science.gov (United States)

Prox, Jordan; Smith, Tory; Holl, Chad; Chehade, Nick; Guo, Liang

2018-04-01

Objective. Implantable neurotechnologies have revolutionized neuromodulatory medicine for treating the dysfunction of diseased neural circuitry. However, challenges with biocompatibility and lack of full control over neural network communication and function limits the potential to create more stable and robust neuromodulation devices. Thus, we propose a platform technology of implantable and programmable cellular systems, namely Integrated Biocircuits, which use only cells as the functional components of the device. Approach. We envision the foundational principles for this concept begins with novel in vitro platforms used for the study and reconstruction of cellular circuitry. Additionally, recent advancements in organoid and 3D culture systems account for microenvironment factors of cytoarchitecture to construct multicellular circuits as they are normally formed in the brain. We explore the current state of the art of these platforms to provide knowledge of their advancements in circuit fabrication and identify the current biological principles that could be applied in designing integrated biocircuit devices. Main results. We have highlighted the exemplary methodologies and techniques of in vitro circuit fabrication and propose the integration of selected controllable parameters, which would be required in creating suitable biodevices. Significance. We provide our perspective and propose new insights into the future of neuromodulaion devices within the scope of living cellular systems that can be applied in designing more reliable and biocompatible stimulation-based neuroprosthetics.

Nonequilibrium landscape theory of neural networks

Science.gov (United States)

Yan, Han; Zhao, Lei; Hu, Liang; Wang, Xidi; Wang, Erkang; Wang, Jin

2013-01-01

The brain map project aims to map out the neuron connections of the human brain. Even with all of the wirings mapped out, the global and physical understandings of the function and behavior are still challenging. Hopfield quantified the learning and memory process of symmetrically connected neural networks globally through equilibrium energy. The energy basins of attractions represent memories, and the memory retrieval dynamics is determined by the energy gradient. However, the realistic neural networks are asymmetrically connected, and oscillations cannot emerge from symmetric neural networks. Here, we developed a nonequilibrium landscape–flux theory for realistic asymmetrically connected neural networks. We uncovered the underlying potential landscape and the associated Lyapunov function for quantifying the global stability and function. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. We applied our theory to rapid-eye movement sleep cycle. We identified the key regulation factors for function through global sensitivity analysis of landscape topography against wirings, which are in good agreements with experiments. PMID:24145451

Nonequilibrium landscape theory of neural networks.

Science.gov (United States)

Yan, Han; Zhao, Lei; Hu, Liang; Wang, Xidi; Wang, Erkang; Wang, Jin

2013-11-05

The brain map project aims to map out the neuron connections of the human brain. Even with all of the wirings mapped out, the global and physical understandings of the function and behavior are still challenging. Hopfield quantified the learning and memory process of symmetrically connected neural networks globally through equilibrium energy. The energy basins of attractions represent memories, and the memory retrieval dynamics is determined by the energy gradient. However, the realistic neural networks are asymmetrically connected, and oscillations cannot emerge from symmetric neural networks. Here, we developed a nonequilibrium landscape-flux theory for realistic asymmetrically connected neural networks. We uncovered the underlying potential landscape and the associated Lyapunov function for quantifying the global stability and function. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. We applied our theory to rapid-eye movement sleep cycle. We identified the key regulation factors for function through global sensitivity analysis of landscape topography against wirings, which are in good agreements with experiments.

Synaptic E-I Balance Underlies Efficient Neural Coding.

Science.gov (United States)

Zhou, Shanglin; Yu, Yuguo

2018-01-01

Both theoretical and experimental evidence indicate that synaptic excitation and inhibition in the cerebral cortex are well-balanced during the resting state and sensory processing. Here, we briefly summarize the evidence for how neural circuits are adjusted to achieve this balance. Then, we discuss how such excitatory and inhibitory balance shapes stimulus representation and information propagation, two basic functions of neural coding. We also point out the benefit of adopting such a balance during neural coding. We conclude that excitatory and inhibitory balance may be a fundamental mechanism underlying efficient coding.

Doubly stochastic Poisson processes in artificial neural learning.

Science.gov (United States)

Card, H C

1998-01-01

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

From biological neural networks to thinking machines: Transitioning biological organizational principles to computer technology

Science.gov (United States)

Ross, Muriel D.

1991-01-01

The three-dimensional organization of the vestibular macula is under study by computer assisted reconstruction and simulation methods as a model for more complex neural systems. One goal of this research is to transition knowledge of biological neural network architecture and functioning to computer technology, to contribute to the development of thinking computers. Maculas are organized as weighted neural networks for parallel distributed processing of information. The network is characterized by non-linearity of its terminal/receptive fields. Wiring appears to develop through constrained randomness. A further property is the presence of two main circuits, highly channeled and distributed modifying, that are connected through feedforward-feedback collaterals and biasing subcircuit. Computer simulations demonstrate that differences in geometry of the feedback (afferent) collaterals affects the timing and the magnitude of voltage changes delivered to the spike initiation zone. Feedforward (efferent) collaterals act as voltage followers and likely inhibit neurons of the distributed modifying circuit. These results illustrate the importance of feedforward-feedback loops, of timing, and of inhibition in refining neural network output. They also suggest that it is the distributed modifying network that is most involved in adaptation, memory, and learning. Tests of macular adaptation, through hyper- and microgravitational studies, support this hypothesis since synapses in the distributed modifying circuit, but not the channeled circuit, are altered. Transitioning knowledge of biological systems to computer technology, however, remains problematical.

Affective neural response to restricted interests in autism spectrum disorders.

Science.gov (United States)

Cascio, Carissa J; Foss-Feig, Jennifer H; Heacock, Jessica; Schauder, Kimberly B; Loring, Whitney A; Rogers, Baxter P; Pryweller, Jennifer R; Newsom, Cassandra R; Cockhren, Jurnell; Cao, Aize; Bolton, Scott

2014-01-01

Restricted interests are a class of repetitive behavior in autism spectrum disorders (ASD) whose intensity and narrow focus often contribute to significant interference with daily functioning. While numerous neuroimaging studies have investigated executive circuits as putative neural substrates of repetitive behavior, recent work implicates affective neural circuits in restricted interests. We sought to explore the role of affective neural circuits and determine how restricted interests are distinguished from hobbies or interests in typical development. We compared a group of children with ASD to a typically developing (TD) group of children with strong interests or hobbies, employing parent report, an operant behavioral task, and functional imaging with personalized stimuli based on individual interests. While performance on the operant task was similar between the two groups, parent report of intensity and interference of interests was significantly higher in the ASD group. Both the ASD and TD groups showed increased BOLD response in widespread affective neural regions to the pictures of their own interest. When viewing pictures of other children's interests, the TD group showed a similar pattern, whereas BOLD response in the ASD group was much more limited. Increased BOLD response in the insula and anterior cingulate cortex distinguished the ASD from the TD group, and parent report of the intensity and interference with daily life of the child's restricted interest predicted insula response. While affective neural network response and operant behavior are comparable in typical and restricted interests, the narrowness of focus that clinically distinguishes restricted interests in ASD is reflected in more interference in daily life and aberrantly enhanced insula and anterior cingulate response to individuals' own interests in the ASD group. These results further support the involvement of affective neural networks in repetitive behaviors in ASD. © 2013 The

Neural Plasticity and Neurorehabilitation: Teaching the New Brain Old Tricks

Science.gov (United States)

Kleim, Jeffrey A.

2011-01-01

Following brain injury or disease there are widespread biochemical, anatomical and physiological changes that result in what might be considered a new, very different brain. This adapted brain is forced to reacquire behaviors lost as a result of the injury or disease and relies on neural plasticity within the residual neural circuits. The same…

Habenula circuit development: past, present and future

Directory of Open Access Journals (Sweden)

Carlo Antonio Beretta

2012-04-01

Full Text Available The habenular neural circuit is attracting increasing attention from researchers in fields as diverse as neuroscience, medicine, behavior, development and evolution. Recent studies have revealed that this part of the limbic system in the dorsal diencephalon is involved in reward, addiction and other behaviors and its impairment is associated with various neurological conditions and diseases. Since the initial description of the Dorsal Diencephalic Conduction system (DDC with the habenulae in its center at the end of the 19th century, increasingly sophisticated techniques have resolved much of its anatomy and have shown that these pathways relay information from different parts of the forebrain to the tegmentum, midbrain and hindbrain. The first part of this review gives a brief historical overview on how the improving experimental approaches have allowed the stepwise uncovering of much of the architecture of the habenula circuit as we know it today. Our brain distributes tasks differentially between left and right and it has become a paradigm that this functional lateralization is a universal feature of vertebrates. Moreover, task dependent differential brain activities have been linked to anatomical differences across the left-right axis in humans. A good way to further explore this fundamental issue will be to study the functional consequences of subtle changes in neural network formation, which requires that we fully understand DDC system development. As the habenular circuit is evolutionarily highly conserved, researchers have the option to perform such difficult experiments in more experimentally amenable vertebrate systems. Indeed, research in the last decade has shown that the zebrafish is well suited for the study of DDC system development and the phenomenon of functional lateralization. We will critically discuss the advantages of the zebrafish model, available techniques and others that are needed to fully understand habenular circuit

Habenula circuit development: past, present, and future.

Science.gov (United States)

Beretta, Carlo A; Dross, Nicolas; Guiterrez-Triana, Jose A; Ryu, Soojin; Carl, Matthias

2012-01-01

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 nineteenth 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 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 development.

A Neural Circuit Mechanism for the Involvements of Dopamine in Effort-Related Choices: Decay of Learned Values, Secondary Effects of Depletion, and Calculation of Temporal Difference Error

Science.gov (United States)

2018-01-01

Abstract Dopamine has been suggested to be crucially involved in effort-related choices. Key findings are that dopamine depletion (i) changed preference for a high-cost, large-reward option to a low-cost, small-reward option, (ii) but not when the large-reward option was also low-cost or the small-reward option gave no reward, (iii) while increasing the latency in all the cases but only transiently, and (iv) that antagonism of either dopamine D1 or D2 receptors also specifically impaired selection of the high-cost, large-reward option. The underlying neural circuit mechanisms remain unclear. Here we show that findings i–iii can be explained by the dopaminergic representation of temporal-difference reward-prediction error (TD-RPE), whose mechanisms have now become clarified, if (1) the synaptic strengths storing the values of actions mildly decay in time and (2) the obtained-reward-representing excitatory input to dopamine neurons increases after dopamine depletion. The former is potentially caused by background neural activity–induced weak synaptic plasticity, and the latter is assumed to occur through post-depletion increase of neural activity in the pedunculopontine nucleus, where neurons representing obtained reward exist and presumably send excitatory projections to dopamine neurons. We further show that finding iv, which is nontrivial given the suggested distinct functions of the D1 and D2 corticostriatal pathways, can also be explained if we additionally assume a proposed mechanism of TD-RPE calculation, in which the D1 and D2 pathways encode the values of actions with a temporal difference. These results suggest a possible circuit mechanism for the involvements of dopamine in effort-related choices and, simultaneously, provide implications for the mechanisms of TD-RPE calculation. PMID:29468191

Mapping Common Aphasia Assessments to Underlying Cognitive Processes and Their Neural Substrates.

Science.gov (United States)

Lacey, Elizabeth H; Skipper-Kallal, Laura M; Xing, Shihui; Fama, Mackenzie E; Turkeltaub, Peter E

2017-05-01

Understanding the relationships between clinical tests, the processes they measure, and the brain networks underlying them, is critical in order for clinicians to move beyond aphasia syndrome classification toward specification of individual language process impairments. To understand the cognitive, language, and neuroanatomical factors underlying scores of commonly used aphasia tests. Twenty-five behavioral tests were administered to a group of 38 chronic left hemisphere stroke survivors and a high-resolution magnetic resonance image was obtained. Test scores were entered into a principal components analysis to extract the latent variables (factors) measured by the tests. Multivariate lesion-symptom mapping was used to localize lesions associated with the factor scores. The principal components analysis yielded 4 dissociable factors, which we labeled Word Finding/Fluency, Comprehension, Phonology/Working Memory Capacity, and Executive Function. While many tests loaded onto the factors in predictable ways, some relied heavily on factors not commonly associated with the tests. Lesion symptom mapping demonstrated discrete brain structures associated with each factor, including frontal, temporal, and parietal areas extending beyond the classical language network. Specific functions mapped onto brain anatomy largely in correspondence with modern neural models of language processing. An extensive clinical aphasia assessment identifies 4 independent language functions, relying on discrete parts of the left middle cerebral artery territory. A better understanding of the processes underlying cognitive tests and the link between lesion and behavior may lead to improved aphasia diagnosis, and may yield treatments better targeted to an individual's specific pattern of deficits and preserved abilities.

Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning.

Science.gov (United States)

Xu, Tao; Xiao, Na; Zhai, Xiaolong; Kwan Chan, Pak; Tin, Chung

2018-02-01

Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network

Science.gov (United States)

Liu, Ziyi; Gao, Junfeng; Yang, Guoguo; Zhang, Huan; He, Yong

2016-01-01

We present a pipeline for the visual localization and classification of agricultural pest insects by computing a saliency map and applying deep convolutional neural network (DCNN) learning. First, we used a global contrast region-based approach to compute a saliency map for localizing pest insect objects. Bounding squares containing targets were then extracted, resized to a fixed size, and used to construct a large standard database called Pest ID. This database was then utilized for self-learning of local image features which were, in turn, used for classification by DCNN. DCNN learning optimized the critical parameters, including size, number and convolutional stride of local receptive fields, dropout ratio and the final loss function. To demonstrate the practical utility of using DCNN, we explored different architectures by shrinking depth and width, and found effective sizes that can act as alternatives for practical applications. On the test set of paddy field images, our architectures achieved a mean Accuracy Precision (mAP) of 0.951, a significant improvement over previous methods. PMID:26864172

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.

Science.gov (United States)

Zenke, Friedemann; Ganguli, Surya

2018-04-13

A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric, and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike time patterns.

Recruitment of prefrontal-striatal circuit in response to skilled motor challenge.

Science.gov (United States)

Guo, Yumei; Wang, Zhuo; Prathap, Sandhya; Holschneider, Daniel P

2017-12-13

A variety of physical fitness regimens have been shown to improve cognition, including executive function, yet our understanding of which parameters of motor training are important in optimizing outcomes remains limited. We used functional brain mapping to compare the ability of two motor challenges to acutely recruit the prefrontal-striatal circuit. The two motor tasks - walking in a complex running wheel with irregularly spaced rungs or walking in a running wheel with a smooth internal surface - differed only in the extent of skill required for their execution. Cerebral perfusion was mapped in rats by intravenous injection of [C]-iodoantipyrine during walking in either a motorized complex wheel or in a simple wheel. Regional cerebral blood flow (rCBF) was quantified by whole-brain autoradiography and analyzed in three-dimensional reconstructed brains by statistical parametric mapping and seed-based functional connectivity. Skilled or simple walking compared with rest, increased rCBF in regions of the motor circuit, somatosensory and visual cortex, as well as the hippocampus. Significantly greater rCBF increases were noted during skilled walking than for simple walking. Skilled walking, unlike simple walking or the resting condition, was associated with a significant positive functional connectivity in the prefrontal-striatal circuit (prelimbic cortex-dorsomedial striatum) and greater negative functional connectivity in the prefrontal-hippocampal circuit. Our findings suggest that the level of skill of a motor training task determines the extent of functional recruitment of the prefrontal-corticostriatal circuit, with implications for a new approach in neurorehabilitation that uses circuit-specific neuroplasticity to improve motor and cognitive functions.

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

Directory of Open Access Journals (Sweden)

Zhuo Wang

Full Text Available Exercise training is widely used for neurorehabilitation of Parkinson's disease (PD. However, little is known about the functional reorganization of the injured brain after long-term aerobic exercise. We examined the effects of 4 weeks of forced running wheel exercise in a rat model of dopaminergic deafferentation (bilateral, dorsal striatal 6-hydroxydopamine lesions. One week after training, cerebral perfusion was mapped during treadmill walking or at rest using [(14C]-iodoantipyrine autoradiography. Regional cerebral blood flow-related tissue radioactivity (rCBF was analyzed in three-dimensionally reconstructed brains by statistical parametric mapping. In non-exercised rats, lesions resulted in persistent motor deficits. Compared to sham-lesioned rats, lesioned rats showed altered functional brain activation during walking, including: 1. hypoactivation of the striatum and motor cortex; 2. hyperactivation of non-lesioned areas in the basal ganglia-thalamocortical circuit; 3. functional recruitment of the red nucleus, superior colliculus and somatosensory cortex; 4. hyperactivation of the ventrolateral thalamus, cerebellar vermis and deep nuclei, suggesting recruitment of the cerebellar-thalamocortical circuit; 5. hyperactivation of limbic areas (amygdala, hippocampus, ventral striatum, septum, raphe, insula. These findings show remarkable similarities to imaging findings reported in PD patients. Exercise progressively improved motor deficits in lesioned rats, while increasing activation in dorsal striatum and rostral secondary motor cortex, attenuating a hyperemia of the zona incerta and eliciting a functional reorganization of regions participating in the cerebellar-thalamocortical circuit. Both lesions and exercise increased activation in mesolimbic areas (amygdala, hippocampus, ventral striatum, laterodorsal tegmental n., ventral pallidum, as well as in related paralimbic regions (septum, raphe, insula. Exercise, but not lesioning, resulted

Functional Reorganization of Motor and Limbic Circuits after Exercise Training in a Rat Model of Bilateral Parkinsonism

Science.gov (United States)

Wang, Zhuo; Myers, Kalisa G.; Guo, Yumei; Ocampo, Marco A.; Pang, Raina D.; Jakowec, Michael W.; Holschneider, Daniel P.

2013-01-01

Exercise training is widely used for neurorehabilitation of Parkinson’s disease (PD). However, little is known about the functional reorganization of the injured brain after long-term aerobic exercise. We examined the effects of 4 weeks of forced running wheel exercise in a rat model of dopaminergic deafferentation (bilateral, dorsal striatal 6-hydroxydopamine lesions). One week after training, cerebral perfusion was mapped during treadmill walking or at rest using [14C]-iodoantipyrine autoradiography. Regional cerebral blood flow-related tissue radioactivity (rCBF) was analyzed in three-dimensionally reconstructed brains by statistical parametric mapping. In non-exercised rats, lesions resulted in persistent motor deficits. Compared to sham-lesioned rats, lesioned rats showed altered functional brain activation during walking, including: 1. hypoactivation of the striatum and motor cortex; 2. hyperactivation of non-lesioned areas in the basal ganglia-thalamocortical circuit; 3. functional recruitment of the red nucleus, superior colliculus and somatosensory cortex; 4. hyperactivation of the ventrolateral thalamus, cerebellar vermis and deep nuclei, suggesting recruitment of the cerebellar-thalamocortical circuit; 5. hyperactivation of limbic areas (amygdala, hippocampus, ventral striatum, septum, raphe, insula). These findings show remarkable similarities to imaging findings reported in PD patients. Exercise progressively improved motor deficits in lesioned rats, while increasing activation in dorsal striatum and rostral secondary motor cortex, attenuating a hyperemia of the zona incerta and eliciting a functional reorganization of regions participating in the cerebellar-thalamocortical circuit. Both lesions and exercise increased activation in mesolimbic areas (amygdala, hippocampus, ventral striatum, laterodorsal tegmental n., ventral pallidum), as well as in related paralimbic regions (septum, raphe, insula). Exercise, but not lesioning, resulted in decreases

Persistent activity in a recurrent circuit underlies courtship memory in Drosophila

Science.gov (United States)

Zhao, Xiaoliang; Lenek, Daniela; Dag, Ugur; Dickson, Barry J

2018-01-01

Recurrent connections are thought to be a common feature of the neural circuits that encode memories, but how memories are laid down in such circuits is not fully understood. Here we present evidence that courtship memory in Drosophila relies on the recurrent circuit between mushroom body gamma (MBγ), M6 output, and aSP13 dopaminergic neurons. We demonstrate persistent neuronal activity of aSP13 neurons and show that it transiently potentiates synaptic transmission from MBγ>M6 neurons. M6 neurons in turn provide input to aSP13 neurons, prolonging potentiation of MBγ>M6 synapses over time periods that match short-term memory. These data support a model in which persistent aSP13 activity within a recurrent circuit lays the foundation for a short-term memory. PMID:29322941

Microendophenotypes of psychiatric disorders: phenotypes of psychiatric disorders at the level of molecular dynamics, synapses, neurons, and neural circuits.

