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Sample records for neurons parallels learning

  1. Neuronal avalanches and learning

    Energy Technology Data Exchange (ETDEWEB)

    Arcangelis, Lucilla de, E-mail: dearcangelis@na.infn.it [Department of Information Engineering and CNISM, Second University of Naples, 81031 Aversa (Italy)

    2011-05-01

    Networks of living neurons represent one of the most fascinating systems of biology. If the physical and chemical mechanisms at the basis of the functioning of a single neuron are quite well understood, the collective behaviour of a system of many neurons is an extremely intriguing subject. Crucial ingredient of this complex behaviour is the plasticity property of the network, namely the capacity to adapt and evolve depending on the level of activity. This plastic ability is believed, nowadays, to be at the basis of learning and memory in real brains. Spontaneous neuronal activity has recently shown features in common to other complex systems. Experimental data have, in fact, shown that electrical information propagates in a cortex slice via an avalanche mode. These avalanches are characterized by a power law distribution for the size and duration, features found in other problems in the context of the physics of complex systems and successful models have been developed to describe their behaviour. In this contribution we discuss a statistical mechanical model for the complex activity in a neuronal network. The model implements the main physiological properties of living neurons and is able to reproduce recent experimental results. Then, we discuss the learning abilities of this neuronal network. Learning occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. The system is able to learn all the tested rules, in particular the exclusive OR (XOR) and a random rule with three inputs. The learning dynamics exhibits universal features as function of the strength of plastic adaptation. Any rule could be learned provided that the plastic adaptation is sufficiently slow.

  2. Neuronal avalanches and learning

    International Nuclear Information System (INIS)

    Arcangelis, Lucilla de

    2011-01-01

    Networks of living neurons represent one of the most fascinating systems of biology. If the physical and chemical mechanisms at the basis of the functioning of a single neuron are quite well understood, the collective behaviour of a system of many neurons is an extremely intriguing subject. Crucial ingredient of this complex behaviour is the plasticity property of the network, namely the capacity to adapt and evolve depending on the level of activity. This plastic ability is believed, nowadays, to be at the basis of learning and memory in real brains. Spontaneous neuronal activity has recently shown features in common to other complex systems. Experimental data have, in fact, shown that electrical information propagates in a cortex slice via an avalanche mode. These avalanches are characterized by a power law distribution for the size and duration, features found in other problems in the context of the physics of complex systems and successful models have been developed to describe their behaviour. In this contribution we discuss a statistical mechanical model for the complex activity in a neuronal network. The model implements the main physiological properties of living neurons and is able to reproduce recent experimental results. Then, we discuss the learning abilities of this neuronal network. Learning occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. The system is able to learn all the tested rules, in particular the exclusive OR (XOR) and a random rule with three inputs. The learning dynamics exhibits universal features as function of the strength of plastic adaptation. Any rule could be learned provided that the plastic adaptation is sufficiently slow.

  3. Parallel Stochastic discrete event simulation of calcium dynamics in neuron.

    Science.gov (United States)

    Ishlam Patoary, Mohammad Nazrul; Tropper, Carl; McDougal, Robert A; Zhongwei, Lin; Lytton, William W

    2017-09-26

    The intra-cellular calcium signaling pathways of a neuron depends on both biochemical reactions and diffusions. Some quasi-isolated compartments (e.g. spines) are so small and calcium concentrations are so low that one extra molecule diffusing in by chance can make a nontrivial difference in its concentration (percentage-wise). These rare events can affect dynamics discretely in such way that they cannot be evaluated by a deterministic simulation. Stochastic models of such a system provide a more detailed understanding of these systems than existing deterministic models because they capture their behavior at a molecular level. Our research focuses on the development of a high performance parallel discrete event simulation environment, Neuron Time Warp (NTW), which is intended for use in the parallel simulation of stochastic reaction-diffusion systems such as intra-calcium signaling. NTW is integrated with NEURON, a simulator which is widely used within the neuroscience community. We simulate two models, a calcium buffer and a calcium wave model. The calcium buffer model is employed in order to verify the correctness and performance of NTW by comparing it to a serial deterministic simulation in NEURON. We also derived a discrete event calcium wave model from a deterministic model using the stochastic IP3R structure.

  4. Mirror Neurons from Associative Learning

    OpenAIRE

    Catmur, Caroline; Press, Clare; Heyes, Cecilia

    2016-01-01

    Mirror neurons fire both when executing actions and observing others perform similar actions. Their sensorimotor matching properties have generally been considered a genetic adaptation for social cognition; however, in the present chapter we argue that the evidence in favor of this account is not compelling. Instead we present evidence supporting an alternative account: that mirror neurons’ matching properties arise from associative learning during individual development. Notably, this proces...

  5. Parallelization of TMVA Machine Learning Algorithms

    CERN Document Server

    Hajili, Mammad

    2017-01-01

    This report reflects my work on Parallelization of TMVA Machine Learning Algorithms integrated to ROOT Data Analysis Framework during summer internship at CERN. The report consists of 4 impor- tant part - data set used in training and validation, algorithms that multiprocessing applied on them, parallelization techniques and re- sults of execution time changes due to number of workers.

  6. Parallel Volunteer Learning during Youth Programs

    Science.gov (United States)

    Lesmeister, Marilyn K.; Green, Jeremy; Derby, Amy; Bothum, Candi

    2012-01-01

    Lack of time is a hindrance for volunteers to participate in educational opportunities, yet volunteer success in an organization is tied to the orientation and education they receive. Meeting diverse educational needs of volunteers can be a challenge for program managers. Scheduling a Volunteer Learning Track for chaperones that is parallel to a…

  7. Parallel strategy for optimal learning in perceptrons

    International Nuclear Information System (INIS)

    Neirotti, J P

    2010-01-01

    We developed a parallel strategy for learning optimally specific realizable rules by perceptrons, in an online learning scenario. Our result is a generalization of the Caticha-Kinouchi (CK) algorithm developed for learning a perceptron with a synaptic vector drawn from a uniform distribution over the N-dimensional sphere, so called the typical case. Our method outperforms the CK algorithm in almost all possible situations, failing only in a denumerable set of cases. The algorithm is optimal in the sense that it saturates Bayesian bounds when it succeeds.

  8. Learning of time series through neuron-to-neuron instruction

    Energy Technology Data Exchange (ETDEWEB)

    Miyazaki, Y [Department of Physics, Kyoto University, Kyoto 606-8502, (Japan); Kinzel, W [Institut fuer Theoretische Physik, Universitaet Wurzburg, 97074 Wurzburg (Germany); Shinomoto, S [Department of Physics, Kyoto University, Kyoto (Japan)

    2003-02-07

    A model neuron with delayline feedback connections can learn a time series generated by another model neuron. It has been known that some student neurons that have completed such learning under the instruction of a teacher's quasi-periodic sequence mimic the teacher's time series over a long interval, even after instruction has ceased. We found that in addition to such faithful students, there are unfaithful students whose time series eventually diverge exponentially from that of the teacher. In order to understand the circumstances that allow for such a variety of students, the orbit dimension was estimated numerically. The quasi-periodic orbits in question were found to be confined in spaces with dimensions significantly smaller than that of the full phase space.

  9. Learning of time series through neuron-to-neuron instruction

    International Nuclear Information System (INIS)

    Miyazaki, Y; Kinzel, W; Shinomoto, S

    2003-01-01

    A model neuron with delayline feedback connections can learn a time series generated by another model neuron. It has been known that some student neurons that have completed such learning under the instruction of a teacher's quasi-periodic sequence mimic the teacher's time series over a long interval, even after instruction has ceased. We found that in addition to such faithful students, there are unfaithful students whose time series eventually diverge exponentially from that of the teacher. In order to understand the circumstances that allow for such a variety of students, the orbit dimension was estimated numerically. The quasi-periodic orbits in question were found to be confined in spaces with dimensions significantly smaller than that of the full phase space

  10. Parallelization of the ROOT Machine Learning Methods

    CERN Document Server

    Vakilipourtakalou, Pourya

    2016-01-01

    Today computation is an inseparable part of scientific research. Specially in Particle Physics when there is a classification problem like discrimination of Signals from Backgrounds originating from the collisions of particles. On the other hand, Monte Carlo simulations can be used in order to generate a known data set of Signals and Backgrounds based on theoretical physics. The aim of Machine Learning is to train some algorithms on known data set and then apply these trained algorithms to the unknown data sets. However, the most common framework for data analysis in Particle Physics is ROOT. In order to use Machine Learning methods, a Toolkit for Multivariate Data Analysis (TMVA) has been added to ROOT. The major consideration in this report is the parallelization of some TMVA methods, specially Cross-Validation and BDT.

  11. Parallel learning in an autoshaping paradigm.

    Science.gov (United States)

    Naeem, Maliha; White, Norman M

    2016-08-01

    In an autoshaping task, a single conditioned stimulus (CS; lever insertion) was repeatedly followed by the delivery of an unconditioned stimulus (US; food pellet into an adjacent food magazine) irrespective of the rats' behavior. After repeated training trials, some rats responded to the onset of the CS by approaching and pressing the lever (sign-trackers). Lesions of dorsolateral striatum almost completely eliminated responding to the lever CS while facilitating responding to the food magazine (US). Lesions of the dorsomedial striatum attenuated but did not eliminate responding to the lever CS. Lesions of the basolateral or central nucleus of the amygdala had no significant effects on sign-tracking, but combined lesions of the 2 structures impaired sign-tracking by significantly increasing latency to the first lever press without affecting the number of lever presses. Lesions of the dorsal hippocampus had no effect on any of the behavioral measures. The findings suggest that sign-tracking with a single lever insertion as the CS may consist of 2 separate behaviors learned in parallel: An amygdala-mediated conditioned orienting and approach response and a dorsal striatum-mediated instrumental response. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  12. Neuronal integration of dynamic sources: Bayesian learning and Bayesian inference.

    Science.gov (United States)

    Siegelmann, Hava T; Holzman, Lars E

    2010-09-01

    One of the brain's most basic functions is integrating sensory data from diverse sources. This ability causes us to question whether the neural system is computationally capable of intelligently integrating data, not only when sources have known, fixed relative dependencies but also when it must determine such relative weightings based on dynamic conditions, and then use these learned weightings to accurately infer information about the world. We suggest that the brain is, in fact, fully capable of computing this parallel task in a single network and describe a neural inspired circuit with this property. Our implementation suggests the possibility that evidence learning requires a more complex organization of the network than was previously assumed, where neurons have different specialties, whose emergence brings the desired adaptivity seen in human online inference.

  13. Parallel optical control of spatiotemporal neuronal spike activity using high-frequency digital light processingtechnology

    Directory of Open Access Journals (Sweden)

    Jason eJerome

    2011-08-01

    Full Text Available Neurons in the mammalian neocortex receive inputs from and communicate back to thousands of other neurons, creating complex spatiotemporal activity patterns. The experimental investigation of these parallel dynamic interactions has been limited due to the technical challenges of monitoring or manipulating neuronal activity at that level of complexity. Here we describe a new massively parallel photostimulation system that can be used to control action potential firing in in vitro brain slices with high spatial and temporal resolution while performing extracellular or intracellular electrophysiological measurements. The system uses Digital-Light-Processing (DLP technology to generate 2-dimensional (2D stimulus patterns with >780,000 independently controlled photostimulation sites that operate at high spatial (5.4 µm and temporal (>13kHz resolution. Light is projected through the quartz-glass bottom of the perfusion chamber providing access to a large area (2.76 x 2.07 mm2 of the slice preparation. This system has the unique capability to induce temporally precise action potential firing in large groups of neurons distributed over a wide area covering several cortical columns. Parallel photostimulation opens up new opportunities for the in vitro experimental investigation of spatiotemporal neuronal interactions at a broad range of anatomical scales.

  14. Parallel expression of synaptophysin and evoked neurotransmitter release during development of cultured neurons

    DEFF Research Database (Denmark)

    Ehrhart-Bornstein, M; Treiman, M; Hansen, Gert Helge

    1991-01-01

    Primary cultures of GABAergic cerebral cortex neurons and glutamatergic cerebellar granule cells were used to study the expression of synaptophysin, a synaptic vesicle marker protein, along with the ability of each cell type to release neurotransmitter upon stimulation. The synaptophysin expression...... by quantitative immunoblotting and light microscope immunocytochemistry, respectively. In both cell types, a close parallelism was found between the temporal pattern of development in synaptophysin expression and neurotransmitter release. This temporal pattern differed between the two types of neurons....... The cerebral cortex neurons showed a biphasic time course of increase in synaptophysin content, paralleled by a biphasic pattern of development in their ability to release [3H]GABA in response to depolarization by glutamate or elevated K+ concentrations. In contrast, a monophasic, approximately linear increase...

  15. Neuronal representations of stimulus associations develop in the temporal lobe during learning.

    Science.gov (United States)

    Messinger, A; Squire, L R; Zola, S M; Albright, T D

    2001-10-09

    Visual stimuli that are frequently seen together become associated in long-term memory, such that the sight of one stimulus readily brings to mind the thought or image of the other. It has been hypothesized that acquisition of such long-term associative memories proceeds via the strengthening of connections between neurons representing the associated stimuli, such that a neuron initially responding only to one stimulus of an associated pair eventually comes to respond to both. Consistent with this hypothesis, studies have demonstrated that individual neurons in the primate inferior temporal cortex tend to exhibit similar responses to pairs of visual stimuli that have become behaviorally associated. In the present study, we investigated the role of these areas in the formation of conditional visual associations by monitoring the responses of individual neurons during the learning of new stimulus pairs. We found that many neurons in both area TE and perirhinal cortex came to elicit more similar neuronal responses to paired stimuli as learning proceeded. Moreover, these neuronal response changes were learning-dependent and proceeded with an average time course that paralleled learning. This experience-dependent plasticity of sensory representations in the cerebral cortex may underlie the learning of associations between objects.

  16. A new supervised learning algorithm for spiking neurons.

    Science.gov (United States)

    Xu, Yan; Zeng, Xiaoqin; Zhong, Shuiming

    2013-06-01

    The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by the precise firing times of spikes. If only running time is considered, the supervised learning for a spiking neuron is equivalent to distinguishing the times of desired output spikes and the other time during the running process of the neuron through adjusting synaptic weights, which can be regarded as a classification problem. Based on this idea, this letter proposes a new supervised learning method for spiking neurons with temporal encoding; it first transforms the supervised learning into a classification problem and then solves the problem by using the perceptron learning rule. The experiment results show that the proposed method has higher learning accuracy and efficiency over the existing learning methods, so it is more powerful for solving complex and real-time problems.

  17. Neuronal Rac1 Is Required for Learning-Evoked Neurogenesis

    Science.gov (United States)

    Anderson, Matthew P.; Freewoman, Julia; Cord, Branden; Babu, Harish; Brakebusch, Cord

    2013-01-01

    Hippocampus-dependent learning and memory relies on synaptic plasticity as well as network adaptations provided by the addition of adult-born neurons. We have previously shown that activity-induced intracellular signaling through the Rho family small GTPase Rac1 is necessary in forebrain projection neurons for normal synaptic plasticity in vivo, and here we show that selective loss of neuronal Rac1 also impairs the learning-evoked increase in neurogenesis in the adult mouse hippocampus. Earlier work has indicated that experience elevates the abundance of adult-born neurons in the hippocampus primarily by enhancing the survival of neurons produced just before the learning event. Loss of Rac1 in mature projection neurons did reduce learning-evoked neurogenesis but, contrary to our expectations, these effects were not mediated by altering the survival of young neurons in the hippocampus. Instead, loss of neuronal Rac1 activation selectively impaired a learning-evoked increase in the proliferation and accumulation of neural precursors generated during the learning event itself. This indicates that experience-induced alterations in neurogenesis can be mechanistically resolved into two effects: (1) the well documented but Rac1-independent signaling cascade that enhances the survival of young postmitotic neurons; and (2) a previously unrecognized Rac1-dependent signaling cascade that stimulates the proliferative production and retention of new neurons generated during learning itself. PMID:23884931

  18. Scaling up machine learning: parallel and distributed approaches

    National Research Council Canada - National Science Library

    Bekkerman, Ron; Bilenko, Mikhail; Langford, John

    2012-01-01

    .... Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements...

  19. Neuron recycling for learning the alphabetic principles.

    Science.gov (United States)

    Scliar-Cabral, Leonor

    2014-01-01

    The main purpose of this paper is to discuss an approach to the phonic method of learning-teaching early literacy development, namely that the visual neurons must be recycled to recognize the small differences among pertinent letter features. In addition to the challenge of segmenting the speech chain and the syllable for learning the alphabetic principles, neuroscience has demonstrated another major challenge: neurons in mammals are programmed to process visual signals symmetrically. In order to develop early literacy, visual neurons must be recycled to overcome this initial programming together with phonological awareness, expanding it with the ability to delimit words, including clitics, as well as assigning stress to words. To achieve this goal, Scliar's Early Literacy Development System was proposed and tested. Sixteen subjects (10 girls and 6 boys) comprised the experimental group (mean age 6.02 years), and 16 subjects (7 girls and 9 boys) formed the control group (mean age 6.10 years). The research instruments were a psychosociolinguistic questionnaire to reveal the subjects' profile and a post-test battery of tests. At the beginning of the experiment, the experimental group was submitted to an intervention program based on Scliar's Early Literacy Development System. One of the tests is discussed in this paper, the grapheme-phoneme test: subjects had to read aloud a pseudoword with 4 graphemes, signaled by the experimenter and designed to assess the subject's ability to convert a grapheme into its correspondent phoneme. The average value for the test group was 25.0 correct answers (SD = 11.4); the control group had an average of 14.3 correct answers (SD = 10.6): The difference was significant. The experimental results validate Scliar's Early Literacy Development System and indicate the need to redesign early literacy development methods. © 2014 S. Karger AG, Basel.

  20. A causal link between prediction errors, dopamine neurons and learning.

    Science.gov (United States)

    Steinberg, Elizabeth E; Keiflin, Ronald; Boivin, Josiah R; Witten, Ilana B; Deisseroth, Karl; Janak, Patricia H

    2013-07-01

    Situations in which rewards are unexpectedly obtained or withheld represent opportunities for new learning. Often, this learning includes identifying cues that predict reward availability. Unexpected rewards strongly activate midbrain dopamine neurons. This phasic signal is proposed to support learning about antecedent cues by signaling discrepancies between actual and expected outcomes, termed a reward prediction error. However, it is unknown whether dopamine neuron prediction error signaling and cue-reward learning are causally linked. To test this hypothesis, we manipulated dopamine neuron activity in rats in two behavioral procedures, associative blocking and extinction, that illustrate the essential function of prediction errors in learning. We observed that optogenetic activation of dopamine neurons concurrent with reward delivery, mimicking a prediction error, was sufficient to cause long-lasting increases in cue-elicited reward-seeking behavior. Our findings establish a causal role for temporally precise dopamine neuron signaling in cue-reward learning, bridging a critical gap between experimental evidence and influential theoretical frameworks.

  1. Learning and Parallelization Boost Constraint Search

    Science.gov (United States)

    Yun, Xi

    2013-01-01

    Constraint satisfaction problems are a powerful way to abstract and represent academic and real-world problems from both artificial intelligence and operations research. A constraint satisfaction problem is typically addressed by a sequential constraint solver running on a single processor. Rather than construct a new, parallel solver, this work…

  2. Mirror Neurons, Embodied Cognitive Agents and Imitation Learning

    OpenAIRE

    Wiedermann, Jiří

    2003-01-01

    Mirror neurons are a relatively recent discovery; it has been conjectured that these neurons play an important role in imitation learning and other cognitive phenomena. We will study a possible place and role of mirror neurons in the neural architecture of embodied cognitive agents. We will formulate and investigate the hypothesis that mirror neurons serve as a mechanism which coordinates the multimodal (i.e., motor, perceptional and proprioceptive) information and completes it so that the ag...

  3. The specificity of learned parallelism in dual-memory retrieval.

    Science.gov (United States)

    Strobach, Tilo; Schubert, Torsten; Pashler, Harold; Rickard, Timothy

    2014-05-01

    Retrieval of two responses from one visually presented cue occurs sequentially at the outset of dual-retrieval practice. Exclusively for subjects who adopt a mode of grouping (i.e., synchronizing) their response execution, however, reaction times after dual-retrieval practice indicate a shift to learned retrieval parallelism (e.g., Nino & Rickard, in Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 373-388, 2003). In the present study, we investigated how this learned parallelism is achieved and why it appears to occur only for subjects who group their responses. Two main accounts were considered: a task-level versus a cue-level account. The task-level account assumes that learned retrieval parallelism occurs at the level of the task as a whole and is not limited to practiced cues. Grouping response execution may thus promote a general shift to parallel retrieval following practice. The cue-level account states that learned retrieval parallelism is specific to practiced cues. This type of parallelism may result from cue-specific response chunking that occurs uniquely as a consequence of grouped response execution. The results of two experiments favored the second account and were best interpreted in terms of a structural bottleneck model.

  4. Scaling up machine learning: parallel and distributed approaches

    National Research Council Canada - National Science Library

    Bekkerman, Ron; Bilenko, Mikhail; Langford, John

    2012-01-01

    ... presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters; concurrent programming frameworks that include CUDA, MPI, MapReduce, and DryadLINQ; and various learning settings: supervised, unsupervised, semi-supervised, and online learning. Extensive coverage of parallelizat...

  5. Large-Scale Modeling of Epileptic Seizures: Scaling Properties of Two Parallel Neuronal Network Simulation Algorithms

    Directory of Open Access Journals (Sweden)

    Lorenzo L. Pesce

    2013-01-01

    Full Text Available Our limited understanding of the relationship between the behavior of individual neurons and large neuronal networks is an important limitation in current epilepsy research and may be one of the main causes of our inadequate ability to treat it. Addressing this problem directly via experiments is impossibly complex; thus, we have been developing and studying medium-large-scale simulations of detailed neuronal networks to guide us. Flexibility in the connection schemas and a complete description of the cortical tissue seem necessary for this purpose. In this paper we examine some of the basic issues encountered in these multiscale simulations. We have determined the detailed behavior of two such simulators on parallel computer systems. The observed memory and computation-time scaling behavior for a distributed memory implementation were very good over the range studied, both in terms of network sizes (2,000 to 400,000 neurons and processor pool sizes (1 to 256 processors. Our simulations required between a few megabytes and about 150 gigabytes of RAM and lasted between a few minutes and about a week, well within the capability of most multinode clusters. Therefore, simulations of epileptic seizures on networks with millions of cells should be feasible on current supercomputers.

  6. Large-scale modeling of epileptic seizures: scaling properties of two parallel neuronal network simulation algorithms.

    Science.gov (United States)

    Pesce, Lorenzo L; Lee, Hyong C; Hereld, Mark; Visser, Sid; Stevens, Rick L; Wildeman, Albert; van Drongelen, Wim

    2013-01-01

    Our limited understanding of the relationship between the behavior of individual neurons and large neuronal networks is an important limitation in current epilepsy research and may be one of the main causes of our inadequate ability to treat it. Addressing this problem directly via experiments is impossibly complex; thus, we have been developing and studying medium-large-scale simulations of detailed neuronal networks to guide us. Flexibility in the connection schemas and a complete description of the cortical tissue seem necessary for this purpose. In this paper we examine some of the basic issues encountered in these multiscale simulations. We have determined the detailed behavior of two such simulators on parallel computer systems. The observed memory and computation-time scaling behavior for a distributed memory implementation were very good over the range studied, both in terms of network sizes (2,000 to 400,000 neurons) and processor pool sizes (1 to 256 processors). Our simulations required between a few megabytes and about 150 gigabytes of RAM and lasted between a few minutes and about a week, well within the capability of most multinode clusters. Therefore, simulations of epileptic seizures on networks with millions of cells should be feasible on current supercomputers.

  7. Hebbian Learning is about contingency, not contiguity, and explains the emergence of predictive mirror neurons

    NARCIS (Netherlands)

    Keysers, C.; Perrett, David I; Gazzola, Valeria

    Hebbian Learning should not be reduced to contiguity, as it detects contingency and causality. Hebbian Learning accounts of mirror neurons make predictions that differ from associative learning: Through Hebbian Learning, mirror neurons become dynamic networks that calculate predictions and

  8. The function of mirror neurons in the learning process

    OpenAIRE

    Mara Daniel

    2017-01-01

    In the last years, Neurosciences have developed very much, being elaborated many important theories scientific research in the field. The main goal of neuroscience is to understand how groups of neurons interact to create the behavior. Neuroscientists studying the action of molecules, genes and cells. It also explores the complex interactions involved in motion perception, thoughts, emotions and learning. Brick fundamental nervous system is the nerve cell, neuron. Neurons exchange information...

  9. A parallel ILP algorithm that incorporates incremental batch learning

    OpenAIRE

    Nuno Fonseca; Rui Camacho; Fernado Silva

    2003-01-01

    In this paper we tackle the problems of eciency and scala-bility faced by Inductive Logic Programming (ILP) systems. We proposethe use of parallelism to improve eciency and the use of an incrementalbatch learning to address the scalability problem. We describe a novelparallel algorithm that incorporates into ILP the method of incremen-tal batch learning. The theoretical complexity of the algorithm indicatesthat a linear speedup can be achieved.

  10. Programmed to Learn? The Ontogeny of Mirror Neurons

    Science.gov (United States)

    Del Giudice, Marco; Manera, Valeria; Keysers, Christian

    2009-01-01

    Mirror neurons are increasingly recognized as a crucial substrate for many developmental processes, including imitation and social learning. Although there has been considerable progress in describing their function and localization in the primate and adult human brain, we still know little about their ontogeny. The idea that mirror neurons result…

  11. Supervised learning with decision margins in pools of spiking neurons.

    Science.gov (United States)

    Le Mouel, Charlotte; Harris, Kenneth D; Yger, Pierre

    2014-10-01

    Learning to categorise sensory inputs by generalising from a few examples whose category is precisely known is a crucial step for the brain to produce appropriate behavioural responses. At the neuronal level, this may be performed by adaptation of synaptic weights under the influence of a training signal, in order to group spiking patterns impinging on the neuron. Here we describe a framework that allows spiking neurons to perform such "supervised learning", using principles similar to the Support Vector Machine, a well-established and robust classifier. Using a hinge-loss error function, we show that requesting a margin similar to that of the SVM improves performance on linearly non-separable problems. Moreover, we show that using pools of neurons to discriminate categories can also increase the performance by sharing the load among neurons.

  12. Exploration Of Deep Learning Algorithms Using Openacc Parallel Programming Model

    KAUST Repository

    Hamam, Alwaleed A.

    2017-03-13

    Deep learning is based on a set of algorithms that attempt to model high level abstractions in data. Specifically, RBM is a deep learning algorithm that used in the project to increase it\\'s time performance using some efficient parallel implementation by OpenACC tool with best possible optimizations on RBM to harness the massively parallel power of NVIDIA GPUs. GPUs development in the last few years has contributed to growing the concept of deep learning. OpenACC is a directive based ap-proach for computing where directives provide compiler hints to accelerate code. The traditional Restricted Boltzmann Ma-chine is a stochastic neural network that essentially perform a binary version of factor analysis. RBM is a useful neural net-work basis for larger modern deep learning model, such as Deep Belief Network. RBM parameters are estimated using an efficient training method that called Contrastive Divergence. Parallel implementation of RBM is available using different models such as OpenMP, and CUDA. But this project has been the first attempt to apply OpenACC model on RBM.

  13. Exploration Of Deep Learning Algorithms Using Openacc Parallel Programming Model

    KAUST Repository

    Hamam, Alwaleed A.; Khan, Ayaz H.

    2017-01-01

    Deep learning is based on a set of algorithms that attempt to model high level abstractions in data. Specifically, RBM is a deep learning algorithm that used in the project to increase it's time performance using some efficient parallel implementation by OpenACC tool with best possible optimizations on RBM to harness the massively parallel power of NVIDIA GPUs. GPUs development in the last few years has contributed to growing the concept of deep learning. OpenACC is a directive based ap-proach for computing where directives provide compiler hints to accelerate code. The traditional Restricted Boltzmann Ma-chine is a stochastic neural network that essentially perform a binary version of factor analysis. RBM is a useful neural net-work basis for larger modern deep learning model, such as Deep Belief Network. RBM parameters are estimated using an efficient training method that called Contrastive Divergence. Parallel implementation of RBM is available using different models such as OpenMP, and CUDA. But this project has been the first attempt to apply OpenACC model on RBM.

  14. On Scalable Deep Learning and Parallelizing Gradient Descent

    CERN Document Server

    AUTHOR|(CDS)2129036; Möckel, Rico; Baranowski, Zbigniew; Canali, Luca

    Speeding up gradient based methods has been a subject of interest over the past years with many practical applications, especially with respect to Deep Learning. Despite the fact that many optimizations have been done on a hardware level, the convergence rate of very large models remains problematic. Therefore, data parallel methods next to mini-batch parallelism have been suggested to further decrease the training time of parameterized models using gradient based methods. Nevertheless, asynchronous optimization was considered too unstable for practical purposes due to a lacking understanding of the underlying mechanisms. Recently, a theoretical contribution has been made which defines asynchronous optimization in terms of (implicit) momentum due to the presence of a queuing model of gradients based on past parameterizations. This thesis mainly builds upon this work to construct a better understanding why asynchronous optimization shows proportionally more divergent behavior when the number of parallel worker...

  15. The function of mirror neurons in the learning process

    Directory of Open Access Journals (Sweden)

    Mara Daniel

    2017-01-01

    Full Text Available In the last years, Neurosciences have developed very much, being elaborated many important theories scientific research in the field. The main goal of neuroscience is to understand how groups of neurons interact to create the behavior. Neuroscientists studying the action of molecules, genes and cells. It also explores the complex interactions involved in motion perception, thoughts, emotions and learning. Brick fundamental nervous system is the nerve cell, neuron. Neurons exchange information by sending electrical signals and chemical through connections called synapses. Discovered by a group of Italian researchers from the University of Parma, neurons - mirror are a special class of nerve cells played an important role in the direct knowledge, automatic and unconscious environment. These cortical neurons are activated not only when an action is fulfilled, but when we see how the same action is performed by someone else, they represent neural mechanism by which the actions, intentions and emotions of others can be understood automatically. In childhood neurons - mirror are extremely important. Thanks to them we learned a lot in the early years: smile, to ask for help and, in fact, all the behaviors and family and group norms. People learn by what they see and sense the others. Neurons - mirror are important to understanding the actions and intentions of other people and learn new skills through mirror image. They are involved in planning and controlling actions, abstract thinking and memory. If a child observes an action, neurons - mirror is activated and forming new neural pathways as if even he takes that action. Efficient activity of mirror neurons leads to good development in all areas at a higher emotional intelligence and the ability to empathize with others.

  16. Bayesian Inference and Online Learning in Poisson Neuronal Networks.

    Science.gov (United States)

    Huang, Yanping; Rao, Rajesh P N

    2016-08-01

    Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.

  17. Parallel, but Dissociable, Processing in Discrete Corticostriatal Inputs Encodes Skill Learning.

    Science.gov (United States)

    Kupferschmidt, David A; Juczewski, Konrad; Cui, Guohong; Johnson, Kari A; Lovinger, David M

    2017-10-11

    Changes in cortical and striatal function underlie the transition from novel actions to refined motor skills. How discrete, anatomically defined corticostriatal projections function in vivo to encode skill learning remains unclear. Using novel fiber photometry approaches to assess real-time activity of associative inputs from medial prefrontal cortex to dorsomedial striatum and sensorimotor inputs from motor cortex to dorsolateral striatum, we show that associative and sensorimotor inputs co-engage early in action learning and disengage in a dissociable manner as actions are refined. Disengagement of associative, but not sensorimotor, inputs predicts individual differences in subsequent skill learning. Divergent somatic and presynaptic engagement in both projections during early action learning suggests potential learning-related in vivo modulation of presynaptic corticostriatal function. These findings reveal parallel processing within associative and sensorimotor circuits that challenges and refines existing views of corticostriatal function and expose neuronal projection- and compartment-specific activity dynamics that encode and predict action learning. Published by Elsevier Inc.

  18. Hebbian Learning is about contingency, not contiguity, and explains the emergence of predictive mirror neurons.

    Science.gov (United States)

    Keysers, Christian; Perrett, David I; Gazzola, Valeria

    2014-04-01

    Hebbian Learning should not be reduced to contiguity, as it detects contingency and causality. Hebbian Learning accounts of mirror neurons make predictions that differ from associative learning: Through Hebbian Learning, mirror neurons become dynamic networks that calculate predictions and prediction errors and relate to ideomotor theories. The social force of imitation is important for mirror neuron emergence and suggests canalization.

  19. Hebbian Learning is about contingency, not contiguity, and explains the emergence of predictive mirror neurons

    OpenAIRE

    Keysers, C.; Perrett, D.I.; Gazzola, V.

    2014-01-01

    Hebbian Learning should not be reduced to contiguity, as it detects contingency and causality. Hebbian Learning accounts of mirror neurons make predictions that differ from associative learning: Through Hebbian Learning, mirror neurons become dynamic networks that calculate predictions and prediction errors and relate to ideomotor theories. The social force of imitation is important for mirror neuron emergence and suggests canalization. Publisher PDF Peer reviewed

  20. Deep Learning with Dynamic Spiking Neurons and Fixed Feedback Weights.

    Science.gov (United States)

    Samadi, Arash; Lillicrap, Timothy P; Tweed, Douglas B

    2017-03-01

    Recent work in computer science has shown the power of deep learning driven by the backpropagation algorithm in networks of artificial neurons. But real neurons in the brain are different from most of these artificial ones in at least three crucial ways: they emit spikes rather than graded outputs, their inputs and outputs are related dynamically rather than by piecewise-smooth functions, and they have no known way to coordinate arrays of synapses in separate forward and feedback pathways so that they change simultaneously and identically, as they do in backpropagation. Given these differences, it is unlikely that current deep learning algorithms can operate in the brain, but we that show these problems can be solved by two simple devices: learning rules can approximate dynamic input-output relations with piecewise-smooth functions, and a variation on the feedback alignment algorithm can train deep networks without having to coordinate forward and feedback synapses. Our results also show that deep spiking networks learn much better if each neuron computes an intracellular teaching signal that reflects that cell's nonlinearity. With this mechanism, networks of spiking neurons show useful learning in synapses at least nine layers upstream from the output cells and perform well compared to other spiking networks in the literature on the MNIST digit recognition task.

  1. The cerebellum: a neuronal learning machine?

    Science.gov (United States)

    Raymond, J. L.; Lisberger, S. G.; Mauk, M. D.

    1996-01-01

    Comparison of two seemingly quite different behaviors yields a surprisingly consistent picture of the role of the cerebellum in motor learning. Behavioral and physiological data about classical conditioning of the eyelid response and motor learning in the vestibulo-ocular reflex suggests that (i) plasticity is distributed between the cerebellar cortex and the deep cerebellar nuclei; (ii) the cerebellar cortex plays a special role in learning the timing of movement; and (iii) the cerebellar cortex guides learning in the deep nuclei, which may allow learning to be transferred from the cortex to the deep nuclei. Because many of the similarities in the data from the two systems typify general features of cerebellar organization, the cerebellar mechanisms of learning in these two systems may represent principles that apply to many motor systems.

  2. Learning and structure of neuronal networks

    Indian Academy of Sciences (India)

    We study the effect of learning dynamics on network topology. Firstly, a network of discrete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the paradigm of spike-time-dependent plasticity (STDP). This incorporates ...

  3. Neuronal Rac1 is required for learning-evoked neurogenesis

    DEFF Research Database (Denmark)

    Haditsch, Ursula; Anderson, Matthew P; Freewoman, Julia

    2013-01-01

    Hippocampus-dependent learning and memory relies on synaptic plasticity as well as network adaptations provided by the addition of adult-born neurons. We have previously shown that activity-induced intracellular signaling through the Rho family small GTPase Rac1 is necessary in forebrain projection...

  4. Programmed to learn? The ontogeny of mirror neurons

    NARCIS (Netherlands)

    Del Giudice, Marco; Manera, Valeria; Keysers, Christian

    Mirror neurons are increasingly recognized as a crucial substrate for many developmental processes, including imitation and social learning. Although there has been considerable progress in describing their function and localization in the primate and adult human brain, we still know little about

  5. Postnatal Gene Therapy Improves Spatial Learning Despite the Presence of Neuronal Ectopia in a Model of Neuronal Migration Disorder

    Directory of Open Access Journals (Sweden)

    Huaiyu Hu

    2016-11-01

    Full Text Available Patients with type II lissencephaly, a neuronal migration disorder with ectopic neurons, suffer from severe mental retardation, including learning deficits. There is no effective therapy to prevent or correct the formation of neuronal ectopia, which is presumed to cause cognitive deficits. We hypothesized that learning deficits were not solely caused by neuronal ectopia and that postnatal gene therapy could improve learning without correcting the neuronal ectopia formed during fetal development. To test this hypothesis, we evaluated spatial learning of cerebral cortex-specific protein O-mannosyltransferase 2 (POMT2, an enzyme required for O-mannosyl glycosylation knockout mice and compared to the knockout mice that were injected with an adeno-associated viral vector (AAV encoding POMT2 into the postnatal brains with Barnes maze. The data showed that the knockout mice exhibited reduced glycosylation in the cerebral cortex, reduced dendritic spine density on CA1 neurons, and increased latency to the target hole in the Barnes maze, indicating learning deficits. Postnatal gene therapy restored functional glycosylation, rescued dendritic spine defects, and improved performance on the Barnes maze by the knockout mice even though neuronal ectopia was not corrected. These results indicate that postnatal gene therapy improves spatial learning despite the presence of neuronal ectopia.

  6. A real-time hybrid neuron network for highly parallel cognitive systems.

    Science.gov (United States)

    Christiaanse, Gerrit Jan; Zjajo, Amir; Galuzzi, Carlo; van Leuken, Rene

    2016-08-01

    For comprehensive understanding of how neurons communicate with each other, new tools need to be developed that can accurately mimic the behaviour of such neurons and neuron networks under `real-time' constraints. In this paper, we propose an easily customisable, highly pipelined, neuron network design, which executes optimally scheduled floating-point operations for maximal amount of biophysically plausible neurons per FPGA family type. To reduce the required amount of resources without adverse effect on the calculation latency, a single exponent instance is used for multiple neuron calculation operations. Experimental results indicate that the proposed network design allows the simulation of up to 1188 neurons on Virtex7 (XC7VX550T) device in brain real-time yielding a speed-up of x12.4 compared to the state-of-the art.

  7. Mirror Neurons, Embodied Cognitive Agents and Imitation Learning

    Czech Academy of Sciences Publication Activity Database

    Wiedermann, Jiří

    2003-01-01

    Roč. 22, č. 6 (2003), s. 545-559 ISSN 1335-9150 R&D Projects: GA ČR GA201/02/1456 Institutional research plan: CEZ:AV0Z1030915 Keywords : complete agents * mirror neurons * embodied cognition * imitation learning * sensorimotor control Subject RIV: BA - General Mathematics Impact factor: 0.254, year: 2003 http://www.cai.sk/ojs/index.php/cai/article/view/468

  8. Deciphering mirror neurons: rational decision versus associative learning.

    Science.gov (United States)

    Khalil, Elias L

    2014-04-01

    The rational-decision approach is superior to the associative-learning approach of Cook et al. at explaining why mirror neurons fire or do not fire - even when the stimulus is the same. The rational-decision approach is superior because it starts with the analysis of the intention of the organism, that is, with the identification of the specific objective or goal that the organism is trying to maximize.

  9. Parallel multiple instance learning for extremely large histopathology image analysis.

    Science.gov (United States)

    Xu, Yan; Li, Yeshu; Shen, Zhengyang; Wu, Ziwei; Gao, Teng; Fan, Yubo; Lai, Maode; Chang, Eric I-Chao

    2017-08-03

    Histopathology images are critical for medical diagnosis, e.g., cancer and its treatment. A standard histopathology slice can be easily scanned at a high resolution of, say, 200,000×200,000 pixels. These high resolution images can make most existing imaging processing tools infeasible or less effective when operated on a single machine with limited memory, disk space and computing power. In this paper, we propose an algorithm tackling this new emerging "big data" problem utilizing parallel computing on High-Performance-Computing (HPC) clusters. Experimental results on a large-scale data set (1318 images at a scale of 10 billion pixels each) demonstrate the efficiency and effectiveness of the proposed algorithm for low-latency real-time applications. The framework proposed an effective and efficient system for extremely large histopathology image analysis. It is based on the multiple instance learning formulation for weakly-supervised learning for image classification, segmentation and clustering. When a max-margin concept is adopted for different clusters, we obtain further improvement in clustering performance.

  10. Symbol manipulation and rule learning in spiking neuronal networks.

    Science.gov (United States)

    Fernando, Chrisantha

    2011-04-21

    It has been claimed that the productivity, systematicity and compositionality of human language and thought necessitate the existence of a physical symbol system (PSS) in the brain. Recent discoveries about temporal coding suggest a novel type of neuronal implementation of a physical symbol system. Furthermore, learning classifier systems provide a plausible algorithmic basis by which symbol re-write rules could be trained to undertake behaviors exhibiting systematicity and compositionality, using a kind of natural selection of re-write rules in the brain, We show how the core operation of a learning classifier system, namely, the replication with variation of symbol re-write rules, can be implemented using spike-time dependent plasticity based supervised learning. As a whole, the aim of this paper is to integrate an algorithmic and an implementation level description of a neuronal symbol system capable of sustaining systematic and compositional behaviors. Previously proposed neuronal implementations of symbolic representations are compared with this new proposal. Copyright © 2011 Elsevier Ltd. All rights reserved.

  11. Learning Recruits Neurons Representing Previously Established Associations in the Corvid Endbrain.

    Science.gov (United States)

    Veit, Lena; Pidpruzhnykova, Galyna; Nieder, Andreas

    2017-10-01

    Crows quickly learn arbitrary associations. As a neuronal correlate of this behavior, single neurons in the corvid endbrain area nidopallium caudolaterale (NCL) change their response properties during association learning. In crows performing a delayed association task that required them to map both familiar and novel sample pictures to the same two choice pictures, NCL neurons established a common, prospective code for associations. Here, we report that neuronal tuning changes during learning were not distributed equally in the recorded population of NCL neurons. Instead, such learning-related changes relied almost exclusively on neurons which were already encoding familiar associations. Only in such neurons did behavioral improvements during learning of novel associations coincide with increasing selectivity over the learning process. The size and direction of selectivity for familiar and newly learned associations were highly correlated. These increases in selectivity for novel associations occurred only late in the delay period. Moreover, NCL neurons discriminated correct from erroneous trial outcome based on feedback signals at the end of the trial, particularly in newly learned associations. Our results indicate that task-relevant changes during association learning are not distributed within the population of corvid NCL neurons but rather are restricted to a specific group of association-selective neurons. Such association neurons in the multimodal cognitive integration area NCL likely play an important role during highly flexible behavior in corvids.

  12. Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels.

    Science.gov (United States)

    Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J

    2014-01-01

    This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively "hiding" its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research.

  13. Examining Neuronal Connectivity and Its Role in Learning and Memory

    Science.gov (United States)

    Gala, Rohan

    Learning and long-term memory formation are accompanied with changes in the patterns and weights of synaptic connections in the underlying neuronal network. However, the fundamental rules that drive connectivity changes, and the precise structure-function relationships within neuronal networks remain elusive. Technological improvements over the last few decades have enabled the observation of large but specific subsets of neurons and their connections in unprecedented detail. Devising robust and automated computational methods is critical to distill information from ever-increasing volumes of raw experimental data. Moreover, statistical models and theoretical frameworks are required to interpret the data and assemble evidence into understanding of brain function. In this thesis, I first describe computational methods to reconstruct connectivity based on light microscopy imaging experiments. Next, I use these methods to quantify structural changes in connectivity based on in vivo time-lapse imaging experiments. Finally, I present a theoretical model of associative learning that can explain many stereotypical features of experimentally observed connectivity.

  14. Aversive learning shapes neuronal orientation tuning in human visual cortex.

    Science.gov (United States)

    McTeague, Lisa M; Gruss, L Forest; Keil, Andreas

    2015-07-28

    The responses of sensory cortical neurons are shaped by experience. As a result perceptual biases evolve, selectively facilitating the detection and identification of sensory events that are relevant for adaptive behaviour. Here we examine the involvement of human visual cortex in the formation of learned perceptual biases. We use classical aversive conditioning to associate one out of a series of oriented gratings with a noxious sound stimulus. After as few as two grating-sound pairings, visual cortical responses to the sound-paired grating show selective amplification. Furthermore, as learning progresses, responses to the orientations with greatest similarity to the sound-paired grating are increasingly suppressed, suggesting inhibitory interactions between orientation-selective neuronal populations. Changes in cortical connectivity between occipital and fronto-temporal regions mirror the changes in visuo-cortical response amplitudes. These findings suggest that short-term behaviourally driven retuning of human visual cortical neurons involves distal top-down projections as well as local inhibitory interactions.

  15. Neurons with two sites of synaptic integration learn invariant representations.

    Science.gov (United States)

    Körding, K P; König, P

    2001-12-01

    Neurons in mammalian cerebral cortex combine specific responses with respect to some stimulus features with invariant responses to other stimulus features. For example, in primary visual cortex, complex cells code for orientation of a contour but ignore its position to a certain degree. In higher areas, such as the inferotemporal cortex, translation-invariant, rotation-invariant, and even view point-invariant responses can be observed. Such properties are of obvious interest to artificial systems performing tasks like pattern recognition. It remains to be resolved how such response properties develop in biological systems. Here we present an unsupervised learning rule that addresses this problem. It is based on a neuron model with two sites of synaptic integration, allowing qualitatively different effects of input to basal and apical dendritic trees, respectively. Without supervision, the system learns to extract invariance properties using temporal or spatial continuity of stimuli. Furthermore, top-down information can be smoothly integrated in the same framework. Thus, this model lends a physiological implementation to approaches of unsupervised learning of invariant-response properties.

  16. Associative (not Hebbian) learning and the mirror neuron system.

    Science.gov (United States)

    Cooper, Richard P; Cook, Richard; Dickinson, Anthony; Heyes, Cecilia M

    2013-04-12

    The associative sequence learning (ASL) hypothesis suggests that sensorimotor experience plays an inductive role in the development of the mirror neuron system, and that it can play this crucial role because its effects are mediated by learning that is sensitive to both contingency and contiguity. The Hebbian hypothesis proposes that sensorimotor experience plays a facilitative role, and that its effects are mediated by learning that is sensitive only to contiguity. We tested the associative and Hebbian accounts by computational modelling of automatic imitation data indicating that MNS responsivity is reduced more by contingent and signalled than by non-contingent sensorimotor training (Cook et al. [7]). Supporting the associative account, we found that the reduction in automatic imitation could be reproduced by an existing interactive activation model of imitative compatibility when augmented with Rescorla-Wagner learning, but not with Hebbian or quasi-Hebbian learning. The work argues for an associative, but against a Hebbian, account of the effect of sensorimotor training on automatic imitation. We argue, by extension, that associative learning is potentially sufficient for MNS development. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  17. Associative and sensorimotor learning for parenting involves mirror neurons under the influence of oxytocin.

    Science.gov (United States)

    Ho, S Shaun; Macdonald, Adam; Swain, James E

    2014-04-01

    Mirror neuron-based associative learning may be understood according to associative learning theories, in addition to sensorimotor learning theories. This is important for a comprehensive understanding of the role of mirror neurons and related hormone modulators, such as oxytocin, in complex social interactions such as among parent-infant dyads and in examples of mirror neuron function that involve abnormal motor systems such as depression.

  18. Parallel and patterned optogenetic manipulation of neurons in the brain slice using a DMD-based projector.

    Science.gov (United States)

    Sakai, Seiichiro; Ueno, Kenichi; Ishizuka, Toru; Yawo, Hiromu

    2013-01-01

    Optical manipulation technologies greatly advanced the understanding of the neuronal network and its dysfunctions. To achieve patterned and parallel optical switching, we developed a microscopic illumination system using a commercial DMD-based projector and a software program. The spatiotemporal patterning of the system was evaluated using acute slices of the hippocampus. The neural activity was optically manipulated, positively by the combination of channelrhodopsin-2 (ChR2) and blue light, and negatively by the combination of archaerhodopsin-T (ArchT) and green light. It is suggested that our projector-managing optical system (PMOS) would effectively facilitate the optogenetic analyses of neurons and their circuits. Copyright © 2012 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

  19. Spatial learning depends on both the addition and removal of new hippocampal neurons.

    Directory of Open Access Journals (Sweden)

    David Dupret

    2007-08-01

    Full Text Available The role of adult hippocampal neurogenesis in spatial learning remains a matter of debate. Here, we show that spatial learning modifies neurogenesis by inducing a cascade of events that resembles the selective stabilization process characterizing development. Learning promotes survival of relatively mature neurons, apoptosis of more immature cells, and finally, proliferation of neural precursors. These are three interrelated events mediating learning. Thus, blocking apoptosis impairs memory and inhibits learning-induced cell survival and cell proliferation. In conclusion, during learning, similar to the selective stabilization process, neuronal networks are sculpted by a tightly regulated selection and suppression of different populations of newly born neurons.

  20. Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception.

    Science.gov (United States)

    Kutschireiter, Anna; Surace, Simone Carlo; Sprekeler, Henning; Pfister, Jean-Pascal

    2017-08-18

    The robust estimation of dynamical hidden features, such as the position of prey, based on sensory inputs is one of the hallmarks of perception. This dynamical estimation can be rigorously formulated by nonlinear Bayesian filtering theory. Recent experimental and behavioral studies have shown that animals' performance in many tasks is consistent with such a Bayesian statistical interpretation. However, it is presently unclear how a nonlinear Bayesian filter can be efficiently implemented in a network of neurons that satisfies some minimum constraints of biological plausibility. Here, we propose the Neural Particle Filter (NPF), a sampling-based nonlinear Bayesian filter, which does not rely on importance weights. We show that this filter can be interpreted as the neuronal dynamics of a recurrently connected rate-based neural network receiving feed-forward input from sensory neurons. Further, it captures properties of temporal and multi-sensory integration that are crucial for perception, and it allows for online parameter learning with a maximum likelihood approach. The NPF holds the promise to avoid the 'curse of dimensionality', and we demonstrate numerically its capability to outperform weighted particle filters in higher dimensions and when the number of particles is limited.

  1. The Languages of Neurons: An Analysis of Coding Mechanisms by Which Neurons Communicate, Learn and Store Information

    Directory of Open Access Journals (Sweden)

    Morris H. Baslow

    2009-11-01

    Full Text Available In this paper evidence is provided that individual neurons possess language, and that the basic unit for communication consists of two neurons and their entire field of interacting dendritic and synaptic connections. While information processing in the brain is highly complex, each neuron uses a simple mechanism for transmitting information. This is in the form of temporal electrophysiological action potentials or spikes (S operating on a millisecond timescale that, along with pauses (P between spikes constitute a two letter “alphabet” that generates meaningful frequency-encoded signals or neuronal S/P “words” in a primary language. However, when a word from an afferent neuron enters the dendritic-synaptic-dendritic field between two neurons, it is translated into a new frequency-encoded word with the same meaning, but in a different spike-pause language, that is delivered to and understood by the efferent neuron. It is suggested that this unidirectional inter-neuronal language-based word translation step is of utmost importance to brain function in that it allows for variations in meaning to occur. Thus, structural or biochemical changes in dendrites or synapses can produce novel words in the second language that have changed meanings, allowing for a specific signaling experience, either external or internal, to modify the meaning of an original word (learning, and store the learned information of that experience (memory in the form of an altered dendritic-synaptic-dendritic field.

  2. Repeated Stimulation of Cultured Networks of Rat Cortical Neurons Induces Parallel Memory Traces

    Science.gov (United States)

    le Feber, Joost; Witteveen, Tim; van Veenendaal, Tamar M.; Dijkstra, Jelle

    2015-01-01

    During systems consolidation, memories are spontaneously replayed favoring information transfer from hippocampus to neocortex. However, at present no empirically supported mechanism to accomplish a transfer of memory from hippocampal to extra-hippocampal sites has been offered. We used cultured neuronal networks on multielectrode arrays and…

  3. Mirror neuron system and observational learning: behavioral and neurophysiological evidence.

    Science.gov (United States)

    Lago-Rodriguez, Angel; Lopez-Alonso, Virginia; Fernández-del-Olmo, Miguel

    2013-07-01

    Three experiments were performed to study observational learning using behavioral, perceptual, and neurophysiological data. Experiment 1 investigated whether observing an execution model, during physical practice of a transitive task that only presented one execution strategy, led to performance improvements compared with physical practice alone. Experiment 2 investigated whether performing an observational learning protocol improves subjects' action perception. In experiment 3 we evaluated whether the type of practice performed determined the activation of the Mirror Neuron System during action observation. Results showed that, compared with physical practice, observing an execution model during a task that only showed one execution strategy does not provide behavioral benefits. However, an observational learning protocol allows subjects to predict more precisely the outcome of the learned task. Finally, intersperse observation of an execution model with physical practice results in changes of primary motor cortex activity during the observation of the motor pattern previously practiced, whereas modulations in the connectivity between primary and non primary motor areas (PMv-M1; PPC-M1) were not affected by the practice protocol performed by the observer. Copyright © 2013 Elsevier B.V. All rights reserved.

  4. Context Fear Learning Specifically Activates Distinct Populations of Neurons in Amygdala and Hypothalamus

    Science.gov (United States)

    Trogrlic, Lidia; Wilson, Yvette M.; Newman, Andrew G.; Murphy, Mark

    2011-01-01

    The identity and distribution of neurons that are involved in any learning or memory event is not known. In previous studies, we identified a discrete population of neurons in the lateral amygdala that show learning-specific activation of a c-"fos"-regulated transgene following context fear conditioning. Here, we have extended these studies to…

  5. Module Six: Parallel Circuits; Basic Electricity and Electronics Individualized Learning System.

    Science.gov (United States)

    Bureau of Naval Personnel, Washington, DC.

    In this module the student will learn the rules that govern the characteristics of parallel circuits; the relationships between voltage, current, resistance and power; and the results of common troubles in parallel circuits. The module is divided into four lessons: rules of voltage and current, rules for resistance and power, variational analysis,…

  6. Shifts in sensory neuron identity parallel differences in pheromone preference in the European corn borer

    Directory of Open Access Journals (Sweden)

    Fotini A Koutroumpa

    2014-10-01

    Full Text Available Pheromone communication relies on highly specific signals sent and received between members of the same species. However, how pheromone specificity is determined in moth olfactory circuits remains unknown. Here we provide the first glimpse into the mechanism that generates this specificity in Ostrinia nubilalis. In Ostrinia nubilalis it was found that a single locus causes strain-specific, diametrically opposed preferences for a 2-component pheromone blend. Previously we found pheromone preference to be correlated with the strain and hybrid-specific relative antennal response to both pheromone components. This led to the current study, in which we detail the underlying mechanism of this differential response, through chemotopically mapping of the pheromone detection circuit in the antenna. We determined that both strains and their hybrids have swapped the neuronal identity of the pheromone-sensitive neurons co-housed within a single sensillum. Furthermore, neurons that mediate behavioral antagonism surprisingly co-express up to five pheromone receptors, mirroring the concordantly broad tuning to heterospecific pheromones. This appears as possible evolutionary adaptation that could prevent cross attraction to a range of heterospecific signals, while keeping the pheromone detection system to its simplest tripartite setup.

  7. Parallel changes in cortical neuron biochemistry and motor function in protein-energy malnourished adult rats.

    Science.gov (United States)

    Alaverdashvili, Mariam; Hackett, Mark J; Caine, Sally; Paterson, Phyllis G

    2017-04-01

    While protein-energy malnutrition in the adult has been reported to induce motor abnormalities and exaggerate motor deficits caused by stroke, it is not known if alterations in mature cortical neurons contribute to the functional deficits. Therefore, we explored if PEM in adult rats provoked changes in the biochemical profile of neurons in the forelimb and hindlimb regions of the motor cortex. Fourier transform infrared spectroscopic imaging using a synchrotron generated light source revealed for the first time altered lipid composition in neurons and subcellular domains (cytosol and nuclei) in a cortical layer and region-specific manner. This change measured by the area under the curve of the δ(CH 2 ) band may indicate modifications in membrane fluidity. These PEM-induced biochemical changes were associated with the development of abnormalities in forelimb use and posture. The findings of this study provide a mechanism by which PEM, if not treated, could exacerbate the course of various neurological disorders and diminish treatment efficacy. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. Dutch Lifelong learning : A Policy Perspective bringing together parallel Worlds

    NARCIS (Netherlands)

    van Dellen, Teije; Klercq, Jumbo; Buiskool, Bert-Jan

    Lifelong learning has never been an integral part of the Dutch educational culture. Nevertheless, nowadays yearly many adults (about 17.8% in 2015) are after either or not finishing initial education in some respect emergently participating in (continuing) second, third or more learning paths

  9. Associative and sensorimotor learning for parenting involves mirror neurons under the influence of oxytocin

    OpenAIRE

    Ho, S. Shaun; MacDonald, Adam; Swain, James E.

    2014-01-01

    Mirror neuron–based associative learning may be understood according to associative learning theories, in addition to sensorimotor learning theories. This is important for a comprehensive understanding of the role of mirror neurons and related hormone modulators, such as oxytocin, in complex social interactions such as among parent–infant dyads and in examples of mirror neuron function that involve abnormal motor systems such as depression.

  10. 'Re-zoning' proximal development in a parallel e-learning course ...

    African Journals Online (AJOL)

    'Re-zoning' proximal development in a parallel e-learning course. ... Journal Home > Vol 22, No 4 (2002) >. Log in or Register to get access to full text downloads. ... This twinning course was introduced to expand learning opportunities in what we ... face-to-face curriculum with less scheduled teaching time than previously.