Science.gov (United States)

Kida, S; Kato, T

2015-01-01

Psychiatric disorders are caused not only by genetic factors but also by complicated factors such as environmental ones. Moreover, environmental factors are rarely quantitated as biological and biochemical indicators, making it extremely difficult to understand the pathological conditions of psychiatric disorders as well as their underlying pathogenic mechanisms. Additionally, we have actually no other option but to perform biological studies on postmortem human brains that display features of psychiatric disorders, thereby resulting in a lack of experimental materials to characterize the basic biology of these disorders. From these backgrounds, animal, tissue, or cell models that can be used in basic research are indispensable to understand biologically the pathogenic mechanisms of psychiatric disorders. In this review, we discuss the importance of microendophenotypes of psychiatric disorders, i.e., phenotypes at the level of molecular dynamics, neurons, synapses, and neural circuits, as targets of basic research on these disorders.

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

Directory of Open Access Journals (Sweden)

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.

Integrated Circuits for Analog Signal Processing

CERN Document Server

2013-01-01

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

Artificial Neural Network with Hardware Training and Hardware Refresh

Science.gov (United States)

Duong, Tuan A. (Inventor)

2003-01-01

A neural network circuit is provided having a plurality of circuits capable of charge storage. Also provided is a plurality of circuits each coupled to at least one of the plurality of charge storage circuits and constructed to generate an output in accordance with a neuron transfer function. Each of a plurality of circuits is coupled to one of the plurality of neuron transfer function circuits and constructed to generate a derivative of the output. A weight update circuit updates the charge storage circuits based upon output from the plurality of transfer function circuits and output from the plurality of derivative circuits. In preferred embodiments, separate training and validation networks share the same set of charge storage circuits and may operate concurrently. The validation network has a separate transfer function circuits each being coupled to the charge storage circuits so as to replicate the training network s coupling of the plurality of charge storage to the plurality of transfer function circuits. The plurality of transfer function circuits may be constructed each having a transconductance amplifier providing differential currents combined to provide an output in accordance with a transfer function. The derivative circuits may have a circuit constructed to generate a biased differential currents combined so as to provide the derivative of the transfer function.

The neuropsychiatry of hyperkinetic movement disorders: insights from neuroimaging into the neural circuit bases of dysfunction.

Science.gov (United States)

Hayhow, Bradleigh D; Hassan, Islam; Looi, Jeffrey C L; Gaillard, Francesco; Velakoulis, Dennis; Walterfang, Mark

2013-01-01

Movement disorders, particularly those associated with basal ganglia disease, have a high rate of comorbid neuropsychiatric illness. We consider the pathophysiological basis of the comorbidity between movement disorders and neuropsychiatric illness by 1) reviewing the epidemiology of neuropsychiatric illness in a range of hyperkinetic movement disorders, and 2) correlating findings to evidence from studies that have utilized modern neuroimaging techniques to investigate these disorders. In addition to diseases classically associated with basal ganglia pathology, such as Huntington disease, Wilson disease, the neuroacanthocytoses, and diseases of brain iron accumulation, we include diseases associated with pathology of subcortical white matter tracts, brain stem nuclei, and the cerebellum, such as metachromatic leukodystrophy, dentatorubropallidoluysian atrophy, and the spinocerebellar ataxias. Neuropsychiatric symptoms are integral to a thorough phenomenological account of hyperkinetic movement disorders. Drawing on modern theories of cortico-subcortical circuits, we argue that these disorders can be conceptualized as disorders of complex subcortical networks with distinct functional architectures. Damage to any component of these complex information-processing networks can have variable and often profound consequences for the function of more remote neural structures, creating a diverse but nonetheless rational pattern of clinical symptomatology.

Retina neural circuitry seen with particle detector technology

CERN Multimedia

CERN Bulletin

2010-01-01

Using particle physics techniques, high energy physics researchers have recently provided new insight into neural circuits inside the retina. After uncovering a new type of retinal cell and mapping how the retina deals with colours, the team from Santa Cruz (US), Krakow and Glasgow is now turning its attention to more complex issues such as how the retina gets wired up and how the brain deals with the signals it receives from the retina. All this using technology derived from high-density, multistrip silicon detectors…   Seen from the point of view of a particle physicist, eyes are image detectors that can gather many different types of data: light and dark, different colours, motion, etc. In particular, the retina, a thin tissue that lines the back of the eye, is a biological pixel detector that detects light and converts it to electrical signals that travel through the optic nerve to the brain. Neurobiologists know that many different cell types are involved in these processes, but they...

Learning sequential control in a Neural Blackboard Architecture for in situ concept reasoning

NARCIS (Netherlands)

van der Velde, Frank; van der Velde, Frank; Besold, Tarek R.; Lamb, Luis; Serafini, Luciano; Tabor, Whitney

2016-01-01

Simulations are presented and discussed of learning sequential control in a Neural Blackboard Architecture (NBA) for in situ concept-based reasoning. Sequential control is learned in a reservoir network, consisting of columns with neural circuits. This allows the reservoir to control the dynamics of

Kernel Temporal Differences for Neural Decoding

Science.gov (United States)

Bae, Jihye; Sanchez Giraldo, Luis G.; Pohlmeyer, Eric A.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

2015-01-01

We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces. PMID:25866504

Psychological Processing in Chronic Pain: A Neural Systems Approach

OpenAIRE

Simons, Laura; Elman, Igor; Borsook, David

2013-01-01

Our understanding of chronic pain involves complex brain circuits that include sensory, emotional, cognitive and interoceptive processing. The feed-forward interactions between physical (e.g., trauma) and emotional pain and the consequences of altered psychological status on the expression of pain have made the evaluation and treatment of chronic pain a challenge in the clinic. By understanding the neural circuits involved in psychological processes, a mechanistic approach to the implementati...

Signal transfer within a cultured asymmetric cortical neuron circuit.

Science.gov (United States)

Isomura, Takuya; Shimba, Kenta; Takayama, Yuzo; Takeuchi, Akimasa; Kotani, Kiyoshi; Jimbo, Yasuhiko

2015-12-01

Simplified neuronal circuits are required for investigating information representation in nervous systems and for validating theoretical neural network models. Here, we developed patterned neuronal circuits using micro fabricated devices, comprising a micro-well array bonded to a microelectrode-array substrate. The micro-well array consisted of micrometre-scale wells connected by tunnels, all contained within a silicone slab called a micro-chamber. The design of the micro-chamber confined somata to the wells and allowed axons to grow through the tunnels bidirectionally but with a designed, unidirectional bias. We guided axons into the point of the arrow structure where one of the two tunnel entrances is located, making that the preferred direction. When rat cortical neurons were cultured in the wells, their axons grew through the tunnels and connected to neurons in adjoining wells. Unidirectional burst transfers and other asymmetric signal-propagation phenomena were observed via the substrate-embedded electrodes. Seventy-nine percent of burst transfers were in the forward direction. We also observed rapid propagation of activity from sites of local electrical stimulation, and significant effects of inhibitory synapse blockade on bursting activity. These results suggest that this simple, substrate-controlled neuronal circuit can be applied to develop in vitro models of the function of cortical microcircuits or deep neural networks, better to elucidate the laws governing the dynamics of neuronal networks.

Signal transfer within a cultured asymmetric cortical neuron circuit

Science.gov (United States)

Isomura, Takuya; Shimba, Kenta; Takayama, Yuzo; Takeuchi, Akimasa; Kotani, Kiyoshi; Jimbo, Yasuhiko

2015-12-01

Objective. Simplified neuronal circuits are required for investigating information representation in nervous systems and for validating theoretical neural network models. Here, we developed patterned neuronal circuits using micro fabricated devices, comprising a micro-well array bonded to a microelectrode-array substrate. Approach. The micro-well array consisted of micrometre-scale wells connected by tunnels, all contained within a silicone slab called a micro-chamber. The design of the micro-chamber confined somata to the wells and allowed axons to grow through the tunnels bidirectionally but with a designed, unidirectional bias. We guided axons into the point of the arrow structure where one of the two tunnel entrances is located, making that the preferred direction. Main results. When rat cortical neurons were cultured in the wells, their axons grew through the tunnels and connected to neurons in adjoining wells. Unidirectional burst transfers and other asymmetric signal-propagation phenomena were observed via the substrate-embedded electrodes. Seventy-nine percent of burst transfers were in the forward direction. We also observed rapid propagation of activity from sites of local electrical stimulation, and significant effects of inhibitory synapse blockade on bursting activity. Significance. These results suggest that this simple, substrate-controlled neuronal circuit can be applied to develop in vitro models of the function of cortical microcircuits or deep neural networks, better to elucidate the laws governing the dynamics of neuronal networks.

Hybrid discrete-time neural networks.

Science.gov (United States)

Cao, Hongjun; Ibarz, Borja

2010-11-13

Hybrid dynamical systems combine evolution equations with state transitions. When the evolution equations are discrete-time (also called map-based), the result is a hybrid discrete-time system. A class of biological neural network models that has recently received some attention falls within this category: map-based neuron models connected by means of fast threshold modulation (FTM). FTM is a connection scheme that aims to mimic the switching dynamics of a neuron subject to synaptic inputs. The dynamic equations of the neuron adopt different forms according to the state (either firing or not firing) and type (excitatory or inhibitory) of their presynaptic neighbours. Therefore, the mathematical model of one such network is a combination of discrete-time evolution equations with transitions between states, constituting a hybrid discrete-time (map-based) neural network. In this paper, we review previous work within the context of these models, exemplifying useful techniques to analyse them. Typical map-based neuron models are low-dimensional and amenable to phase-plane analysis. In bursting models, fast-slow decomposition can be used to reduce dimensionality further, so that the dynamics of a pair of connected neurons can be easily understood. We also discuss a model that includes electrical synapses in addition to chemical synapses with FTM. Furthermore, we describe how master stability functions can predict the stability of synchronized states in these networks. The main results are extended to larger map-based neural networks.

Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning

Science.gov (United States)

Xu, Tao; Xiao, Na; Zhai, Xiaolong; Chan, Pak Kwan; Tin, Chung

2018-02-01

Objective. Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). Approach. The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. Main results. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. Significance. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

Possibilities of introducing different functional circuits on top of a structural neuron triplet: Where do the gains lie?

International Nuclear Information System (INIS)

Franović, Igor; Miljković, Vladimir

2012-01-01

Highlights: ► We identify different types of functional circuits in structural triplets. ► The approach utilizes the properties of synapses with fast-threshold modulation. ► We observe correspondence between sequences in the time series and the triads. ► Triads are assigned roles with respect to synchronization. ► Regularization of bursting series is explained by classifying all the bursts. - Abstract: Instigated by the research on clusterization phenomena in complex neural networks, we study a triplet of bursting Rulkov map neurons connected via inhibitory synapses with delay. It is demonstrated how on a background of structural motif one can build different types of functional circuits. The approach is based on utilizing the properties of the chemical synapses, whose gating is modeled by the fast threshold modulation, in conjunction with the phase plane analysis, allowing the system state to be represented in terms of maps the neurons reside on. For both the dynamical configurations, monitoring the layout of active neurons, and the functional motifs, following the maps where the synchronized neurons lie, we establish a one-on-one correspondence between sequences in the time series and the triads, making up the subgraphs of the original graph. By introducing the appropriate sets of quantities, one obtains not only the distributions of triads as a function of synaptic parameters, but is also able to identify a distinct triad whose presence may be viewed as a signature of the burst synchronization process. In another setup, the regularization of burst cycles for an arbitrary neuron is explained by classifying all the bursts as long or short, with their fractions linked to the abundances of triads under variation of synaptic parameters.

Integrated circuits and molecular components for stress and feeding: implications for eating disorders.

Science.gov (United States)

Hardaway, J A; Crowley, N A; Bulik, C M; Kash, T L

2015-01-01

Eating disorders are complex brain disorders that afflict millions of individuals worldwide. The etiology of these diseases is not fully understood, but a growing body of literature suggests that stress and anxiety may play a critical role in their development. As our understanding of the genetic and environmental factors that contribute to disease in clinical populations like anorexia nervosa, bulimia nervosa and binge eating disorder continue to grow, neuroscientists are using animal models to understand the neurobiology of stress and feeding. We hypothesize that eating disorder clinical phenotypes may result from stress-induced maladaptive alterations in neural circuits that regulate feeding, and that these circuits can be neurochemically isolated using animal model of eating disorders. © 2014 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

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

Science.gov (United States)

Foutz, Thomas J; Ackermann, D Michael; Kilgore, Kevin L; McIntyre, Cameron C

2012-01-01

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

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

Directory of Open Access Journals (Sweden)

Thomas J Foutz

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

Active Neural Localization

OpenAIRE

Chaplot, Devendra Singh; Parisotto, Emilio; Salakhutdinov, Ruslan

2018-01-01

Localization is the problem of estimating the location of an autonomous agent from an observation and a map of the environment. Traditional methods of localization, which filter the belief based on the observations, are sub-optimal in the number of steps required, as they do not decide the actions taken by the agent. We propose "Active Neural Localizer", a fully differentiable neural network that learns to localize accurately and efficiently. The proposed model incorporates ideas of tradition...

Transcranial magnetic stimulation and connectivity mapping: tools for studying the neural bases of brain disorders.

Science.gov (United States)

Hampson, M; Hoffman, R E

2010-01-01

There has been an increasing emphasis on characterizing pathophysiology underlying psychiatric and neurological disorders in terms of altered neural connectivity and network dynamics. Transcranial magnetic stimulation (TMS) provides a unique opportunity for investigating connectivity in the human brain. TMS allows researchers and clinicians to directly stimulate cortical regions accessible to electromagnetic coils positioned on the scalp. The induced activation can then propagate through long-range connections to other brain areas. Thus, by identifying distal regions activated during TMS, researchers can infer connectivity patterns in the healthy human brain and can examine how those patterns may be disrupted in patients with different brain disorders. Conversely, connectivity maps derived using neuroimaging methods can identify components of a dysfunctional network. Nodes in this dysfunctional network accessible as targets for TMS by virtue of their proximity to the scalp may then permit TMS-induced alterations of components of the network not directly accessible to TMS via propagated effects. Thus TMS can provide a portal for accessing and altering neural dynamics in networks that are widely distributed anatomically. Finally, when long-term modulation of network dynamics is induced by trains of repetitive TMS, changes in functional connectivity patterns can be studied in parallel with changes in patient symptoms. These correlational data can elucidate neural mechanisms underlying illness and recovery. In this review, we focus on the application of these approaches to the study of psychiatric and neurological illnesses.

Transcranial magnetic stimulation and connectivity mapping: tools for studying the neural bases of brain disorders.

Directory of Open Access Journals (Sweden)

Michelle Hampson

2010-08-01

Full Text Available There has been an increasing emphasis on characterizing pathophysiology underlying psychiatric and neurological disorders in terms of altered neural connectivity and network dynamics. Transcranial magnetic stimulation (TMS provides a unique opportunity for investigating connectivity in the human brain. TMS allows researchers and clinicians to directly stimulate cortical regions accessible to electromagnetic coils positioned on the scalp. The induced activation can then propagate through long-range connections to other brain areas. Thus, by identifying distal regions activated during TMS, researchers can infer connectivity patterns in the healthy human brain and can examine how those patterns may be disrupted in patients with different brain disorders. Conversely, connectivity maps derived using neuroimaging methods can identify components of a dysfunctional network. Nodes in this dysfunctional network accessible as targets for TMS by virtue of their proximity to the scalp may then permit TMS-induced alterations of components of the network not directly accessible to TMS via propagated effects. Thus TMS can provide a portal for accessing and altering neural dynamics in networks that are widely distributed anatomically. Finally, when long-term modulation of network dynamics is induced by trains of repetitive TMS, changes in functional connectivity patterns can be studied in parallel with changes in patient symptoms. These correlational data can elucidate neural mechanisms underlying illness and recovery. In this review, we focus on the application of these approaches to the study of psychiatric and neurological illnesses.

Analog Multilayer Perceptron Circuit with On-chip Learning: Portable Electronic Nose

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Pan, Chih-Heng; Tang, Kea-Tiong

2011-09-01

This article presents an analog multilayer perceptron (MLP) neural network circuit with on-chip back propagation learning. This low power and small area analog MLP circuit is proposed to implement as a classifier in an electronic nose (E-nose). Comparing with the E-nose using microprocessor or FPGA as a classifier, the E-nose applying analog circuit as a classifier can be faster and much smaller, demonstrate greater power efficiency and be capable of developing a portable E-nose [1]. The system contains four inputs, four hidden neurons, and only one output neuron; this simple structure allows the circuit to have a smaller area and less power consumption. The circuit is fabricated using TSMC 0.18 μm 1P6M CMOS process with 1.8 V supply voltage. The area of this chip is 1.353×1.353 mm2 and the power consumption is 0.54 mW. Post-layout simulations show that the proposed analog MLP circuit can be successively trained to identify three kinds of fruit odors.

High on food: the interaction between the neural circuits for feeding and for reward.

Science.gov (United States)

Liu, Jing-Jing; Mukherjee, Diptendu; Haritan, Doron; Ignatowska-Jankowska, Bogna; Liu, Ji; Citri, Ami; Pang, Zhiping P

2015-04-01

Hunger, mostly initiated by a deficiency in energy, induces food seeking and intake. However, the drive toward food is not only regulated by physiological needs, but is motivated by the pleasure derived from ingestion of food, in particular palatable foods. Therefore, feeding is viewed as an adaptive motivated behavior that involves integrated communication between homeostatic feeding circuits and reward circuits. The initiation and termination of a feeding episode are instructed by a variety of neuronal signals, and maladaptive plasticity in almost any component of the network may lead to the development of pathological eating disorders. In this review we will summarize the latest understanding of how the feeding circuits and reward circuits in the brain interact. We will emphasize communication between the hypothalamus and the mesolimbic dopamine system and highlight complexities, discrepancies, open questions and future directions for the field.

Persistent activity in a recurrent circuit underlies courtship memory in Drosophila.

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Zhao, Xiaoliang; Lenek, Daniela; Dag, Ugur; Dickson, Barry J; Keleman, Krystyna

2018-01-11

Recurrent connections are thought to be a common feature of the neural circuits that encode memories, but how memories are laid down in such circuits is not fully understood. Here we present evidence that courtship memory in Drosophila relies on the recurrent circuit between mushroom body gamma (MBγ), M6 output, and aSP13 dopaminergic neurons. We demonstrate persistent neuronal activity of aSP13 neurons and show that it transiently potentiates synaptic transmission from MBγ>M6 neurons. M6 neurons in turn provide input to aSP13 neurons, prolonging potentiation of MB γ >M6 synapses over time periods that match short-term memory. These data support a model in which persistent aSP13 activity within a recurrent circuit lays the foundation for a short-term memory. © 2018, Zhao et al.

Artificial neural networks in NDT

International Nuclear Information System (INIS)

Abdul Aziz Mohamed

2001-01-01

Artificial neural networks, simply known as neural networks, have attracted considerable interest in recent years largely because of a growing recognition of the potential of these computational paradigms as powerful alternative models to conventional pattern recognition or function approximation techniques. The neural networks approach is having a profound effect on almost all fields, and has been utilised in fields Where experimental inter-disciplinary work is being carried out. Being a multidisciplinary subject with a broad knowledge base, Nondestructive Testing (NDT) or Nondestructive Evaluation (NDE) is no exception. This paper explains typical applications of neural networks in NDT/NDE. Three promising types of neural networks are highlighted, namely, back-propagation, binary Hopfield and Kohonen's self-organising maps. (Author)

Spherical Harmonics Reveal Standing EEG Waves and Long-Range Neural Synchronization during Non-REM Sleep

OpenAIRE

Sivakumar, Siddharth S.; Namath, Amalia G.; Galán, Roberto F.

2016-01-01

Previous work from our lab has demonstrated how the connectivity of brain circuits constrains the repertoire of activity patterns that those circuits can display. Specifically, we have shown that the principal components of spontaneous neural activity are uniquely determined by the underlying circuit connections, and that although the principal components do not uniquely resolve the circuit structure, they do reveal important features about it. Expanding upon this framework on a larger scale ...

Mapping visual stimuli to perceptual decisions via sparse decoding of mesoscopic neural activity.

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Sajda, Paul

2010-01-01

In this talk I will describe our work investigating sparse decoding of neural activity, given a realistic mapping of the visual scene to neuronal spike trains generated by a model of primary visual cortex (V1). We use a linear decoder which imposes sparsity via an L1 norm. The decoder can be viewed as a decoding neuron (linear summation followed by a sigmoidal nonlinearity) in which there are relatively few non-zero synaptic weights. We find: (1) the best decoding performance is for a representation that is sparse in both space and time, (2) decoding of a temporal code results in better performance than a rate code and is also a better fit to the psychophysical data, (3) the number of neurons required for decoding increases monotonically as signal-to-noise in the stimulus decreases, with as little as 1% of the neurons required for decoding at the highest signal-to-noise levels, and (4) sparse decoding results in a more accurate decoding of the stimulus and is a better fit to psychophysical performance than a distributed decoding, for example one imposed by an L2 norm. We conclude that sparse coding is well-justified from a decoding perspective in that it results in a minimum number of neurons and maximum accuracy when sparse representations can be decoded from the neural dynamics.

Neural basis of feature-based contextual effects on visual search behavior

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Kelly eShen

2012-01-01

Full Text Available Searching for a visual object is known to be adaptable to context, and it is thought to result from the selection of neural representations distributed on a visual salience map, wherein stimulus-driven and goal-directed signals are combined. Here we investigated the neural basis of this adaptability by recording superior colliculus (SC neurons while three female rhesus monkeys (Macaca mulatta searched with saccadic eye movements for a target presented in an array of visual stimuli whose feature composition varied from trial to trial. We found that sensory-motor activity associated with distracters was enhanced or suppressed depending on the search array composition and that it corresponded to the monkey's search strategy, as assessed by the distribution of the occasional errant saccades. This feature-related modulation occurred independently from the saccade goal and facilitated the process of saccade target selection. We also observed feature-related enhancement in the activity associated with distracters that had been the search target during the previous session. Consistent with recurrent processing, both feature-related neuronal modulations occurred more than 60 ms after the onset of the visually evoked responses, and their near coincidence with the time of saccade target selection suggests that they are integral to this process. These results suggest that SC neuronal activity is shaped by the visual context as dictated by both stimulus-driven and goal-directed signals. Given the close proximity of the SC to the motor circuit, our findings suggest a direct link between perception and action and no need for distinct salience and motor maps.

Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems

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Broccard, Frédéric D.; Joshi, Siddharth; Wang, Jun; Cauwenberghs, Gert

2017-08-01

Objective. Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. Approach. This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity. Main results. Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. Significance. Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a

A simple miniature device for wireless stimulation of neural circuits in small behaving animals.

Science.gov (United States)

Zhang, Yisi; Langford, Bruce; Kozhevnikov, Alexay

2011-10-30

The use of wireless neural stimulation devices offers significant advantages for neural stimulation experiments in behaving animals. We demonstrate a simple, low-cost and extremely lightweight wireless neural stimulation device which is made from off-the-shelf components. The device has low power consumption and does not require a high-power RF preamplifier. Neural stimulation can be carried out in either a voltage source mode or a current source mode. Using the device, we carry out wireless stimulation in the premotor brain area HVC of a songbird and demonstrate that such stimulation causes rapid perturbations of the acoustic structure of the song. Published by Elsevier B.V.

Neural responses to macronutrients: hedonic and homeostatic mechanisms.

Science.gov (United States)

Tulloch, Alastair J; Murray, Susan; Vaicekonyte, Regina; Avena, Nicole M

2015-05-01

The brain responds to macronutrients via intricate mechanisms. We review how the brain's neural systems implicated in homeostatic control of feeding and hedonic responses are influenced by the ingestion of specific types of food. We discuss how these neural systems are dysregulated in preclinical models of obesity. Findings from these studies can increase our understanding of overeating and, perhaps in some cases, the development of obesity. In addition, a greater understanding of the neural circuits affected by the consumption of specific macronutrients, and by obesity, might lead to new treatments and strategies for preventing unhealthy weight gain. Copyright © 2015 AGA Institute. Published by Elsevier Inc. All rights reserved.

Quantized Synchronization of Chaotic Neural Networks With Scheduled Output Feedback Control.

Science.gov (United States)

Wan, Ying; Cao, Jinde; Wen, Guanghui

In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control

Statistical modeling implicates neuroanatomical circuit mediating stress relief by 'comfort' food.

Science.gov (United States)

Ulrich-Lai, Yvonne M; Christiansen, Anne M; Wang, Xia; Song, Seongho; Herman, James P

2016-07-01

A history of eating highly palatable foods reduces physiological and emotional responses to stress. For instance, we have previously shown that limited sucrose intake (4 ml of 30 % sucrose twice daily for 14 days) reduces hypothalamic-pituitary-adrenocortical (HPA) axis responses to stress. However, the neural mechanisms underlying stress relief by such 'comfort' foods are unclear, and could reveal an endogenous brain pathway for stress mitigation. As such, the present work assessed the expression of several proteins related to neuronal activation and/or plasticity in multiple stress- and reward-regulatory brain regions of rats after limited sucrose (vs. water control) intake. These data were then subjected to a series of statistical analyses, including Bayesian modeling, to identify the most likely neurocircuit mediating stress relief by sucrose. The analyses suggest that sucrose reduces HPA activation by dampening an excitatory basolateral amygdala-medial amygdala circuit, while also potentiating an inhibitory bed nucleus of the stria terminalis principle subdivision-mediated circuit, resulting in reduced HPA activation after stress. Collectively, the results support the hypothesis that sucrose limits stress responses via plastic changes to the structure and function of stress-regulatory neural circuits. The work also illustrates that advanced statistical methods are useful approaches to identify potentially novel and important underlying relationships in biological datasets.

A Simple Quantum Neural Net with a Periodic Activation Function

OpenAIRE

Daskin, Ammar

2018-01-01

In this paper, we propose a simple neural net that requires only $O(nlog_2k)$ number of qubits and $O(nk)$ quantum gates: Here, $n$ is the number of input parameters, and $k$ is the number of weights applied to these parameters in the proposed neural net. We describe the network in terms of a quantum circuit, and then draw its equivalent classical neural net which involves $O(k^n)$ nodes in the hidden layer. Then, we show that the network uses a periodic activation function of cosine values o...

Dynamic neural network modeling of HF radar current maps for forecasting oil spill trajectories

International Nuclear Information System (INIS)

Tissot, P.; Perez, J.; Kelly, F.J.; Bonner, J.; Michaud, P.

2001-01-01

This paper examined the concept of dynamic neural network (NN) modeling for short-term forecasts of coastal high-frequency (HF) radar current maps offshore of Galveston Texas. HF radar technology is emerging as a viable and affordable way to measure surface currents in real time and the number of users applying the technology is increasing. A 25 megahertz, two site, Seasonde HF radar system was used to map ocean and bay surface currents along the coast of Texas where wind and river discharge create complex and rapidly changing current patters that override the weaker tidal flow component. The HF radar system is particularly useful in this type of setting because its mobility makes it a good marine spill response tool that could provide hourly current maps. This capability helps improve deployment of response resources. In addition, the NN model recently developed by the Conrad Blucher Institute can be used to forecast water levels during storm events. Forecasted currents are based on time series of current vectors from HF radar plus wind speed, wind direction, and water levels, as well as tidal forecasts. The dynamic NN model was tested to evaluate its performance and the results were compared with a baseline model which assumes the currents do not change from the time of the forecast up to the forecasted time. The NN model showed improvements over the baseline model for forecasting time equal or greater than 3 hours, but the difference was relatively small. The test demonstrated the ability of the dynamic NN model to link meteorological forcing functions with HF radar current maps. Development of the dynamic NN modeling is still ongoing. 18 refs., 1 tab., 5 figs

Neuromorphic Implementation of Attractor Dynamics in a Two-Variable Winner-Take-All Circuit with NMDARs: A Simulation Study.

Science.gov (United States)

You, Hongzhi; Wang, Da-Hui

2017-01-01

Neural networks configured with winner-take-all (WTA) competition and N-methyl-D-aspartate receptor (NMDAR)-mediated synaptic dynamics are endowed with various dynamic characteristics of attractors underlying many cognitive functions. This paper presents a novel method for neuromorphic implementation of a two-variable WTA circuit with NMDARs aimed at implementing decision-making, working memory and hysteresis in visual perceptions. The method proposed is a dynamical system approach of circuit synthesis based on a biophysically plausible WTA model. Notably, slow and non-linear temporal dynamics of NMDAR-mediated synapses was generated. Circuit simulations in Cadence reproduced ramping neural activities observed in electrophysiological recordings in experiments of decision-making, the sustained activities observed in the prefrontal cortex during working memory, and classical hysteresis behavior during visual discrimination tasks. Furthermore, theoretical analysis of the dynamical system approach illuminated the underlying mechanisms of decision-making, memory capacity and hysteresis loops. The consistence between the circuit simulations and theoretical analysis demonstrated that the WTA circuit with NMDARs was able to capture the attractor dynamics underlying these cognitive functions. Their physical implementations as elementary modules are promising for assembly into integrated neuromorphic cognitive systems.

Thermoacoustic and thermoreflectance imaging of biased integrated circuits: Voltage and temperature maps

Energy Technology Data Exchange (ETDEWEB)

Hernández-Rosales, E.; Cedeño, E. [Gleb Wataghin Physics Institute, University of Campinas - Unicamp, 13083-859 Campinas, SP (Brazil); Instituto Politécnico Nacional, Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Legaria 694, Colonia Irrigación, CP 11500, México, DF (Mexico); Hernandez-Wong, J. [Instituto Politécnico Nacional, Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Legaria 694, Colonia Irrigación, CP 11500, México, DF (Mexico); CONACYT, México, DF, México (Mexico); Rojas-Trigos, J. B.; Marin, E. [Instituto Politécnico Nacional, Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Legaria 694, Colonia Irrigación, CP 11500, México, DF (Mexico); Gandra, F. C. G.; Mansanares, A. M., E-mail: manoel@ifi.unicamp.br [Gleb Wataghin Physics Institute, University of Campinas - Unicamp, 13083-859 Campinas, SP (Brazil)

2016-07-25

In this work a combined thermoacoustic and thermoreflectance set-up was designed for imaging biased microelectronic circuits. In particular, it was used with polycrystalline silicon resistive tracks grown on a monocrystalline Si substrate mounted on a test chip. Thermoreflectance images, obtained by scanning a probe laser beam on the sample surface, clearly show the regions periodically heated by Joule effect, which are associated to the electric current distribution in the circuit. The thermoacoustic signal, detected by a pyroelectric/piezoelectric sensor beneath the chip, also discloses the Joule contribution of the whole sample. However, additional information emerges when a non-modulated laser beam is focused on the sample surface in a raster scan mode allowing imaging of the sample. The distribution of this supplementary signal is related to the voltage distribution along the circuit.

CAD-CAM printed circuit board design

Science.gov (United States)

Agy, W. E.

A step-by-step procedure for a printed circuit design achieved by CAD is presented. The operator at the interactive CRT station moves a stylus across a graphics tablet and intersperses commands which result in computer-generated pictorial forms on the screen that were drawn on the pad. Standard symbols are used for commands allowing, for instance, connections to be made of specific types in certain locations, which can be automatically edited from a materials list. An entire network of drawn lines can be referenced by a signal name for recall, and a finished circuit schematic can be checked for designs rules compliance, including fault reporting in terms of designator/pin number. A map may be present delineating the boundaries of the circuitry area, and previously completed circuitry segments can be recalled for piece-by-piece assembly of the circuit board.

CNNcon: improved protein contact maps prediction using cascaded neural networks.

Directory of Open Access Journals (Sweden)

Wang Ding

Full Text Available BACKGROUNDS: Despite continuing progress in X-ray crystallography and high-field NMR spectroscopy for determination of three-dimensional protein structures, the number of unsolved and newly discovered sequences grows much faster than that of determined structures. Protein modeling methods can possibly bridge this huge sequence-structure gap with the development of computational science. A grand challenging problem is to predict three-dimensional protein structure from its primary structure (residues sequence alone. However, predicting residue contact maps is a crucial and promising intermediate step towards final three-dimensional structure prediction. Better predictions of local and non-local contacts between residues can transform protein sequence alignment to structure alignment, which can finally improve template based three-dimensional protein structure predictors greatly. METHODS: CNNcon, an improved multiple neural networks based contact map predictor using six sub-networks and one final cascade-network, was developed in this paper. Both the sub-networks and the final cascade-network were trained and tested with their corresponding data sets. While for testing, the target protein was first coded and then input to its corresponding sub-networks for prediction. After that, the intermediate results were input to the cascade-network to finish the final prediction. RESULTS: The CNNcon can accurately predict 58.86% in average of contacts at a distance cutoff of 8 Å for proteins with lengths ranging from 51 to 450. The comparison results show that the present method performs better than the compared state-of-the-art predictors. Particularly, the prediction accuracy keeps steady with the increase of protein sequence length. It indicates that the CNNcon overcomes the thin density problem, with which other current predictors have trouble. This advantage makes the method valuable to the prediction of long length proteins. As a result, the effective

Artificial neural network with self-organizing mapping for reactor stability monitoring

International Nuclear Information System (INIS)

Okumura, Motofumi; Tsuji, Masashi; Shimazu, Yoichiro

2009-01-01

In boiling water reactor (BWR) stability monitoring, damping ratio has been used as a stability index. A method for estimating the damping ratio by applying Principal Component Analysis (PCA) to neutron detector signals measured with local power range monitors (LPRMs) had been developed; in this method, measured fluctuating signal is decomposed into some independent components and the signal components directly related to stability are extracted among them to determine the damping ratio. For online monitoring, it is necessary to select stability related signal components efficiently. The self-organizing map (SOM) is one of the artificial neural networks (ANNs) and has the characteristics such that online learning is possible without supervised learning within a relatively short time. In the present study, the SOM was applied to extract the relevant signal components more quickly and more accurately, and the availability was confirmed through the feasibility study. For realizing online stability monitoring only with ANNs, another type of ANN that performs online processing of PCA was combined with SOM. And stability monitoring performance was investigated. (author)

Spike Neural Models Part II: Abstract Neural Models

Directory of Open Access Journals (Sweden)

Johnson, Melissa G.

2018-02-01

Full Text Available Neurons are complex cells that require a lot of time and resources to model completely. In spiking neural networks (SNN though, not all that complexity is required. Therefore simple, abstract models are often used. These models save time, use less computer resources, and are easier to understand. This tutorial presents two such models: Izhikevich's model, which is biologically realistic in the resulting spike trains but not in the parameters, and the Leaky Integrate and Fire (LIF model which is not biologically realistic but does quickly and easily integrate input to produce spikes. Izhikevich's model is based on Hodgkin-Huxley's model but simplified such that it uses only two differentiation equations and four parameters to produce various realistic spike patterns. LIF is based on a standard electrical circuit and contains one equation. Either of these two models, or any of the many other models in literature can be used in a SNN. Choosing a neural model is an important task that depends on the goal of the research and the resources available. Once a model is chosen, network decisions such as connectivity, delay, and sparseness, need to be made. Understanding neural models and how they are incorporated into the network is the first step in creating a SNN.

Stochastic resonance in an ensemble of single-electron neuromorphic devices and its application to competitive neural networks

International Nuclear Information System (INIS)

Oya, Takahide; Asai, Tetsuya; Amemiya, Yoshihito

2007-01-01

Neuromorphic computing based on single-electron circuit technology is gaining prominence because of its massively increased computational efficiency and the increasing relevance of computer technology and nanotechnology [Likharev K, Mayr A, Muckra I, Tuerel O. CrossNets: High-performance neuromorphic architectures for CMOL circuits. Molec Electron III: Ann NY Acad Sci 1006;2003:146-63; Oya T, Schmid A, Asai T, Leblebici Y, Amemiya Y. On the fault tolerance of a clustered single-electron neural network for differential enhancement. IEICE Electron Expr 2;2005:76-80]. The maximum impact of these technologies will be strongly felt when single-electron circuits based on fault- and noise-tolerant neural structures can operate at room temperature. In this paper, inspired by stochastic resonance (SR) in an ensemble of spiking neurons [Collins JJ, Chow CC, Imhoff TT. Stochastic resonance without tuning. Nature 1995;376:236-8], we propose our design of a basic single-electron neural component and report how we examined its statistical results on a network

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

Science.gov (United States)

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

2013-01-01

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

Neural ECM in addiction, schizophrenia, and mood disorder

NARCIS (Netherlands)

Lubbers, B.R.; Smit, A.B.; Spijker, S.; van den Oever, M.C.

2014-01-01

The extracellular matrix (ECM) has a prominent role in brain development, maturation of neural circuits, and adult neuroplasticity. This multifactorial role of the ECM suggests that processes that affect composition or turnover of ECM in the brain could lead to altered brain function, possibly

A neuromorphic circuit mimicking biological short-term memory.

Science.gov (United States)

Barzegarjalali, Saeid; Parker, Alice C

2016-08-01

Research shows that the way we remember things for a few seconds is a different mechanism from the way we remember things for a longer time. Short-term memory is based on persistently firing neurons, whereas storing information for a longer time is based on strengthening the synapses or even forming new neural connections. Information about location and appearance of an object is segregated and processed by separate neurons. Furthermore neurons can continue firing using different mechanisms. Here, we have designed a biomimetic neuromorphic circuit that mimics short-term memory by firing neurons, using biological mechanisms to remember location and shape of an object. Our neuromorphic circuit has a hybrid architecture. Neurons are designed with CMOS 45nm technology and synapses are designed with carbon nanotubes (CNT).

Vertically integrated circuit development at Fermilab for detectors

International Nuclear Information System (INIS)

Yarema, R; Deptuch, G; Hoff, J; Khalid, F; Lipton, R; Shenai, A; Trimpl, M; Zimmerman, T

2013-01-01

Today vertically integrated circuits, (a.k.a. 3D integrated circuits) is a popular topic in many trade journals. The many advantages of these circuits have been described such as higher speed due to shorter trace lenghts, the ability to reduce cross talk by placing analog and digital circuits on different levels, higher circuit density without the going to smaller feature sizes, lower interconnect capacitance leading to lower power, reduced chip size, and different processing for the various layers to optimize performance. There are some added advantages specifically for MAPS (Monolithic Active Pixel Sensors) in High Energy Physics: four side buttable pixel arrays, 100% diode fill factor, the ability to move PMOS transistors out of the diode sensing layer, and a increase in channel density. Fermilab began investigating 3D circuits in 2006. Many different bonding processes have been described for fabricating 3D circuits [1]. Fermilab has used three different processes to fabricate several circuits for specific applications in High Energy Physics and X-ray imaging. This paper covers some of the early 3D work at Fermilab and then moves to more recent activities. The major processes we have used are discussed and some of the problems encountered are described. An overview of pertinent 3D circuit designs is presented along with test results thus far.

An Overview of Bayesian Methods for Neural Spike Train Analysis

Directory of Open Access Journals (Sweden)

Zhe Chen

2013-01-01

Full Text Available Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation. On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony. Some research challenges and opportunities for neural spike train analysis are discussed.

The Reference Ability Neural Network Study: Life-time stability of reference-ability neural networks derived from task maps of young adults.

Science.gov (United States)

Habeck, C; Gazes, Y; Razlighi, Q; Steffener, J; Brickman, A; Barulli, D; Salthouse, T; Stern, Y

2016-01-15

Analyses of large test batteries administered to individuals ranging from young to old have consistently yielded a set of latent variables representing reference abilities (RAs) that capture the majority of the variance in age-related cognitive change: Episodic Memory, Fluid Reasoning, Perceptual Processing Speed, and Vocabulary. In a previous paper (Stern et al., 2014), we introduced the Reference Ability Neural Network Study, which administers 12 cognitive neuroimaging tasks (3 for each RA) to healthy adults age 20-80 in order to derive unique neural networks underlying these 4 RAs and investigate how these networks may be affected by aging. We used a multivariate approach, linear indicator regression, to derive a unique covariance pattern or Reference Ability Neural Network (RANN) for each of the 4 RAs. The RANNs were derived from the neural task data of 64 younger adults of age 30 and below. We then prospectively applied the RANNs to fMRI data from the remaining sample of 227 adults of age 31 and above in order to classify each subject-task map into one of the 4 possible reference domains. Overall classification accuracy across subjects in the sample age 31 and above was 0.80±0.18. Classification accuracy by RA domain was also good, but variable; memory: 0.72±0.32; reasoning: 0.75±0.35; speed: 0.79±0.31; vocabulary: 0.94±0.16. Classification accuracy was not associated with cross-sectional age, suggesting that these networks, and their specificity to the respective reference domain, might remain intact throughout the age range. Higher mean brain volume was correlated with increased overall classification accuracy; better overall performance on the tasks in the scanner was also associated with classification accuracy. For the RANN network scores, we observed for each RANN that a higher score was associated with a higher corresponding classification accuracy for that reference ability. Despite the absence of behavioral performance information in the

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

Science.gov (United States)

Kobayashi, Kyogo; Nakano, Shunji; Amano, Mutsuki; Tsuboi, Daisuke; Nishioka, Tomoki; Ikeda, Shingo; Yokoyama, Genta; Kaibuchi, Kozo; Mori, Ikue

2016-01-05

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. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Functional changes of neural circuits in stroke patients with dysphagia: A meta-analysis.