  11. Allopregnanolone-induced rise in intracellular calcium in embryonic hippocampal neurons parallels their proliferative potential

    Directory of Open Access Journals (Sweden)

    Brinton Roberta

    2008-12-01

    Full Text Available Abstract Background Factors that regulate intracellular calcium concentration are known to play a critical role in brain function and neural development, including neural plasticity and neurogenesis. We previously demonstrated that the neurosteroid allopregnanolone (APα; 5α-pregnan-3α-ol-20-one promotes neural progenitor proliferation in vitro in cultures of rodent hippocampal and human cortical neural progenitors, and in vivo in triple transgenic Alzheimer's disease mice dentate gyrus. We also found that APα-induced proliferation of neural progenitors is abolished by a calcium channel blocker, nifedipine, indicating a calcium dependent mechanism for the proliferation. Methods In the present study, we investigated the effect of APα on the regulation of intracellular calcium concentration in E18 rat hippocampal neurons using ratiometric Fura2-AM imaging. Results Results indicate that APα rapidly increased intracellular calcium concentration in a dose-dependent and developmentally regulated manner, with an EC50 of 110 ± 15 nM and a maximal response occurring at three days in vitro. The stereoisomers 3β-hydroxy-5α-hydroxy-pregnan-20-one, and 3β-hydroxy-5β-hydroxy-pregnan-20-one, as well as progesterone, were without significant effect. APα-induced intracellular calcium concentration increase was not observed in calcium depleted medium and was blocked in the presence of the broad spectrum calcium channel blocker La3+, or the L-type calcium channel blocker nifedipine. Furthermore, the GABAA receptor blockers bicuculline and picrotoxin abolished APα-induced intracellular calcium concentration rise. Conclusion Collectively, these data indicate that APα promotes a rapid, dose-dependent, stereo-specific, and developmentally regulated increase of intracellular calcium concentration in rat embryonic hippocampal neurons via a mechanism that requires both the GABAA receptor and L-type calcium channel. These data suggest that AP

  12. Changes in prefrontal neuronal activity after learning to perform a spatial working memory task.

    Science.gov (United States)

    Qi, Xue-Lian; Meyer, Travis; Stanford, Terrence R; Constantinidis, Christos

    2011-12-01

    The prefrontal cortex is considered essential for learning to perform cognitive tasks though little is known about how the representation of stimulus properties is altered by learning. To address this issue, we recorded neuronal activity in monkeys before and after training on a task that required visual working memory. After the subjects learned to perform the task, we observed activation of more prefrontal neurons and increased activity during working memory maintenance. The working memory-related increase in firing rate was due mostly to regular-spiking putative pyramidal neurons. Unexpectedly, the selectivity of neurons for stimulus properties and the ability of neurons to discriminate between stimuli decreased as the information about stimulus properties was apparently present in neural firing prior to training and neuronal selectivity degraded after training in the task. The effect was robust and could not be accounted for by differences in sampling sites, selection of neurons, level of performance, or merely the elapse of time. The results indicate that, in contrast to the effects of perceptual learning, mastery of a cognitive task degrades the apparent stimulus selectivity as neurons represent more abstract information related to the task. This effect is countered by the recruitment of more neurons after training.

  13. The chronotron: a neuron that learns to fire temporally precise spike patterns.

    Directory of Open Access Journals (Sweden)

    Răzvan V Florian

    Full Text Available In many cases, neurons process information carried by the precise timings of spikes. Here we show how neurons can learn to generate specific temporally precise output spikes in response to input patterns of spikes having precise timings, thus processing and memorizing information that is entirely temporally coded, both as input and as output. We introduce two new supervised learning rules for spiking neurons with temporal coding of information (chronotrons, one that provides high memory capacity (E-learning, and one that has a higher biological plausibility (I-learning. With I-learning, the neuron learns to fire the target spike trains through synaptic changes that are proportional to the synaptic currents at the timings of real and target output spikes. We study these learning rules in computer simulations where we train integrate-and-fire neurons. Both learning rules allow neurons to fire at the desired timings, with sub-millisecond precision. We show how chronotrons can learn to classify their inputs, by firing identical, temporally precise spike trains for different inputs belonging to the same class. When the input is noisy, the classification also leads to noise reduction. We compute lower bounds for the memory capacity of chronotrons and explore the influence of various parameters on chronotrons' performance. The chronotrons can model neurons that encode information in the time of the first spike relative to the onset of salient stimuli or neurons in oscillatory networks that encode information in the phases of spikes relative to the background oscillation. Our results show that firing one spike per cycle optimizes memory capacity in neurons encoding information in the phase of firing relative to a background rhythm.

  14. Learning to read ‘properly’ by moving between parallel literacy classes

    OpenAIRE

    Robertson, Leena Helavaara

    2006-01-01

    This paper explores what kinds of advantages and strengths the process of learning to read simultaneously in different languages and scripts might bring about. It is based on a socio-cultural view of learning and literacy and examines early literacy in three parallel literacy classes in Watford, England. It analyses the learning experiences of five bilingual children who are of second or third generation Pakistani background. At the start of the study the children are five years old and they ...

  15. An online supervised learning method based on gradient descent for spiking neurons.

    Science.gov (United States)

    Xu, Yan; Yang, Jing; Zhong, Shuiming

    2017-09-01

    The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by precise firing times of spikes. The gradient-descent-based (GDB) learning methods are widely used and verified in the current research. Although the existing GDB multi-spike learning (or spike sequence learning) methods have good performance, they work in an offline manner and still have some limitations. This paper proposes an online GDB spike sequence learning method for spiking neurons that is based on the online adjustment mechanism of real biological neuron synapses. The method constructs error function and calculates the adjustment of synaptic weights as soon as the neurons emit a spike during their running process. We analyze and synthesize desired and actual output spikes to select appropriate input spikes in the calculation of weight adjustment in this paper. The experimental results show that our method obviously improves learning performance compared with the offline learning manner and has certain advantage on learning accuracy compared with other learning methods. Stronger learning ability determines that the method has large pattern storage capacity. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. Parallel Alterations of Functional Connectivity during Execution and Imagination after Motor Imagery Learning

    Science.gov (United States)

    Zhang, Rushao; Hui, Mingqi; Long, Zhiying; Zhao, Xiaojie; Yao, Li

    2012-01-01

    Background Neural substrates underlying motor learning have been widely investigated with neuroimaging technologies. Investigations have illustrated the critical regions of motor learning and further revealed parallel alterations of functional activation during imagination and execution after learning. However, little is known about the functional connectivity associated with motor learning, especially motor imagery learning, although benefits from functional connectivity analysis attract more attention to the related explorations. We explored whether motor imagery (MI) and motor execution (ME) shared parallel alterations of functional connectivity after MI learning. Methodology/Principal Findings Graph theory analysis, which is widely used in functional connectivity exploration, was performed on the functional magnetic resonance imaging (fMRI) data of MI and ME tasks before and after 14 days of consecutive MI learning. The control group had no learning. Two measures, connectivity degree and interregional connectivity, were calculated and further assessed at a statistical level. Two interesting results were obtained: (1) The connectivity degree of the right posterior parietal lobe decreased in both MI and ME tasks after MI learning in the experimental group; (2) The parallel alterations of interregional connectivity related to the right posterior parietal lobe occurred in the supplementary motor area for both tasks. Conclusions/Significance These computational results may provide the following insights: (1) The establishment of motor schema through MI learning may induce the significant decrease of connectivity degree in the posterior parietal lobe; (2) The decreased interregional connectivity between the supplementary motor area and the right posterior parietal lobe in post-test implicates the dissociation between motor learning and task performing. These findings and explanations further revealed the neural substrates underpinning MI learning and supported that

  17. Learning Enhances Intrinsic Excitability in a Subset of Lateral Amygdala Neurons

    Science.gov (United States)

    Sehgal, Megha; Ehlers, Vanessa L.; Moyer, James R., Jr.

    2014-01-01

    Learning-induced modulation of neuronal intrinsic excitability is a metaplasticity mechanism that can impact the acquisition of new memories. Although the amygdala is important for emotional learning and other behaviors, including fear and anxiety, whether learning alters intrinsic excitability within the amygdala has received very little…

  18. Reconciling genetic evolution and the associative learning account of mirror neurons through data-acquisition mechanisms.

    Science.gov (United States)

    Lotem, Arnon; Kolodny, Oren

    2014-04-01

    An associative learning account of mirror neurons should not preclude genetic evolution of its underlying mechanisms. On the contrary, an associative learning framework for cognitive development should seek heritable variation in the learning rules and in the data-acquisition mechanisms that construct associative networks, demonstrating how small genetic modifications of associative elements can give rise to the evolution of complex cognition.

  19. The Mirror Neuron System and Observational Learning: Implications for the Effectiveness of Dynamic Visualizations

    Science.gov (United States)

    van Gog, Tamara; Paas, Fred; Marcus, Nadine; Ayres, Paul; Sweller, John

    2009-01-01

    Learning by observing and imitating others has long been recognized as constituting a powerful learning strategy for humans. Recent findings from neuroscience research, more specifically on the mirror neuron system, begin to provide insight into the neural bases of learning by observation and imitation. These findings are discussed here, along…

  20. 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.

  1. 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.

  2. A Simple Deep Learning Method for Neuronal Spike Sorting

    Science.gov (United States)

    Yang, Kai; Wu, Haifeng; Zeng, Yu

    2017-10-01

    Spike sorting is one of key technique to understand brain activity. With the development of modern electrophysiology technology, some recent multi-electrode technologies have been able to record the activity of thousands of neuronal spikes simultaneously. The spike sorting in this case will increase the computational complexity of conventional sorting algorithms. In this paper, we will focus spike sorting on how to reduce the complexity, and introduce a deep learning algorithm, principal component analysis network (PCANet) to spike sorting. The introduced method starts from a conventional model and establish a Toeplitz matrix. Through the column vectors in the matrix, we trains a PCANet, where some eigenvalue vectors of spikes could be extracted. Finally, support vector machine (SVM) is used to sort spikes. In experiments, we choose two groups of simulated data from public databases availably and compare this introduced method with conventional methods. The results indicate that the introduced method indeed has lower complexity with the same sorting errors as the conventional methods.

  3. The mirror-neuron system and observational learning: Implications for the effectiveness of dynamic visualizations.

    OpenAIRE

    Van Gog, Tamara; Paas, Fred; Marcus, Nadine; Ayres, Paul; Sweller, John

    2009-01-01

    Van Gog, T., Paas, F., Marcus, N., Ayres, P., & Sweller, J. (2009). The mirror-neuron system and observational learning: Implications for the effectiveness of dynamic visualizations. Educational Psychology Review, 21, 21-30.

  4. CAMKII activation is not required for maintenance of learning-induced enhancement of neuronal excitability.

    Directory of Open Access Journals (Sweden)

    Ori Liraz

    Full Text Available Pyramidal neurons in the piriform cortex from olfactory-discrimination trained rats show enhanced intrinsic neuronal excitability that lasts for several days after learning. Such enhanced intrinsic excitability is mediated by long-term reduction in the post-burst after-hyperpolarization (AHP which is generated by repetitive spike firing. AHP reduction is due to decreased conductance of a calcium-dependent potassium current, the sI(AHP. We have previously shown that learning-induced AHP reduction is maintained by persistent protein kinase C (PKC and extracellular regulated kinase (ERK activation. However, the molecular machinery underlying this long-lasting modulation of intrinsic excitability is yet to be fully described. Here we examine whether the CaMKII, which is known to be crucial in learning, memory and synaptic plasticity processes, is instrumental for the maintenance of learning-induced AHP reduction. KN93, that selectively blocks CaMKII autophosphorylation at Thr286, reduced the AHP in neurons from trained and control rat to the same extent. Consequently, the differences in AHP amplitude and neuronal adaptation between neurons from trained rats and controls remained. Accordingly, the level of activated CaMKII was similar in pirifrom cortex samples taken form trained and control rats. Our data show that although CaMKII modulates the amplitude of AHP of pyramidal neurons in the piriform cortex, its activation is not required for maintaining learning-induced enhancement of neuronal excitability.

  5. Hebbian learning and predictive mirror neurons for actions, sensations and emotions

    OpenAIRE

    Keysers, C.; Gazzola, Valeria

    2014-01-01

    Spike-timing-dependent plasticity is considered the neurophysiological basis of Hebbian learning and has been shown to be sensitive to both contingency and contiguity between pre- and postsynaptic activity. Here, we will examine how applying this Hebbian learning rule to a system of interconnected neurons in the presence of direct or indirect re-afference (e.g. seeing/hearing one's own actions) predicts the emergence of mirror neurons with predictive properties. In this framework, we analyse ...

  6. Sensorimotor learning and the ontogeny of the mirror neuron system

    OpenAIRE

    Catmur, C

    2013-01-01

    Mirror neurons, which have now been found in the human and songbird as well as the macaque, respond to both the observation and the performance of the same action. It has been suggested that their matching response properties have evolved as an adaptation for action understanding; alternatively, these properties may arise through sensorimotor experience. Here I review mirror neuron response characteristics from the perspective of ontogeny; I discuss the limited evidence for mirror neurons in ...

  7. Direct Neuronal Reprogramming for Disease Modeling Studies Using Patient-Derived Neurons: What Have We Learned?

    Directory of Open Access Journals (Sweden)

    Janelle Drouin-Ouellet

    2017-09-01

    Full Text Available Direct neuronal reprogramming, by which a neuron is formed via direct conversion from a somatic cell without going through a pluripotent intermediate stage, allows for the possibility of generating patient-derived neurons. A unique feature of these so-called induced neurons (iNs is the potential to maintain aging and epigenetic signatures of the donor, which is critical given that many diseases of the CNS are age related. Here, we review the published literature on the work that has been undertaken using iNs to model human brain disorders. Furthermore, as disease-modeling studies using this direct neuronal reprogramming approach are becoming more widely adopted, it is important to assess the criteria that are used to characterize the iNs, especially in relation to the extent to which they are mature adult neurons. In particular: i what constitutes an iN cell, ii which stages of conversion offer the earliest/optimal time to assess features that are specific to neurons and/or a disorder and iii whether generating subtype-specific iNs is critical to the disease-related features that iNs express. Finally, we discuss the range of potential biomedical applications that can be explored using patient-specific models of neurological disorders with iNs, and the challenges that will need to be overcome in order to realize these applications.

  8. Maximization of learning speed in the motor cortex due to neuronal redundancy.

    Directory of Open Access Journals (Sweden)

    Ken Takiyama

    2012-01-01

    Full Text Available Many redundancies play functional roles in motor control and motor learning. For example, kinematic and muscle redundancies contribute to stabilizing posture and impedance control, respectively. Another redundancy is the number of neurons themselves; there are overwhelmingly more neurons than muscles, and many combinations of neural activation can generate identical muscle activity. The functional roles of this neuronal redundancy remains unknown. Analysis of a redundant neural network model makes it possible to investigate these functional roles while varying the number of model neurons and holding constant the number of output units. Our analysis reveals that learning speed reaches its maximum value if and only if the model includes sufficient neuronal redundancy. This analytical result does not depend on whether the distribution of the preferred direction is uniform or a skewed bimodal, both of which have been reported in neurophysiological studies. Neuronal redundancy maximizes learning speed, even if the neural network model includes recurrent connections, a nonlinear activation function, or nonlinear muscle units. Furthermore, our results do not rely on the shape of the generalization function. The results of this study suggest that one of the functional roles of neuronal redundancy is to maximize learning speed.

  9. PBODL : Parallel Bayesian Online Deep Learning for Click-Through Rate Prediction in Tencent Advertising System

    OpenAIRE

    Liu, Xun; Xue, Wei; Xiao, Lei; Zhang, Bo

    2017-01-01

    We describe a parallel bayesian online deep learning framework (PBODL) for click-through rate (CTR) prediction within today's Tencent advertising system, which provides quick and accurate learning of user preferences. We first explain the framework with a deep probit regression model, which is trained with probabilistic back-propagation in the mode of assumed Gaussian density filtering. Then we extend the model family to a variety of bayesian online models with increasing feature embedding ca...

  10. Sensorimotor learning and the ontogeny of the mirror neuron system.

    Science.gov (United States)

    Catmur, Caroline

    2013-04-12

    Mirror neurons, which have now been found in the human and songbird as well as the macaque, respond to both the observation and the performance of the same action. It has been suggested that their matching response properties have evolved as an adaptation for action understanding; alternatively, these properties may arise through sensorimotor experience. Here I review mirror neuron response characteristics from the perspective of ontogeny; I discuss the limited evidence for mirror neurons in early development; and I describe the growing body of evidence suggesting that mirror neuron responses can be modified through experience, and that sensorimotor experience is the critical type of experience for producing mirror neuron responses. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  11. Hebbian learning and predictive mirror neurons for actions, sensations and emotions.

    Science.gov (United States)

    Keysers, Christian; Gazzola, Valeria

    2014-01-01

    Spike-timing-dependent plasticity is considered the neurophysiological basis of Hebbian learning and has been shown to be sensitive to both contingency and contiguity between pre- and postsynaptic activity. Here, we will examine how applying this Hebbian learning rule to a system of interconnected neurons in the presence of direct or indirect re-afference (e.g. seeing/hearing one's own actions) predicts the emergence of mirror neurons with predictive properties. In this framework, we analyse how mirror neurons become a dynamic system that performs active inferences about the actions of others and allows joint actions despite sensorimotor delays. We explore how this system performs a projection of the self onto others, with egocentric biases to contribute to mind-reading. Finally, we argue that Hebbian learning predicts mirror-like neurons for sensations and emotions and review evidence for the presence of such vicarious activations outside the motor system.

  12. Immature doublecortin-positive hippocampal neurons are important for learning but not for remembering.

    Science.gov (United States)

    Vukovic, Jana; Borlikova, Gilyana G; Ruitenberg, Marc J; Robinson, Gregory J; Sullivan, Robert K P; Walker, Tara L; Bartlett, Perry F

    2013-04-10

    It is now widely accepted that hippocampal neurogenesis underpins critical cognitive functions, such as learning and memory. To assess the behavioral importance of adult-born neurons, we developed a novel knock-in mouse model that allowed us to specifically and reversibly ablate hippocampal neurons at an immature stage. In these mice, the diphtheria toxin receptor (DTR) is expressed under control of the doublecortin (DCX) promoter, which allows for specific ablation of immature DCX-expressing neurons after administration of diphtheria toxin while leaving the neural precursor pool intact. Using a spatially challenging behavioral test (a modified version of the active place avoidance test), we present direct evidence that immature DCX-expressing neurons are required for successful acquisition of spatial learning, as well as reversal learning, but are not necessary for the retrieval of stored long-term memories. Importantly, the observed learning deficits were rescued as newly generated immature neurons repopulated the granule cell layer upon termination of the toxin treatment. Repeat (or cyclic) depletion of immature neurons reinstated behavioral deficits if the mice were challenged with a novel task. Together, these findings highlight the potential of stimulating neurogenesis as a means to enhance learning.

  13. Large-scale Exploration of Neuronal Morphologies Using Deep Learning and Augmented Reality.

    Science.gov (United States)

    Li, Zhongyu; Butler, Erik; Li, Kang; Lu, Aidong; Ji, Shuiwang; Zhang, Shaoting

    2018-02-12

    Recently released large-scale neuron morphological data has greatly facilitated the research in neuroinformatics. However, the sheer volume and complexity of these data pose significant challenges for efficient and accurate neuron exploration. In this paper, we propose an effective retrieval framework to address these problems, based on frontier techniques of deep learning and binary coding. For the first time, we develop a deep learning based feature representation method for the neuron morphological data, where the 3D neurons are first projected into binary images and then learned features using an unsupervised deep neural network, i.e., stacked convolutional autoencoders (SCAEs). The deep features are subsequently fused with the hand-crafted features for more accurate representation. Considering the exhaustive search is usually very time-consuming in large-scale databases, we employ a novel binary coding method to compress feature vectors into short binary codes. Our framework is validated on a public data set including 58,000 neurons, showing promising retrieval precision and efficiency compared with state-of-the-art methods. In addition, we develop a novel neuron visualization program based on the techniques of augmented reality (AR), which can help users take a deep exploration of neuron morphologies in an interactive and immersive manner.

  14. VTA GABA neurons modulate specific learning behaviours through the control of dopamine and cholinergic systems

    Directory of Open Access Journals (Sweden)

    Meaghan C Creed

    2014-01-01

    Full Text Available The mesolimbic reward system is primarily comprised of the ventral tegmental area (VTA and the nucleus accumbens (NAc as well as their afferent and efferent connections. This circuitry is essential for learning about stimuli associated with motivationally-relevant outcomes. Moreover, addictive drugs affect and remodel this system, which may underlie their addictive properties. In addition to DA neurons, the VTA also contains approximately 30% ɣ-aminobutyric acid (GABA neurons. The task of signalling both rewarding and aversive events from the VTA to the NAc has mostly been ascribed to DA neurons and the role of GABA neurons has been largely neglected until recently. GABA neurons provide local inhibition of DA neurons and also long-range inhibition of projection regions, including the NAc. Here we review studies using a combination of in vivo and ex vivo electrophysiology, pharmacogenetic and optogenetic manipulations that have characterized the functional neuroanatomy of inhibitory circuits in the mesolimbic system, and describe how GABA neurons of the VTA regulate reward and aversion-related learning. We also discuss pharmacogenetic manipulation of this system with benzodiazepines (BDZs, a class of addictive drugs, which act directly on GABAA receptors located on GABA neurons of the VTA. The results gathered with each of these approaches suggest that VTA GABA neurons bi-directionally modulate activity of local DA neurons, underlying reward or aversion at the behavioural level. Conversely, long-range GABA projections from the VTA to the NAc selectively target cholinergic interneurons (CINs to pause their firing and temporarily reduce cholinergic tone in the NAc, which modulates associative learning. Further characterization of inhibitory circuit function within and beyond the VTA is needed in order to fully understand the function of the mesolimbic system under normal and pathological conditions.

  15. Adaptive Load Balancing of Parallel Applications with Multi-Agent Reinforcement Learning on Heterogeneous Systems

    Directory of Open Access Journals (Sweden)

    Johan Parent

    2004-01-01

    Full Text Available We report on the improvements that can be achieved by applying machine learning techniques, in particular reinforcement learning, for the dynamic load balancing of parallel applications. The applications being considered in this paper are coarse grain data intensive applications. Such applications put high pressure on the interconnect of the hardware. Synchronization and load balancing in complex, heterogeneous networks need fast, flexible, adaptive load balancing algorithms. Viewing a parallel application as a one-state coordination game in the framework of multi-agent reinforcement learning, and by using a recently introduced multi-agent exploration technique, we are able to improve upon the classic job farming approach. The improvements are achieved with limited computation and communication overhead.

  16. Roles for Drosophila Mushroom Body Neurons in Olfactory Learning and Memory

    Science.gov (United States)

    Zong, Lin; Tanaka, Nobuaki K.; Ito, Kei; Davis, Ronald L.; Akalal, David-Benjamin G.; Wilson, Curtis F.

    2006-01-01

    Olfactory learning assays in Drosophila have revealed that distinct brain structures known as mushroom bodies (MBs) are critical for the associative learning and memory of olfactory stimuli. However, the precise roles of the different neurons comprising the MBs are still under debate. The confusion surrounding the roles of the different neurons…

  17. Neuronal mechanisms of motor learning and motor memory consolidation in healthy old adults

    NARCIS (Netherlands)

    Berghuis, K. M. M.; Veldman, M. P.; Solnik, S.; Koch, G.; Zijdewind, I.; Hortobagyi, T.

    It is controversial whether or not old adults are capable of learning new motor skills and consolidate the performance gains into motor memory in the offline period. The underlying neuronal mechanisms are equally unclear. We determined the magnitude of motor learning and motor memory consolidation

  18. The R package "sperrorest" : Parallelized spatial error estimation and variable importance assessment for geospatial machine learning

    Science.gov (United States)

    Schratz, Patrick; Herrmann, Tobias; Brenning, Alexander

    2017-04-01

    Computational and statistical prediction methods such as the support vector machine have gained popularity in remote-sensing applications in recent years and are often compared to more traditional approaches like maximum-likelihood classification. However, the accuracy assessment of such predictive models in a spatial context needs to account for the presence of spatial autocorrelation in geospatial data by using spatial cross-validation and bootstrap strategies instead of their now more widely used non-spatial equivalent. The R package sperrorest by A. Brenning [IEEE International Geoscience and Remote Sensing Symposium, 1, 374 (2012)] provides a generic interface for performing (spatial) cross-validation of any statistical or machine-learning technique available in R. Since spatial statistical models as well as flexible machine-learning algorithms can be computationally expensive, parallel computing strategies are required to perform cross-validation efficiently. The most recent major release of sperrorest therefore comes with two new features (aside from improved documentation): The first one is the parallelized version of sperrorest(), parsperrorest(). This function features two parallel modes to greatly speed up cross-validation runs. Both parallel modes are platform independent and provide progress information. par.mode = 1 relies on the pbapply package and calls interactively (depending on the platform) parallel::mclapply() or parallel::parApply() in the background. While forking is used on Unix-Systems, Windows systems use a cluster approach for parallel execution. par.mode = 2 uses the foreach package to perform parallelization. This method uses a different way of cluster parallelization than the parallel package does. In summary, the robustness of parsperrorest() is increased with the implementation of two independent parallel modes. A new way of partitioning the data in sperrorest is provided by partition.factor.cv(). This function gives the user the

  19. Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy.

    Science.gov (United States)

    Tian, Yuling; Zhang, Hongxian

    2016-01-01

    For the purposes of information retrieval, users must find highly relevant documents from within a system (and often a quite large one comprised of many individual documents) based on input query. Ranking the documents according to their relevance within the system to meet user needs is a challenging endeavor, and a hot research topic-there already exist several rank-learning methods based on machine learning techniques which can generate ranking functions automatically. This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity. The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together, the experimental results show that the proposed algorithm indeed effectively and rapidly identifies optimal ranking functions.

  20. Neuronal mechanisms of motor learning and motor memory consolidation in healthy old adults.

    Science.gov (United States)

    Berghuis, K M M; Veldman, M P; Solnik, S; Koch, G; Zijdewind, I; Hortobágyi, T

    2015-06-01

    It is controversial whether or not old adults are capable of learning new motor skills and consolidate the performance gains into motor memory in the offline period. The underlying neuronal mechanisms are equally unclear. We determined the magnitude of motor learning and motor memory consolidation in healthy old adults and examined if specific metrics of neuronal excitability measured by magnetic brain stimulation mediate the practice and retention effects. Eleven healthy old adults practiced a wrist extension-flexion visuomotor skill for 20 min (MP, 71.3 years), while a second group only watched the templates without movements (attentional control, AC, n = 11, 70.5 years). There was 40 % motor learning in MP but none in AC (interaction, p learn a new motor skill and consolidate the learned skill into motor memory, processes that are most likely mediated by disinhibitory mechanisms. These results are relevant for the increasing number of old adults who need to learn and relearn movements during motor rehabilitation.

  1. Autism and the mirror neuron system: Insights from learning and teaching

    OpenAIRE

    Vivanti, G; Rogers, SJ

    2014-01-01

    Individuals with autism have difficulties in social learning domains which typically involve mirror neuron system (MNS) activation. However, the precise role of the MNS in the development of autism and its relevance to treatment remain unclear. In this paper, we argue that three distinct aspects of social learning are critical for advancing knowledge in this area: (i) the mechanisms that allow for the implicit mapping of and learning from others' behaviour, (ii) the motivation to attend to an...

  2. Associative learning alone is insufficient for the evolution and maintenance of the human mirror neuron system.

    Science.gov (United States)

    Oberman, Lindsay M; Hubbard, Edward M; McCleery, Joseph P

    2014-04-01

    Cook et al. argue that mirror neurons originate from associative learning processes, without evolutionary influence from social-cognitive mechanisms. We disagree with this claim and present arguments based upon cross-species comparisons, EEG findings, and developmental neuroscience that the evolution of mirror neurons is most likely driven simultaneously and interactively by evolutionarily adaptive psychological mechanisms and lower-level biological mechanisms that support them.

  3. Bioinformatics algorithm based on a parallel implementation of a machine learning approach using transducers

    International Nuclear Information System (INIS)

    Roche-Lima, Abiel; Thulasiram, Ruppa K

    2012-01-01

    Finite automata, in which each transition is augmented with an output label in addition to the familiar input label, are considered finite-state transducers. Transducers have been used to analyze some fundamental issues in bioinformatics. Weighted finite-state transducers have been proposed to pairwise alignments of DNA and protein sequences; as well as to develop kernels for computational biology. Machine learning algorithms for conditional transducers have been implemented and used for DNA sequence analysis. Transducer learning algorithms are based on conditional probability computation. It is calculated by using techniques, such as pair-database creation, normalization (with Maximum-Likelihood normalization) and parameters optimization (with Expectation-Maximization - EM). These techniques are intrinsically costly for computation, even worse when are applied to bioinformatics, because the databases sizes are large. In this work, we describe a parallel implementation of an algorithm to learn conditional transducers using these techniques. The algorithm is oriented to bioinformatics applications, such as alignments, phylogenetic trees, and other genome evolution studies. Indeed, several experiences were developed using the parallel and sequential algorithm on Westgrid (specifically, on the Breeze cluster). As results, we obtain that our parallel algorithm is scalable, because execution times are reduced considerably when the data size parameter is increased. Another experience is developed by changing precision parameter. In this case, we obtain smaller execution times using the parallel algorithm. Finally, number of threads used to execute the parallel algorithm on the Breezy cluster is changed. In this last experience, we obtain as result that speedup is considerably increased when more threads are used; however there is a convergence for number of threads equal to or greater than 16.

  4. Learning causes reorganization of neuronal firing patterns to represent related experiences within a hippocampal schema.

    Science.gov (United States)

    McKenzie, Sam; Robinson, Nick T M; Herrera, Lauren; Churchill, Jordana C; Eichenbaum, Howard

    2013-06-19

    According to schema theory as proposed by Piaget and Bartlett, learning involves the assimilation of new memories into networks of preexisting knowledge, as well as alteration of the original networks to accommodate the new information. Recent evidence has shown that rats form a schema of goal locations and that the hippocampus plays an essential role in adding new memories to the spatial schema. Here we examined the nature of hippocampal contributions to schema updating by monitoring firing patterns of multiple CA1 neurons as rats learned new goal locations in an environment in which there already were multiple goals. Before new learning, many neurons that fired on arrival at one goal location also fired at other goals, whereas ensemble activity patterns also distinguished different goal events, thus constituting a neural representation that linked distinct goals within a spatial schema. During new learning, some neurons began to fire as animals approached the new goals. These were primarily the same neurons that fired at original goals, the activity patterns at new goals were similar to those associated with the original goals, and new learning also produced changes in the preexisting goal-related firing patterns. After learning, activity patterns associated with the new and original goals gradually diverged, such that initial generalization was followed by a prolonged period in which new memories became distinguished within the ensemble representation. These findings support the view that consolidation involves assimilation of new memories into preexisting neural networks that accommodate relationships among new and existing memories.

  5. Focal adhesion kinase regulates neuronal growth, synaptic plasticity and hippocampus-dependent spatial learning and memory.

    Science.gov (United States)

    Monje, Francisco J; Kim, Eun-Jung; Pollak, Daniela D; Cabatic, Maureen; Li, Lin; Baston, Arthur; Lubec, Gert

    2012-01-01

    The focal adhesion kinase (FAK) is a non-receptor tyrosine kinase abundantly expressed in the mammalian brain and highly enriched in neuronal growth cones. Inhibitory and facilitatory activities of FAK on neuronal growth have been reported and its role in neuritic outgrowth remains controversial. Unlike other tyrosine kinases, such as the neurotrophin receptors regulating neuronal growth and plasticity, the relevance of FAK for learning and memory in vivo has not been clearly defined yet. A comprehensive study aimed at determining the role of FAK in neuronal growth, neurotransmitter release and synaptic plasticity in hippocampal neurons and in hippocampus-dependent learning and memory was therefore undertaken using the mouse model. Gain- and loss-of-function experiments indicated that FAK is a critical regulator of hippocampal cell morphology. FAK mediated neurotrophin-induced neuritic outgrowth and FAK inhibition affected both miniature excitatory postsynaptic potentials and activity-dependent hippocampal long-term potentiation prompting us to explore the possible role of FAK in spatial learning and memory in vivo. Our data indicate that FAK has a growth-promoting effect, is importantly involved in the regulation of the synaptic function and mediates in vivo hippocampus-dependent spatial learning and memory. Copyright © 2011 S. Karger AG, Basel.

  6. Expressions of multiple neuronal dynamics during sensorimotor learning in the motor cortex of behaving monkeys.

    Directory of Open Access Journals (Sweden)

    Yael Mandelblat-Cerf

    Full Text Available Previous studies support the notion that sensorimotor learning involves multiple processes. We investigated the neuronal basis of these processes by recording single-unit activity in motor cortex of non-human primates (Macaca fascicularis, during adaptation to force-field perturbations. Perturbed trials (reaching to one direction were practiced along with unperturbed trials (to other directions. The number of perturbed trials relative to the unperturbed ones was either low or high, in two separate practice schedules. Unsurprisingly, practice under high-rate resulted in faster learning with more pronounced generalization, as compared to the low-rate practice. However, generalization and retention of behavioral and neuronal effects following practice in high-rate were less stable; namely, the faster learning was forgotten faster. We examined two subgroups of cells and showed that, during learning, the changes in firing-rate in one subgroup depended on the number of practiced trials, but not on time. In contrast, changes in the second subgroup depended on time and practice; the changes in firing-rate, following the same number of perturbed trials, were larger under high-rate than low-rate learning. After learning, the neuronal changes gradually decayed. In the first subgroup, the decay pace did not depend on the practice rate, whereas in the second subgroup, the decay pace was greater following high-rate practice. This group shows neuronal representation that mirrors the behavioral performance, evolving faster but also decaying faster at learning under high-rate, as compared to low-rate. The results suggest that the stability of a new learned skill and its neuronal representation are affected by the acquisition schedule.

  7. A Model to Explain the Emergence of Reward Expectancy neurons using Reinforcement Learning and Neural Network

    OpenAIRE

    Shinya, Ishii; Munetaka, Shidara; Katsunari, Shibata

    2006-01-01

    In an experiment of multi-trial task to obtain a reward, reward expectancy neurons,###which responded only in the non-reward trials that are necessary to advance###toward the reward, have been observed in the anterior cingulate cortex of monkeys.###In this paper, to explain the emergence of the reward expectancy neuron in###terms of reinforcement learning theory, a model that consists of a recurrent neural###network trained based on reinforcement learning is proposed. The analysis of the###hi...

  8. Local-learning-based neuron selection for grasping gesture prediction in motor brain machine interfaces

    Science.gov (United States)

    Xu, Kai; Wang, Yiwen; Wang, Yueming; Wang, Fang; Hao, Yaoyao; Zhang, Shaomin; Zhang, Qiaosheng; Chen, Weidong; Zheng, Xiaoxiang

    2013-04-01

    Objective. The high-dimensional neural recordings bring computational challenges to movement decoding in motor brain machine interfaces (mBMI), especially for portable applications. However, not all recorded neural activities relate to the execution of a certain movement task. This paper proposes to use a local-learning-based method to perform neuron selection for the gesture prediction in a reaching and grasping task. Approach. Nonlinear neural activities are decomposed into a set of linear ones in a weighted feature space. A margin is defined to measure the distance between inter-class and intra-class neural patterns. The weights, reflecting the importance of neurons, are obtained by minimizing a margin-based exponential error function. To find the most dominant neurons in the task, 1-norm regularization is introduced to the objective function for sparse weights, where near-zero weights indicate irrelevant neurons. Main results. The signals of only 10 neurons out of 70 selected by the proposed method could achieve over 95% of the full recording's decoding accuracy of gesture predictions, no matter which different decoding methods are used (support vector machine and K-nearest neighbor). The temporal activities of the selected neurons show visually distinguishable patterns associated with various hand states. Compared with other algorithms, the proposed method can better eliminate the irrelevant neurons with near-zero weights and provides the important neuron subset with the best decoding performance in statistics. The weights of important neurons converge usually within 10-20 iterations. In addition, we study the temporal and spatial variation of neuron importance along a period of one and a half months in the same task. A high decoding performance can be maintained by updating the neuron subset. Significance. The proposed algorithm effectively ascertains the neuronal importance without assuming any coding model and provides a high performance with different

  9. Activity strengths of cortical glutamatergic and GABAergic neurons are correlated with transgenerational inheritance of learning ability.

    Science.gov (United States)

    Liu, Yulong; Ge, Rongjing; Zhao, Xin; Guo, Rui; Huang, Li; Zhao, Shidi; Guan, Sudong; Lu, Wei; Cui, Shan; Wang, Shirlene; Wang, Jin-Hui

    2017-12-22

    The capabilities of learning and memory in parents are presumably transmitted to their offsprings, in which genetic codes and epigenetic regulations are thought as molecular bases. As neural plasticity occurs during memory formation as cellular mechanism, we aim to examine the correlation of activity strengths at cortical glutamatergic and GABAergic neurons to the transgenerational inheritance of learning ability. In a mouse model of associative learning, paired whisker and odor stimulations led to odorant-induced whisker motion, whose onset appeared fast (high learning efficiency, HLE) or slow (low learning efficiency, LLE). HLE male and female mice, HLE female and LLE male mice as well as HLE male and LLE female mice were cross-mated to have their first generation of offsprings, filials (F1). The onset of odorant-induced whisker motion appeared a sequence of high-to-low efficiency in three groups of F1 mice that were from HLE male and female mice, HLE female and LLE male mice as well as HLE male and LLE female mice. Activities related to glutamatergic neurons in barrel cortices appeared a sequence of high-to-low strength in these F1 mice from HLE male and female mice, HLE female and LLE male mice as well as HLE male and LLE female mice. Activities related to GABAergic neurons in barrel cortices appeared a sequence of low-to-high strength in these F1 mice from HLE male and female mice, HLE female and LLE male mice as well as HLE male and LLE female mice. Neuronal activity strength was linearly correlated to learning efficiency among three groups. Thus, the coordinated activities at glutamatergic and GABAergic neurons may constitute the cellular basis for the transgenerational inheritance of learning ability.

  10. Contextual Learning Requires Functional Diversity at Excitatory and Inhibitory Synapses onto CA1 Pyramidal Neurons

    Directory of Open Access Journals (Sweden)

    Dai Mitsushima

    2015-01-01

    Full Text Available Although the hippocampus is processing temporal and spatial information in particular context, the encoding rule creating memory is completely unknown. To examine the mechanism, we trained rats on an inhibitory avoidance (IA task, a hippocampus-dependent rapid one-trial contextual learning paradigm. By combining Herpes virus-mediated in vivo gene delivery with in vitro patch-clamp recordings, I reported contextual learning drives GluR1-containing AMPA receptors into CA3-CA1 synapses. The molecular event is required for contextual memory, since bilateral expression of delivery blocker in CA1 successfully blocked IA learning. Moreover, I found a logarithmic correlation between the number of delivery blocking cells and learning performance. Considering that one all-or-none device can process 1-bit of data per clock (Nobert Wiener 1961, the logarithmic correlation may provides evidence that CA1 neurons transmit essential data of contextual information. Further, I recently reported critical role of acetylcholine as an intrinsic trigger of learning-dependent synaptic plasticity. IA training induced ACh release in CA1 that strengthened not only AMPA receptor-mediated excitatory synapses, but also GABAA receptor-mediated inhibitory synapses on each CA1 neuron. More importantly, IA-trained rats showed individually different excitatory and inhibitory synaptic inputs with wide variation on each CA1 neuron. Here I propose a new hypothesis that the diversity of synaptic inputs on CA1 neurons may depict cell-specific outputs processing experienced episodes after training.

  11. A hierarchical model for structure learning based on the physiological characteristics of neurons

    Institute of Scientific and Technical Information of China (English)

    WEI Hui

    2007-01-01

    Almost all applications of Artificial Neural Networks (ANNs) depend mainly on their memory ability.The characteristics of typical ANN models are fixed connections,with evolved weights,globalized representations,and globalized optimizations,all based on a mathematical approach.This makes those models to be deficient in robustness,efficiency of learning,capacity,anti-jamming between training sets,and correlativity of samples,etc.In this paper,we attempt to address these problems by adopting the characteristics of biological neurons in morphology and signal processing.A hierarchical neural network was designed and realized to implement structure learning and representations based on connected structures.The basic characteristics of this model are localized and random connections,field limitations of neuron fan-in and fan-out,dynamic behavior of neurons,and samples represented through different sub-circuits of neurons specialized into different response patterns.At the end of this paper,some important aspects of error correction,capacity,learning efficiency,and soundness of structural representation are analyzed theoretically.This paper has demonstrated the feasibility and advantages of structure learning and representation.This model can serve as a fundamental element of cognitive systems such as perception and associative memory.Key-words structure learning,representation,associative memory,computational neuroscience

  12. Arc mRNA induction in striatal efferent neurons associated with response learning.

    Science.gov (United States)

    Daberkow, D P; Riedy, M D; Kesner, R P; Keefe, K A

    2007-07-01

    The dorsal striatum is involved in motor-response learning, but the extent to which distinct populations of striatal efferent neurons are differentially involved in such learning is unknown. Activity-regulated, cytoskeleton-associated (Arc) protein is an effector immediate-early gene implicated in synaptic plasticity. We examined arc mRNA expression in striatopallidal vs. striatonigral efferent neurons in dorsomedial and dorsolateral striatum of rats engaged in reversal learning on a T-maze motor-response task. Male Sprague-Dawley rats learned to turn right or left for 3 days. Half of the rats then underwent reversal training. The remaining rats were yoked to rats undergoing reversal training, such that they ran the same number of trials but ran them as continued-acquisition trials. Brains were removed and processed using double-label fluorescent in situ hybridization for arc and preproenkephalin (PPE) mRNA. In the reversal, but not the continued-acquisition, group there was a significant relation between the overall arc mRNA signal in dorsomedial striatum and the number of trials run, with rats reaching criterion in fewer trials having higher levels of arc mRNA expression. A similar relation was seen between the numbers of PPE(+) and PPE(-) neurons in dorsomedial striatum with cytoplasmic arc mRNA expression. Interestingly, in behaviourally activated animals significantly more PPE(-) neurons had cytoplasmic arc mRNA expression. These data suggest that Arc in both striatonigral and striatopallidal efferent neurons is involved in striatal synaptic plasticity mediating motor-response learning in the T-maze and that there is differential processing of arc mRNA in distinct subpopulations of striatal efferent neurons.

  13. Precise auditory-vocal mirroring in neurons for learned vocal communication.

    Science.gov (United States)

    Prather, J F; Peters, S; Nowicki, S; Mooney, R

    2008-01-17

    Brain mechanisms for communication must establish a correspondence between sensory and motor codes used to represent the signal. One idea is that this correspondence is established at the level of single neurons that are active when the individual performs a particular gesture or observes a similar gesture performed by another individual. Although neurons that display a precise auditory-vocal correspondence could facilitate vocal communication, they have yet to be identified. Here we report that a certain class of neurons in the swamp sparrow forebrain displays a precise auditory-vocal correspondence. We show that these neurons respond in a temporally precise fashion to auditory presentation of certain note sequences in this songbird's repertoire and to similar note sequences in other birds' songs. These neurons display nearly identical patterns of activity when the bird sings the same sequence, and disrupting auditory feedback does not alter this singing-related activity, indicating it is motor in nature. Furthermore, these neurons innervate striatal structures important for song learning, raising the possibility that singing-related activity in these cells is compared to auditory feedback to guide vocal learning.

  14. Synaptic potentiation onto habenula neurons in learned helplessness model of depression

    Science.gov (United States)

    Li, Bo; Piriz, Joaquin; Mirrione, Martine; Chung, ChiHye; Proulx, Christophe D.; Schulz, Daniela; Henn, Fritz; Malinow, Roberto

    2010-01-01

    The cellular basis of depressive disorders is poorly understood1. Recent studies in monkeys indicate that neurons in the lateral habenula (LHb), a nucleus that mediates communication between forebrain and midbrain structures, can increase their activity when an animal fails to receive an expected positive reward or receives a stimulus that predicts aversive conditions (i.e. disappointment or anticipation of a negative outcome)2, 3, 4. LHb neurons project to and modulate dopamine-rich regions such as the ventral-tegmental area (VTA)2, 5 that control reward-seeking behavior6 and participate in depressive disorders7. Here we show in two learned helplessness models of depression that excitatory synapses onto LHb neurons projecting to the VTA are potentiated. Synaptic potentiation correlates with an animal’s helplessness behavior and is due to an enhanced presynaptic release probability. Depleting transmitter release by repeated electrical stimulation of LHb afferents, using a protocol that can be effective on depressed patients8, 9, dramatically suppresses synaptic drive onto VTA-projecting LHb neurons in brain slices and can significantly reduce learned helplessness behavior in rats. Our results indicate that increased presynaptic action onto LHb neurons contributes to the rodent learned helplessness model of depression. PMID:21350486

  15. Synaptic potentiation onto habenula neurons in the learned helplessness model of depression.

    Science.gov (United States)

    Li, Bo; Piriz, Joaquin; Mirrione, Martine; Chung, ChiHye; Proulx, Christophe D; Schulz, Daniela; Henn, Fritz; Malinow, Roberto

    2011-02-24

    The cellular basis of depressive disorders is poorly understood. Recent studies in monkeys indicate that neurons in the lateral habenula (LHb), a nucleus that mediates communication between forebrain and midbrain structures, can increase their activity when an animal fails to receive an expected positive reward or receives a stimulus that predicts aversive conditions (that is, disappointment or anticipation of a negative outcome). LHb neurons project to, and modulate, dopamine-rich regions, such as the ventral tegmental area (VTA), that control reward-seeking behaviour and participate in depressive disorders. Here we show that in two learned helplessness models of depression, excitatory synapses onto LHb neurons projecting to the VTA are potentiated. Synaptic potentiation correlates with an animal's helplessness behaviour and is due to an enhanced presynaptic release probability. Depleting transmitter release by repeated electrical stimulation of LHb afferents, using a protocol that can be effective for patients who are depressed, markedly suppresses synaptic drive onto VTA-projecting LHb neurons in brain slices and can significantly reduce learned helplessness behaviour in rats. Our results indicate that increased presynaptic action onto LHb neurons contributes to the rodent learned helplessness model of depression.

  16. Synaptic potentiation onto habenula neurons in the learned helplessness model of depression

    International Nuclear Information System (INIS)

    Li, B.; Schulz, D.; Piriz, J.; Mirrione, M.; Chung, C.H.; Proulx, C.D.; Schulz, D.; Henn, F.; Malinow, R.

    2011-01-01

    The cellular basis of depressive disorders is poorly understood. Recent studies in monkeys indicate that neurons in the lateral habenula (LHb), a nucleus that mediates communication between forebrain and midbrain structures, can increase their activity when an animal fails to receive an expected positive reward or receives a stimulus that predicts aversive conditions (that is, disappointment or anticipation of a negative outcome). LHb neurons project to, and modulate, dopamine-rich regions, such as the ventral tegmental area (VTA), that control reward-seeking behaviour and participate in depressive disorders. Here we show that in two learned helplessness models of depression, excitatory synapses onto LHb neurons projecting to the VTA are potentiated. Synaptic potentiation correlates with an animal's helplessness behaviour and is due to an enhanced presynaptic release probability. Depleting transmitter release by repeated electrical stimulation of LHb afferents, using a protocol that can be effective for patients who are depressed, markedly suppresses synaptic drive onto VTA-projecting LHb neurons in brain slices and can significantly reduce learned helplessness behaviour in rats. Our results indicate that increased presynaptic action onto LHb neurons contributes to the rodent learned helplessness model of depression.

  17. Dictionary Learning Based on Nonnegative Matrix Factorization Using Parallel Coordinate Descent

    Directory of Open Access Journals (Sweden)

    Zunyi Tang

    2013-01-01

    Full Text Available Sparse representation of signals via an overcomplete dictionary has recently received much attention as it has produced promising results in various applications. Since the nonnegativities of the signals and the dictionary are required in some applications, for example, multispectral data analysis, the conventional dictionary learning methods imposed simply with nonnegativity may become inapplicable. In this paper, we propose a novel method for learning a nonnegative, overcomplete dictionary for such a case. This is accomplished by posing the sparse representation of nonnegative signals as a problem of nonnegative matrix factorization (NMF with a sparsity constraint. By employing the coordinate descent strategy for optimization and extending it to multivariable case for processing in parallel, we develop a so-called parallel coordinate descent dictionary learning (PCDDL algorithm, which is structured by iteratively solving the two optimal problems, the learning process of the dictionary and the estimating process of the coefficients for constructing the signals. Numerical experiments demonstrate that the proposed algorithm performs better than the conventional nonnegative K-SVD (NN-KSVD algorithm and several other algorithms for comparison. What is more, its computational consumption is remarkably lower than that of the compared algorithms.

  18. Functional architecture of reward learning in mushroom body extrinsic neurons of larval Drosophila.

    Science.gov (United States)

    Saumweber, Timo; Rohwedder, Astrid; Schleyer, Michael; Eichler, Katharina; Chen, Yi-Chun; Aso, Yoshinori; Cardona, Albert; Eschbach, Claire; Kobler, Oliver; Voigt, Anne; Durairaja, Archana; Mancini, Nino; Zlatic, Marta; Truman, James W; Thum, Andreas S; Gerber, Bertram

    2018-03-16

    The brain adaptively integrates present sensory input, past experience, and options for future action. The insect mushroom body exemplifies how a central brain structure brings about such integration. Here we use a combination of systematic single-cell labeling, connectomics, transgenic silencing, and activation experiments to study the mushroom body at single-cell resolution, focusing on the behavioral architecture of its input and output neurons (MBINs and MBONs), and of the mushroom body intrinsic APL neuron. Our results reveal the identity and morphology of almost all of these 44 neurons in stage 3 Drosophila larvae. Upon an initial screen, functional analyses focusing on the mushroom body medial lobe uncover sparse and specific functions of its dopaminergic MBINs, its MBONs, and of the GABAergic APL neuron across three behavioral tasks, namely odor preference, taste preference, and associative learning between odor and taste. Our results thus provide a cellular-resolution study case of how brains organize behavior.

  19. CREB Selectively Controls Learning-Induced Structural Remodeling of Neurons

    Science.gov (United States)

    Middei, Silvia; Spalloni, Alida; Longone, Patrizia; Pittenger, Christopher; O'Mara, Shane M.; Marie, Helene; Ammassari-Teule, Martine

    2012-01-01

    The modulation of synaptic strength associated with learning is post-synaptically regulated by changes in density and shape of dendritic spines. The transcription factor CREB (cAMP response element binding protein) is required for memory formation and in vitro dendritic spine rearrangements, but its role in learning-induced remodeling of neurons…

  20. Histone Deacetylase (HDAC) Inhibitors - emerging roles in neuronal memory, learning, synaptic plasticity and neural regeneration.

    Science.gov (United States)

    Ganai, Shabir Ahmad; Ramadoss, Mahalakshmi; Mahadevan, Vijayalakshmi

    2016-01-01

    Epigenetic regulation of neuronal signalling through histone acetylation dictates transcription programs that govern neuronal memory, plasticity and learning paradigms. Histone Acetyl Transferases (HATs) and Histone Deacetylases (HDACs) are antagonistic enzymes that regulate gene expression through acetylation and deacetylation of histone proteins around which DNA is wrapped inside a eukaryotic cell nucleus. The epigenetic control of HDACs and the cellular imbalance between HATs and HDACs dictate disease states and have been implicated in muscular dystrophy, loss of memory, neurodegeneration and autistic disorders. Altering gene expression profiles through inhibition of HDACs is now emerging as a powerful technique in therapy. This review presents evolving applications of HDAC inhibitors as potential drugs in neurological research and therapy. Mechanisms that govern their expression profiles in neuronal signalling, plasticity and learning will be covered. Promising and exciting possibilities of HDAC inhibitors in memory formation, fear conditioning, ischemic stroke and neural regeneration have been detailed.

  1. SPAN: spike pattern association neuron for learning spatio-temporal sequences

    OpenAIRE

    Mohemmed, A; Schliebs, S; Matsuda, S; Kasabov, N

    2012-01-01

    Spiking Neural Networks (SNN) were shown to be suitable tools for the processing of spatio-temporal information. However, due to their inherent complexity, the formulation of efficient supervised learning algorithms for SNN is difficult and remains an important problem in the research area. This article presents SPAN — a spiking neuron that is able to learn associations of arbitrary spike trains in a supervised fashion allowing the processing of spatio-temporal information encoded in the prec...

  2. Fully parallel write/read in resistive synaptic array for accelerating on-chip learning

    Science.gov (United States)

    Gao, Ligang; Wang, I.-Ting; Chen, Pai-Yu; Vrudhula, Sarma; Seo, Jae-sun; Cao, Yu; Hou, Tuo-Hung; Yu, Shimeng

    2015-11-01

    A neuro-inspired computing paradigm beyond the von Neumann architecture is emerging and it generally takes advantage of massive parallelism and is aimed at complex tasks that involve intelligence and learning. The cross-point array architecture with synaptic devices has been proposed for on-chip implementation of the weighted sum and weight update in the learning algorithms. In this work, forming-free, silicon-process-compatible Ta/TaO x /TiO2/Ti synaptic devices are fabricated, in which >200 levels of conductance states could be continuously tuned by identical programming pulses. In order to demonstrate the advantages of parallelism of the cross-point array architecture, a novel fully parallel write scheme is designed and experimentally demonstrated in a small-scale crossbar array to accelerate the weight update in the training process, at a speed that is independent of the array size. Compared to the conventional row-by-row write scheme, it achieves >30× speed-up and >30× improvement in energy efficiency as projected in a large-scale array. If realistic synaptic device characteristics such as device variations are taken into an array-level simulation, the proposed array architecture is able to achieve ∼95% recognition accuracy of MNIST handwritten digits, which is close to the accuracy achieved by software using the ideal sparse coding algorithm.

  3. Fully parallel write/read in resistive synaptic array for accelerating on-chip learning

    International Nuclear Information System (INIS)

    Gao, Ligang; Chen, Pai-Yu; Seo, Jae-sun; Cao, Yu; Yu, Shimeng; Wang, I-Ting; Hou, Tuo-Hung; Vrudhula, Sarma

    2015-01-01

    A neuro-inspired computing paradigm beyond the von Neumann architecture is emerging and it generally takes advantage of massive parallelism and is aimed at complex tasks that involve intelligence and learning. The cross-point array architecture with synaptic devices has been proposed for on-chip implementation of the weighted sum and weight update in the learning algorithms. In this work, forming-free, silicon-process-compatible Ta/TaO_x/TiO_2/Ti synaptic devices are fabricated, in which >200 levels of conductance states could be continuously tuned by identical programming pulses. In order to demonstrate the advantages of parallelism of the cross-point array architecture, a novel fully parallel write scheme is designed and experimentally demonstrated in a small-scale crossbar array to accelerate the weight update in the training process, at a speed that is independent of the array size. Compared to the conventional row-by-row write scheme, it achieves >30× speed-up and >30× improvement in energy efficiency as projected in a large-scale array. If realistic synaptic device characteristics such as device variations are taken into an array-level simulation, the proposed array architecture is able to achieve ∼95% recognition accuracy of MNIST handwritten digits, which is close to the accuracy achieved by software using the ideal sparse coding algorithm. (paper)

  4. [Changes of the neuronal membrane excitability as cellular mechanisms of learning and memory].

    Science.gov (United States)

    Gaĭnutdinov, Kh L; Andrianov, V V; Gaĭnutdinova, T Kh

    2011-01-01

    In the presented review given literature and results of own studies of dynamics of electrical characteristics of neurons, which change are included in processes both an elaboration of learning, and retention of the long-term memory. Literary datas and our results allow to conclusion, that long-term retention of behavioural reactions during learning is accompanied not only by changing efficiency of synaptic transmission, as well as increasing of excitability of command neurons of the defensive reflex. This means, that in the process of learning are involved long-term changes of the characteristics a membrane of certain elements of neuronal network, dependent from the metabolism of the cells. see text). Thou phenomena possible mark as cellular (electrophysiological) correlates of long-term plastic modifications of the behaviour. The analyses of having results demonstrates an important role of membrane characteristics of neurons (their excitability) and parameters an synaptic transmission not only in initial stage of learning, as well as in long-term modifications of the behaviour (long-term memory).

  5. Code-specific learning rules improve action selection by populations of spiking neurons.

    Science.gov (United States)

    Friedrich, Johannes; Urbanczik, Robert; Senn, Walter

    2014-08-01

    Population coding is widely regarded as a key mechanism for achieving reliable behavioral decisions. We previously introduced reinforcement learning for population-based decision making by spiking neurons. Here we generalize population reinforcement learning to spike-based plasticity rules that take account of the postsynaptic neural code. We consider spike/no-spike, spike count and spike latency codes. The multi-valued and continuous-valued features in the postsynaptic code allow for a generalization of binary decision making to multi-valued decision making and continuous-valued action selection. We show that code-specific learning rules speed up learning both for the discrete classification and the continuous regression tasks. The suggested learning rules also speed up with increasing population size as opposed to standard reinforcement learning rules. Continuous action selection is further shown to explain realistic learning speeds in the Morris water maze. Finally, we introduce the concept of action perturbation as opposed to the classical weight- or node-perturbation as an exploration mechanism underlying reinforcement learning. Exploration in the action space greatly increases the speed of learning as compared to exploration in the neuron or weight space.

  6. Parallel processing and learning in simple systems. Final report, 10 January 1986-14 January 1989

    Energy Technology Data Exchange (ETDEWEB)

    Mpitsos, G.J.

    1989-03-15

    Work over the three-year tenure of this grant has dealt with interrelated studies of (1) neuropharmacology, (2) behavior, and (3) distributed/parallel processing in the generation of variable motor patterns in the buccal-oral system of the sea slug Pleurobranchaea californica. (4) Computer simulations of simple neutral networks have been undertaken to examine neurointegrative principles that could not be examined in biological preparations. The simulation work has set the basis for further simulations dealing with networks having characteristics relating to real neurons. All of the work has had the goal of developing interdisciplinary tools for understanding the scale-independent problem of how individuals, each possessing only local knowledge of group activity, act within a group to produce different and variable adaptive outputs, and, in turn, of how the group influences the activity of the individual. The pharmacologic studies have had the goal of developing biochemical tools with which to identify groups of neurons that perform specific tasks during the production of a given behavior but are multifunctional by being critically involved in generating several different behaviors.

  7. The ENU-3 protein family members function in the Wnt pathway parallel to UNC-6/Netrin to promote motor neuron axon outgrowth in C. elegans.

    Science.gov (United States)

    Florica, Roxana Oriana; Hipolito, Victoria; Bautista, Stephen; Anvari, Homa; Rapp, Chloe; El-Rass, Suzan; Asgharian, Alimohammad; Antonescu, Costin N; Killeen, Marie T

    2017-10-01

    The axons of the DA and DB classes of motor neurons fail to reach the dorsal cord in the absence of the guidance cue UNC-6/Netrin or its receptor UNC-5 in C. elegans. However, the axonal processes usually exit their cell bodies in the ventral cord in the absence of both molecules. Strains lacking functional versions of UNC-6 or UNC-5 have a low level of DA and DB motor neuron axon outgrowth defects. We found that mutations in the genes for all six of the ENU-3 proteins function to enhance the outgrowth defects of the DA and DB axons in strains lacking either UNC-6 or UNC-5. A mutation in the gene for the MIG-14/Wntless protein also enhances defects in a strain lacking either UNC-5 or UNC-6, suggesting that the ENU-3 and Wnt pathways function parallel to the Netrin pathway in directing motor neuron axon outgrowth. Our evidence suggests that the ENU-3 proteins are novel members of the Wnt pathway in nematodes. Five of the six members of the ENU-3 family are predicted to be single-pass trans-membrane proteins. The expression pattern of ENU-3.1 was consistent with plasma membrane localization. One family member, ENU-3.6, lacks the predicted signal peptide and the membrane-spanning domain. In HeLa cells ENU-3.6 had a cytoplasmic localization and caused actin dependent processes to appear. We conclude that the ENU-3 family proteins function in a pathway parallel to the UNC-6/Netrin pathway for motor neuron axon outgrowth, most likely in the Wnt pathway. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. Mlifdect: Android Malware Detection Based on Parallel Machine Learning and Information Fusion

    Directory of Open Access Journals (Sweden)

    Xin Wang

    2017-01-01

    Full Text Available In recent years, Android malware has continued to grow at an alarming rate. More recent malicious apps’ employing highly sophisticated detection avoidance techniques makes the traditional machine learning based malware detection methods far less effective. More specifically, they cannot cope with various types of Android malware and have limitation in detection by utilizing a single classification algorithm. To address this limitation, we propose a novel approach in this paper that leverages parallel machine learning and information fusion techniques for better Android malware detection, which is named Mlifdect. To implement this approach, we first extract eight types of features from static analysis on Android apps and build two kinds of feature sets after feature selection. Then, a parallel machine learning detection model is developed for speeding up the process of classification. Finally, we investigate the probability analysis based and Dempster-Shafer theory based information fusion approaches which can effectively obtain the detection results. To validate our method, other state-of-the-art detection works are selected for comparison with real-world Android apps. The experimental results demonstrate that Mlifdect is capable of achieving higher detection accuracy as well as a remarkable run-time efficiency compared to the existing malware detection solutions.