Science.gov (United States)

Liu, Lu; Xiao, Yuan; Zhang, Wenjing; Yao, Li; Gao, Xin; Chandan, Shah; Lui, Su

2017-08-01

Dysphagia is a common problem in stroke patients with unclear pathogenesis. Several recent functional magnetic resonance imaging (fMRI) studies had been carried out to explore the cerebral functional changes in dysphagic stroke patients. The aim of this study was to analysis these imaging findings using a meta-analysis. We used seed-based d mapping (SDM) to conduct a meta-analysis for dysphagic stroke patients prior to any kind of special treatment for dysphagia. A systematic search was conducted for the relevant studies. SDM meta-analysis method was used to examine regions of increased and decreased functional activation between dysphagic stroke patients and healthy controls. Finally, six studies including 81 stroke patients with dysphagia and 78 healthy controls met the inclusion standards. When compared with healthy controls, stroke patients with dysphagia showed hyperactivation in left cingulate gyrus, left precentral gyrus and right posterior cingulate gyrus, and hypoactivation in right cuneus and left middle frontal gyrus. The hyperactivity of precentral gyrus is crucial in stroke patients with dysphagia and may be associated with the severity of stroke. Besides the motor areas, the default-mode network regions (DMN) and affective network regions (AN) circuits are also involved in dysphagia after stroke. © 2017 Chinese Cochrane Center, West China Hospital of Sichuan University and John Wiley & Sons Australia, Ltd.

Signal Processing and Neural Network Simulator

Science.gov (United States)

Tebbe, Dennis L.; Billhartz, Thomas J.; Doner, John R.; Kraft, Timothy T.

1995-04-01

The signal processing and neural network simulator (SPANNS) is a digital signal processing simulator with the capability to invoke neural networks into signal processing chains. This is a generic tool which will greatly facilitate the design and simulation of systems with embedded neural networks. The SPANNS is based on the Signal Processing WorkSystemTM (SPWTM), a commercial-off-the-shelf signal processing simulator. SPW provides a block diagram approach to constructing signal processing simulations. Neural network paradigms implemented in the SPANNS include Backpropagation, Kohonen Feature Map, Outstar, Fully Recurrent, Adaptive Resonance Theory 1, 2, & 3, and Brain State in a Box. The SPANNS was developed by integrating SAIC's Industrial Strength Neural Networks (ISNN) Software into SPW.

Local Dynamics in Trained Recurrent Neural Networks.

Science.gov (United States)

Rivkind, Alexander; Barak, Omri

2017-06-23

Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.

Local Dynamics in Trained Recurrent Neural Networks

Science.gov (United States)

Rivkind, Alexander; Barak, Omri

2017-06-01

Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.

An Improved Neural Network for Regional Giant Panda Habitat Suitability Mapping: A Case Study in Ya’an Prefecture

Directory of Open Access Journals (Sweden)

Jingwei Song

2014-06-01

Full Text Available Expert knowledge is a combination of prior information and subjective opinions based on long-experience; as such it is often not sufficiently objective to produce convincing results in animal habitat suitability index mapping. In this study, an animal habitat assessment method based on a learning neural network is proposed to reduce the level of subjectivity in animal habitat assessments. Based on two hypotheses, this method substitutes habitat suitability index with apparent density and has advantages over conventional ones such as those based on analytical hierarchy process or multivariate regression approaches. Besides, this method is integrated with a learning neural network and is suitable for building non-linear transferring functions to fit complex relationships between multiple factors influencing habitat suitability. Once the neural network is properly trained, new earth observation data can be integrated for rapid habitat suitability monitoring which could save time and resources needed for traditional data collecting approaches through extensive field surveys. Giant panda (Ailuropoda melanoleuca natural habitat in Ya’an prefecture and corresponding landsat images, DEM and ground observations are tested for validity of using the methodology reported. Results show that the method scores well in key efficiency and performance indicators and could be extended for habitat assessments, particularly of other large, rare and widely distributed animal species.

Statistical modeling implicates neuroanatomical circuit mediating stress relief by ‘comfort’ food

Science.gov (United States)

Ulrich-Lai, Yvonne M.; Christiansen, Anne M.; Wang, Xia; Song, Seongho; Herman, James P.

2015-01-01

A history of eating highly-palatable foods reduces physiological and emotional responses to stress. For instance, we have previously shown that limited sucrose intake (4 ml of 30% sucrose twice daily for 14 days) reduces hypothalamic-pituitary-adrenocortical (HPA) axis responses to stress. However, the neural mechanisms underlying stress relief by such ‘comfort’ foods are unclear, and could reveal an endogenous brain pathway for stress mitigation. As such, the present work assessed the expression of several proteins related to neuronal activation and/or plasticity in multiple stress- and reward-regulatory brain regions of rats after limited sucrose (vs. water control) intake. These data were then subjected to a series of statistical analyses, including Bayesian modeling, to identify the most likely neurocircuit mediating stress relief by sucrose. The analyses suggest that sucrose reduces HPA activation by dampening an excitatory basolateral amygdala - medial amygdala circuit, while also potentiating an inhibitory bed nucleus of the stria terminalis principle subdivision-mediated circuit, resulting in reduced HPA activation after stress. Collectively, the results support the hypothesis that sucrose limits stress responses via plastic changes to the structure and function of stress-regulatory neural circuits. The work also illustrates that advanced statistical methods are useful approaches to identify potentially novel and important underlying relationships in biological data sets. PMID:26246177

Circuit Mechanisms Governing Local vs. Global Motion Processing in Mouse Visual Cortex

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Rune Rasmussen

2017-12-01

Full Text Available A withstanding question in neuroscience is how neural circuits encode representations and perceptions of the external world. A particularly well-defined visual computation is the representation of global object motion by pattern direction-selective (PDS cells from convergence of motion of local components represented by component direction-selective (CDS cells. However, how PDS and CDS cells develop their distinct response properties is still unresolved. The visual cortex of the mouse is an attractive model for experimentally solving this issue due to the large molecular and genetic toolbox available. Although mouse visual cortex lacks the highly ordered orientation columns of primates, it is organized in functional sub-networks and contains striate- and extrastriate areas like its primate counterparts. In this Perspective article, we provide an overview of the experimental and theoretical literature on global motion processing based on works in primates and mice. Lastly, we propose what types of experiments could illuminate what circuit mechanisms are governing cortical global visual motion processing. We propose that PDS cells in mouse visual cortex appear as the perfect arena for delineating and solving how individual sensory features extracted by neural circuits in peripheral brain areas are integrated to build our rich cohesive sensory experiences.

Intracellular recording, sensory field mapping, and culturing identified neurons in the leech, Hirudo medicinalis.

Science.gov (United States)

Titlow, Josh; Majeed, Zana R; Nicholls, John G; Cooper, Robin L

2013-11-04

The freshwater leech, Hirudo medicinalis, is a versatile model organism that has been used to address scientific questions in the fields of neurophysiology, neuroethology, and developmental biology. The goal of this report is to consolidate experimental techniques from the leech system into a single article that will be of use to physiologists with expertise in other nervous system preparations, or to biology students with little or no electrophysiology experience. We demonstrate how to dissect the leech for recording intracellularly from identified neural circuits in the ganglion. Next we show how individual cells of known function can be removed from the ganglion to be cultured in a Petri dish, and how to record from those neurons in culture. Then we demonstrate how to prepare a patch of innervated skin to be used for mapping sensory or motor fields. These leech preparations are still widely used to address basic electrical properties of neural networks, behavior, synaptogenesis, and development. They are also an appropriate training module for neuroscience or physiology teaching laboratories.

Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns.

Directory of Open Access Journals (Sweden)

Andrea Maesani

2015-11-01

Full Text Available The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs-locomotor bouts-matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior.

Operator Spreading in Random Unitary Circuits

Science.gov (United States)

Nahum, Adam; Vijay, Sagar; Haah, Jeongwan

2018-04-01

Random quantum circuits yield minimally structured models for chaotic quantum dynamics, which are able to capture, for example, universal properties of entanglement growth. We provide exact results and coarse-grained models for the spreading of operators by quantum circuits made of Haar-random unitaries. We study both 1 +1 D and higher dimensions and argue that the coarse-grained pictures carry over to operator spreading in generic many-body systems. In 1 +1 D , we demonstrate that the out-of-time-order correlator (OTOC) satisfies a biased diffusion equation, which gives exact results for the spatial profile of the OTOC and determines the butterfly speed vB. We find that in 1 +1 D , the "front" of the OTOC broadens diffusively, with a width scaling in time as t1 /2. We address fluctuations in the OTOC between different realizations of the random circuit, arguing that they are negligible in comparison to the broadening of the front within a realization. Turning to higher dimensions, we show that the averaged OTOC can be understood exactly via a remarkable correspondence with a purely classical droplet growth problem. This implies that the width of the front of the averaged OTOC scales as t1 /3 in 2 +1 D and as t0.240 in 3 +1 D (exponents of the Kardar-Parisi-Zhang universality class). We support our analytic argument with simulations in 2 +1 D . We point out that, in two or higher spatial dimensions, the shape of the spreading operator at late times is affected by underlying lattice symmetries and, in general, is not spherical. However, when full spatial rotational symmetry is present in 2 +1 D , our mapping implies an exact asymptotic form for the OTOC, in terms of the Tracy-Widom distribution. For an alternative perspective on the OTOC in 1 +1 D , we map it to the partition function of an Ising-like statistical mechanics model. As a result of special structure arising from unitarity, this partition function reduces to a random walk calculation which can be

High speed VLSI neural network for high energy physics

NARCIS (Netherlands)

Masa, P.; Masa, P.; Hoen, K.; Hoen, Klaas; Wallinga, Hans

1994-01-01

A CMOS neural network IC is discussed which was designed for very high speed applications. The parallel architecture, analog computing and digital weight storage provides unprecedented computing speed combined with ease of use. The circuit classifies up to 70 dimensional vectors within 20

Metabolic activation of amygdala, lateral septum and accumbens circuits during food anticipatory behavior.

Science.gov (United States)

Olivo, Diana; Caba, Mario; Gonzalez-Lima, Francisco; Rodríguez-Landa, Juan F; Corona-Morales, Aleph A

2017-01-01

When food is restricted to a brief fixed period every day, animals show an increase in temperature, corticosterone concentration and locomotor activity for 2-3h before feeding time, termed food anticipatory activity. Mechanisms and neuroanatomical circuits responsible for food anticipatory activity remain unclear, and may involve both oscillators and networks related to temporal conditioning. Rabbit pups are nursed once-a-day so they represent a natural model of circadian food anticipatory activity. Food anticipatory behavior in pups may be associated with neural circuits that temporally anticipate feeding, while the nursing event may produce consummatory effects. Therefore, we used New Zealand white rabbit pups entrained to circadian feeding to investigate the hypothesis that structures related to reward expectation and conditioned emotional responses would show a metabolic rhythm anticipatory of the nursing event, different from that shown by structures related to reward delivery. Quantitative cytochrome oxidase histochemistry was used to measure regional brain metabolic activity at eight different times during the day. We found that neural metabolism peaked before nursing, during food anticipatory behavior, in nuclei of the extended amygdala (basolateral, medial and central nuclei, bed nucleus of the stria terminalis), lateral septum and accumbens core. After pups were fed, however, maximal metabolic activity was expressed in the accumbens shell, caudate, putamen and cortical amygdala. Neural and behavioral activation persisted when animals were fasted by two cycles, at the time of expected nursing. These findings suggest that metabolic activation of amygdala-septal-accumbens circuits involved in temporal conditioning may contribute to food anticipatory activity. Copyright © 2016 Elsevier B.V. All rights reserved.

Entrainment and high-density three-dimensional mapping in right atrial macroreentry provide critical complementary information: Entrainment may unmask "visual reentry" as passive.

Science.gov (United States)

Pathik, Bhupesh; Lee, Geoffrey; Nalliah, Chrishan; Joseph, Stephen; Morton, Joseph B; Sparks, Paul B; Sanders, Prashanthan; Kistler, Peter M; Kalman, Jonathan M

2017-10-01

With the recent advent of high-density (HD) 3-dimensional (3D) mapping, the utility of entrainment is uncertain. However, the limitations of visual representation and interpretation of these high-resolution 3D maps are unclear. The purpose of this study was to determine the strengths and limitations of both HD 3D mapping and entrainment mapping during mapping of right atrial macroreentry. Fifteen patients were studied. The number and type of circuits accounting for ≥90% of the tachycardia cycle length using HD 3D mapping were verified using systematic entrainment mapping. Entrainment sites with an unexpectedly long postpacing interval despite proximity to the active circuit were evaluated. Based on HD 3D mapping, 27 circuits were observed: 12 peritricuspid, 2 upper loop reentry, 10 lower loop reentry, and 3 lateral wall circuits. With entrainment, 17 of the 27 circuits were active: all 12 peritricuspid and 2 upper loop reentry. However, lower loop reentry was confirmed in only 3 of 10, and none of the 3 lateral wall circuits were present. Mean percentage of tachycardia cycle length covered by active circuits was 98% ± 1% vs 97% ± 2% for passive circuits (P = .09). None of the 345 entrainment runs terminated tachycardia or changed tachycardia mechanism. In 8 of 15 patients, 13 examples of unexpectedly long postpacing interval were observed at entrainment sites located distal to localized zones of slow conduction seen on HD 3D mapping. Using HD 3D mapping, "visual reentry" may be due to passive circuitous propagation rather than a critical reentrant circuit. HD 3D mapping provides new insights into regional conduction and helps explain unusual entrainment phenomena. Copyright © 2017 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.

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

Directory of Open Access Journals (Sweden)

Chung-Chuan Lo

2016-08-01

Full Text Available 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.

A Bayesian framework for cosmic string searches in CMB maps

Energy Technology Data Exchange (ETDEWEB)

Ciuca, Razvan; Hernández, Oscar F., E-mail: razvan.ciuca@mail.mcgill.ca, E-mail: oscarh@physics.mcgill.ca [Department of Physics, McGill University, 3600 rue University, Montréal, QC, H3A 2T8 (Canada)

2017-08-01

There exists various proposals to detect cosmic strings from Cosmic Microwave Background (CMB) or 21 cm temperature maps. Current proposals do not aim to find the location of strings on sky maps, all of these approaches can be thought of as a statistic on a sky map. We propose a Bayesian interpretation of cosmic string detection and within that framework, we derive a connection between estimates of cosmic string locations and cosmic string tension G μ. We use this Bayesian framework to develop a machine learning framework for detecting strings from sky maps and outline how to implement this framework with neural networks. The neural network we trained was able to detect and locate cosmic strings on noiseless CMB temperature map down to a string tension of G μ=5 ×10{sup −9} and when analyzing a CMB temperature map that does not contain strings, the neural network gives a 0.95 probability that G μ≤2.3×10{sup −9}.

Dissecting OCD Circuits: From Animal Models to Targeted Treatments

Science.gov (United States)

Ahmari, Susanne E.; Dougherty, Darin D.

2015-01-01

Obsessive Compulsive Disorder (OCD) is a chronic, severe mental illness with up to 2–3% prevalence worldwide, which has been classified as one of the world’s 10 leading causes of illness-related disability according to the World Health Organization, largely because of the chronic nature of disabling symptoms 1. Despite the severity and high prevalence of this chronic and disabling disorder, there is still relatively limited understanding of its pathophysiology. However, this is now rapidly changing due to development of powerful technologies that can be used to dissect the neural circuits underlying pathologic behaviors. In this article, we describe recent technical advances that have allowed neuroscientists to start identifying the circuits underlying complex repetitive behaviors using animal model systems. In addition, we review current surgical and stimulation-based treatments for OCD that target circuit dysfunction. Finally, we discuss how findings from animal models may be applied in the clinical arena to help inform and refine targeted brain stimulation-based treatment approaches. PMID:25952989

Brain/MINDS: brain-mapping project in Japan

Science.gov (United States)

Okano, Hideyuki; Miyawaki, Atsushi; Kasai, Kiyoto

2015-01-01

There is an emerging interest in brain-mapping projects in countries across the world, including the USA, Europe, Australia and China. In 2014, Japan started a brain-mapping project called Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS). Brain/MINDS aims to map the structure and function of neuronal circuits to ultimately understand the vast complexity of the human brain, and takes advantage of a unique non-human primate animal model, the common marmoset (Callithrix jacchus). In Brain/MINDS, the RIKEN Brain Science Institute acts as a central institute. The objectives of Brain/MINDS can be categorized into the following three major subject areas: (i) structure and functional mapping of a non-human primate brain (the marmoset brain); (ii) development of innovative neurotechnologies for brain mapping; and (iii) human brain mapping; and clinical research. Brain/MINDS researchers are highly motivated to identify the neuronal circuits responsible for the phenotype of neurological and psychiatric disorders, and to understand the development of these devastating disorders through the integration of these three subject areas. PMID:25823872

Brain/MINDS: brain-mapping project in Japan.

Science.gov (United States)

Okano, Hideyuki; Miyawaki, Atsushi; Kasai, Kiyoto

2015-05-19

There is an emerging interest in brain-mapping projects in countries across the world, including the USA, Europe, Australia and China. In 2014, Japan started a brain-mapping project called Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS). Brain/MINDS aims to map the structure and function of neuronal circuits to ultimately understand the vast complexity of the human brain, and takes advantage of a unique non-human primate animal model, the common marmoset (Callithrix jacchus). In Brain/MINDS, the RIKEN Brain Science Institute acts as a central institute. The objectives of Brain/MINDS can be categorized into the following three major subject areas: (i) structure and functional mapping of a non-human primate brain (the marmoset brain); (ii) development of innovative neurotechnologies for brain mapping; and (iii) human brain mapping; and clinical research. Brain/MINDS researchers are highly motivated to identify the neuronal circuits responsible for the phenotype of neurological and psychiatric disorders, and to understand the development of these devastating disorders through the integration of these three subject areas.

Global exponential stability and periodicity of reaction-diffusion recurrent neural networks with distributed delays and Dirichlet boundary conditions

International Nuclear Information System (INIS)

Lu Junguo; Lu Linji

2009-01-01

In this paper, global exponential stability and periodicity of a class of reaction-diffusion recurrent neural networks with distributed delays and Dirichlet boundary conditions are studied by constructing suitable Lyapunov functionals and utilizing some inequality techniques. We first prove global exponential convergence to 0 of the difference between any two solutions of the original neural networks, the existence and uniqueness of equilibrium is the direct results of this procedure. This approach is different from the usually used one where the existence, uniqueness of equilibrium and stability are proved in two separate steps. Secondly, we prove periodicity. Sufficient conditions ensuring the existence, uniqueness, and global exponential stability of the equilibrium and periodic solution are given. These conditions are easy to verify and our results play an important role in the design and application of globally exponentially stable neural circuits and periodic oscillatory neural circuits.

Application of neural network to CT

International Nuclear Information System (INIS)

Ma, Xiao-Feng; Takeda, Tatsuoki

1999-01-01

This paper presents a new method for two-dimensional image reconstruction by using a multilayer neural network. Multilayer neural networks are extensively investigated and practically applied to solution of various problems such as inverse problems or time series prediction problems. From learning an input-output mapping from a set of examples, neural networks can be regarded as synthesizing an approximation of multidimensional function (that is, solving the problem of hypersurface reconstruction, including smoothing and interpolation). From this viewpoint, neural networks are well suited to the solution of CT image reconstruction. Though a conventionally used object function of a neural network is composed of a sum of squared errors of the output data, we can define an object function composed of a sum of residue of an integral equation. By employing an appropriate line integral for this integral equation, we can construct a neural network that can be used for CT. We applied this method to some model problems and obtained satisfactory results. As it is not necessary to discretized the integral equation using this reconstruction method, therefore it is application to the problem of complicated geometrical shapes is also feasible. Moreover, in neural networks, interpolation is performed quite smoothly, as a result, inverse mapping can be achieved smoothly even in case of including experimental and numerical errors, However, use of conventional back propagation technique for optimization leads to an expensive computation cost. To overcome this drawback, 2nd order optimization methods or parallel computing will be applied in future. (J.P.N.)

Computational Models and Emergent Properties of Respiratory Neural Networks

Science.gov (United States)

Lindsey, Bruce G.; Rybak, Ilya A.; Smith, Jeffrey C.

2012-01-01

Computational models of the neural control system for breathing in mammals provide a theoretical and computational framework bringing together experimental data obtained from different animal preparations under various experimental conditions. Many of these models were developed in parallel and iteratively with experimental studies and provided predictions guiding new experiments. This data-driven modeling approach has advanced our understanding of respiratory network architecture and neural mechanisms underlying generation of the respiratory rhythm and pattern, including their functional reorganization under different physiological conditions. Models reviewed here vary in neurobiological details and computational complexity and span multiple spatiotemporal scales of respiratory control mechanisms. Recent models describe interacting populations of respiratory neurons spatially distributed within the Bötzinger and pre-Bötzinger complexes and rostral ventrolateral medulla that contain core circuits of the respiratory central pattern generator (CPG). Network interactions within these circuits along with intrinsic rhythmogenic properties of neurons form a hierarchy of multiple rhythm generation mechanisms. The functional expression of these mechanisms is controlled by input drives from other brainstem components, including the retrotrapezoid nucleus and pons, which regulate the dynamic behavior of the core circuitry. The emerging view is that the brainstem respiratory network has rhythmogenic capabilities at multiple levels of circuit organization. This allows flexible, state-dependent expression of different neural pattern-generation mechanisms under various physiological conditions, enabling a wide repertoire of respiratory behaviors. Some models consider control of the respiratory CPG by pulmonary feedback and network reconfiguration during defensive behaviors such as cough. Future directions in modeling of the respiratory CPG are considered. PMID:23687564

Circuit variability interacts with excitatory-inhibitory diversity of interneurons to regulate network encoding capacity.