  9. Learning to see the difference specifically alters the most informative V4 neurons.

    Science.gov (United States)

    Raiguel, Steven; Vogels, Rufin; Mysore, Santosh G; Orban, Guy A

    2006-06-14

    Perceptual learning is an instance of adult plasticity whereby training in a sensory (e.g., a visual task) results in neuronal changes leading to an improved ability to perform the task. Yet studies in primary visual cortex have found that changes in neuronal response properties were relatively modest. The present study examines the effects of training in an orientation discrimination task on the response properties of V4 neurons in awake rhesus monkeys. Results indicate that the changes induced in V4 are indeed larger than those in V1. Nonspecific effects of training included a decrease in response variance, and an increase in overall orientation selectivity in V4. The orientation-specific changes involved a local steepening in the orientation tuning curve around the trained orientation that selectively improved orientation discriminability at the trained orientation. Moreover, these changes were largely confined to the population of neurons whose orientation tuning was optimal for signaling small differences in orientation at the trained orientation. Finally, the modifications were restricted to the part of the tuning curve close to the trained orientation. Thus, we conclude that it is the most informative V4 neurons, those most directly involved in the discrimination, that are specifically modified by perceptual learning.

  10. DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons.

    Science.gov (United States)

    Taherkhani, Aboozar; Belatreche, Ammar; Li, Yuhua; Maguire, Liam P

    2015-12-01

    Recent research has shown the potential capability of spiking neural networks (SNNs) to model complex information processing in the brain. There is biological evidence to prove the use of the precise timing of spikes for information coding. However, the exact learning mechanism in which the neuron is trained to fire at precise times remains an open problem. The majority of the existing learning methods for SNNs are based on weight adjustment. However, there is also biological evidence that the synaptic delay is not constant. In this paper, a learning method for spiking neurons, called delay learning remote supervised method (DL-ReSuMe), is proposed to merge the delay shift approach and ReSuMe-based weight adjustment to enhance the learning performance. DL-ReSuMe uses more biologically plausible properties, such as delay learning, and needs less weight adjustment than ReSuMe. Simulation results have shown that the proposed DL-ReSuMe approach achieves learning accuracy and learning speed improvements compared with ReSuMe.

  11. Complex population response of dorsal putamen neurons predicts the ability to learn.

    Science.gov (United States)

    Laquitaine, Steeve; Piron, Camille; Abellanas, David; Loewenstein, Yonatan; Boraud, Thomas

    2013-01-01

    Day-to-day variability in performance is a common experience. We investigated its neural correlate by studying learning behavior of monkeys in a two-alternative forced choice task, the two-armed bandit task. We found substantial session-to-session variability in the monkeys' learning behavior. Recording the activity of single dorsal putamen neurons we uncovered a dual function of this structure. It has been previously shown that a population of neurons in the DLP exhibits firing activity sensitive to the reward value of chosen actions. Here, we identify putative medium spiny neurons in the dorsal putamen that are cue-selective and whose activity builds up with learning. Remarkably we show that session-to-session changes in the size of this population and in the intensity with which this population encodes cue-selectivity is correlated with session-to-session changes in the ability to learn the task. Moreover, at the population level, dorsal putamen activity in the very beginning of the session is correlated with the performance at the end of the session, thus predicting whether the monkey will have a "good" or "bad" learning day. These results provide important insights on the neural basis of inter-temporal performance variability.

  12. Tissue Plasminogen Activator Induction in Purkinje Neurons After Cerebellar Motor Learning

    Science.gov (United States)

    Seeds, Nicholas W.; Williams, Brian L.; Bickford, Paula C.

    1995-12-01

    The cerebellar cortex is implicated in the learning of complex motor skills. This learning may require synaptic remodeling of Purkinje cell inputs. An extracellular serine protease, tissue plasminogen activator (tPA), is involved in remodeling various nonneural tissues and is associated with developing and regenerating neurons. In situ hybridization showed that expression of tPA messenger RNA was increased in the Purkinje neurons of rats within an hour of their being trained for a complex motor task. Antibody to tPA also showed the induction of tPA protein associated with cerebellar Purkinje cells. Thus, the induction of tPA during motor learning may play a role in activity-dependent synaptic plasticity.

  13. Bifurcation of learning and structure formation in neuronal maps

    DEFF Research Database (Denmark)

    Marschler, Christian; Faust-Ellsässer, Carmen; Starke, Jens

    2014-01-01

    to map formation in the laminar nucleus of the barn owl's auditory system. Using equation-free methods, we perform a bifurcation analysis of spatio-temporal structure formation in the associated synaptic-weight matrix. This enables us to analyze learning as a bifurcation process and follow the unstable...... states as well. A simple time translation of the learning window function shifts the bifurcation point of structure formation and goes along with traveling waves in the map, without changing the animal's sound localization performance....

  14. Biologically Predisposed Learning and Selective Associations in Amygdalar Neurons

    Science.gov (United States)

    Chung, Ain; Barot, Sabiha K.; Kim, Jeansok J.; Bernstein, Ilene L.

    2011-01-01

    Modern views on learning and memory accept the notion of biological constraints--that the formation of association is not uniform across all stimuli. Yet cellular evidence of the encoding of selective associations is lacking. Here, conditioned stimuli (CSs) and unconditioned stimuli (USs) commonly employed in two basic associative learning…

  15. Parallelization of learning problems by artificial neural networks. Application in external radiotherapy

    International Nuclear Information System (INIS)

    Sauget, M.

    2007-12-01

    This research is about the application of neural networks used in the external radiotherapy domain. The goal is to elaborate a new evaluating system for the radiation dose distributions in heterogeneous environments. The al objective of this work is to build a complete tool kit to evaluate the optimal treatment planning. My st research point is about the conception of an incremental learning algorithm. The interest of my work is to combine different optimizations specialized in the function interpolation and to propose a new algorithm allowing to change the neural network architecture during the learning phase. This algorithm allows to minimise the al size of the neural network while keeping a good accuracy. The second part of my research is to parallelize the previous incremental learning algorithm. The goal of that work is to increase the speed of the learning step as well as the size of the learned dataset needed in a clinical case. For that, our incremental learning algorithm presents an original data decomposition with overlapping, together with a fault tolerance mechanism. My last research point is about a fast and accurate algorithm computing the radiation dose deposit in any heterogeneous environment. At the present time, the existing solutions used are not optimal. The fast solution are not accurate and do not give an optimal treatment planning. On the other hand, the accurate solutions are far too slow to be used in a clinical context. Our algorithm answers to this problem by bringing rapidity and accuracy. The concept is to use a neural network adequately learned together with a mechanism taking into account the environment changes. The advantages of this algorithm is to avoid the use of a complex physical code while keeping a good accuracy and reasonable computation times. (author)

  16. Learning Joint-Sparse Codes for Calibration-Free Parallel MR Imaging.

    Science.gov (United States)

    Wang, Shanshan; Tan, Sha; Gao, Yuan; Liu, Qiegen; Ying, Leslie; Xiao, Taohui; Liu, Yuanyuan; Liu, Xin; Zheng, Hairong; Liang, Dong

    2018-01-01

    The integration of compressed sensing and parallel imaging (CS-PI) has shown an increased popularity in recent years to accelerate magnetic resonance (MR) imaging. Among them, calibration-free techniques have presented encouraging performances due to its capability in robustly handling the sensitivity information. Unfortunately, existing calibration-free methods have only explored joint-sparsity with direct analysis transform projections. To further exploit joint-sparsity and improve reconstruction accuracy, this paper proposes to Learn joINt-sparse coDes for caliBration-free parallEl mR imaGing (LINDBERG) by modeling the parallel MR imaging problem as an - - minimization objective with an norm constraining data fidelity, Frobenius norm enforcing sparse representation error and the mixed norm triggering joint sparsity across multichannels. A corresponding algorithm has been developed to alternatively update the sparse representation, sensitivity encoded images and K-space data. Then, the final image is produced as the square root of sum of squares of all channel images. Experimental results on both physical phantom and in vivo data sets show that the proposed method is comparable and even superior to state-of-the-art CS-PI reconstruction approaches. Specifically, LINDBERG has presented strong capability in suppressing noise and artifacts while reconstructing MR images from highly undersampled multichannel measurements.

  17. Machine Learning and Parallelism in the Reconstruction of LHCb and its Upgrade

    International Nuclear Information System (INIS)

    Cian, Michel De

    2016-01-01

    The LHCb detector at the LHC is a general purpose detector in the forward region with a focus on reconstructing decays of c- and b-hadrons. For Run II of the LHC, a new trigger strategy with a real-time reconstruction, alignment and calibration was employed. This was made possible by implementing an offline-like track reconstruction in the high level trigger. However, the ever increasing need for a higher throughput and the move to parallelism in the CPU architectures in the last years necessitated the use of vectorization techniques to achieve the desired speed and a more extensive use of machine learning to veto bad events early on. This document discusses selected improvements in computationally expensive parts of the track reconstruction, like the Kalman filter, as well as an improved approach to get rid of fake tracks using fast machine learning techniques. In the last part, a short overview of the track reconstruction challenges for the upgrade of LHCb, is given. Running a fully software-based trigger, a large gain in speed in the reconstruction has to be achieved to cope with the 40 MHz bunch-crossing rate. Two possible approaches for techniques exploiting massive parallelization are discussed

  18. Machine Learning and Parallelism in the Reconstruction of LHCb and its Upgrade

    Science.gov (United States)

    De Cian, Michel

    2016-11-01

    The LHCb detector at the LHC is a general purpose detector in the forward region with a focus on reconstructing decays of c- and b-hadrons. For Run II of the LHC, a new trigger strategy with a real-time reconstruction, alignment and calibration was employed. This was made possible by implementing an offline-like track reconstruction in the high level trigger. However, the ever increasing need for a higher throughput and the move to parallelism in the CPU architectures in the last years necessitated the use of vectorization techniques to achieve the desired speed and a more extensive use of machine learning to veto bad events early on. This document discusses selected improvements in computationally expensive parts of the track reconstruction, like the Kalman filter, as well as an improved approach to get rid of fake tracks using fast machine learning techniques. In the last part, a short overview of the track reconstruction challenges for the upgrade of LHCb, is given. Running a fully software-based trigger, a large gain in speed in the reconstruction has to be achieved to cope with the 40 MHz bunch-crossing rate. Two possible approaches for techniques exploiting massive parallelization are discussed.

  19. A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses.

    Science.gov (United States)

    Qiao, Ning; Mostafa, Hesham; Corradi, Federico; Osswald, Marc; Stefanini, Fabio; Sumislawska, Dora; Indiveri, Giacomo

    2015-01-01

    Implementing compact, low-power artificial neural processing systems with real-time on-line learning abilities is still an open challenge. In this paper we present a full-custom mixed-signal VLSI device with neuromorphic learning circuits that emulate the biophysics of real spiking neurons and dynamic synapses for exploring the properties of computational neuroscience models and for building brain-inspired computing systems. The proposed architecture allows the on-chip configuration of a wide range of network connectivities, including recurrent and deep networks, with short-term and long-term plasticity. The device comprises 128 K analog synapse and 256 neuron circuits with biologically plausible dynamics and bi-stable spike-based plasticity mechanisms that endow it with on-line learning abilities. In addition to the analog circuits, the device comprises also asynchronous digital logic circuits for setting different synapse and neuron properties as well as different network configurations. This prototype device, fabricated using a 180 nm 1P6M CMOS process, occupies an area of 51.4 mm(2), and consumes approximately 4 mW for typical experiments, for example involving attractor networks. Here we describe the details of the overall architecture and of the individual circuits and present experimental results that showcase its potential. By supporting a wide range of cortical-like computational modules comprising plasticity mechanisms, this device will enable the realization of intelligent autonomous systems with on-line learning capabilities.

  20. Bidirectional Modulation of Intrinsic Excitability in Rat Prelimbic Cortex Neuronal Ensembles and Non-Ensembles after Operant Learning.

    Science.gov (United States)

    Whitaker, Leslie R; Warren, Brandon L; Venniro, Marco; Harte, Tyler C; McPherson, Kylie B; Beidel, Jennifer; Bossert, Jennifer M; Shaham, Yavin; Bonci, Antonello; Hope, Bruce T

    2017-09-06

    Learned associations between environmental stimuli and rewards drive goal-directed learning and motivated behavior. These memories are thought to be encoded by alterations within specific patterns of sparsely distributed neurons called neuronal ensembles that are activated selectively by reward-predictive stimuli. Here, we use the Fos promoter to identify strongly activated neuronal ensembles in rat prelimbic cortex (PLC) and assess altered intrinsic excitability after 10 d of operant food self-administration training (1 h/d). First, we used the Daun02 inactivation procedure in male FosLacZ-transgenic rats to ablate selectively Fos-expressing PLC neurons that were active during operant food self-administration. Selective ablation of these neurons decreased food seeking. We then used male FosGFP-transgenic rats to assess selective alterations of intrinsic excitability in Fos-expressing neuronal ensembles (FosGFP + ) that were activated during food self-administration and compared these with alterations in less activated non-ensemble neurons (FosGFP - ). Using whole-cell recordings of layer V pyramidal neurons in an ex vivo brain slice preparation, we found that operant self-administration increased excitability of FosGFP + neurons and decreased excitability of FosGFP - neurons. Increased excitability of FosGFP + neurons was driven by increased steady-state input resistance. Decreased excitability of FosGFP - neurons was driven by increased contribution of small-conductance calcium-activated potassium (SK) channels. Injections of the specific SK channel antagonist apamin into PLC increased Fos expression but had no effect on food seeking. Overall, operant learning increased intrinsic excitability of PLC Fos-expressing neuronal ensembles that play a role in food seeking but decreased intrinsic excitability of Fos - non-ensembles. SIGNIFICANCE STATEMENT Prefrontal cortex activity plays a critical role in operant learning, but the underlying cellular mechanisms are

  1. Relationship between mathematical abstraction in learning parallel coordinates concept and performance in learning analytic geometry of pre-service mathematics teachers: an investigation

    Science.gov (United States)

    Nurhasanah, F.; Kusumah, Y. S.; Sabandar, J.; Suryadi, D.

    2018-05-01

    As one of the non-conventional mathematics concepts, Parallel Coordinates is potential to be learned by pre-service mathematics teachers in order to give them experiences in constructing richer schemes and doing abstraction process. Unfortunately, the study related to this issue is still limited. This study wants to answer a research question “to what extent the abstraction process of pre-service mathematics teachers in learning concept of Parallel Coordinates could indicate their performance in learning Analytic Geometry”. This is a case study that part of a larger study in examining mathematical abstraction of pre-service mathematics teachers in learning non-conventional mathematics concept. Descriptive statistics method is used in this study to analyze the scores from three different tests: Cartesian Coordinate, Parallel Coordinates, and Analytic Geometry. The participants in this study consist of 45 pre-service mathematics teachers. The result shows that there is a linear association between the score on Cartesian Coordinate and Parallel Coordinates. There also found that the higher levels of the abstraction process in learning Parallel Coordinates are linearly associated with higher student achievement in Analytic Geometry. The result of this study shows that the concept of Parallel Coordinates has a significant role for pre-service mathematics teachers in learning Analytic Geometry.

  2. Recording single neurons' action potentials from freely moving pigeons across three stages of learning.

    Science.gov (United States)

    Starosta, Sarah; Stüttgen, Maik C; Güntürkün, Onur

    2014-06-02

    While the subject of learning has attracted immense interest from both behavioral and neural scientists, only relatively few investigators have observed single-neuron activity while animals are acquiring an operantly conditioned response, or when that response is extinguished. But even in these cases, observation periods usually encompass only a single stage of learning, i.e. acquisition or extinction, but not both (exceptions include protocols employing reversal learning; see Bingman et al.(1) for an example). However, acquisition and extinction entail different learning mechanisms and are therefore expected to be accompanied by different types and/or loci of neural plasticity. Accordingly, we developed a behavioral paradigm which institutes three stages of learning in a single behavioral session and which is well suited for the simultaneous recording of single neurons' action potentials. Animals are trained on a single-interval forced choice task which requires mapping each of two possible choice responses to the presentation of different novel visual stimuli (acquisition). After having reached a predefined performance criterion, one of the two choice responses is no longer reinforced (extinction). Following a certain decrement in performance level, correct responses are reinforced again (reacquisition). By using a new set of stimuli in every session, animals can undergo the acquisition-extinction-reacquisition process repeatedly. Because all three stages of learning occur in a single behavioral session, the paradigm is ideal for the simultaneous observation of the spiking output of multiple single neurons. We use pigeons as model systems, but the task can easily be adapted to any other species capable of conditioned discrimination learning.

  3. Parallel Processing and Learning: Variability and Chaos in Self- Organization of Activity in Groups of Neurons

    Science.gov (United States)

    1993-03-09

    neurotransmission and neuromodulation (Soinila and Mpitsos, 1992; Soinila ct al., 1992). It is necessary, as these and other publications (e.g., Mpitsos and...neurotransmitters and neuromodulators affect the activity of neural assemblies, and (b) how individual transmitters act within the framework of the many...examined mammalian tissues that may he useful ajs model s~sqerni to examine distributed function in neurotransmission and neuromodulation (Soinila and

  4. Roles of dopamine neurons in mediating the prediction error in aversive learning in insects.

    Science.gov (United States)

    Terao, Kanta; Mizunami, Makoto

    2017-10-31

    In associative learning in mammals, it is widely accepted that the discrepancy, or error, between actual and predicted reward determines whether learning occurs. The prediction error theory has been proposed to account for the finding of a blocking phenomenon, in which pairing of a stimulus X with an unconditioned stimulus (US) could block subsequent association of a second stimulus Y to the US when the two stimuli were paired in compound with the same US. Evidence for this theory, however, has been imperfect since blocking can also be accounted for by competitive theories. We recently reported blocking in classical conditioning of an odor with water reward in crickets. We also reported an "auto-blocking" phenomenon in appetitive learning, which supported the prediction error theory and rejected alternative theories. The presence of auto-blocking also suggested that octopamine neurons mediate reward prediction error signals. Here we show that blocking and auto-blocking occur in aversive learning to associate an odor with salt water (US) in crickets, and our results suggest that dopamine neurons mediate aversive prediction error signals. We conclude that the prediction error theory is applicable to both appetitive learning and aversive learning in insects.

  5. Aging in Sensory and Motor Neurons Results in Learning Failure in Aplysia californica.

    Directory of Open Access Journals (Sweden)

    Andrew T Kempsell

    Full Text Available The physiological and molecular mechanisms of age-related memory loss are complicated by the complexity of vertebrate nervous systems. This study takes advantage of a simple neural model to investigate nervous system aging, focusing on changes in learning and memory in the form of behavioral sensitization in vivo and synaptic facilitation in vitro. The effect of aging on the tail withdrawal reflex (TWR was studied in Aplysia californica at maturity and late in the annual lifecycle. We found that short-term sensitization in TWR was absent in aged Aplysia. This implied that the neuronal machinery governing nonassociative learning was compromised during aging. Synaptic plasticity in the form of short-term facilitation between tail sensory and motor neurons decreased during aging whether the sensitizing stimulus was tail shock or the heterosynaptic modulator serotonin (5-HT. Together, these results suggest that the cellular mechanisms governing behavioral sensitization are compromised during aging, thereby nearly eliminating sensitization in aged Aplysia.

  6. [Effect of electroacupuncture intervention on learning-memory ability and injured hippocampal neurons in depression rats].

    Science.gov (United States)

    Bao, Wu-Ye; Jiao, Shuang; Lu, Jun; Tu, Ya; Song, Ying-Zhou; Wu, Qian; A, Ying-Ge

    2014-04-01

    To observe the effect of electroacupuncture (EA) stimulation of "Baihui" (GV 20)-"Yintang" (EX-HN 3) on changes of learning-memory ability and hippocampal neuron structure in chronic stress-stimulation induced depression rats. Forty-eight SD rats were randomly divided into normal, model, EA and medication (Fluoxetine) groups, with 12 rats in each group. The depression model was established by chronic unpredictable mild stress stimulation (swimming in 4 degrees C water, fasting, water deprivation, reversed day and night, etc). Treatment was applied to "Baihui" (GV 20) and "Yintang" (EX-HN 3) for 20 min, once every day for 21 days. For rats of the medication group, Fluoxetine (3.3 mg/kg) was given by gavage (p.o.), once daily for 21 days. The learning-memory ability was detected by Morris water maze tests. The pathological and ultrastructural changes of the hippocampal tissue and neurons were assessed by H.E. staining, light microscope and transmission electron microscopy, respectively. Compared to the normal group, the rats' body weight on day 14 and day 21 after modeling was significantly decreased in the model group (P learning-memory ability. Observations of light microscope and transmission electron microscope showed that modeling induced pathological changes such as reduction in hippocampal cell layers, vague and broken cellular membrane, and ultrastructural changes of hippocampal neurons including swelling and reduction of mitochondria and mitochondrial crests were relived after EA and Fluoxetine treatment. EA intervention can improve the learning-memory ability and relieving impairment of hippocampal neurons in depression rats, which may be one of its mechanisms underlying bettering depression.

  7. Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons.

    Science.gov (United States)

    Burbank, Kendra S

    2015-12-01

    The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field's Sparse Coding algorithm, can be seen as autoencoder variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult to implement in a biologically realistic fashion. The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to identical changes at feedforward and feedback connections. Here, we study a biologically realistic network of integrate-and-fire neurons with anatomical connectivity and synaptic plasticity that closely matches that observed in cortical sensory areas. Our choice of synaptic plasticity rules is inspired by recent experimental and theoretical results suggesting that learning at feedback connections may have a different form from learning at feedforward connections, and our results depend critically on this novel choice of plasticity rules. Specifically, we propose that plasticity rules at feedforward versus feedback connections are temporally opposed versions of spike-timing dependent plasticity (STDP), leading to a symmetric combined rule we call Mirrored STDP (mSTDP). We show that with mSTDP, our network follows a learning rule that approximately minimizes an autoencoder loss function. When trained with whitened natural image patches, the learned synaptic weights resemble the receptive fields seen in V1. Our results use realistic synaptic plasticity rules to show that the powerful autoencoder learning algorithm could be within the reach of real biological networks.

  8. Span: spike pattern association neuron for learning spatio-temporal spike patterns.

    Science.gov (United States)

    Mohemmed, Ammar; Schliebs, Stefan; Matsuda, Satoshi; Kasabov, Nikola

    2012-08-01

    Spiking Neural Networks (SNN) were shown to be suitable tools for the processing of spatio-temporal information. However, due to their inherent complexity, the formulation of efficient supervised learning algorithms for SNN is difficult and remains an important problem in the research area. This article presents SPAN - a spiking neuron that is able to learn associations of arbitrary spike trains in a supervised fashion allowing the processing of spatio-temporal information encoded in the precise timing of spikes. The idea of the proposed algorithm is to transform spike trains during the learning phase into analog signals so that common mathematical operations can be performed on them. Using this conversion, it is possible to apply the well-known Widrow-Hoff rule directly to the transformed spike trains in order to adjust the synaptic weights and to achieve a desired input/output spike behavior of the neuron. In the presented experimental analysis, the proposed learning algorithm is evaluated regarding its learning capabilities, its memory capacity, its robustness to noisy stimuli and its classification performance. Differences and similarities of SPAN regarding two related algorithms, ReSuMe and Chronotron, are discussed.

  9. Reinforcement learning of targeted movement in a spiking neuronal model of motor cortex.

    Directory of Open Access Journals (Sweden)

    George L Chadderdon

    Full Text Available Sensorimotor control has traditionally been considered from a control theory perspective, without relation to neurobiology. In contrast, here we utilized a spiking-neuron model of motor cortex and trained it to perform a simple movement task, which consisted of rotating a single-joint "forearm" to a target. Learning was based on a reinforcement mechanism analogous to that of the dopamine system. This provided a global reward or punishment signal in response to decreasing or increasing distance from hand to target, respectively. Output was partially driven by Poisson motor babbling, creating stochastic movements that could then be shaped by learning. The virtual forearm consisted of a single segment rotated around an elbow joint, controlled by flexor and extensor muscles. The model consisted of 144 excitatory and 64 inhibitory event-based neurons, each with AMPA, NMDA, and GABA synapses. Proprioceptive cell input to this model encoded the 2 muscle lengths. Plasticity was only enabled in feedforward connections between input and output excitatory units, using spike-timing-dependent eligibility traces for synaptic credit or blame assignment. Learning resulted from a global 3-valued signal: reward (+1, no learning (0, or punishment (-1, corresponding to phasic increases, lack of change, or phasic decreases of dopaminergic cell firing, respectively. Successful learning only occurred when both reward and punishment were enabled. In this case, 5 target angles were learned successfully within 180 s of simulation time, with a median error of 8 degrees. Motor babbling allowed exploratory learning, but decreased the stability of the learned behavior, since the hand continued moving after reaching the target. Our model demonstrated that a global reinforcement signal, coupled with eligibility traces for synaptic plasticity, can train a spiking sensorimotor network to perform goal-directed motor behavior.

  10. Reinforcement learning of targeted movement in a spiking neuronal model of motor cortex.

    Science.gov (United States)

    Chadderdon, George L; Neymotin, Samuel A; Kerr, Cliff C; Lytton, William W

    2012-01-01

    Sensorimotor control has traditionally been considered from a control theory perspective, without relation to neurobiology. In contrast, here we utilized a spiking-neuron model of motor cortex and trained it to perform a simple movement task, which consisted of rotating a single-joint "forearm" to a target. Learning was based on a reinforcement mechanism analogous to that of the dopamine system. This provided a global reward or punishment signal in response to decreasing or increasing distance from hand to target, respectively. Output was partially driven by Poisson motor babbling, creating stochastic movements that could then be shaped by learning. The virtual forearm consisted of a single segment rotated around an elbow joint, controlled by flexor and extensor muscles. The model consisted of 144 excitatory and 64 inhibitory event-based neurons, each with AMPA, NMDA, and GABA synapses. Proprioceptive cell input to this model encoded the 2 muscle lengths. Plasticity was only enabled in feedforward connections between input and output excitatory units, using spike-timing-dependent eligibility traces for synaptic credit or blame assignment. Learning resulted from a global 3-valued signal: reward (+1), no learning (0), or punishment (-1), corresponding to phasic increases, lack of change, or phasic decreases of dopaminergic cell firing, respectively. Successful learning only occurred when both reward and punishment were enabled. In this case, 5 target angles were learned successfully within 180 s of simulation time, with a median error of 8 degrees. Motor babbling allowed exploratory learning, but decreased the stability of the learned behavior, since the hand continued moving after reaching the target. Our model demonstrated that a global reinforcement signal, coupled with eligibility traces for synaptic plasticity, can train a spiking sensorimotor network to perform goal-directed motor behavior.

  11. Homemade Buckeye-Pi: A Learning Many-Node Platform for High-Performance Parallel Computing

    Science.gov (United States)

    Amooie, M. A.; Moortgat, J.

    2017-12-01

    We report on the "Buckeye-Pi" cluster, the supercomputer developed in The Ohio State University School of Earth Sciences from 128 inexpensive Raspberry Pi (RPi) 3 Model B single-board computers. Each RPi is equipped with fast Quad Core 1.2GHz ARMv8 64bit processor, 1GB of RAM, and 32GB microSD card for local storage. Therefore, the cluster has a total RAM of 128GB that is distributed on the individual nodes and a flash capacity of 4TB with 512 processors, while it benefits from low power consumption, easy portability, and low total cost. The cluster uses the Message Passing Interface protocol to manage the communications between each node. These features render our platform the most powerful RPi supercomputer to date and suitable for educational applications in high-performance-computing (HPC) and handling of large datasets. In particular, we use the Buckeye-Pi to implement optimized parallel codes in our in-house simulator for subsurface media flows with the goal of achieving a massively-parallelized scalable code. We present benchmarking results for the computational performance across various number of RPi nodes. We believe our project could inspire scientists and students to consider the proposed unconventional cluster architecture as a mainstream and a feasible learning platform for challenging engineering and scientific problems.

  12. Bidirectional coupling between astrocytes and neurons mediates learning and dynamic coordination in the brain: a multiple modeling approach.

    Directory of Open Access Journals (Sweden)

    John J Wade

    Full Text Available In recent years research suggests that astrocyte networks, in addition to nutrient and waste processing functions, regulate both structural and synaptic plasticity. To understand the biological mechanisms that underpin such plasticity requires the development of cell level models that capture the mutual interaction between astrocytes and neurons. This paper presents a detailed model of bidirectional signaling between astrocytes and neurons (the astrocyte-neuron model or AN model which yields new insights into the computational role of astrocyte-neuronal coupling. From a set of modeling studies we demonstrate two significant findings. Firstly, that spatial signaling via astrocytes can relay a "learning signal" to remote synaptic sites. Results show that slow inward currents cause synchronized postsynaptic activity in remote neurons and subsequently allow Spike-Timing-Dependent Plasticity based learning to occur at the associated synapses. Secondly, that bidirectional communication between neurons and astrocytes underpins dynamic coordination between neuron clusters. Although our composite AN model is presently applied to simplified neural structures and limited to coordination between localized neurons, the principle (which embodies structural, functional and dynamic complexity, and the modeling strategy may be extended to coordination among remote neuron clusters.

  13. 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.