Science.gov (United States)

Tsai, Kuo-Ting; Hu, Chin-Kun; Li, Kuan-Wei; Hwang, Wen-Liang; Chou, Ya-Hui

2018-05-23

Local interneurons (LNs) in the Drosophila olfactory system exhibit neuronal diversity and variability, yet it is still unknown how these features impact information encoding capacity and reliability in a complex LN network. We employed two strategies to construct a diverse excitatory-inhibitory neural network beginning with a ring network structure and then introduced distinct types of inhibitory interneurons and circuit variability to the simulated network. The continuity of activity within the node ensemble (oscillation pattern) was used as a readout to describe the temporal dynamics of network activity. We found that inhibitory interneurons enhance the encoding capacity by protecting the network from extremely short activation periods when the network wiring complexity is very high. In addition, distinct types of interneurons have differential effects on encoding capacity and reliability. Circuit variability may enhance the encoding reliability, with or without compromising encoding capacity. Therefore, we have described how circuit variability of interneurons may interact with excitatory-inhibitory diversity to enhance the encoding capacity and distinguishability of neural networks. In this work, we evaluate the effects of different types and degrees of connection diversity on a ring model, which may simulate interneuron networks in the Drosophila olfactory system or other biological systems.

Neuromimetic Circuits with Synaptic Devices Based on Strongly Correlated Electron Systems

Science.gov (United States)

Ha, Sieu D.; Shi, Jian; Meroz, Yasmine; Mahadevan, L.; Ramanathan, Shriram

2014-12-01

Strongly correlated electron systems such as the rare-earth nickelates (R NiO3 , R denotes a rare-earth element) can exhibit synapselike continuous long-term potentiation and depression when gated with ionic liquids; exploiting the extreme sensitivity of coupled charge, spin, orbital, and lattice degrees of freedom to stoichiometry. We present experimental real-time, device-level classical conditioning and unlearning using nickelate-based synaptic devices in an electronic circuit compatible with both excitatory and inhibitory neurons. We establish a physical model for the device behavior based on electric-field-driven coupled ionic-electronic diffusion that can be utilized for design of more complex systems. We use the model to simulate a variety of associate and nonassociative learning mechanisms, as well as a feedforward recurrent network for storing memory. Our circuit intuitively parallels biological neural architectures, and it can be readily generalized to other forms of cellular learning and extinction. The simulation of neural function with electronic device analogs may provide insight into biological processes such as decision making, learning, and adaptation, while facilitating advanced parallel information processing in hardware.

Neural coding in the visual system of Drosophila melanogaster: How do small neural populations support visually guided behaviours?

Science.gov (United States)

Dewar, Alex D M; Wystrach, Antoine; Philippides, Andrew; Graham, Paul

2017-10-01

All organisms wishing to survive and reproduce must be able to respond adaptively to a complex, changing world. Yet the computational power available is constrained by biology and evolution, favouring mechanisms that are parsimonious yet robust. Here we investigate the information carried in small populations of visually responsive neurons in Drosophila melanogaster. These so-called 'ring neurons', projecting to the ellipsoid body of the central complex, are reported to be necessary for complex visual tasks such as pattern recognition and visual navigation. Recently the receptive fields of these neurons have been mapped, allowing us to investigate how well they can support such behaviours. For instance, in a simulation of classic pattern discrimination experiments, we show that the pattern of output from the ring neurons matches observed fly behaviour. However, performance of the neurons (as with flies) is not perfect and can be easily improved with the addition of extra neurons, suggesting the neurons' receptive fields are not optimised for recognising abstract shapes, a conclusion which casts doubt on cognitive explanations of fly behaviour in pattern recognition assays. Using artificial neural networks, we then assess how easy it is to decode more general information about stimulus shape from the ring neuron population codes. We show that these neurons are well suited for encoding information about size, position and orientation, which are more relevant behavioural parameters for a fly than abstract pattern properties. This leads us to suggest that in order to understand the properties of neural systems, one must consider how perceptual circuits put information at the service of behaviour.

Deciphering Neural Codes of Memory during Sleep

Science.gov (United States)

Chen, Zhe; Wilson, Matthew A.

2017-01-01

Memories of experiences are stored in the cerebral cortex. Sleep is critical for consolidating hippocampal memory of wake experiences into the neocortex. Understanding representations of neural codes of hippocampal-neocortical networks during sleep would reveal important circuit mechanisms on memory consolidation, and provide novel insights into memory and dreams. Although sleep-associated ensemble spike activity has been investigated, identifying the content of memory in sleep remains challenging. Here, we revisit important experimental findings on sleep-associated memory (i.e., neural activity patterns in sleep that reflect memory processing) and review computational approaches for analyzing sleep-associated neural codes (SANC). We focus on two analysis paradigms for sleep-associated memory, and propose a new unsupervised learning framework (“memory first, meaning later”) for unbiased assessment of SANC. PMID:28390699

Synthetic Biology: A Unifying View and Review Using Analog Circuits.

Science.gov (United States)

Teo, Jonathan J Y; Woo, Sung Sik; Sarpeshkar, Rahul

2015-08-01

We review the field of synthetic biology from an analog circuits and analog computation perspective, focusing on circuits that have been built in living cells. This perspective is well suited to pictorially, symbolically, and quantitatively representing the nonlinear, dynamic, and stochastic (noisy) ordinary and partial differential equations that rigorously describe the molecular circuits of synthetic biology. This perspective enables us to construct a canonical analog circuit schematic that helps unify and review the operation of many fundamental circuits that have been built in synthetic biology at the DNA, RNA, protein, and small-molecule levels over nearly two decades. We review 17 circuits in the literature as particular examples of feedforward and feedback analog circuits that arise from special topological cases of the canonical analog circuit schematic. Digital circuit operation of these circuits represents a special case of saturated analog circuit behavior and is automatically incorporated as well. Many issues that have prevented synthetic biology from scaling are naturally represented in analog circuit schematics. Furthermore, the deep similarity between the Boltzmann thermodynamic equations that describe noisy electronic current flow in subthreshold transistors and noisy molecular flux in biochemical reactions has helped map analog circuit motifs in electronics to analog circuit motifs in cells and vice versa via a `cytomorphic' approach. Thus, a body of knowledge in analog electronic circuit design, analysis, simulation, and implementation may also be useful in the robust and efficient design of molecular circuits in synthetic biology, helping it to scale to more complex circuits in the future.

Liquefaction Microzonation of Babol City Using Artificial Neural Network

DEFF Research Database (Denmark)

Farrokhzad, F.; Choobbasti, A.J.; Barari, Amin

2012-01-01

that will be less susceptible to damage during earthquakes. The scope of present study is to prepare the liquefaction microzonation map for the Babol city based on Seed and Idriss (1983) method using artificial neural network. Artificial neural network (ANN) is one of the artificial intelligence (AI) approaches...... microzonation map is produced for research area. Based on the obtained results, it can be stated that the trained neural network is capable in prediction of liquefaction potential with an acceptable level of confidence. At the end, zoning of the city is carried out based on the prediction of liquefaction...... that can be classified as machine learning. Simplified methods have been practiced by researchers to assess nonlinear liquefaction potential of soil. In order to address the collective knowledge built-up in conventional liquefaction engineering, an alternative general regression neural network model...

Behavioral Synthesis of Asynchronous Circuits Using Syntax Directed Translation as Backend

DEFF Research Database (Denmark)

Nielsen, Sune Fallgaard; Sparsø, Jens; Madsen, Jan

2009-01-01

The current state-of-the art in high-level synthesis of asynchronous circuits is syntax directed translation, which performs a one-to-one mapping of a HDL-description into a corresponding circuit. This paper presents a method for behavioral synthesis of asynchronous circuits which builds on top...... description language Balsa [1]. This ”conventional” template architecture allows us to adapt traditional synchronous synthesis techniques for resource sharing, scheduling, binding etc, to the domain of asynchronous circuits. A prototype tool has been implemented on top of the Balsa framework, and the method...... is illustrated through the implementation of a set of example circuits. The main contributions of the paper are: the fundamental idea, the template architecture and its implementation using asynchronous handshake components, and the implementation of a prototype tool....

Pharmacologically-mediated reactivation and reconsolidation blockade of the psychostimulant-abuse circuit: A novel treatment strategy

Science.gov (United States)

Lee, Tong H.; Szabo, Steven T.; Fowler, J. Corey; Mannelli, Paolo; Mangum, O. Barry; Beyer, Wayne F.; Patkar, Ashwin; Wetsel, William C.

2012-01-01

Psychostimulant abuse continues to present legal, socioeconomic and medical challenges as a primary psychiatric disorder, and represents a significant comorbid factor in major psychiatric and medical illnesses. To date, monotherapeutic drug treatments have not proven effective in promoting long-term abstinence in psychostimulant abusers. In contrast to clinical trials utilizing monotherapies, combinations of dopamine (DA) agonists and selective 5-HT3, 5HT2A/2C, or NK1 antagonists have shown robust efficacy in reversing behavioral and neurobiological alterations in animal models of psychostimulant abuse. One important temporal requirement for these treatments is that the 5-HT or NK1 receptor antagonist be given at a critical time window after DA agonist administration. This requirement may reflect a necessary dosing regimen towards normalizing underlying dysfunctional neural circuits and “addiction memory” states. Indeed, chronic psychostimulant abuse can be conceptualized as a consolidated form of dysfunctional memory maintained by repeated drug- or cue-induced reactivation of neural circuit and subsequent reconsolidation. According to this concept, the DA agonist given first may reactivate this memory circuit, thereby rendering it transiently labile. The subsequent antagonist is hypothesized to disrupt reconsolidation necessary for restabilization, thus leading progressively to a therapeutically-mediated abolishment of dysfunctional synaptic plasticity. We propose that long-term abstinence in psychostimulant abusers may be achieved not only by targeting putative mechanistic pathways, but also by optimizing drug treatment regimens designed to disrupt the neural processes underlying the addicted state. PMID:22356892

Hypothalamic Circuits for Predation and Evasion.

Science.gov (United States)

Li, Yi; Zeng, Jiawei; Zhang, Juen; Yue, Chenyu; Zhong, Weixin; Liu, Zhixiang; Feng, Qiru; Luo, Minmin

2018-02-21

The interactions between predator and prey represent some of the most dramatic events in nature and constitute a matter of life and death for both sides. The hypothalamus has been implicated in driving predation and evasion; however, the exact hypothalamic neural circuits underlying these behaviors remain poorly defined. Here, we demonstrate that inhibitory and excitatory projections from the mouse lateral hypothalamus (LH) to the periaqueductal gray (PAG) in the midbrain drive, respectively, predation and evasion. LH GABA neurons were activated during predation. Optogenetically stimulating PAG-projecting LH GABA neurons drove strong predatory attack, and inhibiting these cells reversibly blocked predation. In contrast, LH glutamate neurons were activated during evasion. Stimulating PAG-projecting LH glutamate neurons drove evasion and inhibiting them impeded predictive evasion. Therefore, the seemingly opposite behaviors of predation and evasion are tightly regulated by two dissociable modular command systems within a single neural projection from the LH to the PAG. VIDEO ABSTRACT. Copyright © 2018 Elsevier Inc. All rights reserved.

Neural signatures of Trail Making Test performance: Evidence from lesion-mapping and neuroimaging studies.

Science.gov (United States)

Varjacic, Andreja; Mantini, Dante; Demeyere, Nele; Gillebert, Celine R

2018-03-27

The Trail Making Test (TMT) is an extensively used neuropsychological instrument for the assessment of set-switching ability across a wide range of neurological conditions. However, the exact nature of the cognitive processes and associated brain regions contributing to the performance on the TMT remains unclear. In this review, we first introduce the TMT by discussing its administration and scoring approaches. We then examine converging evidence and divergent findings concerning the brain regions related to TMT performance, as identified by lesion-symptom mapping studies conducted in brain-injured patients and functional magnetic resonance imaging studies conducted in healthy participants. After addressing factors that may account for the heterogeneity in the brain regions reported by these studies, we identify future research endeavours that may permit disentangling the different processes contributing to TMT performance and relating them to specific brain circuits. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Olfactory systems and neural circuits that modulate predator odor fear

OpenAIRE

Takahashi, Lorey K.

2014-01-01

When prey animals detect the odor of a predator a constellation of fear-related autonomic, endocrine, and behavioral responses rapidly occur to facilitate survival. How olfactory sensory systems process predator odor and channel that information to specific brain circuits is a fundamental issue that is not clearly understood. However, research in the last 15 years has begun to identify some of the essential features of the sensory detection systems and brain structures that underlie predator ...

Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework.

Directory of Open Access Journals (Sweden)

H Francis Song

2016-02-01

Full Text Available The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs that have been optimized to perform the same tasks as behaving animals. Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, "trained" networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them arbitrarily, make trained networks a convenient proxy for biological circuits and a valuable platform for theoretical investigation. However, existing RNNs lack basic biological features such as the distinction between excitatory and inhibitory units (Dale's principle, which are essential if RNNs are to provide insights into the operation of biological circuits. Moreover, trained networks can achieve the same behavioral performance but differ substantially in their structure and dynamics, highlighting the need for a simple and flexible framework for the exploratory training of RNNs. Here, we describe a framework for gradient descent-based training of excitatory-inhibitory RNNs that can incorporate a variety of biological knowledge. We provide an implementation based on the machine learning library Theano, whose automatic differentiation capabilities facilitate modifications and extensions. We validate this framework by applying it to well-known experimental paradigms such as perceptual decision-making, context-dependent integration, multisensory integration, parametric working memory, and motor sequence generation. Our results demonstrate the wide range of neural

Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework

Science.gov (United States)

Wang, Xiao-Jing

2016-01-01

The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs) that have been optimized to perform the same tasks as behaving animals. Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, “trained” networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them arbitrarily, make trained networks a convenient proxy for biological circuits and a valuable platform for theoretical investigation. However, existing RNNs lack basic biological features such as the distinction between excitatory and inhibitory units (Dale’s principle), which are essential if RNNs are to provide insights into the operation of biological circuits. Moreover, trained networks can achieve the same behavioral performance but differ substantially in their structure and dynamics, highlighting the need for a simple and flexible framework for the exploratory training of RNNs. Here, we describe a framework for gradient descent-based training of excitatory-inhibitory RNNs that can incorporate a variety of biological knowledge. We provide an implementation based on the machine learning library Theano, whose automatic differentiation capabilities facilitate modifications and extensions. We validate this framework by applying it to well-known experimental paradigms such as perceptual decision-making, context-dependent integration, multisensory integration, parametric working memory, and motor sequence generation. Our results demonstrate the wide range of neural activity

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

Science.gov (United States)

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

2015-12-01

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

A NEW RECOGNITION TECHNIQUE NAMED SOMP BASED ON PALMPRINT USING NEURAL NETWORK BASED SELF ORGANIZING MAPS

Directory of Open Access Journals (Sweden)

A. S. Raja

2012-08-01

Full Text Available The word biometrics refers to the use of physiological or biological characteristics of human to recognize and verify the identity of an individual. Palmprint has become a new class of human biometrics for passive identification with uniqueness and stability. This is considered to be reliable due to the lack of expressions and the lesser effect of aging. In this manuscript a new Palmprint based biometric system based on neural networks self organizing maps (SOM is presented. The method is named as SOMP. The paper shows that the proposed SOMP method improves the performance and robustness of recognition. The proposed method is applied to a variety of datasets and the results are shown.

The challenges of neural mind-reading paradigms

OpenAIRE

Vilarroya, Oscar

2013-01-01

Neural mind-reading studies, based on multivariate pattern analysis (MVPA) methods, are providing exciting new studies. Some of the results obtained with these paradigms have raised high expectations, such as the possibility of creating brain reading devices. However, such hopes are based on the assumptions that: (a) the BOLD signal is a marker of neural activity; (b) the BOLD pattern identified by a MVPA is a neurally sound pattern; (c) the MVPA's feature space is a good mapping of the neura...

Standardization of Schwarz-Christoffel transformation for engineering design of semiconductor and hybrid integrated-circuit elements

Science.gov (United States)

Yashin, A. A.

1985-04-01

A semiconductor or hybrid structure into a calculable two-dimensional region mapped by the Schwarz-Christoffel transformation and a universal algorithm can be constructed on the basis of Maxwell's electro-magnetic-thermal similarity principle for engineering design of integrated-circuit elements. The design procedure involves conformal mapping of the original region into a polygon and then the latter into a rectangle with uniform field distribution, where conductances and capacitances are calculated, using tabulated standard mapping functions. Subsequent synthesis of a device requires inverse conformal mapping. Devices adaptable as integrated-circuit elements are high-resistance film resistors with periodic serration, distributed-resistance film attenuators with high transformation ratio, coplanar microstrip lines, bipolar transistors, directional couplers with distributed coupling to microstrip lines for microwave bulk devices, and quasirregular smooth matching transitions from asymmetric to coplanar microstrip lines.

Neonatal ghrelin programs development of hypothalamic feeding circuits

Science.gov (United States)

Steculorum, Sophie M.; Collden, Gustav; Coupe, Berengere; Croizier, Sophie; Lockie, Sarah; Andrews, Zane B.; Jarosch, Florian; Klussmann, Sven; Bouret, Sebastien G.

2015-01-01

A complex neural network regulates body weight and energy balance, and dysfunction in the communication between the gut and this neural network is associated with metabolic diseases, such as obesity. The stomach-derived hormone ghrelin stimulates appetite through interactions with neurons in the arcuate nucleus of the hypothalamus (ARH). Here, we evaluated the physiological and neurobiological contribution of ghrelin during development by specifically blocking ghrelin action during early postnatal development in mice. Ghrelin blockade in neonatal mice resulted in enhanced ARH neural projections and long-term metabolic effects, including increased body weight, visceral fat, and blood glucose levels and decreased leptin sensitivity. In addition, chronic administration of ghrelin during postnatal life impaired the normal development of ARH projections and caused metabolic dysfunction. Consistent with these observations, direct exposure of postnatal ARH neuronal explants to ghrelin blunted axonal growth and blocked the neurotrophic effect of the adipocyte-derived hormone leptin. Moreover, chronic ghrelin exposure in neonatal mice also attenuated leptin-induced STAT3 signaling in ARH neurons. Collectively, these data reveal that ghrelin plays an inhibitory role in the development of hypothalamic neural circuits and suggest that proper expression of ghrelin during neonatal life is pivotal for lifelong metabolic regulation. PMID:25607843

Methods and Tools for the Analysis, Verification and Synthesis of Genetic Logic Circuits,

DEFF Research Database (Denmark)

Baig, Hasan

2017-01-01

. This usually requires simulating the mathematical models of these genetic circuits and perceive whether or not the circuit behaves appropriately. Furthermore, synthetic biology utilizes the concepts from electronic design automation (EDA) of abstraction and automated construction to generate genetic circuits...... that the proposed approach is effective to determine the variation in the behavior of genetic circuits when the circuit’s parameters are changed. In addition, the thesis also attempts to propose a synthesis and technology mapping tool, called GeneTech, for genetic circuits. It allows users to construct a genetic...... important design characteristics. This thesis also introduces an automated approach to analyze the behavior of genetic logic circuits from the simulation data. With this capability, the boolean logic of complex genetic circuits can be analyzed and/or verified automatically. It is also shown in this thesis...

Characterization of the central neural projections to brown, white, and beige adipose tissue.

Science.gov (United States)

Wiedmann, Nicole M; Stefanidis, Aneta; Oldfield, Brian J

2017-11-01

The functional recruitment of classic brown adipose tissue (BAT) and inducible brown-like or beige fat is, to a large extent, dependent on intact sympathetic neural input. Whereas the central neural circuits directed specifically to BAT or white adipose tissue (WAT) are well established, there is only a developing insight into the nature of neural inputs common to both fat types. Moreover, there is no clear view of the specific central and peripheral innervation of the browned component of WAT: beige fat. The objective of the present study is to examine the neural input to both BAT and WAT in the same animal and, by exposing different cohorts of rats to either thermoneutral or cold conditions, define changes in central neural organization that will ensure that beige fat is appropriately recruited and modulated after browning of inguinal WAT (iWAT). At thermoneutrality, injection of the neurotropic (pseudorabies) viruses into BAT and WAT demonstrates that there are dedicated axonal projections, as well as collateral axonal branches of command neurons projecting to both types of fat. After cold exposure, central neural circuits directed to iWAT showed evidence of reorganization with a greater representation of command neurons projecting to both brown and beiged WAT in hypothalamic (paraventricular nucleus and lateral hypothalamus) and brainstem (raphe pallidus and locus coeruleus) sites. This shift was driven by a greater number of supraspinal neurons projecting to iWAT under cold conditions. These data provide evidence for a reorganization of the nervous system at the level of neural connectivity following browning of WAT.-Wiedmann, N. M., Stefanidis, A., Oldfield, B. J. Characterization of the central neural projections to brown, white, and beige adipose tissue. © FASEB.