  14. Reinforcement learning using a continuous time actor-critic framework with spiking neurons.

    Directory of Open Access Journals (Sweden)

    Nicolas Frémaux

    2013-04-01

    Full Text Available Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD learning of Doya (2000 to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.

  15. PHYLOGENETIC ANALYSIS OF LEARNING-RELATED NEUROMODULATION IN MOLLUSCAN MECHANOSENSORY NEURONS.

    Science.gov (United States)

    Wright, William G; Kirschman, David; Rozen, Danny; Maynard, Barbara

    1996-12-01

    In spite of significant advances in our understanding of mechanisms of learning and memory in a variety of organisms, little is known about how such mechanisms evolve. Even mechanisms of simple forms of learning, such as habituation and sensitization, have not been studied phylogenetically. Here we begin an evolutionary analysis of learning-related neuromodulation in species related to the well-studied opisthobranch gastropod, Aplysia californica. In Aplysia, increased spike duration and excitability in mechanosensory neurons contribute to several forms of learning-related changes to defensive withdrawal reflexes. The modulatory transmitter serotonin (5-hydroxytryptamine, or 5-HT), is thought to play a critical role in producing these firing property changes. In the present study, we tested mechanosensory homologs of the tail-withdrawal reflex in species related to Aplysia for 5-HT-mediated increases in spike duration and excitability. Criteria used to identify homologous tail-sensory neurons included position, relative size, resting electrical properties, expression of a sensory neuron-specific protein, neuroanatomy, and receptive field. The four ingroup species studied (Aplysia californica, Dolabella auricularia, Bursatella leachii, and Dolabrifera dolabrifera) belong to two clades (two species each) within the family Aplysiidae. In the first clade (Aplysia/Dolabella), we found that the tail-sensory neurons of A. californica and tail-sensory homologs of a closely related species, D. auricularia, responded to bath-applied serotonin in essentially similar fashion: significant increases in spike duration as well as excitability. In the other clade (Dolabrifera/Bursatella), more distantly related to Aplysia, one species (B. leachii) showed spike broadening and increased excitability. However, the other species (D. dolabrifera) showed neither spike broadening nor increased excitability. The firing properties of tail-sensory homologs of D. dolabrifera were insensitive

  16. Neuron splitting in compute-bound parallel network simulations enables runtime scaling with twice as many processors.

    Science.gov (United States)

    Hines, Michael L; Eichner, Hubert; Schürmann, Felix

    2008-08-01

    Neuron tree topology equations can be split into two subtrees and solved on different processors with no change in accuracy, stability, or computational effort; communication costs involve only sending and receiving two double precision values by each subtree at each time step. Splitting cells is useful in attaining load balance in neural network simulations, especially when there is a wide range of cell sizes and the number of cells is about the same as the number of processors. For compute-bound simulations load balance results in almost ideal runtime scaling. Application of the cell splitting method to two published network models exhibits good runtime scaling on twice as many processors as could be effectively used with whole-cell balancing.

  17. A Cross-Correlated Delay Shift Supervised Learning Method for Spiking Neurons with Application to Interictal Spike Detection in Epilepsy.

    Science.gov (United States)

    Guo, Lilin; Wang, Zhenzhong; Cabrerizo, Mercedes; Adjouadi, Malek

    2017-05-01

    This study introduces a novel learning algorithm for spiking neurons, called CCDS, which is able to learn and reproduce arbitrary spike patterns in a supervised fashion allowing the processing of spatiotemporal information encoded in the precise timing of spikes. Unlike the Remote Supervised Method (ReSuMe), synapse delays and axonal delays in CCDS are variants which are modulated together with weights during learning. The CCDS rule is both biologically plausible and computationally efficient. The properties of this learning rule are investigated extensively through experimental evaluations in terms of reliability, adaptive learning performance, generality to different neuron models, learning in the presence of noise, effects of its learning parameters and classification performance. Results presented show that the CCDS learning method achieves learning accuracy and learning speed comparable with ReSuMe, but improves classification accuracy when compared to both the Spike Pattern Association Neuron (SPAN) learning rule and the Tempotron learning rule. The merit of CCDS rule is further validated on a practical example involving the automated detection of interictal spikes in EEG records of patients with epilepsy. Results again show that with proper encoding, the CCDS rule achieves good recognition performance.

  18. Machine learning and parallelism in the reconstruction of LHCb and its upgrade

    CERN Document Server

    AUTHOR|(INSPIRE)INSPIRE-00260810

    2016-01-01

    The LHCb detector at the LHC is a general purpose detector in the forward region with a focus on reconstructing decays of c- and b-hadrons. For Run II of the LHC, a new trigger strategy with a real-time reconstruction, alignment and calibration was employed. This was made possible by implementing an oine-like track reconstruction in the high level trigger. However, the ever increasing need for a higher throughput and the move to parallelism in the CPU architectures in the last years necessitated the use of vectorization techniques to achieve the desired speed and a more extensive use of machine learning to veto bad events early on. This document discusses selected improvements in computationally expensive parts of the track reconstruction, like the Kalman filter, as well as an improved approach to get rid of fake tracks using fast machine learning techniques. In the last part, a short overview of the track reconstruction challenges for the upgrade of LHCb, is given. Running a fully software-based trigger, a l...

  19. Engineering Computer Games: A Parallel Learning Opportunity for Undergraduate Engineering and Primary (K-5 Students

    Directory of Open Access Journals (Sweden)

    Mark Michael Budnik

    2011-04-01

    Full Text Available In this paper, we present how our College of Engineering is developing a growing portfolio of engineering computer games as a parallel learning opportunity for undergraduate engineering and primary (grade K-5 students. Around the world, many schools provide secondary students (grade 6-12 with opportunities to pursue pre-engineering classes. However, by the time students reach this age, many of them have already determined their educational goals and preferred careers. Our College of Engineering is developing resources to provide primary students, still in their educational formative years, with opportunities to learn more about engineering. One of these resources is a library of engineering games targeted to the primary student population. The games are designed by sophomore students in our College of Engineering. During their Introduction to Computational Techniques course, the students use the LabVIEW environment to develop the games. This software provides a wealth of design resources for the novice programmer; using it to develop the games strengthens the undergraduates

  20. Autism and the mirror neuron system: insights from learning and teaching.

    Science.gov (United States)

    Vivanti, Giacomo; Rogers, Sally J

    2014-01-01

    Individuals with autism have difficulties in social learning domains which typically involve mirror neuron system (MNS) activation. However, the precise role of the MNS in the development of autism and its relevance to treatment remain unclear. In this paper, we argue that three distinct aspects of social learning are critical for advancing knowledge in this area: (i) the mechanisms that allow for the implicit mapping of and learning from others' behaviour, (ii) the motivation to attend to and model conspecifics and (iii) the flexible and selective use of social learning. These factors are key targets of the Early Start Denver Model, an autism treatment approach which emphasizes social imitation, dyadic engagement, verbal and non-verbal communication and affect sharing. Analysis of the developmental processes and treatment-related changes in these different aspects of social learning in autism can shed light on the nature of the neuropsychological mechanisms underlying social learning and positive treatment outcomes in autism. This knowledge in turn may assist in developing more successful pedagogic approaches to autism spectrum disorder. Thus, intervention research can inform the debate on relations among neuropsychology of social learning, the role of the MNS, and educational practice in autism.

  1. Autism and the mirror neuron system: insights from learning and teaching

    Science.gov (United States)

    Vivanti, Giacomo; Rogers, Sally J.

    2014-01-01

    Individuals with autism have difficulties in social learning domains which typically involve mirror neuron system (MNS) activation. However, the precise role of the MNS in the development of autism and its relevance to treatment remain unclear. In this paper, we argue that three distinct aspects of social learning are critical for advancing knowledge in this area: (i) the mechanisms that allow for the implicit mapping of and learning from others' behaviour, (ii) the motivation to attend to and model conspecifics and (iii) the flexible and selective use of social learning. These factors are key targets of the Early Start Denver Model, an autism treatment approach which emphasizes social imitation, dyadic engagement, verbal and non-verbal communication and affect sharing. Analysis of the developmental processes and treatment-related changes in these different aspects of social learning in autism can shed light on the nature of the neuropsychological mechanisms underlying social learning and positive treatment outcomes in autism. This knowledge in turn may assist in developing more successful pedagogic approaches to autism spectrum disorder. Thus, intervention research can inform the debate on relations among neuropsychology of social learning, the role of the MNS, and educational practice in autism. PMID:24778379

  2. Towards a HPC-oriented parallel implementation of a learning algorithm for bioinformatics applications.

    Science.gov (United States)

    D'Angelo, Gianni; Rampone, Salvatore

    2014-01-01

    The huge quantity of data produced in Biomedical research needs sophisticated algorithmic methodologies for its storage, analysis, and processing. High Performance Computing (HPC) appears as a magic bullet in this challenge. However, several hard to solve parallelization and load balancing problems arise in this context. Here we discuss the HPC-oriented implementation of a general purpose learning algorithm, originally conceived for DNA analysis and recently extended to treat uncertainty on data (U-BRAIN). The U-BRAIN algorithm is a learning algorithm that finds a Boolean formula in disjunctive normal form (DNF), of approximately minimum complexity, that is consistent with a set of data (instances) which may have missing bits. The conjunctive terms of the formula are computed in an iterative way by identifying, from the given data, a family of sets of conditions that must be satisfied by all the positive instances and violated by all the negative ones; such conditions allow the computation of a set of coefficients (relevances) for each attribute (literal), that form a probability distribution, allowing the selection of the term literals. The great versatility that characterizes it, makes U-BRAIN applicable in many of the fields in which there are data to be analyzed. However the memory and the execution time required by the running are of O(n(3)) and of O(n(5)) order, respectively, and so, the algorithm is unaffordable for huge data sets. We find mathematical and programming solutions able to lead us towards the implementation of the algorithm U-BRAIN on parallel computers. First we give a Dynamic Programming model of the U-BRAIN algorithm, then we minimize the representation of the relevances. When the data are of great size we are forced to use the mass memory, and depending on where the data are actually stored, the access times can be quite different. According to the evaluation of algorithmic efficiency based on the Disk Model, in order to reduce the costs of

  3. Trim9 Deletion Alters the Morphogenesis of Developing and Adult-Born Hippocampal Neurons and Impairs Spatial Learning and Memory.

    Science.gov (United States)

    Winkle, Cortney C; Olsen, Reid H J; Kim, Hyojin; Moy, Sheryl S; Song, Juan; Gupton, Stephanie L

    2016-05-04

    During hippocampal development, newly born neurons migrate to appropriate destinations, extend axons, and ramify dendritic arbors to establish functional circuitry. These developmental stages are recapitulated in the dentate gyrus of the adult hippocampus, where neurons are continuously generated and subsequently incorporate into existing, local circuitry. Here we demonstrate that the E3 ubiquitin ligase TRIM9 regulates these developmental stages in embryonic and adult-born mouse hippocampal neurons in vitro and in vivo Embryonic hippocampal and adult-born dentate granule neurons lacking Trim9 exhibit several morphological defects, including excessive dendritic arborization. Although gross anatomy of the hippocampus was not detectably altered by Trim9 deletion, a significant number of Trim9(-/-) adult-born dentate neurons localized inappropriately. These morphological and localization defects of hippocampal neurons in Trim9(-/-) mice were associated with extreme deficits in spatial learning and memory, suggesting that TRIM9-directed neuronal morphogenesis may be involved in hippocampal-dependent behaviors. Appropriate generation and incorporation of adult-born neurons in the dentate gyrus are critical for spatial learning and memory and other hippocampal functions. Here we identify the brain-enriched E3 ubiquitin ligase TRIM9 as a novel regulator of embryonic and adult hippocampal neuron shape acquisition and hippocampal-dependent behaviors. Genetic deletion of Trim9 elevated dendritic arborization of hippocampal neurons in vitro and in vivo Adult-born dentate granule cells lacking Trim9 similarly exhibited excessive dendritic arborization and mislocalization of cell bodies in vivo These cellular defects were associated with severe deficits in spatial learning and memory. Copyright © 2016 the authors 0270-6474/16/364940-19$15.00/0.

  4. Diverse Assessment and Active Student Engagement Sustain Deep Learning: A Comparative Study of Outcomes in Two Parallel Introductory Biochemistry Courses

    Science.gov (United States)

    Bevan, Samantha J.; Chan, Cecilia W. L.; Tanner, Julian A.

    2014-01-01

    Although there is increasing evidence for a relationship between courses that emphasize student engagement and achievement of student deep learning, there is a paucity of quantitative comparative studies in a biochemistry and molecular biology context. Here, we present a pedagogical study in two contrasting parallel biochemistry introductory…

  5. Artificial neuron-glia networks learning approach based on cooperative coevolution.

    Science.gov (United States)

    Mesejo, Pablo; Ibáñez, Oscar; Fernández-Blanco, Enrique; Cedrón, Francisco; Pazos, Alejandro; Porto-Pazos, Ana B

    2015-06-01

    Artificial Neuron-Glia Networks (ANGNs) are a novel bio-inspired machine learning approach. They extend classical Artificial Neural Networks (ANNs) by incorporating recent findings and suppositions about the way information is processed by neural and astrocytic networks in the most evolved living organisms. Although ANGNs are not a consolidated method, their performance against the traditional approach, i.e. without artificial astrocytes, was already demonstrated on classification problems. However, the corresponding learning algorithms developed so far strongly depends on a set of glial parameters which are manually tuned for each specific problem. As a consequence, previous experimental tests have to be done in order to determine an adequate set of values, making such manual parameter configuration time-consuming, error-prone, biased and problem dependent. Thus, in this paper, we propose a novel learning approach for ANGNs that fully automates the learning process, and gives the possibility of testing any kind of reasonable parameter configuration for each specific problem. This new learning algorithm, based on coevolutionary genetic algorithms, is able to properly learn all the ANGNs parameters. Its performance is tested on five classification problems achieving significantly better results than ANGN and competitive results with ANN approaches.

  6. TGF-β Signaling in Dopaminergic Neurons Regulates Dendritic Growth, Excitatory-Inhibitory Synaptic Balance, and Reversal Learning

    Directory of Open Access Journals (Sweden)

    Sarah X. Luo

    2016-12-01

    Full Text Available Neural circuits involving midbrain dopaminergic (DA neurons regulate reward and goal-directed behaviors. Although local GABAergic input is known to modulate DA circuits, the mechanism that controls excitatory/inhibitory synaptic balance in DA neurons remains unclear. Here, we show that DA neurons use autocrine transforming growth factor β (TGF-β signaling to promote the growth of axons and dendrites. Surprisingly, removing TGF-β type II receptor in DA neurons also disrupts the balance in TGF-β1 expression in DA neurons and neighboring GABAergic neurons, which increases inhibitory input, reduces excitatory synaptic input, and alters phasic firing patterns in DA neurons. Mice lacking TGF-β signaling in DA neurons are hyperactive and exhibit inflexibility in relinquishing learned behaviors and re-establishing new stimulus-reward associations. These results support a role for TGF-β in regulating the delicate balance of excitatory/inhibitory synaptic input in local microcircuits involving DA and GABAergic neurons and its potential contributions to neuropsychiatric disorders.

  7. QSpike Tools: a Generic Framework for Parallel Batch Preprocessing of Extracellular Neuronal Signals Recorded by Substrate Microelectrode Arrays

    Directory of Open Access Journals (Sweden)

    Mufti eMahmud

    2014-03-01

    Full Text Available Micro-Electrode Arrays (MEAs have emerged as a mature technique to investigate brain (dysfunctions in vivo and in in vitro animal models. Often referred to as smart Petri dishes, MEAs has demonstrated a great potential particularly for medium-throughput studies in vitro, both in academic and pharmaceutical industrial contexts. Enabling rapid comparison of ionic/pharmacological/genetic manipulations with control conditions, MEAs are often employed to screen compounds by monitoring non-invasively the spontaneous and evoked neuronal electrical activity in longitudinal studies, with relatively inexpensive equipment. However, in order to acquire sufficient statistical significance, recordings last up to tens of minutes and generate large amount of raw data (e.g., 60 channels/MEA, 16 bits A/D conversion, 20kHz sampling rate: ~8GB/MEA,h uncompressed. Thus, when the experimental conditions to be tested are numerous, the availability of fast, standardized, and automated signal preprocessing becomes pivotal for any subsequent analysis and data archiving. To this aim, we developed an in-house cloud-computing system, named QSpike Tools, where CPU-intensive operations, required for preprocessing of each recorded channel (e.g., filtering, multi-unit activity detection, spike-sorting, etc., are decomposed and batch-queued to a multi-core architecture or to computer cluster. With the commercial availability of new and inexpensive high-density MEAs, we believe that disseminating QSpike Tools might facilitate its wide adoption and customization, and possibly inspire the creation of community-supported cloud-computing facilities for MEAs users.

  8. QSpike tools: a generic framework for parallel batch preprocessing of extracellular neuronal signals recorded by substrate microelectrode arrays.

    Science.gov (United States)

    Mahmud, Mufti; Pulizzi, Rocco; Vasilaki, Eleni; Giugliano, Michele

    2014-01-01

    Micro-Electrode Arrays (MEAs) have emerged as a mature technique to investigate brain (dys)functions in vivo and in in vitro animal models. Often referred to as "smart" Petri dishes, MEAs have demonstrated a great potential particularly for medium-throughput studies in vitro, both in academic and pharmaceutical industrial contexts. Enabling rapid comparison of ionic/pharmacological/genetic manipulations with control conditions, MEAs are employed to screen compounds by monitoring non-invasively the spontaneous and evoked neuronal electrical activity in longitudinal studies, with relatively inexpensive equipment. However, in order to acquire sufficient statistical significance, recordings last up to tens of minutes and generate large amount of raw data (e.g., 60 channels/MEA, 16 bits A/D conversion, 20 kHz sampling rate: approximately 8 GB/MEA,h uncompressed). Thus, when the experimental conditions to be tested are numerous, the availability of fast, standardized, and automated signal preprocessing becomes pivotal for any subsequent analysis and data archiving. To this aim, we developed an in-house cloud-computing system, named QSpike Tools, where CPU-intensive operations, required for preprocessing of each recorded channel (e.g., filtering, multi-unit activity detection, spike-sorting, etc.), are decomposed and batch-queued to a multi-core architecture or to a computers cluster. With the commercial availability of new and inexpensive high-density MEAs, we believe that disseminating QSpike Tools might facilitate its wide adoption and customization, and inspire the creation of community-supported cloud-computing facilities for MEAs users.

  9. Adaptive Neuron Model: An architecture for the rapid learning of nonlinear topological transformations

    Science.gov (United States)

    Tawel, Raoul (Inventor)

    1994-01-01

    A method for the rapid learning of nonlinear mappings and topological transformations using a dynamically reconfigurable artificial neural network is presented. This fully-recurrent Adaptive Neuron Model (ANM) network was applied to the highly degenerate inverse kinematics problem in robotics, and its performance evaluation is bench-marked. Once trained, the resulting neuromorphic architecture was implemented in custom analog neural network hardware and the parameters capturing the functional transformation downloaded onto the system. This neuroprocessor, capable of 10(exp 9) ops/sec, was interfaced directly to a three degree of freedom Heathkit robotic manipulator. Calculation of the hardware feed-forward pass for this mapping was benchmarked at approximately 10 microsec.

  10. A parallel spatiotemporal saliency and discriminative online learning method for visual target tracking in aerial videos.

    Science.gov (United States)

    Aghamohammadi, Amirhossein; Ang, Mei Choo; A Sundararajan, Elankovan; Weng, Ng Kok; Mogharrebi, Marzieh; Banihashem, Seyed Yashar

    2018-01-01

    Visual tracking in aerial videos is a challenging task in computer vision and remote sensing technologies due to appearance variation difficulties. Appearance variations are caused by camera and target motion, low resolution noisy images, scale changes, and pose variations. Various approaches have been proposed to deal with appearance variation difficulties in aerial videos, and amongst these methods, the spatiotemporal saliency detection approach reported promising results in the context of moving target detection. However, it is not accurate for moving target detection when visual tracking is performed under appearance variations. In this study, a visual tracking method is proposed based on spatiotemporal saliency and discriminative online learning methods to deal with appearance variations difficulties. Temporal saliency is used to represent moving target regions, and it was extracted based on the frame difference with Sauvola local adaptive thresholding algorithms. The spatial saliency is used to represent the target appearance details in candidate moving regions. SLIC superpixel segmentation, color, and moment features can be used to compute feature uniqueness and spatial compactness of saliency measurements to detect spatial saliency. It is a time consuming process, which prompted the development of a parallel algorithm to optimize and distribute the saliency detection processes that are loaded into the multi-processors. Spatiotemporal saliency is then obtained by combining the temporal and spatial saliencies to represent moving targets. Finally, a discriminative online learning algorithm was applied to generate a sample model based on spatiotemporal saliency. This sample model is then incrementally updated to detect the target in appearance variation conditions. Experiments conducted on the VIVID dataset demonstrated that the proposed visual tracking method is effective and is computationally efficient compared to state-of-the-art methods.

  11. A parallel spatiotemporal saliency and discriminative online learning method for visual target tracking in aerial videos

    Science.gov (United States)

    2018-01-01

    Visual tracking in aerial videos is a challenging task in computer vision and remote sensing technologies due to appearance variation difficulties. Appearance variations are caused by camera and target motion, low resolution noisy images, scale changes, and pose variations. Various approaches have been proposed to deal with appearance variation difficulties in aerial videos, and amongst these methods, the spatiotemporal saliency detection approach reported promising results in the context of moving target detection. However, it is not accurate for moving target detection when visual tracking is performed under appearance variations. In this study, a visual tracking method is proposed based on spatiotemporal saliency and discriminative online learning methods to deal with appearance variations difficulties. Temporal saliency is used to represent moving target regions, and it was extracted based on the frame difference with Sauvola local adaptive thresholding algorithms. The spatial saliency is used to represent the target appearance details in candidate moving regions. SLIC superpixel segmentation, color, and moment features can be used to compute feature uniqueness and spatial compactness of saliency measurements to detect spatial saliency. It is a time consuming process, which prompted the development of a parallel algorithm to optimize and distribute the saliency detection processes that are loaded into the multi-processors. Spatiotemporal saliency is then obtained by combining the temporal and spatial saliencies to represent moving targets. Finally, a discriminative online learning algorithm was applied to generate a sample model based on spatiotemporal saliency. This sample model is then incrementally updated to detect the target in appearance variation conditions. Experiments conducted on the VIVID dataset demonstrated that the proposed visual tracking method is effective and is computationally efficient compared to state-of-the-art methods. PMID:29438421

  12. Whole-Brain Mapping of Neuronal Activity in the Learned Helplessness Model of Depression.

    Science.gov (United States)

    Kim, Yongsoo; Perova, Zinaida; Mirrione, Martine M; Pradhan, Kith; Henn, Fritz A; Shea, Stephen; Osten, Pavel; Li, Bo

    2016-01-01

    Some individuals are resilient, whereas others succumb to despair in repeated stressful situations. The neurobiological mechanisms underlying such divergent behavioral responses remain unclear. Here, we employed an automated method for mapping neuronal activity in search of signatures of stress responses in the entire mouse brain. We used serial two-photon tomography to detect expression of c-FosGFP - a marker of neuronal activation - in c-fosGFP transgenic mice subjected to the learned helplessness (LH) procedure, a widely used model of stress-induced depression-like phenotype in laboratory animals. We found that mice showing "helpless" behavior had an overall brain-wide reduction in the level of neuronal activation compared with mice showing "resilient" behavior, with the exception of a few brain areas, including the locus coeruleus, that were more activated in the helpless mice. In addition, the helpless mice showed a strong trend of having higher similarity in whole-brain activity profile among individuals, suggesting that helplessness is represented by a more stereotypic brain-wide activation pattern. This latter effect was confirmed in rats subjected to the LH procedure, using 2-deoxy-2[18F]fluoro-D-glucose positron emission tomography to assess neural activity. Our findings reveal distinct brain activity markings that correlate with adaptive and maladaptive behavioral responses to stress, and provide a framework for further studies investigating the contribution of specific brain regions to maladaptive stress responses.

  13. Whole-brain mapping of neuronal activity in the learned helplessness model of depression

    Directory of Open Access Journals (Sweden)

    Yongsoo eKim

    2016-02-01

    Full Text Available Some individuals are resilient, whereas others succumb to despair in repeated stressful situations. The neurobiological mechanisms underlying such divergent behavioral responses remain unclear. Here, we employed an automated method for mapping neuronal activity in search of signatures of stress responses in the entire mouse brain. We used serial two-photon tomography to detect expression of c-FosGFP – a marker of neuronal activation – in c-fosGFP transgenic mice subjected to the learned helplessness (LH procedure, a widely used model of stress-induced depression-like phenotype in laboratory animals. We found that mice showing helpless behavior had an overall brain-wide reduction in the level of neuronal activation compared with mice showing resilient behavior, with the exception of a few brain areas, including the locus coeruleus, that were more activated in the helpless mice. In addition, the helpless mice showed a strong trend of having higher similarity in whole brain activity profile among individuals, suggesting that helplessness is represented by a more stereotypic brain-wide activation pattern. This latter effect was confirmed in rats subjected to the LH procedure, using 2-deoxy-2[18F]fluoro-D-glucose positron emission tomography to assess neural activity. Our findings reveal distinct brain activity markings that correlate with adaptive and maladaptive behavioral responses to stress, and provide a framework for further studies investigating the contribution of specific brain regions to maladaptive stress responses.

  14. Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning.

    Science.gov (United States)

    Wu, Jiayi; Ma, Yong-Bei; Congdon, Charles; Brett, Bevin; Chen, Shuobing; Xu, Yaofang; Ouyang, Qi; Mao, Youdong

    2017-01-01

    Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR) in the image data, yet demand increased computational costs. Overcoming these limitations requires further development of clustering algorithms for high-performance cryo-EM data processing. Here we introduce an unsupervised single-particle clustering algorithm derived from a statistical manifold learning framework called generative topographic mapping (GTM). We show that unsupervised GTM clustering improves classification accuracy by about 40% in the absence of input references for data with lower SNRs. Applications to several experimental datasets suggest that our algorithm can detect subtle structural differences among classes via a hierarchical clustering strategy. After code optimization over a high-performance computing (HPC) environment, our software implementation was able to generate thousands of reference-free class averages within hours in a massively parallel fashion, which allows a significant improvement on ab initio 3D reconstruction and assists in the computational purification of homogeneous datasets for high-resolution visualization.

  15. Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning.

    Directory of Open Access Journals (Sweden)

    Jiayi Wu

    Full Text Available Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR in the image data, yet demand increased computational costs. Overcoming these limitations requires further development of clustering algorithms for high-performance cryo-EM data processing. Here we introduce an unsupervised single-particle clustering algorithm derived from a statistical manifold learning framework called generative topographic mapping (GTM. We show that unsupervised GTM clustering improves classification accuracy by about 40% in the absence of input references for data with lower SNRs. Applications to several experimental datasets suggest that our algorithm can detect subtle structural differences among classes via a hierarchical clustering strategy. After code optimization over a high-performance computing (HPC environment, our software implementation was able to generate thousands of reference-free class averages within hours in a massively parallel fashion, which allows a significant improvement on ab initio 3D reconstruction and assists in the computational purification of homogeneous datasets for high-resolution visualization.

  16. Parallel and interactive learning processes within the basal ganglia: relevance for the understanding of addiction.

    Science.gov (United States)

    Belin, David; Jonkman, Sietse; Dickinson, Anthony; Robbins, Trevor W; Everitt, Barry J

    2009-04-12

    In this review we discuss the evidence that drug addiction, defined as a maladaptive compulsive habit, results from the progressive subversion by addictive drugs of striatum-dependent operant and Pavlovian learning mechanisms that are usually involved in the control over behaviour by stimuli associated with natural reinforcement. Although mainly organized through segregated parallel cortico-striato-pallido-thalamo-cortical loops involved in motor or emotional functions, the basal ganglia, and especially the striatum, are key mediators of the modulation of behavioural responses, under the control of both action-outcome and stimulus-response mechanisms, by incentive motivational processes and Pavlovian associations. Here we suggest that protracted exposure to addictive drugs recruits serial and dopamine-dependent, striato-nigro-striatal ascending spirals from the nucleus accumbens to more dorsal regions of the striatum that underlie a shift from action-outcome to stimulus-response mechanisms in the control over drug seeking. When this progressive ventral to dorsal striatum shift is combined with drug-associated Pavlovian influences from limbic structures such as the amygdala and the orbitofrontal cortex, drug seeking behaviour becomes established as an incentive habit. This instantiation of implicit sub-cortical processing of drug-associated stimuli and instrumental responding might be a key mechanism underlying the development of compulsive drug seeking and the high vulnerability to relapse which are hallmarks of drug addiction.

  17. Reward-dependent learning in neuronal networks for planning and decision making.

    Science.gov (United States)

    Dehaene, S; Changeux, J P

    2000-01-01

    Neuronal network models have been proposed for the organization of evaluation and decision processes in prefrontal circuitry and their putative neuronal and molecular bases. The models all include an implementation and simulation of an elementary reward mechanism. Their central hypothesis is that tentative rules of behavior, which are coded by clusters of active neurons in prefrontal cortex, are selected or rejected based on an evaluation by this reward signal, which may be conveyed, for instance, by the mesencephalic dopaminergic neurons with which the prefrontal cortex is densely interconnected. At the molecular level, the reward signal is postulated to be a neurotransmitter such as dopamine, which exerts a global modulatory action on prefrontal synaptic efficacies, either via volume transmission or via targeted synaptic triads. Negative reinforcement has the effect of destabilizing the currently active rule-coding clusters; subsequently, spontaneous activity varies again from one cluster to another, giving the organism the chance to discover and learn a new rule. Thus, reward signals function as effective selection signals that either maintain or suppress currently active prefrontal representations as a function of their current adequacy. Simulations of this variation-selection have successfully accounted for the main features of several major tasks that depend on prefrontal cortex integrity, such as the delayed-response test, the Wisconsin card sorting test, the Tower of London test and the Stroop test. For the more complex tasks, we have found it necessary to supplement the external reward input with a second mechanism that supplies an internal reward; it consists of an auto-evaluation loop which short-circuits the reward input from the exterior. This allows for an internal evaluation of covert motor intentions without actualizing them as behaviors, by simply testing them covertly by comparison with memorized former experiences. This element of architecture

  18. On the sample complexity of learning for networks of spiking neurons with nonlinear synaptic interactions.

    Science.gov (United States)

    Schmitt, Michael

    2004-09-01

    We study networks of spiking neurons that use the timing of pulses to encode information. Nonlinear interactions model the spatial groupings of synapses on the neural dendrites and describe the computations performed at local branches. Within a theoretical framework of learning we analyze the question of how many training examples these networks must receive to be able to generalize well. Bounds for this sample complexity of learning can be obtained in terms of a combinatorial parameter known as the pseudodimension. This dimension characterizes the computational richness of a neural network and is given in terms of the number of network parameters. Two types of feedforward architectures are considered: constant-depth networks and networks of unconstrained depth. We derive asymptotically tight bounds for each of these network types. Constant depth networks are shown to have an almost linear pseudodimension, whereas the pseudodimension of general networks is quadratic. Networks of spiking neurons that use temporal coding are becoming increasingly more important in practical tasks such as computer vision, speech recognition, and motor control. The question of how well these networks generalize from a given set of training examples is a central issue for their successful application as adaptive systems. The results show that, although coding and computation in these networks is quite different and in many cases more powerful, their generalization capabilities are at least as good as those of traditional neural network models.

  19. Model-based iterative learning control of Parkinsonian state in thalamic relay neuron

    Science.gov (United States)

    Liu, Chen; Wang, Jiang; Li, Huiyan; Xue, Zhiqin; Deng, Bin; Wei, Xile

    2014-09-01

    Although the beneficial effects of chronic deep brain stimulation on Parkinson's disease motor symptoms are now largely confirmed, the underlying mechanisms behind deep brain stimulation remain unclear and under debate. Hence, the selection of stimulation parameters is full of challenges. Additionally, due to the complexity of neural system, together with omnipresent noises, the accurate model of thalamic relay neuron is unknown. Thus, the iterative learning control of the thalamic relay neuron's Parkinsonian state based on various variables is presented. Combining the iterative learning control with typical proportional-integral control algorithm, a novel and efficient control strategy is proposed, which does not require any particular knowledge on the detailed physiological characteristics of cortico-basal ganglia-thalamocortical loop and can automatically adjust the stimulation parameters. Simulation results demonstrate the feasibility of the proposed control strategy to restore the fidelity of thalamic relay in the Parkinsonian condition. Furthermore, through changing the important parameter—the maximum ionic conductance densities of low-threshold calcium current, the dominant characteristic of the proposed method which is independent of the accurate model can be further verified.

  20. Striatal and Tegmental Neurons Code Critical Signals for Temporal-Difference Learning of State Value in Domestic Chicks

    Directory of Open Access Journals (Sweden)

    Chentao Wen

    2016-11-01

    Full Text Available To ensure survival, animals must update the internal representations of their environment in a trial-and-error fashion. Psychological studies of associative learning and neurophysiological analyses of dopaminergic neurons have suggested that this updating process involves the temporal-difference (TD method in the basal ganglia network. However, the way in which the component variables of the TD method are implemented at the neuronal level is unclear. To investigate the underlying neural mechanisms, we trained domestic chicks to associate color cues with food rewards. We recorded neuronal activities from the medial striatum or tegmentum in a freely behaving condition and examined how reward omission changed neuronal firing. To compare neuronal activities with the signals assumed in the TD method, we simulated the behavioral task in the form of a finite sequence composed of discrete steps of time. The three signals assumed in the simulated task were the prediction signal, the target signal for updating, and the TD-error signal. In both the medial striatum and tegmentum, the majority of recorded neurons were categorized into three types according to their fitness for three models, though these neurons tended to form a continuum spectrum without distinct differences in the firing rate. Specifically, two types of striatal neurons successfully mimicked the target signal and the prediction signal. A linear summation of these two types of striatum neurons was a good fit for the activity of one type of tegmental neurons mimicking the TD-error signal. The present study thus demonstrates that the striatum and tegmentum can convey the signals critically required for the TD method. Based on the theoretical and neurophysiological studies, together with tract-tracing data, we propose a novel model to explain how the convergence of signals represented in the striatum could lead to the computation of TD error in tegmental dopaminergic neurons.

  1. Mirror neurons, procedural learning, and the positive new experience: a developmental systems self psychology approach.

    Science.gov (United States)

    Wolf, N S; Gales, M; Shane, E; Shane, M

    2000-01-01

    In summary, we are impressed with the existence of a mirror neuron system in the prefrontal cortex that serves as part of a complex neural network, including afferent and efferent connections to the limbic system, in particular the amygdala, in addition to the premotor and motor cortex. We think it is possible to arrive at an integration that postulates the mirror neuron system and its many types of associated multimodal neurons as contributing significantly to implicit procedural learning, a process that underlies a range of complex nonconscious, unconscious, preconscious and conscious cognitive activities, from playing musical instruments to character formation and traumatic configurations. This type of brain circuitry may establish an external coherence with developmental systems self psychology which implies that positive new experience is meliorative and that the intentional revival of old-old traumatic relational configurations might enhance maladaptive procedural patterns that would lead to the opposite of the intended beneficial change. When analysts revive traumatic transference patterns for the purpose of clarification and interpretation, they may fail to appreciate that such traumatic transference patterns make interpretation ineffective because, as we have stated above, the patient lacks self-reflection under such traumatic conditions. The continued plasticity and immediacy of the mirror neuron system can contribute to positive new experiences that promote the formation of new, adaptive, implicit-procedural patterns. Perhaps this broadened repertoire in the patient of ways of understanding interrelational events through the psychoanalytic process allows the less adaptive patterns ultimately to become vestigial and the newer, more adaptive patterns to emerge as dominant. Finally, as we have stated, we believe that the intentional transferential revival of trauma (i.e., the old-old relational configuration) may not contribute to therapeutic benefit. In

  2. An efficient implementation of a backpropagation learning algorithm on quadrics parallel supercomputer

    International Nuclear Information System (INIS)

    Taraglio, S.; Massaioli, F.

    1995-08-01

    A parallel implementation of a library to build and train Multi Layer Perceptrons via the Back Propagation algorithm is presented. The target machine is the SIMD massively parallel supercomputer Quadrics. Performance measures are provided on three different machines with different number of processors, for two network examples. A sample source code is given

  3. Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimized Implementations in the bnlearn R Package

    Directory of Open Access Journals (Sweden)

    Marco Scutari

    2017-03-01

    Full Text Available It is well known in the literature that the problem of learning the structure of Bayesian networks is very hard to tackle: Its computational complexity is super-exponential in the number of nodes in the worst case and polynomial in most real-world scenarios. Efficient implementations of score-based structure learning benefit from past and current research in optimization theory, which can be adapted to the task by using the network score as the objective function to maximize. This is not true for approaches based on conditional independence tests, called constraint-based learning algorithms. The only optimization in widespread use, backtracking, leverages the symmetries implied by the definitions of neighborhood and Markov blanket. In this paper we illustrate how backtracking is implemented in recent versions of the bnlearn R package, and how it degrades the stability of Bayesian network structure learning for little gain in terms of speed. As an alternative, we describe a software architecture and framework that can be used to parallelize constraint-based structure learning algorithms (also implemented in bnlearn and we demonstrate its performance using four reference networks and two real-world data sets from genetics and systems biology. We show that on modern multi-core or multiprocessor hardware parallel implementations are preferable over backtracking, which was developed when single-processor machines were the norm.

  4. Learning by statistical cooperation of self-interested neuron-like computing elements.

    Science.gov (United States)

    Barto, A G

    1985-01-01

    Since the usual approaches to cooperative computation in networks of neuron-like computating elements do not assume that network components have any "preferences", they do not make substantive contact with game theoretic concepts, despite their use of some of the same terminology. In the approach presented here, however, each network component, or adaptive element, is a self-interested agent that prefers some inputs over others and "works" toward obtaining the most highly preferred inputs. Here we describe an adaptive element that is robust enough to learn to cooperate with other elements like itself in order to further its self-interests. It is argued that some of the longstanding problems concerning adaptation and learning by networks might be solvable by this form of cooperativity, and computer simulation experiments are described that show how networks of self-interested components that are sufficiently robust can solve rather difficult learning problems. We then place the approach in its proper historical and theoretical perspective through comparison with a number of related algorithms. A secondary aim of this article is to suggest that beyond what is explicitly illustrated here, there is a wealth of ideas from game theory and allied disciplines such as mathematical economics that can be of use in thinking about cooperative computation in both nervous systems and man-made systems.

  5. Learning of Spatial Relationships between Observed and Imitated Actions allows Invariant Inverse Computation in the Frontal Mirror Neuron System

    Science.gov (United States)

    Oh, Hyuk; Gentili, Rodolphe J.; Reggia, James A.; Contreras-Vidal, José L.

    2014-01-01

    It has been suggested that the human mirror neuron system can facilitate learning by imitation through coupling of observation and action execution. During imitation of observed actions, the functional relationship between and within the inferior frontal cortex, the posterior parietal cortex, and the superior temporal sulcus can be modeled within the internal model framework. The proposed biologically plausible mirror neuron system model extends currently available models by explicitly modeling the intraparietal sulcus and the superior parietal lobule in implementing the function of a frame of reference transformation during imitation. Moreover, the model posits the ventral premotor cortex as performing an inverse computation. The simulations reveal that: i) the transformation system can learn and represent the changes in extrinsic to intrinsic coordinates when an imitator observes a demonstrator; ii) the inverse model of the imitator’s frontal mirror neuron system can be trained to provide the motor plans for the imitated actions. PMID:22255261

  6. Learning of spatial relationships between observed and imitated actions allows invariant inverse computation in the frontal mirror neuron system.

    Science.gov (United States)

    Oh, Hyuk; Gentili, Rodolphe J; Reggia, James A; Contreras-Vidal, José L

    2011-01-01

    It has been suggested that the human mirror neuron system can facilitate learning by imitation through coupling of observation and action execution. During imitation of observed actions, the functional relationship between and within the inferior frontal cortex, the posterior parietal cortex, and the superior temporal sulcus can be modeled within the internal model framework. The proposed biologically plausible mirror neuron system model extends currently available models by explicitly modeling the intraparietal sulcus and the superior parietal lobule in implementing the function of a frame of reference transformation during imitation. Moreover, the model posits the ventral premotor cortex as performing an inverse computation. The simulations reveal that: i) the transformation system can learn and represent the changes in extrinsic to intrinsic coordinates when an imitator observes a demonstrator; ii) the inverse model of the imitator's frontal mirror neuron system can be trained to provide the motor plans for the imitated actions.

  7. Learning-induced Dependence of Neuronal Activity in Primary Motor Cortex on Motor Task Condition.

    Science.gov (United States)

    Cai, X; Shimansky, Y; He, Jiping

    2005-01-01

    A brain-computer interface (BCI) system such as a cortically controlled robotic arm must have a capacity of adjusting its function to a specific environmental condition. We studied this capacity in non-human primates based on chronic multi-electrode recording from the primary motor cortex of a monkey during the animal's performance of a center-out 3D reaching task and adaptation to external force perturbations. The main condition-related feature of motor cortical activity observed before the onset of force perturbation was a phasic raise of activity immediately before the perturbation onset. This feature was observed during a series of perturbation trials, but were absent under no perturbations. After adaptation has been completed, it usually was taking the subject only one trial to recognize a change in the condition to switch the neuronal activity accordingly. These condition-dependent features of neuronal activity can be used by a BCI for recognizing a change in the environmental condition and making corresponding adjustments, which requires that the BCI-based control system possess such advanced properties of the neural motor control system as capacity to learn and adapt.

  8. Scalable, incremental learning with MapReduce parallelization for cell detection in high-resolution 3D microscopy data

    KAUST Repository

    Sung, Chul

    2013-08-01

    Accurate estimation of neuronal count and distribution is central to the understanding of the organization and layout of cortical maps in the brain, and changes in the cell population induced by brain disorders. High-throughput 3D microscopy techniques such as Knife-Edge Scanning Microscopy (KESM) are enabling whole-brain survey of neuronal distributions. Data from such techniques pose serious challenges to quantitative analysis due to the massive, growing, and sparsely labeled nature of the data. In this paper, we present a scalable, incremental learning algorithm for cell body detection that can address these issues. Our algorithm is computationally efficient (linear mapping, non-iterative) and does not require retraining (unlike gradient-based approaches) or retention of old raw data (unlike instance-based learning). We tested our algorithm on our rat brain Nissl data set, showing superior performance compared to an artificial neural network-based benchmark, and also demonstrated robust performance in a scenario where the data set is rapidly growing in size. Our algorithm is also highly parallelizable due to its incremental nature, and we demonstrated this empirically using a MapReduce-based implementation of the algorithm. We expect our scalable, incremental learning approach to be widely applicable to medical imaging domains where there is a constant flux of new data. © 2013 IEEE.

  9. Neuronal Nitric-Oxide Synthase Deficiency Impairs the Long-Term Memory of Olfactory Fear Learning and Increases Odor Generalization

    Science.gov (United States)

    Pavesi, Eloisa; Heldt, Scott A.; Fletcher, Max L.

    2013-01-01

    Experience-induced changes associated with odor learning are mediated by a number of signaling molecules, including nitric oxide (NO), which is predominantly synthesized by neuronal nitric oxide synthase (nNOS) in the brain. In the current study, we investigated the role of nNOS in the acquisition and retention of conditioned olfactory fear. Mice…

  10. A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models

    Directory of Open Access Journals (Sweden)

    Alexander eHanuschkin

    2013-06-01

    Full Text Available Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop a correlation-based theory of interactions between a sensory and a motor area. We show that a simple eligibility-weighted Hebbian learning rule, operating within a sensorimotor loop during motor explorations and stabilized by heterosynaptic competition, naturally gives rise to mirror neurons as well as control theoretic inverse models encoded in the synaptic weights from sensory to motor neurons. Crucially, we find that the correlational structure or stereotypy of the neural code underlying motor explorations determines the nature of the learned inverse model: Random motor codes lead to causal inverses that map sensory activity patterns to their motor causes; such inverses are maximally useful, they allow for imitating arbitrary sensory target sequences. By contrast, stereotyped motor codes lead to less useful predictive inverses that map sensory activity to future motor actions.Our theory generalizes previous work on inverse models by showing that such models can be learned in a simple Hebbian framework without the need for error signals or backpropagation, and it makes new conceptual connections between the causal nature of inverse models, the statistical structure of motor variability, and the time-lag between sensory and motor responses of mirror neurons. Applied to bird song learning, our theory can account for puzzling aspects of the song system, including necessity of sensorimotor gating and selectivity of auditory responses to bird’s own song

  11. A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models.

    Science.gov (United States)

    Hanuschkin, A; Ganguli, S; Hahnloser, R H R

    2013-01-01

    Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map-desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop a correlation-based theory of interactions between a sensory and a motor area. We show that a simple eligibility-weighted Hebbian learning rule, operating within a sensorimotor loop during motor explorations and stabilized by heterosynaptic competition, naturally gives rise to mirror neurons as well as control theoretic inverse models encoded in the synaptic weights from sensory to motor neurons. Crucially, we find that the correlational structure or stereotypy of the neural code underlying motor explorations determines the nature of the learned inverse model: random motor codes lead to causal inverses that map sensory activity patterns to their motor causes; such inverses are maximally useful, by allowing the imitation of arbitrary sensory target sequences. By contrast, stereotyped motor codes lead to less useful predictive inverses that map sensory activity to future motor actions. Our theory generalizes previous work on inverse models by showing that such models can be learned in a simple Hebbian framework without the need for error signals or backpropagation, and it makes new conceptual connections between the causal nature of inverse models, the statistical structure of motor variability, and the time-lag between sensory and motor responses of mirror neurons. Applied to bird song learning, our theory can account for puzzling aspects of the song system, including necessity of sensorimotor gating and selectivity of auditory responses to bird's own song (BOS) stimuli.

  12. Learning tasks as a possible treatment for DNA lesions induced by oxidative stress in hippocampal neurons

    Institute of Scientific and Technical Information of China (English)

    DragoCrneci; Radu Silaghi-Dumitrescu

    2013-01-01

    Reactive oxygen species have been implicated in conditions ranging from cardiovascular dysfunc-tion, arthritis, cancer, to aging and age-related disorders. The organism developed several path-ways to counteract these effects, with base excision repair being responsible for repairing one of the major base lesions (8-oxoG) in al organisms. Epidemiological evidence suggests that cognitive stimulation makes the brain more resilient to damage or degeneration. Recent studies have linked enriched environment to reduction of oxidative stressin neurons of mice with Alzheimer’s dis-ease-like disease, but given its complexity it is not clear what specific aspect of enriched environ-ment has therapeutic effects. Studies from molecular biology have shown that the protein p300, which is a transcription co-activator required for consolidation of memories during specific learning tasks, is at the same time involved in DNA replication and repair, playing a central role in the long-patch pathway of base excision repair. Based on the evidence, we propose that learning tasks such as novel object recognition could be tested as possible methods of base excision repair faci-litation, hence inducing DNA repair in the hippocampal neurons. If this method proves to be effective, it could be the start for designing similar tasks for humans, as a behavioral therapeutic complement to the classical drug-based therapy in treating neurodegenerative disorders. This review presents the current status of therapeutic methods used in treating neurodegenerative diseases induced by reactive oxygen species and proposes a new approach based on existing data.

  13. Compromised NMDA/Glutamate Receptor Expression in Dopaminergic Neurons Impairs Instrumental Learning, But Not Pavlovian Goal Tracking or Sign Tracking

    Science.gov (United States)

    James, Alex S; Pennington, Zachary T; Tran, Phu; Jentsch, James David

    2015-01-01

    Two theories regarding the role for dopamine neurons in learning include the concepts that their activity serves as a (1) mechanism that confers incentive salience onto rewards and associated cues and/or (2) contingency teaching signal reflecting reward prediction error. While both theories are provocative, the causal role for dopamine cell activity in either mechanism remains controversial. In this study mice that either fully or partially lacked NMDARs in dopamine neurons exclusively, as well as appropriate controls, were evaluated for reward-related learning; this experimental design allowed for a test of the premise that NMDA/glutamate receptor (NMDAR)-mediated mechanisms in dopamine neurons, including NMDA-dependent regulation of phasic discharge activity of these cells, modulate either the instrumental learning processes or the likelihood of pavlovian cues to become highly motivating incentive stimuli that directly attract behavior. Loss of NMDARs in dopamine neurons did not significantly affect baseline dopamine utilization in the striatum, novelty evoked locomotor behavior, or consumption of a freely available, palatable food solution. On the other hand, animals lacking NMDARs in dopamine cells exhibited a selective reduction in reinforced lever responses that emerged over the course of instrumental learning. Loss of receptor expression did not, however, influence the likelihood of an animal acquiring a pavlovian conditional response associated with attribution of incentive salience to reward-paired cues (sign tracking). These data support the view that reductions in NMDAR signaling in dopamine neurons affect instrumental reward-related learning but do not lend support to hypotheses that suggest that the behavioral significance of this signaling includes incentive salience attribution.

  14. Synaptic neurotransmission depression in ventral tegmental dopamine neurons and cannabinoid-associated addictive learning.

    Science.gov (United States)

    Liu, Zhiqiang; Han, Jing; Jia, Lintao; Maillet, Jean-Christian; Bai, Guang; Xu, Lin; Jia, Zhengping; Zheng, Qiaohua; Zhang, Wandong; Monette, Robert; Merali, Zul; Zhu, Zhou; Wang, Wei; Ren, Wei; Zhang, Xia

    2010-12-20

    Drug addiction is an association of compulsive drug use with long-term associative learning/memory. Multiple forms of learning/memory are primarily subserved by activity- or experience-dependent synaptic long-term potentiation (LTP) and long-term depression (LTD). Recent studies suggest LTP expression in locally activated glutamate synapses onto dopamine neurons (local Glu-DA synapses) of the midbrain ventral tegmental area (VTA) following a single or chronic exposure to many drugs of abuse, whereas a single exposure to cannabinoid did not significantly affect synaptic plasticity at these synapses. It is unknown whether chronic exposure of cannabis (marijuana or cannabinoids), the most commonly used illicit drug worldwide, induce LTP or LTD at these synapses. More importantly, whether such alterations in VTA synaptic plasticity causatively contribute to drug addictive behavior has not previously been addressed. Here we show in rats that chronic cannabinoid exposure activates VTA cannabinoid CB1 receptors to induce transient neurotransmission depression at VTA local Glu-DA synapses through activation of NMDA receptors and subsequent endocytosis of AMPA receptor GluR2 subunits. A GluR2-derived peptide blocks cannabinoid-induced VTA synaptic depression and conditioned place preference, i.e., learning to associate drug exposure with environmental cues. These data not only provide the first evidence, to our knowledge, that NMDA receptor-dependent synaptic depression at VTA dopamine circuitry requires GluR2 endocytosis, but also suggest an essential contribution of such synaptic depression to cannabinoid-associated addictive learning, in addition to pointing to novel pharmacological strategies for the treatment of cannabis addiction.

  15. Synaptic neurotransmission depression in ventral tegmental dopamine neurons and cannabinoid-associated addictive learning.

    Directory of Open Access Journals (Sweden)

    Zhiqiang Liu

    2010-12-01

    Full Text Available Drug addiction is an association of compulsive drug use with long-term associative learning/memory. Multiple forms of learning/memory are primarily subserved by activity- or experience-dependent synaptic long-term potentiation (LTP and long-term depression (LTD. Recent studies suggest LTP expression in locally activated glutamate synapses onto dopamine neurons (local Glu-DA synapses of the midbrain ventral tegmental area (VTA following a single or chronic exposure to many drugs of abuse, whereas a single exposure to cannabinoid did not significantly affect synaptic plasticity at these synapses. It is unknown whether chronic exposure of cannabis (marijuana or cannabinoids, the most commonly used illicit drug worldwide, induce LTP or LTD at these synapses. More importantly, whether such alterations in VTA synaptic plasticity causatively contribute to drug addictive behavior has not previously been addressed. Here we show in rats that chronic cannabinoid exposure activates VTA cannabinoid CB1 receptors to induce transient neurotransmission depression at VTA local Glu-DA synapses through activation of NMDA receptors and subsequent endocytosis of AMPA receptor GluR2 subunits. A GluR2-derived peptide blocks cannabinoid-induced VTA synaptic depression and conditioned place preference, i.e., learning to associate drug exposure with environmental cues. These data not only provide the first evidence, to our knowledge, that NMDA receptor-dependent synaptic depression at VTA dopamine circuitry requires GluR2 endocytosis, but also suggest an essential contribution of such synaptic depression to cannabinoid-associated addictive learning, in addition to pointing to novel pharmacological strategies for the treatment of cannabis addiction.

  16. Synaptic Neurotransmission Depression in Ventral Tegmental Dopamine Neurons and Cannabinoid-Associated Addictive Learning

    Science.gov (United States)

    Liu, Zhiqiang; Han, Jing; Jia, Lintao; Maillet, Jean-Christian; Bai, Guang; Xu, Lin; Jia, Zhengping; Zheng, Qiaohua; Zhang, Wandong; Monette, Robert; Merali, Zul; Zhu, Zhou; Wang, Wei; Ren, Wei; Zhang, Xia

    2010-01-01

    Drug addiction is an association of compulsive drug use with long-term associative learning/memory. Multiple forms of learning/memory are primarily subserved by activity- or experience-dependent synaptic long-term potentiation (LTP) and long-term depression (LTD). Recent studies suggest LTP expression in locally activated glutamate synapses onto dopamine neurons (local Glu-DA synapses) of the midbrain ventral tegmental area (VTA) following a single or chronic exposure to many drugs of abuse, whereas a single exposure to cannabinoid did not significantly affect synaptic plasticity at these synapses. It is unknown whether chronic exposure of cannabis (marijuana or cannabinoids), the most commonly used illicit drug worldwide, induce LTP or LTD at these synapses. More importantly, whether such alterations in VTA synaptic plasticity causatively contribute to drug addictive behavior has not previously been addressed. Here we show in rats that chronic cannabinoid exposure activates VTA cannabinoid CB1 receptors to induce transient neurotransmission depression at VTA local Glu-DA synapses through activation of NMDA receptors and subsequent endocytosis of AMPA receptor GluR2 subunits. A GluR2-derived peptide blocks cannabinoid-induced VTA synaptic depression and conditioned place preference, i.e., learning to associate drug exposure with environmental cues. These data not only provide the first evidence, to our knowledge, that NMDA receptor-dependent synaptic depression at VTA dopamine circuitry requires GluR2 endocytosis, but also suggest an essential contribution of such synaptic depression to cannabinoid-associated addictive learning, in addition to pointing to novel pharmacological strategies for the treatment of cannabis addiction. PMID:21187978

  17. Barrier Function-Based Neural Adaptive Control With Locally Weighted Learning and Finite Neuron Self-Growing Strategy.

    Science.gov (United States)

    Jia, Zi-Jun; Song, Yong-Duan

    2017-06-01

    This paper presents a new approach to construct neural adaptive control for uncertain nonaffine systems. By integrating locally weighted learning with barrier Lyapunov function (BLF), a novel control design method is presented to systematically address the two critical issues in neural network (NN) control field: one is how to fulfill the compact set precondition for NN approximation, and the other is how to use varying rather than a fixed NN structure to improve the functionality of NN control. A BLF is exploited to ensure the NN inputs to remain bounded during the entire system operation. To account for system nonlinearities, a neuron self-growing strategy is proposed to guide the process for adding new neurons to the system, resulting in a self-adjustable NN structure for better learning capabilities. It is shown that the number of neurons needed to accomplish the control task is finite, and better performance can be obtained with less number of neurons as compared with traditional methods. The salient feature of the proposed method also lies in the continuity of the control action everywhere. Furthermore, the resulting control action is smooth almost everywhere except for a few time instants at which new neurons are added. Numerical example illustrates the effectiveness of the proposed approach.

  18. Changes in Olfactory Sensory Neuron Physiology and Olfactory Perceptual Learning After Odorant Exposure in Adult Mice.

    Science.gov (United States)

    Kass, Marley D; Guang, Stephanie A; Moberly, Andrew H; McGann, John P

    2016-02-01

    The adult olfactory system undergoes experience-dependent plasticity to adapt to the olfactory environment. This plasticity may be accompanied by perceptual changes, including improved olfactory discrimination. Here, we assessed experience-dependent changes in the perception of a homologous aldehyde pair by testing mice in a cross-habituation/dishabituation behavioral paradigm before and after a week-long ester-odorant exposure protocol. In a parallel experiment, we used optical neurophysiology to observe neurotransmitter release from olfactory sensory neuron (OSN) terminals in vivo, and thus compared primary sensory representations of the aldehydes before and after the week-long ester-odorant exposure in individual animals. Mice could not discriminate between the aldehydes during pre-exposure testing, but ester-exposed subjects spontaneously discriminated between the homologous pair after exposure, whereas home cage control mice cross-habituated. Ester exposure did not alter the spatial pattern, peak magnitude, or odorant-selectivity of aldehyde-evoked OSN input to olfactory bulb glomeruli, but did alter the temporal dynamics of that input to make the time course of OSN input more dissimilar between odorants. Together, these findings demonstrate that odor exposure can induce both physiological and perceptual changes in odor processing, and suggest that changes in the temporal patterns of OSN input to olfactory bulb glomeruli could induce differences in odor quality. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  19. Special Issues on Learning Strategies: Parallels and Contrasts between Australian and Chinese Tertiary Education

    Science.gov (United States)

    Yao, Yuzuo

    2017-01-01

    Learning strategies are crucial to student learning in higher education. In this paper, there are comparisons of student engagement, feedback mechanism and workload arrangements at some typical universities in Australia and China, which are followed by practical suggestions for active learning. First, an inclusive class would allow learners from…

  20. Teaching and Learning: Highlighting the Parallels between Education and Participatory Evaluation.

    Science.gov (United States)

    Vanden Berk, Eric J.; Cassata, Jennifer Coyne; Moye, Melinda J.; Yarbrough, Donald B.; Siddens, Stephanie K.

    As an evaluation team trained in educational psychology and committed to participatory evaluation and its evolution, the researchers have found the parallel between evaluator-stakeholder roles in the participatory evaluation process and educator-student roles in educational psychology theory to be important. One advantage then is that the theories…

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

    Directory of Open Access Journals (Sweden)

    Ning eQiao

    2015-04-01

    Full Text Available Implementing compact, low-power artificial neural processing systems with real-time on-line learning abilities is still an open challenge. In this paper we present a full-custom mixed-signal VLSI device with neuromorphic learning circuits that emulate the biophysics of real spiking neurons and dynamic synapses for exploring the properties of computational neuroscience models and for building brain-inspired computing systems. The proposed architecture allows the on-chip configuration of a wide range of network connectivities, including recurrent and deep networks with short-term and long-term plasticity. The device comprises 128 K analog synapse and 256 neuron circuits with biologically plausible dynamics and bi-stable spike-based plasticity mechanisms that endow it with on-line learning abilities. In addition to the analog circuits, the device comprises also asynchronous digital logic circuits for setting different synapse and neuron properties as well as different network configurations. This prototype device, fabricated using a 180 nm 1P6M CMOS process, occupies an area of 51.4 mm 2 , and consumes approximately 4 mW for typical experiments, for example involving attractor networks. Here we describe the details of the overall architecture and of the individual circuits and present experimental results that showcase its potential. By supporting a wide range of cortical-like computational modules comprising plasticity mechanisms, this device will enable the realization of intelligent autonomous systems with on-line learning capabilities.