GABAergic circuit dysfunctions in neurodevelopmental disorders

Directory of Open Access Journals (Sweden)

Bidisha eChattopadhyaya

2012-05-01

Full Text Available GABAergic interneurons control neuronal excitability, integration, and plasticity. Further, they regulate the generation of temporal synchrony and oscillatory behavior among networks of pyramidal neurons. Such oscillations within and across neural systems are believed to serve various complex functions, such as perception, movement initiation, and memory. Alterations in the development of GABAergic circuits have been implicated in various brain diseases with neurodevelopmental origin. Here, we highlight recent studies suggesting a role for alterations of GABA transmission in the pathophysiology of two neurodevelopmental diseases, schizophrenia and autism. We further discuss how manipulations of GABA signaling may be used for novel therapeutic interventions.

Association of contextual cues with morphine reward increases neural and synaptic plasticity in the ventral hippocampus of rats

NARCIS (Netherlands)

Alvandi, M.S.; Bourmpoula, M.; Homberg, J.R.; Fathollahi, Y.

2017-01-01

Drug addiction is associated with aberrant memory and permanent functional changes in neural circuits. It is known that exposure to drugs like morphine is associated with positive emotional states and reward-related memory. However, the underlying mechanisms in terms of neural plasticity in the

Prediction based chaos control via a new neural network

International Nuclear Information System (INIS)

Shen Liqun; Wang Mao; Liu Wanyu; Sun Guanghui

2008-01-01

In this Letter, a new chaos control scheme based on chaos prediction is proposed. To perform chaos prediction, a new neural network architecture for complex nonlinear approximation is proposed. And the difficulty in building and training the neural network is also reduced. Simulation results of Logistic map and Lorenz system show the effectiveness of the proposed chaos control scheme and the proposed neural network

Memory and cognitive control circuits in mathematical cognition and learning

Science.gov (United States)

Menon, V.

2018-01-01

Numerical cognition relies on interactions within and between multiple functional brain systems, including those subserving quantity processing, working memory, declarative memory, and cognitive control. This chapter describes recent advances in our understanding of memory and control circuits in mathematical cognition and learning. The working memory system involves multiple parietal–frontal circuits which create short-term representations that allow manipulation of discrete quantities over several seconds. In contrast, hippocampal–frontal circuits underlying the declarative memory system play an important role in formation of associative memories and binding of new and old information, leading to the formation of long-term memories that allow generalization beyond individual problem attributes. The flow of information across these systems is regulated by flexible cognitive control systems which facilitate the integration and manipulation of quantity and mnemonic information. The implications of recent research for formulating a more comprehensive systems neuroscience view of the neural basis of mathematical learning and knowledge acquisition in both children and adults are discussed. PMID:27339012

An Attractor-Based Complexity Measurement for Boolean Recurrent Neural Networks

Science.gov (United States)

Cabessa, Jérémie; Villa, Alessandro E. P.

2014-01-01

We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics. This complexity measurement is achieved by first proving a computational equivalence between Boolean recurrent neural networks and some specific class of -automata, and then translating the most refined classification of -automata to the Boolean neural network context. As a result, a hierarchical classification of Boolean neural networks based on their attractive dynamics is obtained, thus providing a novel refined attractor-based complexity measurement for Boolean recurrent neural networks. These results provide new theoretical insights to the computational and dynamical capabilities of neural networks according to their attractive potentialities. An application of our findings is illustrated by the analysis of the dynamics of a simplified model of the basal ganglia-thalamocortical network simulated by a Boolean recurrent neural network. This example shows the significance of measuring network complexity, and how our results bear new founding elements for the understanding of the complexity of real brain circuits. PMID:24727866

Neural network based approach for tuning of SNS feedback and feedforward controllers

International Nuclear Information System (INIS)

Kwon, Sung-Il; Prokop, Mark S.; Regan, Amy H.

2002-01-01

The primary controllers in the SNS low level RF system are proportional-integral (PI) feedback controllers. To obtain the best performance of the linac control systems, approximately 91 individual PI controller gains should be optimally tuned. Tuning is time consuming and requires automation. In this paper, a neural network is used for the controller gain tuning. A neural network can approximate any continuous mapping through learning. In a sense, the cavity loop PI controller is a continuous mapping of the tracking error and its one-sample-delay inputs to the controller output. Also, monotonic cavity output with respect to its input makes knowing the detailed parameters of the cavity unnecessary. Hence the PI controller is a prime candidate for approximation through a neural network. Using mean square error minimization to train the neural network along with a continuous mapping of appropriate weights, optimally tuned PI controller gains can be determined. The same neural network approximation property is also applied to enhance the adaptive feedforward controller performance. This is done by adjusting the feedforward controller gains, forgetting factor, and learning ratio. Lastly, the automation of the tuning procedure data measurement, neural network training, tuning and loading the controller gain to the DSP is addressed.

A new paradigm in the design of energy-efficient digital circuits using laterally-actuated double-gate NEMS

KAUST Repository

Dadgour, Hamed F.; Hussain, Muhammad Mustafa; Banerjee, Kaustav

2010-01-01

. While this in itself is a major improvement, its implications for minimizing Boolean functions using Karnaugh maps (K-maps) are even more significant. In the standard K-map technique, which is used in digital circuit design, adjacent "1s" (minterms

Music Signal Processing Using Vector Product Neural Networks

Science.gov (United States)

Fan, Z. C.; Chan, T. S.; Yang, Y. H.; Jang, J. S. R.

2017-05-01

We propose a novel neural network model for music signal processing using vector product neurons and dimensionality transformations. Here, the inputs are first mapped from real values into three-dimensional vectors then fed into a three-dimensional vector product neural network where the inputs, outputs, and weights are all three-dimensional values. Next, the final outputs are mapped back to the reals. Two methods for dimensionality transformation are proposed, one via context windows and the other via spectral coloring. Experimental results on the iKala dataset for blind singing voice separation confirm the efficacy of our model.

Topological schemas of cognitive maps and spatial learning

Directory of Open Access Journals (Sweden)

Andrey eBabichev

2016-03-01

Full Text Available Spatial navigation in mammals is based on building a mental representation of their environment---a cognitive map. However, both the nature of this cognitive map and its underpinning in neural structures and activity remains vague. A key difficulty is that these maps are collective, emergent phenomena that cannot be reduced to a simple combination of inputs provided by individual neurons. In this paper we suggest computational frameworks for integrating the spiking signals of individual cells into a spatial map, which we call schemas. We provide examples of four schemas defined by different types of topological relations that may be neurophysiologically encoded in the brain and demonstrate that each schema provides its own large-scale characteristics of the environment---the schema integrals. Moreover, we find that, in all cases, these integrals are learned at a rate which is faster than the rate of complete training of neural networks. Thus, the proposed schema framework differentiates between the cognitive aspect of spatial learning and the physiological aspect at the neural network level.

Topological Schemas of Cognitive Maps and Spatial Learning.

Science.gov (United States)

Babichev, Andrey; Cheng, Sen; Dabaghian, Yuri A

2016-01-01

Spatial navigation in mammals is based on building a mental representation of their environment-a cognitive map. However, both the nature of this cognitive map and its underpinning in neural structures and activity remains vague. A key difficulty is that these maps are collective, emergent phenomena that cannot be reduced to a simple combination of inputs provided by individual neurons. In this paper we suggest computational frameworks for integrating the spiking signals of individual cells into a spatial map, which we call schemas. We provide examples of four schemas defined by different types of topological relations that may be neurophysiologically encoded in the brain and demonstrate that each schema provides its own large-scale characteristics of the environment-the schema integrals. Moreover, we find that, in all cases, these integrals are learned at a rate which is faster than the rate of complete training of neural networks. Thus, the proposed schema framework differentiates between the cognitive aspect of spatial learning and the physiological aspect at the neural network level.

Mapping real-time air pollution health risk for environmental management: Combining mobile and stationary air pollution monitoring with neural network models.

Science.gov (United States)

Adams, Matthew D; Kanaroglou, Pavlos S

2016-03-01

Air pollution poses health concerns at the global scale. The challenge of managing air pollution is significant because of the many air pollutants, insufficient funds for monitoring and abatement programs, and political and social challenges in defining policy to limit emissions. Some governments provide citizens with air pollution health risk information to allow them to limit their exposure. However, many regions still have insufficient air pollution monitoring networks to provide real-time mapping. Where available, these risk mapping systems either provide absolute concentration data or the concentrations are used to derive an Air Quality Index, which provides the air pollution risk for a mix of air pollutants with a single value. When risk information is presented as a single value for an entire region it does not inform on the spatial variation within the region. Without an understanding of the local variation residents can only make a partially informed decision when choosing daily activities. The single value is typically provided because of a limited number of active monitoring units in the area. In our work, we overcome this issue by leveraging mobile air pollution monitoring techniques, meteorological information and land use information to map real-time air pollution health risks. We propose an approach that can provide improved health risk information to the public by applying neural network models within a framework that is inspired by land use regression. Mobile air pollution monitoring campaigns were conducted across Hamilton from 2005 to 2013. These mobile air pollution data were modelled with a number of predictor variables that included information on the surrounding land use characteristics, the meteorological conditions, air pollution concentrations from fixed location monitors, and traffic information during the time of collection. Fine particulate matter and nitrogen dioxide were both modelled. During the model fitting process we reserved

Compiling quantum circuits to realistic hardware architectures using temporal planners

Science.gov (United States)

Venturelli, Davide; Do, Minh; Rieffel, Eleanor; Frank, Jeremy

2018-04-01

To run quantum algorithms on emerging gate-model quantum hardware, quantum circuits must be compiled to take into account constraints on the hardware. For near-term hardware, with only limited means to mitigate decoherence, it is critical to minimize the duration of the circuit. We investigate the application of temporal planners to the problem of compiling quantum circuits to newly emerging quantum hardware. While our approach is general, we focus on compiling to superconducting hardware architectures with nearest neighbor constraints. Our initial experiments focus on compiling Quantum Alternating Operator Ansatz (QAOA) circuits whose high number of commuting gates allow great flexibility in the order in which the gates can be applied. That freedom makes it more challenging to find optimal compilations but also means there is a greater potential win from more optimized compilation than for less flexible circuits. We map this quantum circuit compilation problem to a temporal planning problem, and generated a test suite of compilation problems for QAOA circuits of various sizes to a realistic hardware architecture. We report compilation results from several state-of-the-art temporal planners on this test set. This early empirical evaluation demonstrates that temporal planning is a viable approach to quantum circuit compilation.

Enhanced surrogate models for statistical design exploiting space mapping technology

DEFF Research Database (Denmark)

Koziel, Slawek; Bandler, John W.; Mohamed, Achmed S.

2005-01-01

We present advances in microwave and RF device modeling exploiting Space Mapping (SM) technology. We propose new SM modeling formulations utilizing input mappings, output mappings, frequency scaling and quadratic approximations. Our aim is to enhance circuit models for statistical analysis...

Very High Resolution Tree Cover Mapping for Continental United States using Deep Convolutional Neural Networks

Science.gov (United States)

Ganguly, Sangram; Kalia, Subodh; Li, Shuang; Michaelis, Andrew; Nemani, Ramakrishna R.; Saatchi, Sassan A

2017-01-01

Uncertainties in input land cover estimates contribute to a significant bias in modeled above ground biomass (AGB) and carbon estimates from satellite-derived data. The resolution of most currently used passive remote sensing products is not sufficient to capture tree canopy cover of less than ca. 10-20 percent, limiting their utility to estimate canopy cover and AGB for trees outside of forest land. In our study, we created a first of its kind Continental United States (CONUS) tree cover map at a spatial resolution of 1-m for the 2010-2012 epoch using the USDA NAIP imagery to address the present uncertainties in AGB estimates. The process involves different tasks including data acquisition ingestion to pre-processing and running a state-of-art encoder-decoder based deep convolutional neural network (CNN) algorithm for automatically generating a tree non-tree map for almost a quarter million scenes. The entire processing chain including generation of the largest open source existing aerial satellite image training database was performed at the NEX supercomputing and storage facility. We believe the resulting forest cover product will substantially contribute to filling the gaps in ongoing carbon and ecological monitoring research and help quantifying the errors and uncertainties in derived products.

Very High Resolution Tree Cover Mapping for Continental United States using Deep Convolutional Neural Networks

Science.gov (United States)

Ganguly, S.; Kalia, S.; Li, S.; Michaelis, A.; Nemani, R. R.; Saatchi, S.

2017-12-01

Uncertainties in input land cover estimates contribute to a significant bias in modeled above gound biomass (AGB) and carbon estimates from satellite-derived data. The resolution of most currently used passive remote sensing products is not sufficient to capture tree canopy cover of less than ca. 10-20 percent, limiting their utility to estimate canopy cover and AGB for trees outside of forest land. In our study, we created a first of its kind Continental United States (CONUS) tree cover map at a spatial resolution of 1-m for the 2010-2012 epoch using the USDA NAIP imagery to address the present uncertainties in AGB estimates. The process involves different tasks including data acquisition/ingestion to pre-processing and running a state-of-art encoder-decoder based deep convolutional neural network (CNN) algorithm for automatically generating a tree/non-tree map for almost a quarter million scenes. The entire processing chain including generation of the largest open source existing aerial/satellite image training database was performed at the NEX supercomputing and storage facility. We believe the resulting forest cover product will substantially contribute to filling the gaps in ongoing carbon and ecological monitoring research and help quantifying the errors and uncertainties in derived products.

A Simple and Robust Gray Image Encryption Scheme Using Chaotic Logistic Map and Artificial Neural Network

Directory of Open Access Journals (Sweden)

Adelaïde Nicole Kengnou Telem

2014-01-01

Full Text Available A robust gray image encryption scheme using chaotic logistic map and artificial neural network (ANN is introduced. In the proposed method, an external secret key is used to derive the initial conditions for the logistic chaotic maps which are employed to generate weights and biases matrices of the multilayer perceptron (MLP. During the learning process with the backpropagation algorithm, ANN determines the weight matrix of the connections. The plain image is divided into four subimages which are used for the first diffusion stage. The subimages obtained previously are divided into the square subimage blocks. In the next stage, different initial conditions are employed to generate a key stream which will be used for permutation and diffusion of the subimage blocks. Some security analyses such as entropy analysis, statistical analysis, and key sensitivity analysis are given to demonstrate the key space of the proposed algorithm which is large enough to make brute force attacks infeasible. Computing validation using experimental data with several gray images has been carried out with detailed numerical analysis, in order to validate the high security of the proposed encryption scheme.

CHEETAH: circuit-switched high-speed end-to-end transport architecture

Science.gov (United States)

Veeraraghavan, Malathi; Zheng, Xuan; Lee, Hyuk; Gardner, M.; Feng, Wuchun

2003-10-01

Leveraging the dominance of Ethernet in LANs and SONET/SDH in MANs and WANs, we propose a service called CHEETAH (Circuit-switched High-speed End-to-End Transport ArcHitecture). The service concept is to provide end hosts with high-speed, end-to-end circuit connectivity on a call-by-call shared basis, where a "circuit" consists of Ethernet segments at the ends that are mapped into Ethernet-over-SONET long-distance circuits. This paper focuses on the file-transfer application for such circuits. For this application, the CHEETAH service is proposed as an add-on to the primary Internet access service already in place for enterprise hosts. This allows an end host that is sending a file to first attempt setting up an end-to-end Ethernet/EoS circuit, and if rejected, fall back to the TCP/IP path. If the circuit setup is successful, the end host will enjoy a much shorter file-transfer delay than on the TCP/IP path. To determine the conditions under which an end host with access to the CHEETAH service should attempt circuit setup, we analyze mean file-transfer delays as a function of call blocking probability in the circuit-switched network, probability of packet loss in the IP network, round-trip times, link rates, and so on.

Replicable Expansion and Differentiation of Neural Precursors from Adult Canine Skin

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Thomas Duncan

2017-08-01

Full Text Available Repopulation of brain circuits by neural precursors is a potential therapeutic strategy for neurodegenerative disorders; however, choice of cell is critical. Previously, we introduced a two-step culture system that generates a high yield of neural precursors from small samples of adult canine skin. Here, we probe their gene and protein expression profiles in comparison with dermal fibroblasts and brain-derived neural stem cells and characterize their neuronal potential. To date, we have produced >50 skin-derived neural precursor (SKN lines. SKNs can be cultured in a highly replicable fashion and uniformly express a panel of identifying markers. Upon differentiation, they self-upregulate neural specification genes, generating neurons with basic electrophysiological functionality. This unique population of neural precursors, derived from mature skin, overcomes many of the practical issues that have limited clinical translation of alternative cell types. Easily accessible, neuronally committed, and patient specific, SKNs may have potential for the treatment of brain disorders.

Dissecting neural pathways for forgetting in Drosophila olfactory aversive memory.

Science.gov (United States)

Shuai, Yichun; Hirokawa, Areekul; Ai, Yulian; Zhang, Min; Li, Wanhe; Zhong, Yi

2015-12-01

Recent studies have identified molecular pathways driving forgetting and supported the notion that forgetting is a biologically active process. The circuit mechanisms of forgetting, however, remain largely unknown. Here we report two sets of Drosophila neurons that account for the rapid forgetting of early olfactory aversive memory. We show that inactivating these neurons inhibits memory decay without altering learning, whereas activating them promotes forgetting. These neurons, including a cluster of dopaminergic neurons (PAM-β'1) and a pair of glutamatergic neurons (MBON-γ4>γ1γ2), terminate in distinct subdomains in the mushroom body and represent parallel neural pathways for regulating forgetting. Interestingly, although activity of these neurons is required for memory decay over time, they are not required for acute forgetting during reversal learning. Our results thus not only establish the presence of multiple neural pathways for forgetting in Drosophila but also suggest the existence of diverse circuit mechanisms of forgetting in different contexts.

Bias-dependent hybrid PKI empirical-neural model of microwave FETs

Science.gov (United States)

Marinković, Zlatica; Pronić-Rančić, Olivera; Marković, Vera

2011-10-01

Empirical models of microwave transistors based on an equivalent circuit are valid for only one bias point. Bias-dependent analysis requires repeated extractions of the model parameters for each bias point. In order to make model bias-dependent, a new hybrid empirical-neural model of microwave field-effect transistors is proposed in this article. The model is a combination of an equivalent circuit model including noise developed for one bias point and two prior knowledge input artificial neural networks (PKI ANNs) aimed at introducing bias dependency of scattering (S) and noise parameters, respectively. The prior knowledge of the proposed ANNs involves the values of the S- and noise parameters obtained by the empirical model. The proposed hybrid model is valid in the whole range of bias conditions. Moreover, the proposed model provides better accuracy than the empirical model, which is illustrated by an appropriate modelling example of a pseudomorphic high-electron mobility transistor device.

Tomonaga-Luttinger physics in electronic quantum circuits.

Science.gov (United States)

Jezouin, S; Albert, M; Parmentier, F D; Anthore, A; Gennser, U; Cavanna, A; Safi, I; Pierre, F

2013-01-01

In one-dimensional conductors, interactions result in correlated electronic systems. At low energy, a hallmark signature of the so-called Tomonaga-Luttinger liquids is the universal conductance curve predicted in presence of an impurity. A seemingly different topic is the quantum laws of electricity, when distinct quantum conductors are assembled in a circuit. In particular, the conductances are suppressed at low energy, a phenomenon called dynamical Coulomb blockade. Here we investigate the conductance of mesoscopic circuits constituted by a short single-channel quantum conductor in series with a resistance, and demonstrate a proposed link to Tomonaga-Luttinger physics. We reformulate and establish experimentally a recently derived phenomenological expression for the conductance using a wide range of circuits, including carbon nanotube data obtained elsewhere. By confronting both conductance data and phenomenological expression with the universal Tomonaga-Luttinger conductance curve, we demonstrate experimentally the predicted mapping between dynamical Coulomb blockade and the transport across a Tomonaga-Luttinger liquid with an impurity.

Short- circuit tests of circuit breakers

OpenAIRE

Chorovský, P.

2015-01-01

This paper deals with short-circuit tests of low voltage electrical devices. In the first part of this paper, there are described basic types of short- circuit tests and their principles. Direct and indirect (synthetic) tests with more details are described in the second part. Each test and principles are explained separately. Oscilogram is obtained from short-circuit tests of circuit breakers at laboratory. The aim of this research work is to propose a test circuit for performing indirect test.

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

Science.gov (United States)

Yu, Lianchun; Yu, Yuguo

2017-11-01

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

Optoisolation circuits nonlinearity applications in engineering : optoisolation nonlinear dynamics and chaos, application in engineering

CERN Document Server

Aluf, Ofer

2012-01-01

This book describes a new concept in analyzing circuits, which includes optoisolation elements. The analysis is based on nonlinear dynamics and chaos models and shows comprehensive benefits and results. All conceptual optoisolation circuits are innovative and can be broadly implemented in engineering applications. The dynamics of optoisolation circuits provides several ways to use them in a variety of applications covering wide areas. The presentation fills the gap of analytical methods for optoisolation circuits analysis, concrete examples, and geometric examples. The optoisolation circuits analysis is developed systematically, starting with basic optoisolation circuits differential equations and their bifurcations, followed by Fixed points analysis, limit cycles and their bifurcations. Optoisolation circuits can be characterized as Lorenz equations, chaos, iterated maps, period doubling and attractors. This book is aimed at electrical and electronic engineers, students and researchers in physics as well. A ...

On the nature and evolution of the neural bases of human language

Science.gov (United States)

Lieberman, Philip

2002-01-01

The traditional theory equating the brain bases of language with Broca's and Wernicke's neocortical areas is wrong. Neural circuits linking activity in anatomically segregated populations of neurons in subcortical structures and the neocortex throughout the human brain regulate complex behaviors such as walking, talking, and comprehending the meaning of sentences. When we hear or read a word, neural structures involved in the perception or real-world associations of the word are activated as well as posterior cortical regions adjacent to Wernicke's area. Many areas of the neocortex and subcortical structures support the cortical-striatal-cortical circuits that confer complex syntactic ability, speech production, and a large vocabulary. However, many of these structures also form part of the neural circuits regulating other aspects of behavior. For example, the basal ganglia, which regulate motor control, are also crucial elements in the circuits that confer human linguistic ability and abstract reasoning. The cerebellum, traditionally associated with motor control, is active in motor learning. The basal ganglia are also key elements in reward-based learning. Data from studies of Broca's aphasia, Parkinson's disease, hypoxia, focal brain damage, and a genetically transmitted brain anomaly (the putative "language gene," family KE), and from comparative studies of the brains and behavior of other species, demonstrate that the basal ganglia sequence the discrete elements that constitute a complete motor act, syntactic process, or thought process. Imaging studies of intact human subjects and electrophysiologic and tracer studies of the brains and behavior of other species confirm these findings. As Dobzansky put it, "Nothing in biology makes sense except in the light of evolution" (cited in Mayr, 1982). That applies with as much force to the human brain and the neural bases of language as it does to the human foot or jaw. The converse follows: the mark of evolution on

Dynamic plasticity in coupled avian midbrain maps

Science.gov (United States)

Atwal, Gurinder Singh

2004-12-01

Internal mapping of the external environment is carried out using the receptive fields of topographic neurons in the brain, and in a normal barn owl the aural and visual subcortical maps are aligned from early experiences. However, instantaneous misalignment of the aural and visual stimuli has been observed to result in adaptive behavior, manifested by functional and anatomical changes of the auditory processing system. Using methods of information theory and statistical mechanics a model of the adaptive dynamics of the aural receptive field is presented and analyzed. The dynamics is determined by maximizing the mutual information between the neural output and the weighted sensory neural inputs, admixed with noise, subject to biophysical constraints. The reduced costs of neural rewiring, as in the case of young barn owls, reveal two qualitatively different types of receptive field adaptation depending on the magnitude of the audiovisual misalignment. By letting the misalignment increase with time, it is shown that the ability to adapt can be increased even when neural rewiring costs are high, in agreement with recent experimental reports of the increased plasticity of the auditory space map in adult barn owls due to incremental learning. Finally, a critical speed of misalignment is identified, demarcating the crossover from adaptive to nonadaptive behavior.

Non-invasive neural stimulation

Science.gov (United States)

Tyler, William J.; Sanguinetti, Joseph L.; Fini, Maria; Hool, Nicholas

2017-05-01

Neurotechnologies for non-invasively interfacing with neural circuits have been evolving from those capable of sensing neural activity to those capable of restoring and enhancing human brain function. Generally referred to as non-invasive neural stimulation (NINS) methods, these neuromodulation approaches rely on electrical, magnetic, photonic, and acoustic or ultrasonic energy to influence nervous system activity, brain function, and behavior. Evidence that has been surmounting for decades shows that advanced neural engineering of NINS technologies will indeed transform the way humans treat diseases, interact with information, communicate, and learn. The physics underlying the ability of various NINS methods to modulate nervous system activity can be quite different from one another depending on the energy modality used as we briefly discuss. For members of commercial and defense industry sectors that have not traditionally engaged in neuroscience research and development, the science, engineering and technology required to advance NINS methods beyond the state-of-the-art presents tremendous opportunities. Within the past few years alone there have been large increases in global investments made by federal agencies, foundations, private investors and multinational corporations to develop advanced applications of NINS technologies. Driven by these efforts NINS methods and devices have recently been introduced to mass markets via the consumer electronics industry. Further, NINS continues to be explored in a growing number of defense applications focused on enhancing human dimensions. The present paper provides a brief introduction to the field of non-invasive neural stimulation by highlighting some of the more common methods in use or under current development today.

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

Directory of Open Access Journals (Sweden)

Andrea E Granstedt

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

Premature Ventricular Contraction Coupling Interval Variability Destabilizes Cardiac Neuronal and Electrophysiological Control: Insights from Simultaneous Cardio-Neural Mapping

Science.gov (United States)

Hamon, David; Rajendran, Pradeep S.; Chui, Ray W.; Ajijola, Olujimi A.; Irie, Tadanobu; Talebi, Ramin; Salavatian, Siamak; Vaseghi, Marmar; Bradfield, Jason S.; Armour, J. Andrew; Ardell, Jeffrey L.; Shivkumar, Kalyanam

2017-01-01

Background Variability in premature ventricular contraction (PVC) coupling interval (CI) increases the risk of cardiomyopathy and sudden death. The autonomic nervous system regulates cardiac electrical and mechanical indices, and its dysregulation plays an important role in cardiac disease pathogenesis. The impact of PVCs on the intrinsic cardiac nervous system (ICNS), a neural network on the heart, remains unknown. The objective was to determine the effect of PVCs and CI on ICNS function in generating cardiac neuronal and electrical instability using a novel cardio-neural mapping approach. Methods and Results In a porcine model (n=8) neuronal activity was recorded from a ventricular ganglion using a microelectrode array, and cardiac electrophysiological mapping was performed. Neurons were functionally classified based on their response to afferent and efferent cardiovascular stimuli, with neurons that responded to both defined as convergent (local reflex processors). Dynamic changes in neuronal activity were then evaluated in response to right ventricular outflow tract PVCs with fixed short, fixed long, and variable CI. PVC delivery elicited a greater neuronal response than all other stimuli (P<0.001). Compared to fixed short and long CI, PVCs with variable CI had a greater impact on neuronal response (P<0.05 versus short CI), particularly on convergent neurons (P<0.05), as well as neurons receiving sympathetic (P<0.05) and parasympathetic input (P<0.05). The greatest cardiac electrical instability was also observed following variable (short) CI PVCs. Conclusions Variable CI PVCs affect critical populations of ICNS neurons and alter cardiac repolarization. These changes may be critical for arrhythmogenesis and remodeling leading to cardiomyopathy. PMID:28408652

A scalable neural chip with synaptic electronics using CMOS integrated memristors

International Nuclear Information System (INIS)

Cruz-Albrecht, Jose M; Derosier, Timothy; Srinivasa, Narayan

2013-01-01

The design and simulation of a scalable neural chip with synaptic electronics using nanoscale memristors fully integrated with complementary metal–oxide–semiconductor (CMOS) is presented. The circuit consists of integrate-and-fire neurons and synapses with spike-timing dependent plasticity (STDP). The synaptic conductance values can be stored in memristors with eight levels, and the topology of connections between neurons is reconfigurable. The circuit has been designed using a 90 nm CMOS process with via connections to on-chip post-processed memristor arrays. The design has about 16 million CMOS transistors and 73 728 integrated memristors. We provide circuit level simulations of the entire chip performing neuronal and synaptic computations that result in biologically realistic functional behavior. (paper)

A Novel Sensory Mapping Design for Bipedal Walking on a Sloped Surface

Directory of Open Access Journals (Sweden)

Chiao-Min Wu

2012-10-01

Full Text Available This paper presents an environment recognition method for bipedal robots using a time-delay neural network. For a robot to walk in a varying terrain, it is desirable that the robot can adapt to any environment encountered in real-time. This paper aims to develop a sensory mapping unit to recognize environment types from the input sensory data based on an artificial neural network approach. With the proposed sensory mapping design, a bipedal walking robot can obtain real-time environment information and select an appropriate walking pattern accordingly. Due to the time-dependent property of sensory data, the sensory mapping is realized by using a time-delay neural network. The sensory data of earlier time sequences combined with current sensory data are sent to the neural network. The proposed method has been implemented on the humanoid robot NAO for verification. Several interesting experiments were carried out to verify the effectiveness of the sensory mapping design. The mapping design is validated for the uphill, downhill and flat surface cases, where three types of environment can be recognized by the NAO robot online.

Nonlinear signal processing using neural networks: Prediction and system modelling

Energy Technology Data Exchange (ETDEWEB)

Lapedes, A.; Farber, R.

1987-06-01

The backpropagation learning algorithm for neural networks is developed into a formalism for nonlinear signal processing. We illustrate the method by selecting two common topics in signal processing, prediction and system modelling, and show that nonlinear applications can be handled extremely well by using neural networks. The formalism is a natural, nonlinear extension of the linear Least Mean Squares algorithm commonly used in adaptive signal processing. Simulations are presented that document the additional performance achieved by using nonlinear neural networks. First, we demonstrate that the formalism may be used to predict points in a highly chaotic time series with orders of magnitude increase in accuracy over conventional methods including the Linear Predictive Method and the Gabor-Volterra-Weiner Polynomial Method. Deterministic chaos is thought to be involved in many physical situations including the onset of turbulence in fluids, chemical reactions and plasma physics. Secondly, we demonstrate the use of the formalism in nonlinear system modelling by providing a graphic example in which it is clear that the neural network has accurately modelled the nonlinear transfer function. It is interesting to note that the formalism provides explicit, analytic, global, approximations to the nonlinear maps underlying the various time series. Furthermore, the neural net seems to be extremely parsimonious in its requirements for data points from the time series. We show that the neural net is able to perform well because it globally approximates the relevant maps by performing a kind of generalized mode decomposition of the maps. 24 refs., 13 figs.

Maternal neural responses to infant cries and faces: relationships with substance use

Directory of Open Access Journals (Sweden)

Nicole eLandi

2011-06-01

Full Text Available Substance abuse in pregnant and recently postpartum women is a major public health concern because of effects on the infant and on the ability of the adult to care for the infant. In addition to the negative health effects of teratogenic substances on fetal development, substance use can contribute to difficulties associated with the social and behavioral aspects of parenting. Neural circuits associated with parenting behavior overlap with circuits involved in addiction (e.g., frontal, striatal and limbic systems and thus may be co-opted for the craving/reward cycle associated with substance use and abuse and be less available for parenting. The current study investigates the degree to which neural circuits associated with parenting are disrupted in mothers who are substance-using. Specifically, we used functional magnetic resonance imaging to examine the neural response to emotional infant cues (faces and cries in substance-using compared to non-using mothers. In response to both faces (of varying emotional valence and cries (of varying distress levels, substance-using mothers evidenced reduced neural activation in regions that have been previously implicated in reward and motivation as well as regions involved in cognitive control. Specifically, in response to faces, substance users showed reduced activation in prefrontal regions, including the dorsolateral and ventromedial prefrontal cortex, as well as visual processing (occipital lobes and limbic regions (parahippocampus and amygdala. Similarly, in response to infant cries substance-using mothers showed reduced activation relative to non-using mothers in prefrontal regions, auditory sensory processing regions, insula and limbic regions (parahippocampus and amygdala. These findings suggest that infant stimuli may be less salient for substance-using mothers, and such reduced saliency may impair developing infant-caregiver attachment and the ability of mothers to respond appropriately to their

Neurog1 Genetic Inducible Fate Mapping (GIFM) Reveals the Existence of Complex Spatiotemporal Cyto-Architectures in the Developing Cerebellum.

Science.gov (United States)

Obana, Edwin A; Lundell, Travis G; Yi, Kevin J; Radomski, Kryslaine L; Zhou, Qiong; Doughty, Martin L

2015-06-01

Neurog1 is a pro-neural basic helix-loop-helix (bHLH) transcription factor expressed in progenitor cells located in the ventricular zone and subsequently the presumptive white matter tracts of the developing mouse cerebellum. We used genetic inducible fate mapping (GIFM) with a transgenic Neurog1-CreER allele to characterize the contributions of Neurog1 lineages to cerebellar circuit formation in mice. GIFM reveals Neurog1-expressing progenitors are fate-mapped to become Purkinje cells and all GABAergic interneuron cell types of the cerebellar cortex but not glia. The spatiotemporal sequence of GIFM is unique to each neuronal cell type. GIFM on embryonic days (E) 10.5 to E12.5 labels Purkinje cells with different medial-lateral settling patterns depending on the day of tamoxifen delivery. GIFM on E11.5 to P7 labels interneurons and the timing of tamoxifen administration correlates with the final inside-to-outside resting position of GABAergic interneurons in the cerebellar cortex. Proliferative status and long-term BrdU retention of GIFM lineages reveals Purkinje cells express Neurog1 around the time they become post-mitotic. In contrast, GIFM labels mitotic and post-mitotic interneurons. Neurog1-CreER GIFM reveals a correlation between the timing of Neurog1 expression and the spatial organization of GABAergic neurons in the cerebellar cortex with possible implications for cerebellar circuit assembly.

Pattern classification and recognition of invertebrate functional groups using self-organizing neural networks.

Science.gov (United States)

Zhang, WenJun

2007-07-01

Self-organizing neural networks can be used to mimic non-linear systems. The main objective of this study is to make pattern classification and recognition on sampling information using two self-organizing neural network models. Invertebrate functional groups sampled in the irrigated rice field were classified and recognized using one-dimensional self-organizing map and self-organizing competitive learning neural networks. Comparisons between neural network models, distance (similarity) measures, and number of neurons were conducted. The results showed that self-organizing map and self-organizing competitive learning neural network models were effective in pattern classification and recognition of sampling information. Overall the performance of one-dimensional self-organizing map neural network was better than self-organizing competitive learning neural network. The number of neurons could determine the number of classes in the classification. Different neural network models with various distance (similarity) measures yielded similar classifications. Some differences, dependent upon the specific network structure, would be found. The pattern of an unrecognized functional group was recognized with the self-organizing neural network. A relative consistent classification indicated that the following invertebrate functional groups, terrestrial blood sucker; terrestrial flyer; tourist (nonpredatory species with no known functional role other than as prey in ecosystem); gall former; collector (gather, deposit feeder); predator and parasitoid; leaf miner; idiobiont (acarine ectoparasitoid), were classified into the same group, and the following invertebrate functional groups, external plant feeder; terrestrial crawler, walker, jumper or hunter; neustonic (water surface) swimmer (semi-aquatic), were classified into another group. It was concluded that reliable conclusions could be drawn from comparisons of different neural network models that use different distance

Magnetic field mapping equipment for KEK 1 m hydrogen bubble chamber magnet

International Nuclear Information System (INIS)

Otake, Yuji

1979-01-01

Magnetic field mapping was carried out for the 1 m hydrogen bubble chamber magnet in November, 1976, and March, 1977 at the National Laboratory for High Energy Physics (KEK). The magnetic field mapping equipment was manufactured for the main purposes of getting low cost and short time in manufacture, sufficient accuracy of measurement, fairly fast speed of measurement and automatic measurement. The design was made, aiming at 18 kgauss of field intensity and 10 -3 of measuring accuracy. The magnetic field mapping system consists of seven parts as shown in the block diagram. The measuring elements are Hall elements, and three elements are mounted on three surfaces of the inner wall so that three directional (X, Y and Z) components can be determined by moving the magnetic field mapping equipment. The control circuit of the equipment regulates the shift of the equipment by 5 cm step in X and Y directions. Other circuits, relays and switches are explained. Since 100 V AC power relays were placed together with TTL's and IC's in this system, relay circuits became serious noise sources. The countermeasures such as separation of IC power supply, independent grounding, are suppression and so on were taken. Of these, it was effective that relay circuit and IC circuit were put in respective shield cases, and wirings in both circuits were made so as not to intersect each other as far as possible. Finally the points to be noticed in manufacturing, obtained from the experiences, are summarized. (Wakatsuki, Y.)

The Challenges of Neural Mind-reading Paradigms

Directory of Open Access Journals (Sweden)

Oscar eVilarroya

2013-06-01

Full Text Available Neural mind-reading studies, based on multivariate pattern analysis (MVPA methods, are providing exciting new studies. Some of the results obtained with these paradigms have raised high expectations, such as the possibility of creating brain reading devices. However, such hopes are based on the assumptions that: a the BOLD signal is a marker of neural activity; b the BOLD pattern identified by a MVPA is a neurally sound pattern; c the MVPA’s feature space is a good mapping of the neural representation of a stimulus, and d the pattern identified by a MVPA corresponds to a representation. I examine here the challenges that still have to be met before fully accepting such assumptions.

The challenges of neural mind-reading paradigms.

Science.gov (United States)

Vilarroya, Oscar

2013-01-01

Neural mind-reading studies, based on multivariate pattern analysis (MVPA) methods, are providing exciting new studies. Some of the results obtained with these paradigms have raised high expectations, such as the possibility of creating brain reading devices. However, such hopes are based on the assumptions that: (a) the BOLD signal is a marker of neural activity; (b) the BOLD pattern identified by a MVPA is a neurally sound pattern; (c) the MVPA's feature space is a good mapping of the neural representation of a stimulus, and (d) the pattern identified by a MVPA corresponds to a representation. I examine here the challenges that still have to be met before fully accepting such assumptions.

Sequential and parallel image restoration: neural network implementations.

Science.gov (United States)

Figueiredo, M T; Leitao, J N

1994-01-01

Sequential and parallel image restoration algorithms and their implementations on neural networks are proposed. For images degraded by linear blur and contaminated by additive white Gaussian noise, maximum a posteriori (MAP) estimation and regularization theory lead to the same high dimension convex optimization problem. The commonly adopted strategy (in using neural networks for image restoration) is to map the objective function of the optimization problem into the energy of a predefined network, taking advantage of its energy minimization properties. Departing from this approach, we propose neural implementations of iterative minimization algorithms which are first proved to converge. The developed schemes are based on modified Hopfield (1985) networks of graded elements, with both sequential and parallel updating schedules. An algorithm supported on a fully standard Hopfield network (binary elements and zero autoconnections) is also considered. Robustness with respect to finite numerical precision is studied, and examples with real images are presented.

Parameter space of experimental chaotic circuits with high-precision control parameters

Energy Technology Data Exchange (ETDEWEB)

Sousa, Francisco F. G. de; Rubinger, Rero M. [Instituto de Física e Química, Universidade Federal de Itajubá, Itajubá, MG (Brazil); Sartorelli, José C., E-mail: sartorelli@if.usp.br [Universidade de São Paulo, São Paulo, SP (Brazil); Albuquerque, Holokx A. [Departamento de Física, Universidade do Estado de Santa Catarina, Joinville, SC (Brazil); Baptista, Murilo S. [Institute of Complex Systems and Mathematical Biology, SUPA, University of Aberdeen, Aberdeen (United Kingdom)

2016-08-15

We report high-resolution measurements that experimentally confirm a spiral cascade structure and a scaling relationship of shrimps in the Chua's circuit. Circuits constructed using this component allow for a comprehensive characterization of the circuit behaviors through high resolution parameter spaces. To illustrate the power of our technological development for the creation and the study of chaotic circuits, we constructed a Chua circuit and study its high resolution parameter space. The reliability and stability of the designed component allowed us to obtain data for long periods of time (∼21 weeks), a data set from which an accurate estimation of Lyapunov exponents for the circuit characterization was possible. Moreover, this data, rigorously characterized by the Lyapunov exponents, allows us to reassure experimentally that the shrimps, stable islands embedded in a domain of chaos in the parameter spaces, can be observed in the laboratory. Finally, we confirm that their sizes decay exponentially with the period of the attractor, a result expected to be found in maps of the quadratic family.

Nonlinear programming with feedforward neural networks.

Energy Technology Data Exchange (ETDEWEB)

Reifman, J.

1999-06-02

We provide a practical and effective method for solving constrained optimization problems by successively training a multilayer feedforward neural network in a coupled neural-network/objective-function representation. Nonlinear programming problems are easily mapped into this representation which has a simpler and more transparent method of solution than optimization performed with Hopfield-like networks and poses very mild requirements on the functions appearing in the problem. Simulation results are illustrated and compared with an off-the-shelf optimization tool.

Configurable Analog-Digital Conversion Using the Neural EngineeringFramework

Directory of Open Access Journals (Sweden)

Christian G Mayr

2014-07-01

Full Text Available Efficient Analog-Digital Converters (ADC are one of the mainstays of mixed-signal integrated circuit design. Besides the conventional ADCs used in mainstream ICs, there have been various attempts in the past to utilize neuromorphic networks to accomplish an efficient crossing between analog and digital domains, i.e. to build neurally inspired ADCs. Generally, these have suffered from the same problems as conventional ADCs, that is they require high-precision, handcrafted analog circuits and are thus not technology portable. In this paper, we present an ADC based on the Neural Engineering Framework (NEF. It carries out a large fraction of the overall ADC process in the digital domain, i.e. it is easily portable across technologies. The analog-digital conversion takes full advantage of the high degree of parallelism inherent in neuromorphic networks, making for a very scalable ADC. In addition, it has a number of features not commonly found in conventional ADCs, such as a runtime reconfigurability of the ADC sampling rate, resolution and transfer characteristic.

Circadian Control of the Estrogenic Circuits Regulating GnRH Secretion and the Preovulatory Luteinizing Hormone Surge

Directory of Open Access Journals (Sweden)

Lance J Kriegsfeld

2012-05-01

Full Text Available Female reproduction requires the precise temporal organization of interacting, estradiol-sensitive neural circuits that converge to optimally drive hypothalamo-pituitary-gonadal (HPG axis functioning. In mammals, the master circadian pacemaker in the suprachaismatic nucleus (SCN of the anterior hypothalamus coordinates reproductively-relevant neuroendocrine events necessary to maximize reproductive success. Likewise, in species where periods of fertility are brief, circadian oversight of reproductive function ensures that estradiol-dependent increases in sexual motivation coincide with ovulation. Across species, including humans, disruptions to circadian timing (e.g., through rotating shift work, night shift work, poor sleep hygiene lead to pronounced deficits in ovulation and fecundity. Despite the well-established roles for the circadian system in female reproductive functioning, the specific neural circuits and neurochemical mediators underlying these interactions are not fully understood. Most work to date has focused on the direct and indirect communication from the SCN to the GnRH system in control of the preovulatory LH surge. However, the same clock genes underlying circadian rhythms at the cellular level in SCN cells are also common to target cell populations of the SCN, including the GnRH neuronal network. Exploring the means by which the master clock synergizes with subordinate clocks in GnRH cells and its upstream modulatory systems represents an exciting opportunity to further understand the role of endogenous timing systems in female reproduction. Herein we provide an overview of the state of knowledge regarding interactions between the circadian timing system and estradiol-sensitive neural circuits driving GnRH secretion and the preovulatory LH surge.

Dendritic nonlinearities are tuned for efficient spike-based computations in cortical circuits.

Science.gov (United States)

Ujfalussy, Balázs B; Makara, Judit K; Branco, Tiago; Lengyel, Máté

2015-12-24

Cortical neurons integrate thousands of synaptic inputs in their dendrites in highly nonlinear ways. It is unknown how these dendritic nonlinearities in individual cells contribute to computations at the level of neural circuits. Here, we show that dendritic nonlinearities are critical for the efficient integration of synaptic inputs in circuits performing analog computations with spiking neurons. We developed a theory that formalizes how a neuron's dendritic nonlinearity that is optimal for integrating synaptic inputs depends on the statistics of its presynaptic activity patterns. Based on their in vivo preynaptic population statistics (firing rates, membrane potential fluctuations, and correlations due to ensemble dynamics), our theory accurately predicted the responses of two different types of cortical pyramidal cells to patterned stimulation by two-photon glutamate uncaging. These results reveal a new computational principle underlying dendritic integration in cortical neurons by suggesting a functional link between cellular and systems--level properties of cortical circuits.

Explicit logic circuits predict local properties of the neocortex's physiology and anatomy.

Directory of Open Access Journals (Sweden)

Lane Yoder

Full Text Available BACKGROUND: Two previous articles proposed an explicit model of how the brain processes information by its organization of synaptic connections. The family of logic circuits was shown to generate neural correlates of complex psychophysical phenomena in different sensory systems. METHODOLOGY/PRINCIPAL FINDINGS: Here it is shown that the most cost-effective architectures for these networks produce correlates of electrophysiological brain phenomena and predict major aspects of the anatomical structure and physiological organization of the neocortex. The logic circuits are markedly efficient in several respects and provide the foundation for all of the brain's combinational processing of information. CONCLUSIONS/SIGNIFICANCE: At the local level, these networks account for much of the physical structure of the neocortex as well its organization of synaptic connections. Electronic implementations of the logic circuits may be more efficient than current electronic logic arrays in generating both Boolean and fuzzy logic.

Commutation circuit for an HVDC circuit breaker

Science.gov (United States)

Premerlani, William J.

1981-01-01

A commutation circuit for a high voltage DC circuit breaker incorporates a resistor capacitor combination and a charging circuit connected to the main breaker, such that a commutating capacitor is discharged in opposition to the load current to force the current in an arc after breaker opening to zero to facilitate arc interruption. In a particular embodiment, a normally open commutating circuit is connected across the contacts of a main DC circuit breaker to absorb the inductive system energy trapped by breaker opening and to limit recovery voltages to a level tolerable by the commutating circuit components.

Using an Insect Mushroom Body Circuit to Encode Route Memory in Complex Natural Environments.

Directory of Open Access Journals (Sweden)

Paul Ardin

2016-02-01

Full Text Available Ants, like many other animals, use visual memory to follow extended routes through complex environments, but it is unknown how their small brains implement this capability. The mushroom body neuropils have been identified as a crucial memory circuit in the insect brain, but their function has mostly been explored for simple olfactory association tasks. We show that a spiking neural model of this circuit originally developed to describe fruitfly (Drosophila melanogaster olfactory association, can also account for the ability of desert ants (Cataglyphis velox to rapidly learn visual routes through complex natural environments. We further demonstrate that abstracting the key computational principles of this circuit, which include one-shot learning of sparse codes, enables the theoretical storage capacity of the ant mushroom body to be estimated at hundreds of independent images.

Memory and cognitive control circuits in mathematical cognition and learning.

Science.gov (United States)

Menon, V

2016-01-01

Numerical cognition relies on interactions within and between multiple functional brain systems, including those subserving quantity processing, working memory, declarative memory, and cognitive control. This chapter describes recent advances in our understanding of memory and control circuits in mathematical cognition and learning. The working memory system involves multiple parietal-frontal circuits which create short-term representations that allow manipulation of discrete quantities over several seconds. In contrast, hippocampal-frontal circuits underlying the declarative memory system play an important role in formation of associative memories and binding of new and old information, leading to the formation of long-term memories that allow generalization beyond individual problem attributes. The flow of information across these systems is regulated by flexible cognitive control systems which facilitate the integration and manipulation of quantity and mnemonic information. The implications of recent research for formulating a more comprehensive systems neuroscience view of the neural basis of mathematical learning and knowledge acquisition in both children and adults are discussed. © 2016 Elsevier B.V. All rights reserved.

Astrocyte regulation of sleep circuits: experimental and modeling perspectives

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Tommaso eFellin

2012-08-01

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

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

Directory of Open Access Journals (Sweden)

Saul S. Siller

2011-09-01

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

Neural basis of disgust perception in racial prejudice.

Science.gov (United States)

Liu, Yunzhe; Lin, Wanjun; Xu, Pengfei; Zhang, Dandan; Luo, Yuejia

2015-12-01

Worldwide racial prejudice is originated from in-group/out-group discrimination. This prejudice can bias face perception at the very beginning of social interaction. However, little is known about the neurocognitive mechanism underlying the influence of racial prejudice on facial emotion perception. Here, we examined the neural basis of disgust perception in racial prejudice using a passive viewing task and functional magnetic resonance imaging. We found that compared with the disgusted faces of in-groups, the disgusted faces of out-groups result in increased amygdala and insular engagement, positive coupling of the insula with amygdala-based emotional system, and negative coupling of the insula with anterior cingulate cortex (ACC)-based regulatory system. Furthermore, machine-learning algorithms revealed that the level of implicit racial prejudice could be predicted by functional couplings of the insula with both the amygdala and the ACC, which suggests that the insula is largely involved in racially biased disgust perception through two distinct neural circuits. In addition, individual difference in disgust sensitivity was found to be predictive of implicit racial prejudice. Taken together, our results suggest a crucial role of insula-centered circuits for disgust perception in racial prejudice. © 2015 Wiley Periodicals, Inc.

A Parallel Adaboost-Backpropagation Neural Network for Massive Image Dataset Classification

Science.gov (United States)

Cao, Jianfang; Chen, Lichao; Wang, Min; Shi, Hao; Tian, Yun

2016-01-01

Image classification uses computers to simulate human understanding and cognition of images by automatically categorizing images. This study proposes a faster image classification approach that parallelizes the traditional Adaboost-Backpropagation (BP) neural network using the MapReduce parallel programming model. First, we construct a strong classifier by assembling the outputs of 15 BP neural networks (which are individually regarded as weak classifiers) based on the Adaboost algorithm. Second, we design Map and Reduce tasks for both the parallel Adaboost-BP neural network and the feature extraction algorithm. Finally, we establish an automated classification model by building a Hadoop cluster. We use the Pascal VOC2007 and Caltech256 datasets to train and test the classification model. The results are superior to those obtained using traditional Adaboost-BP neural network or parallel BP neural network approaches. Our approach increased the average classification accuracy rate by approximately 14.5% and 26.0% compared to the traditional Adaboost-BP neural network and parallel BP neural network, respectively. Furthermore, the proposed approach requires less computation time and scales very well as evaluated by speedup, sizeup and scaleup. The proposed approach may provide a foundation for automated large-scale image classification and demonstrates practical value. PMID:27905520

Combining neural networks for protein secondary structure prediction

DEFF Research Database (Denmark)

Riis, Søren Kamaric

1995-01-01

In this paper structured neural networks are applied to the problem of predicting the secondary structure of proteins. A hierarchical approach is used where specialized neural networks are designed for each structural class and then combined using another neural network. The submodels are designed...... by using a priori knowledge of the mapping between protein building blocks and the secondary structure and by using weight sharing. Since none of the individual networks have more than 600 adjustable weights over-fitting is avoided. When ensembles of specialized experts are combined the performance...

Oscillator circuits

CERN Document Server

Graf, Rudolf F

1996-01-01

This series of circuits provides designers with a quick source for oscillator 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 listing

Measuring circuits

CERN Document Server

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

Neural Behavior Chain Learning of Mobile Robot Actions

Directory of Open Access Journals (Sweden)

Lejla Banjanovic-Mehmedovic

2012-01-01

Full Text Available This paper presents a visual/motor behavior learning approach, based on neural networks. We propose Behavior Chain Model (BCM in order to create a way of behavior learning. Our behavior-based system evolution task is a mobile robot detecting a target and driving/acting towards it. First, the mapping relations between the image feature domain of the object and the robot action domain are derived. Second, a multilayer neural network for offline learning of the mapping relations is used. This learning structure through neural network training process represents a connection between the visual perceptions and motor sequence of actions in order to grip a target. Last, using behavior learning through a noticed action chain, we can predict mobile robot behavior for a variety of similar tasks in similar environment. Prediction results suggest that the methodology is adequate and could be recognized as an idea for designing different mobile robot behaviour assistance.

Recurrent Neural Network for Computing the Drazin Inverse.

Science.gov (United States)

Stanimirović, Predrag S; Zivković, Ivan S; Wei, Yimin

2015-11-01

This paper presents a recurrent neural network (RNN) for computing the Drazin inverse of a real matrix in real time. This recurrent neural network (RNN) is composed of n independent parts (subnetworks), where n is the order of the input matrix. These subnetworks can operate concurrently, so parallel and distributed processing can be achieved. In this way, the computational advantages over the existing sequential algorithms can be attained in real-time applications. The RNN defined in this paper is convenient for an implementation in an electronic circuit. The number of neurons in the neural network is the same as the number of elements in the output matrix, which represents the Drazin inverse. The difference between the proposed RNN and the existing ones for the Drazin inverse computation lies in their network architecture and dynamics. The conditions that ensure the stability of the defined RNN as well as its convergence toward the Drazin inverse are considered. In addition, illustrative examples and examples of application to the practical engineering problems are discussed to show the efficacy of the proposed neural network.

Satisfiability of logic programming based on radial basis function neural networks

International Nuclear Information System (INIS)

Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged; Choon, Ong Hong

2014-01-01

In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We applied the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems

Satisfiability of logic programming based on radial basis function neural networks

Energy Technology Data Exchange (ETDEWEB)

Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged; Choon, Ong Hong [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)

2014-07-10

In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We applied the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems.

Optical waveguides with memory effect using photochromic material for neural network

Science.gov (United States)

Tanimoto, Keisuke; Amemiya, Yoshiteru; Yokoyama, Shin

2018-04-01

An optical neural network using a waveguide with a memory effect, a photodiode, CMOS circuits and LEDs was proposed. To realize the neural network, optical waveguides with a memory effect were fabricated using a cladding layer containing the photochromic material “diarylethene”. The transmittance of green light was decreased by UV light irradiation and recovered by the passage of green light through the waveguide. It was confirmed that the transmittance versus total energy of the green light that passed through the waveguide well fit the universal exponential curve.

Detecting tactical patterns in basketball: comparison of merge self-organising maps and dynamic controlled neural networks.

Science.gov (United States)

Kempe, Matthias; Grunz, Andreas; Memmert, Daniel

2015-01-01

The soaring amount of data, especially spatial-temporal data, recorded in recent years demands for advanced analysis methods. Neural networks derived from self-organizing maps established themselves as a useful tool to analyse static and temporal data. In this study, we applied the merge self-organising map (MSOM) to spatio-temporal data. To do so, we investigated the ability of MSOM's to analyse spatio-temporal data and compared its performance to the common dynamical controlled network (DyCoN) approach to analyse team sport position data. The position data of 10 players were recorded via the Ubisense tracking system during a basketball game. Furthermore, three different pre-selected plays were recorded for classification. Following data preparation, the different nets were trained with the data of the first half. The training success of both networks was evaluated by achieved entropy. The second half of the basketball game was presented to both nets for automatic classification. Both approaches were able to present the trained data extremely well and to detect the pre-selected plays correctly. In conclusion, MSOMs are a useful tool to analyse spatial-temporal data, especially in team sports. By their direct inclusion of different time length of tactical patterns, they open up new opportunities within team sports.

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

Directory of Open Access Journals (Sweden)

Haichen eNiu

2015-03-01

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

Neural systems for control

National Research Council Canada - National Science Library

Omidvar, Omid; Elliott, David L

1997-01-01

... is reprinted with permission from A. Barto, "Reinforcement Learning," Handbook of Brain Theory and Neural Networks, M.A. Arbib, ed.. The MIT Press, Cambridge, MA, pp. 804-809, 1995. Chapter 4, Figures 4-5 and 7-9 and Tables 2-5, are reprinted with permission, from S. Cho, "Map Formation in Proprioceptive Cortex," International Jour...

Neural mechanisms mediating degrees of strategic uncertainty.

Science.gov (United States)

Nagel, Rosemarie; Brovelli, Andrea; Heinemann, Frank; Coricelli, Giorgio

2018-01-01

In social interactions, strategic uncertainty arises when the outcome of one's choice depends on the choices of others. An important question is whether strategic uncertainty can be resolved by assessing subjective probabilities to the counterparts' behavior, as if playing against nature, and thus transforming the strategic interaction into a risky (individual) situation. By means of functional magnetic resonance imaging with human participants we tested the hypothesis that choices under strategic uncertainty are supported by the neural circuits mediating choices under individual risk and deliberation in social settings (i.e. strategic thinking). Participants were confronted with risky lotteries and two types of coordination games requiring different degrees of strategic thinking of the kind 'I think that you think that I think etc.' We found that the brain network mediating risk during lotteries (anterior insula, dorsomedial prefrontal cortex and parietal cortex) is also engaged in the processing of strategic uncertainty in games. In social settings, activity in this network is modulated by the level of strategic thinking that is reflected in the activity of the dorsomedial and dorsolateral prefrontal cortex. These results suggest that strategic uncertainty is resolved by the interplay between the neural circuits mediating risk and higher order beliefs (i.e. beliefs about others' beliefs). © The Author(s) (2017). Published by Oxford University Press.

Global exponential stability of fuzzy cellular neural networks with delays and reaction-diffusion terms

International Nuclear Information System (INIS)

Wang Jian; Lu Junguo

2008-01-01

In this paper, we study the global exponential stability of fuzzy cellular neural networks with delays and reaction-diffusion terms. By constructing a suitable Lyapunov functional and utilizing some inequality techniques, we obtain a sufficient condition for the uniqueness and global exponential stability of the equilibrium solution for a class of fuzzy cellular neural networks with delays and reaction-diffusion terms. The result imposes constraint conditions on the network parameters independently of the delay parameter. The result is also easy to check and plays an important role in the design and application of globally exponentially stable fuzzy neural circuits

Neural reuse of action perception circuits for language, concepts and communication.

Science.gov (United States)

Pulvermüller, Friedemann

2018-01-01

Neurocognitive and neurolinguistics theories make explicit statements relating specialized cognitive and linguistic processes to specific brain loci. These linking hypotheses are in need of neurobiological justification and explanation. Recent mathematical models of human language mechanisms constrained by fundamental neuroscience principles and established knowledge about comparative neuroanatomy offer explanations for where, when and how language is processed in the human brain. In these models, network structure and connectivity along with action- and perception-induced correlation of neuronal activity co-determine neurocognitive mechanisms. Language learning leads to the formation of action perception circuits (APCs) with specific distributions across cortical areas. Cognitive and linguistic processes such as speech production, comprehension, verbal working memory and prediction are modelled by activity dynamics in these APCs, and combinatorial and communicative-interactive knowledge is organized in the dynamics within, and connections between APCs. The network models and, in particular, the concept of distributionally-specific circuits, can account for some previously not well understood facts about the cortical 'hubs' for semantic processing and the motor system's role in language understanding and speech sound recognition. A review of experimental data evaluates predictions of the APC model and alternative theories, also providing detailed discussion of some seemingly contradictory findings. Throughout, recent disputes about the role of mirror neurons and grounded cognition in language and communication are assessed critically. Copyright © 2017 The Author. Published by Elsevier Ltd.. All rights reserved.

Resonance circuits for adiabatic circuits

Directory of Open Access Journals (Sweden)

C. Schlachta

2003-01-01

Full Text Available One of the possible techniques to reduces the power consumption in digital CMOS circuits is to slow down the charge transport. This slowdown can be achieved by introducing an inductor in the charging path. Additionally, the inductor can act as an energy storage element, conserving the energy that is normally dissipated during discharging. Together with the parasitic capacitances from the circuit a LCresonant circuit is formed.

Low-Noise CMOS Circuits for On-Chip Signal Processing in Focal-Plane Arrays

Science.gov (United States)

Pain, Bedabrata

The performance of focal-plane arrays can be significantly enhanced through the use of on-chip signal processing. Novel, in-pixel, on-focal-plane, analog signal-processing circuits for high-performance imaging are presented in this thesis. The presence of a high background-radiation is a major impediment for infrared focal-plane array design. An in-pixel, background-suppression scheme, using dynamic analog current memory circuit, is described. The scheme also suppresses spatial noise that results from response non-uniformities of photo-detectors, leading to background limited infrared detector readout performance. Two new, low-power, compact, current memory circuits, optimized for operation at ultra-low current levels required in infrared-detection, are presented. The first one is a self-cascading current memory that increases the output impedance, and the second one is a novel, switch feed-through reducing current memory, implemented using error-current feedback. This circuit can operate with a residual absolute -error of less than 0.1%. The storage-time of the memory is long enough to also find applications in neural network circuits. In addition, a voltage-mode, accurate, low-offset, low-power, high-uniformity, random-access sample-and-hold cell, implemented using a CCD with feedback, is also presented for use in background-suppression and neural network applications. A new, low noise, ultra-low level signal readout technique, implemented by individually counting photo-electrons within the detection pixel, is presented. The output of each unit-cell is a digital word corresponding to the intensity of the photon flux, and the readout is noise free. This technique requires the use of unit-cell amplifiers that feature ultra-high-gain, low-power, self-biasing capability and noise in sub-electron levels. Both single-input and differential-input implementations of such amplifiers are investigated. A noise analysis technique is presented for analyzing sampled