  2. Superior Generalization Capability of Hardware-Learing Algorithm Developed for Self-Learning Neuron-MOS Neural Networks

    Science.gov (United States)

    Kondo, Shuhei; Shibata, Tadashi; Ohmi, Tadahiro

    1995-02-01

    We have investigated the learning performance of the hardware backpropagation (HBP) algorithm, a hardware-oriented learning algorithm developed for the self-learning architecture of neural networks constructed using neuron MOS (metal-oxide-semiconductor) transistors. The solution to finding a mirror symmetry axis in a 4×4 binary pixel array was tested by computer simulation based on the HBP algorithm. Despite the inherent restrictions imposed on the hardware-learning algorithm, HBP exhibits equivalent learning performance to that of the original backpropagation (BP) algorithm when all the pertinent parameters are optimized. Very importantly, we have found that HBP has a superior generalization capability over BP; namely, HBP exhibits higher performance in solving problems that the network has not yet learnt.

  3. Newborn neurons in the olfactory bulb selected for long-term survival through olfactory learning are prematurely suppressed when the olfactory memory is erased.

    Science.gov (United States)

    Sultan, Sébastien; Rey, Nolwen; Sacquet, Joelle; Mandairon, Nathalie; Didier, Anne

    2011-10-19

    A role for newborn neurons in olfactory memory has been proposed based on learning-dependent modulation of olfactory bulb neurogenesis in adults. We hypothesized that if newborn neurons support memory, then they should be suppressed by memory erasure. Using an ecological approach in mice, we showed that behaviorally breaking a previously learned odor-reward association prematurely suppressed newborn neurons selected to survive during initial learning. Furthermore, intrabulbar infusions of the caspase pan-inhibitor ZVAD (benzyloxycarbonyl-Val-Ala-Asp) during the behavioral odor-reward extinction prevented newborn neurons death and erasure of the odor-reward association. Newborn neurons thus contribute to the bulbar network plasticity underlying long-term memory.

  4. The island model for parallel implementation of evolutionary algorithm of Population-Based Incremental Learning (PBIL) optimization

    International Nuclear Information System (INIS)

    Lima, Alan M.M. de; Schirru, Roberto

    2000-01-01

    Genetic algorithms are biologically motivated adaptive systems which have been used, with good results, for function optimization. The purpose of this work is to introduce a new parallelization method to be applied to the Population-Based Incremental Learning (PBIL) algorithm. PBIL combines standard genetic algorithm mechanisms with simple competitive learning and has ben successfully used in combinatorial optimization problems. The development of this algorithm aims its application to the reload optimization of PWR nuclear reactors. Tests have been performed with combinatorial optimization problems similar to the reload problem. Results are compared to the serial PBIL ones, showing the new method's superiority and its viability as a tool for the nuclear core reload problem solution. (author)

  5. Temporal sequence learning in winner-take-all networks of spiking neurons demonstrated in a brain-based device.

    Science.gov (United States)

    McKinstry, Jeffrey L; Edelman, Gerald M

    2013-01-01

    Animal behavior often involves a temporally ordered sequence of actions learned from experience. Here we describe simulations of interconnected networks of spiking neurons that learn to generate patterns of activity in correct temporal order. The simulation consists of large-scale networks of thousands of excitatory and inhibitory neurons that exhibit short-term synaptic plasticity and spike-timing dependent synaptic plasticity. The neural architecture within each area is arranged to evoke winner-take-all (WTA) patterns of neural activity that persist for tens of milliseconds. In order to generate and switch between consecutive firing patterns in correct temporal order, a reentrant exchange of signals between these areas was necessary. To demonstrate the capacity of this arrangement, we used the simulation to train a brain-based device responding to visual input by autonomously generating temporal sequences of motor actions.

  6. Open Source Parallel Image Analysis and Machine Learning Pipeline, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — Continuum Analytics proposes a Python-based open-source data analysis machine learning pipeline toolkit for satellite data processing, weather and climate data...

  7. The advantage of flexible neuronal tunings in neural network models for motor learning

    Directory of Open Access Journals (Sweden)

    Ellisha N Marongelli

    2013-07-01

    Full Text Available Human motor adaptation to novel environments is often modeled by a basis function network that transforms desired movement properties into estimated forces. This network employs a layer of nodes that have fixed broad tunings that generalize across the input domain. Learning is achieved by updating the weights of these nodes in response to training experience. This conventional model is unable to account for rapid flexibility observed in human spatial generalization during motor adaptation. However, added plasticity in the breadths of the basis function tunings can achieve this flexibility, and several neurophysiological experiments have revealed flexibility in tunings of sensorimotor neurons. We found a model, Locally Weighted Projection Regression (LWPR, which uniquely possesses the structure of a basis function network in which both the weights and tuning widths of the nodes are updated incrementally during adaptation. We presented this LWPR model with training functions of different spatial complexities and monitored incremental updates to receptive field sizes. An inverse pattern of dependence of receptive field adaptation on experienced error became evident, underlying both a relationship between generalization and complexity, and a unique behavior in which generalization always narrows after a sudden switch in environmental complexity. These results implicate a model with a flexible structure, like LWPR, as a viable alternative model for human motor adaptation that can account for previously observed plasticity in spatial generalization. This theory can be tested by using the behaviors observed in our experiments as novel hypotheses in human studies.

  8. The advantage of flexible neuronal tunings in neural network models for motor learning

    Science.gov (United States)

    Marongelli, Ellisha N.; Thoroughman, Kurt A.

    2013-01-01

    Human motor adaptation to novel environments is often modeled by a basis function network that transforms desired movement properties into estimated forces. This network employs a layer of nodes that have fixed broad tunings that generalize across the input domain. Learning is achieved by updating the weights of these nodes in response to training experience. This conventional model is unable to account for rapid flexibility observed in human spatial generalization during motor adaptation. However, added plasticity in the widths of the basis function tunings can achieve this flexibility, and several neurophysiological experiments have revealed flexibility in tunings of sensorimotor neurons. We found a model, Locally Weighted Projection Regression (LWPR), which uniquely possesses the structure of a basis function network in which both the weights and tuning widths of the nodes are updated incrementally during adaptation. We presented this LWPR model with training functions of different spatial complexities and monitored incremental updates to receptive field widths. An inverse pattern of dependence of receptive field adaptation on experienced error became evident, underlying both a relationship between generalization and complexity, and a unique behavior in which generalization always narrows after a sudden switch in environmental complexity. These results implicate a model that is flexible in both basis function widths and weights, like LWPR, as a viable alternative model for human motor adaptation that can account for previously observed plasticity in spatial generalization. This theory can be tested by using the behaviors observed in our experiments as novel hypotheses in human studies. PMID:23888141

  9. The Growth of m-Learning and the Growth of Mobile Computing: Parallel developments

    Directory of Open Access Journals (Sweden)

    Jason G. Caudill

    2007-06-01

    Full Text Available m-Learning is made possible by the existence and application of mobile hardware and networking technology. By exploring the capabilities of these technologies, it is possible to construct a picture of how different components of m-Learning can be implemented. This paper will explore the major technologies currently in use: portable digital assistants (PDAs, Short Message Service (SMS messaging via mobile phone, and podcasts via MP3 players.

  10. Top-down inputs enhance orientation selectivity in neurons of the primary visual cortex during perceptual learning.

    Directory of Open Access Journals (Sweden)

    Samat Moldakarimov

    2014-08-01

    Full Text Available Perceptual learning has been used to probe the mechanisms of cortical plasticity in the adult brain. Feedback projections are ubiquitous in the cortex, but little is known about their role in cortical plasticity. Here we explore the hypothesis that learning visual orientation discrimination involves learning-dependent plasticity of top-down feedback inputs from higher cortical areas, serving a different function from plasticity due to changes in recurrent connections within a cortical area. In a Hodgkin-Huxley-based spiking neural network model of visual cortex, we show that modulation of feedback inputs to V1 from higher cortical areas results in shunting inhibition in V1 neurons, which changes the response properties of V1 neurons. The orientation selectivity of V1 neurons is enhanced without changing orientation preference, preserving the topographic organizations in V1. These results provide new insights to the mechanisms of plasticity in the adult brain, reconciling apparently inconsistent experiments and providing a new hypothesis for a functional role of the feedback connections.

  11. Consolidation of an olfactory memory trace in the olfactory bulb is required for learning-induced survival of adult-born neurons and long-term memory.

    Directory of Open Access Journals (Sweden)

    Florence Kermen

    Full Text Available BACKGROUND: It has recently been proposed that adult-born neurons in the olfactory bulb, whose survival is modulated by learning, support long-term olfactory memory. However, the mechanism used to select which adult-born neurons following learning will participate in the long-term retention of olfactory information is unknown. We addressed this question by investigating the effect of bulbar consolidation of olfactory learning on memory and neurogenesis. METHODOLOGY/PRINCIPAL FINDINGS: Initially, we used a behavioral ecological approach using adult mice to assess the impact of consolidation on neurogenesis. Using learning paradigms in which consolidation time was varied, we showed that a spaced (across days, but not a massed (within day, learning paradigm increased survival of adult-born neurons and allowed long-term retention of the task. Subsequently, we used a pharmacological approach to block consolidation in the olfactory bulb, consisting in intrabulbar infusion of the protein synthesis inhibitor anisomycin, and found impaired learning and no increase in neurogenesis, while basic olfactory processing and the basal rate of adult-born neuron survival remained unaffected. Taken together these data indicate that survival of adult-born neurons during learning depends on consolidation processes taking place in the olfactory bulb. CONCLUSION/SIGNIFICANCE: We can thus propose a model in which consolidation processes in the olfactory bulb determine both survival of adult-born neurons and long-term olfactory memory. The finding that adult-born neuron survival during olfactory learning is governed by consolidation in the olfactory bulb strongly argues in favor of a role for bulbar adult-born neurons in supporting olfactory memory.

  12. Consolidation of an olfactory memory trace in the olfactory bulb is required for learning-induced survival of adult-born neurons and long-term memory.

    Science.gov (United States)

    Kermen, Florence; Sultan, Sébastien; Sacquet, Joëlle; Mandairon, Nathalie; Didier, Anne

    2010-08-13

    It has recently been proposed that adult-born neurons in the olfactory bulb, whose survival is modulated by learning, support long-term olfactory memory. However, the mechanism used to select which adult-born neurons following learning will participate in the long-term retention of olfactory information is unknown. We addressed this question by investigating the effect of bulbar consolidation of olfactory learning on memory and neurogenesis. Initially, we used a behavioral ecological approach using adult mice to assess the impact of consolidation on neurogenesis. Using learning paradigms in which consolidation time was varied, we showed that a spaced (across days), but not a massed (within day), learning paradigm increased survival of adult-born neurons and allowed long-term retention of the task. Subsequently, we used a pharmacological approach to block consolidation in the olfactory bulb, consisting in intrabulbar infusion of the protein synthesis inhibitor anisomycin, and found impaired learning and no increase in neurogenesis, while basic olfactory processing and the basal rate of adult-born neuron survival remained unaffected. Taken together these data indicate that survival of adult-born neurons during learning depends on consolidation processes taking place in the olfactory bulb. We can thus propose a model in which consolidation processes in the olfactory bulb determine both survival of adult-born neurons and long-term olfactory memory. The finding that adult-born neuron survival during olfactory learning is governed by consolidation in the olfactory bulb strongly argues in favor of a role for bulbar adult-born neurons in supporting olfactory memory.

  13. Birth of projection neurons in adult avian brain may be related to perceptual or motor learning

    International Nuclear Information System (INIS)

    Alvarez-Buylla, A.; Kirn, J.R.; Nottebohm, F.

    1990-01-01

    Projection neurons that form part of the motor pathway for song control continue to be produced and to replace older projection neurons in adult canaries and zebra finches. This is shown by combining [3H]thymidine, a cell birth marker, and fluorogold, a retrogradely transported tracer of neuronal connectivity. Species and seasonal comparisons suggest that this process is related to the acquisition of perceptual or motor memories. The ability of an adult brain to produce and replace projection neurons should influence our thinking on brain repair

  14. A Scalable Weight-Free Learning Algorithm for Regulatory Control of Cell Activity in Spiking Neuronal Networks.

    Science.gov (United States)

    Zhang, Xu; Foderaro, Greg; Henriquez, Craig; Ferrari, Silvia

    2018-03-01

    Recent developments in neural stimulation and recording technologies are providing scientists with the ability of recording and controlling the activity of individual neurons in vitro or in vivo, with very high spatial and temporal resolution. Tools such as optogenetics, for example, are having a significant impact in the neuroscience field by delivering optical firing control with the precision and spatiotemporal resolution required for investigating information processing and plasticity in biological brains. While a number of training algorithms have been developed to date for spiking neural network (SNN) models of biological neuronal circuits, exiting methods rely on learning rules that adjust the synaptic strengths (or weights) directly, in order to obtain the desired network-level (or functional-level) performance. As such, they are not applicable to modifying plasticity in biological neuronal circuits, in which synaptic strengths only change as a result of pre- and post-synaptic neuron firings or biological mechanisms beyond our control. This paper presents a weight-free training algorithm that relies solely on adjusting the spatiotemporal delivery of neuron firings in order to optimize the network performance. The proposed weight-free algorithm does not require any knowledge of the SNN model or its plasticity mechanisms. As a result, this training approach is potentially realizable in vitro or in vivo via neural stimulation and recording technologies, such as optogenetics and multielectrode arrays, and could be utilized to control plasticity at multiple scales of biological neuronal circuits. The approach is demonstrated by training SNNs with hundreds of units to control a virtual insect navigating in an unknown environment.

  15. Associative learning is necessary but not sufficient for mirror neuron development

    OpenAIRE

    Bonaiuto, James

    2014-01-01

    Existing computational models of the mirror system demonstrate the additional circuitry needed for mirror neurons to display the range of properties that they exhibit. Such models emphasize the need for existing connectivity to form visuomotor associations, processing to reduce the space of possible inputs, and demonstrate the role neurons with mirror properties might play in monitoring one's own actions.

  16. Associative learning is necessary but not sufficient for mirror neuron development.

    Science.gov (United States)

    Bonaiuto, James

    2014-04-01

    Existing computational models of the mirror system demonstrate the additional circuitry needed for mirror neurons to display the range of properties that they exhibit. Such models emphasize the need for existing connectivity to form visuomotor associations, processing to reduce the space of possible inputs, and demonstrate the role neurons with mirror properties might play in monitoring one's own actions.

  17. Hydrocephalus compacted cortex and hippocampus and altered their output neurons in association with spatial learning and memory deficits in rats.

    Science.gov (United States)

    Chen, Li-Jin; Wang, Yueh-Jan; Chen, Jeng-Rung; Tseng, Guo-Fang

    2017-07-01

    Hydrocephalus is a common neurological disorder in children characterized by abnormal dilation of cerebral ventricles as a result of the impairment of cerebrospinal fluid flow or absorption. Clinical presentation of hydrocephalus varies with chronicity and often shows cognitive dysfunction. Here we used a kaolin-induction method in rats and studied the effects of hydrocephalus on cerebral cortex and hippocampus, the two regions highly related to cognition. Hydrocephalus impaired rats' performance in Morris water maze task. Serial three-dimensional reconstruction from sections of the whole brain freshly froze in situ with skull shows that the volumes of both structures were reduced. Morphologically, pyramidal neurons of the somatosensory cortex and hippocampus appear to be distorted. Intracellular dye injection and subsequent three-dimensional reconstruction and analyses revealed that the dendritic arbors of layer III and V cortical pyramid neurons were reduced. The total dendritic length of CA1, but not CA3, pyramidal neurons was also reduced. Dendritic spine densities on both cortical and hippocampal pyramidal neurons were decreased, consistent with our concomitant findings that the expressions of both synaptophysin and postsynaptic density protein 95 were reduced. These cortical and hippocampal changes suggest reductions of excitatory connectivity, which could underlie the learning and memory deficits in hydrocephalus. © 2016 International Society of Neuropathology.

  18. Learning-Induced Gene Expression in the Hippocampus Reveals a Role of Neuron -Astrocyte Metabolic Coupling in Long Term Memory

    KAUST Repository

    Tadi, Monika; Allaman, Igor; Lengacher, Sylvain; Grenningloh, Gabriele; Magistretti, Pierre J.

    2015-01-01

    We examined the expression of genes related to brain energy metabolism and particularly those encoding glia (astrocyte)-specific functions in the dorsal hippocampus subsequent to learning. Context-dependent avoidance behavior was tested in mice using the step-through Inhibitory Avoidance (IA) paradigm. Animals were sacrificed 3, 9, 24, or 72 hours after training or 3 hours after retention testing. The quantitative determination of mRNA levels revealed learning-induced changes in the expression of genes thought to be involved in astrocyte-neuron metabolic coupling in a time dependent manner. Twenty four hours following IA training, an enhanced gene expression was seen, particularly for genes encoding monocarboxylate transporters 1 and 4 (MCT1, MCT4), alpha2 subunit of the Na/K-ATPase and glucose transporter type 1. To assess the functional role for one of these genes in learning, we studied MCT1 deficient mice and found that they exhibit impaired memory in the inhibitory avoidance task. Together, these observations indicate that neuron-glia metabolic coupling undergoes metabolic adaptations following learning as indicated by the change in expression of key metabolic genes.

  19. Learning-Induced Gene Expression in the Hippocampus Reveals a Role of Neuron -Astrocyte Metabolic Coupling in Long Term Memory.

    Directory of Open Access Journals (Sweden)

    Monika Tadi

    Full Text Available We examined the expression of genes related to brain energy metabolism and particularly those encoding glia (astrocyte-specific functions in the dorsal hippocampus subsequent to learning. Context-dependent avoidance behavior was tested in mice using the step-through Inhibitory Avoidance (IA paradigm. Animals were sacrificed 3, 9, 24, or 72 hours after training or 3 hours after retention testing. The quantitative determination of mRNA levels revealed learning-induced changes in the expression of genes thought to be involved in astrocyte-neuron metabolic coupling in a time dependent manner. Twenty four hours following IA training, an enhanced gene expression was seen, particularly for genes encoding monocarboxylate transporters 1 and 4 (MCT1, MCT4, alpha2 subunit of the Na/K-ATPase and glucose transporter type 1. To assess the functional role for one of these genes in learning, we studied MCT1 deficient mice and found that they exhibit impaired memory in the inhibitory avoidance task. Together, these observations indicate that neuron-glia metabolic coupling undergoes metabolic adaptations following learning as indicated by the change in expression of key metabolic genes.

  20. Learning-Induced Gene Expression in the Hippocampus Reveals a Role of Neuron -Astrocyte Metabolic Coupling in Long Term Memory

    KAUST Repository

    Tadi, Monika

    2015-10-29

    We examined the expression of genes related to brain energy metabolism and particularly those encoding glia (astrocyte)-specific functions in the dorsal hippocampus subsequent to learning. Context-dependent avoidance behavior was tested in mice using the step-through Inhibitory Avoidance (IA) paradigm. Animals were sacrificed 3, 9, 24, or 72 hours after training or 3 hours after retention testing. The quantitative determination of mRNA levels revealed learning-induced changes in the expression of genes thought to be involved in astrocyte-neuron metabolic coupling in a time dependent manner. Twenty four hours following IA training, an enhanced gene expression was seen, particularly for genes encoding monocarboxylate transporters 1 and 4 (MCT1, MCT4), alpha2 subunit of the Na/K-ATPase and glucose transporter type 1. To assess the functional role for one of these genes in learning, we studied MCT1 deficient mice and found that they exhibit impaired memory in the inhibitory avoidance task. Together, these observations indicate that neuron-glia metabolic coupling undergoes metabolic adaptations following learning as indicated by the change in expression of key metabolic genes.

  1. Parallel R

    CERN Document Server

    McCallum, Ethan

    2011-01-01

    It's tough to argue with R as a high-quality, cross-platform, open source statistical software product-unless you're in the business of crunching Big Data. This concise book introduces you to several strategies for using R to analyze large datasets. You'll learn the basics of Snow, Multicore, Parallel, and some Hadoop-related tools, including how to find them, how to use them, when they work well, and when they don't. With these packages, you can overcome R's single-threaded nature by spreading work across multiple CPUs, or offloading work to multiple machines to address R's memory barrier.

  2. A natural form of learning can increase and decrease the survival of new neurons in the dentate gyrus.

    Science.gov (United States)

    Olariu, Ana; Cleaver, Kathryn M; Shore, Lauren E; Brewer, Michelle D; Cameron, Heather A

    2005-01-01

    Granule cells born in the adult dentate gyrus undergo a 4-week developmental period characterized by high susceptibility to cell death. Two forms of hippocampus-dependent learning have been shown to rescue many of the new neurons during this critical period. Here, we show that a natural form of associative learning, social transmission of food preference (STFP), can either increase or decrease the survival of young granule cells in adult rats. Increased numbers of pyknotic as well as phospho-Akt-expressing BrdU-labeled cells were seen 1 day after STFP training, indicating that training rapidly induces both cell death and active suppression of cell death in different subsets. A single day of training for STFP increased the survival of 8-day-old BrdU-labeled cells when examined 1 week later. In contrast, 2 days of training decreased the survival of BrdU-labeled cells and the density of immature neurons, identified with crmp-4. This change from increased to decreased survival could not be accounted for by the ages of the cells. Instead, we propose that training may initially increase young granule cell survival, then, if continued, cause them to die. This complex regulation of cell death could potentially serve to maintain granule cells that are actively involved in memory consolidation, while rapidly using and discarding young granule cells whose training is complete to make space for new naïve neurons. Published 2005 Wiley-Liss, Inc.

  3. Effect of Cistanche Desertica Polysaccharides on Learning and Memory Functions and Ultrastructure of Cerebral Neurons in Experimental Aging Mice

    Institute of Scientific and Technical Information of China (English)

    孙云; 邓杨梅; 王德俊; 沈春锋; 刘晓梅; 张洪泉

    2001-01-01

    To observe the effects of Cistanche desertica polysaccharides (CDP) on the learning and memory functions and cerebral ultrastructure in experimental aging mice. Methods: CDP was administrated intragastrically 50 or 100 mg/kg per day for 64 successive days to experimental aging model mice induced by D-galactose, then the learning and memory functions of mice were estimated by step-down test and Y-maze test; organelles of brain tissue and cerebral ultrastructure were observed by transmission electron microscope and physical strength was determined by swimming test. Results: CDP could obviously enhance the learning and memory functions (P<0.01) and prolong the swimming time (P<0.05), decrease the number of lipofuscin and slow down the degeneration of mitochondria in neurons(P<0.05), and improve the degeneration of cerebral ultra-structure in aging mice. Conclusion: CDP could improve the impaired physiological function and alleviate cerebral morphological change in experimental aging mice.

  4. Rivastigmine lowers Aβ and increases sAPPα levels, which parallel elevated synaptic markers and metabolic activity in degenerating primary rat neurons.

    Directory of Open Access Journals (Sweden)

    Jason A Bailey

    Full Text Available Overproduction of amyloid-β (Aβ protein in the brain has been hypothesized as the primary toxic insult that, via numerous mechanisms, produces cognitive deficits in Alzheimer's disease (AD. Cholinesterase inhibition is a primary strategy for treatment of AD, and specific compounds of this class have previously been demonstrated to influence Aβ precursor protein (APP processing and Aβ production. However, little information is available on the effects of rivastigmine, a dual acetylcholinesterase and butyrylcholinesterase inhibitor, on APP processing. As this drug is currently used to treat AD, characterization of its various activities is important to optimize its clinical utility. We have previously shown that rivastigmine can preserve or enhance neuronal and synaptic terminal markers in degenerating primary embryonic cerebrocortical cultures. Given previous reports on the effects of APP and Aβ on synapses, regulation of APP processing represents a plausible mechanism for the synaptic effects of rivastigmine. To test this hypothesis, we treated degenerating primary cultures with rivastigmine and measured secreted APP (sAPP and Aβ. Rivastigmine treatment increased metabolic activity in these cultured cells, and elevated APP secretion. Analysis of the two major forms of APP secreted by these cultures, attributed to neurons or glia based on molecular weight showed that rivastigmine treatment significantly increased neuronal relative to glial secreted APP. Furthermore, rivastigmine treatment increased α-secretase cleaved sAPPα and decreased Aβ secretion, suggesting a therapeutic mechanism wherein rivastigmine alters the relative activities of the secretase pathways. Assessment of sAPP levels in rodent CSF following once daily rivastigmine administration for 21 days confirmed that elevated levels of APP in cell culture translated in vivo. Taken together, rivastigmine treatment enhances neuronal sAPP and shifts APP processing toward the

  5. Neuronal representations of stimulus associations develop in the temporal lobe during learning

    OpenAIRE

    Messinger, Adam; Squire, Larry R.; Zola, Stuart M.; Albright, Thomas D.

    2001-01-01

    Visual stimuli that are frequently seen together become associated in long-term memory, such that the sight of one stimulus readily brings to mind the thought or image of the other. It has been hypothesized that acquisition of such long-term associative memories proceeds via the strengthening of connections between neurons representing the associated stimuli, such that a neuron initially responding only to one stimulus of an associated pair eventually comes to respond to both. Consistent with...

  6. Military Curricula for Vocational & Technical Education. Basic Electricity and Electronics Individualized Learning System. CANTRAC A-100-0010. Module Fourteen: Parallel AC Resistive-Reactive Circuits. Study Booklet.

    Science.gov (United States)

    Chief of Naval Education and Training Support, Pensacola, FL.

    This individualized learning module on parallel alternating current resistive-reaction circuits is one in a series of modules for a course in basic electricity and electronics. The course is one of a number of military-developed curriculum packages selected for adaptation to vocational instructional and curriculum development in a civilian…

  7. Military Curricula for Vocational & Technical Education. Basic Electricity and Electronics Individualized Learning System. CANTRAC A-100-0010. Module Six: Parallel Circuits. Study Booklet.

    Science.gov (United States)

    Chief of Naval Education and Training Support, Pensacola, FL.

    This individualized learning module on parallel circuits is one in a series of modules for a course in basic electricity and electronics. The course is one of a number of military-developed curriculum packages selected for adaptation to vocational instructional and curriculum development in a civilian setting. Four lessons are included in the…

  8. Multichannel microformulators for massively parallel machine learning and automated design of biological experiments

    Science.gov (United States)

    Wikswo, John; Kolli, Aditya; Shankaran, Harish; Wagoner, Matthew; Mettetal, Jerome; Reiserer, Ronald; Gerken, Gregory; Britt, Clayton; Schaffer, David

    Genetic, proteomic, and metabolic networks describing biological signaling can have 102 to 103 nodes. Transcriptomics and mass spectrometry can quantify 104 different dynamical experimental variables recorded from in vitro experiments with a time resolution approaching 1 s. It is difficult to infer metabolic and signaling models from such massive data sets, and it is unlikely that causality can be determined simply from observed temporal correlations. There is a need to design and apply specific system perturbations, which will be difficult to perform manually with 10 to 102 externally controlled variables. Machine learning and optimal experimental design can select an experiment that best discriminates between multiple conflicting models, but a remaining problem is to control in real time multiple variables in the form of concentrations of growth factors, toxins, nutrients and other signaling molecules. With time-division multiplexing, a microfluidic MicroFormulator (μF) can create in real time complex mixtures of reagents in volumes suitable for biological experiments. Initial 96-channel μF implementations control the exposure profile of cells in a 96-well plate to different temporal profiles of drugs; future experiments will include challenge compounds. Funded in part by AstraZeneca, NIH/NCATS HHSN271201600009C and UH3TR000491, and VIIBRE.

  9. Resistor Combinations for Parallel Circuits.

    Science.gov (United States)

    McTernan, James P.

    1978-01-01

    To help simplify both teaching and learning of parallel circuits, a high school electricity/electronics teacher presents and illustrates the use of tables of values for parallel resistive circuits in which total resistances are whole numbers. (MF)

  10. Teaching and learning the Hodgkin-Huxley model based on software developed in NEURON's programming language hoc.

    Science.gov (United States)

    Hernández, Oscar E; Zurek, Eduardo E

    2013-05-15

    We present a software tool called SENB, which allows the geometric and biophysical neuronal properties in a simple computational model of a Hodgkin-Huxley (HH) axon to be changed. The aim of this work is to develop a didactic and easy-to-use computational tool in the NEURON simulation environment, which allows graphical visualization of both the passive and active conduction parameters and the geometric characteristics of a cylindrical axon with HH properties. The SENB software offers several advantages for teaching and learning electrophysiology. First, SENB offers ease and flexibility in determining the number of stimuli. Second, SENB allows immediate and simultaneous visualization, in the same window and time frame, of the evolution of the electrophysiological variables. Third, SENB calculates parameters such as time and space constants, stimuli frequency, cellular area and volume, sodium and potassium equilibrium potentials, and propagation velocity of the action potentials. Furthermore, it allows the user to see all this information immediately in the main window. Finally, with just one click SENB can save an image of the main window as evidence. The SENB software is didactic and versatile, and can be used to improve and facilitate the teaching and learning of the underlying mechanisms in the electrical activity of an axon using the biophysical properties of the squid giant axon.

  11. Relationship between neuronal network architecture and naming performance in temporal lobe epilepsy: A connectome based approach using machine learning.

    Science.gov (United States)

    Munsell, B C; Wu, G; Fridriksson, J; Thayer, K; Mofrad, N; Desisto, N; Shen, D; Bonilha, L

    2017-09-09

    Impaired confrontation naming is a common symptom of temporal lobe epilepsy (TLE). The neurobiological mechanisms underlying this impairment are poorly understood but may indicate a structural disorganization of broadly distributed neuronal networks that support naming ability. Importantly, naming is frequently impaired in other neurological disorders and by contrasting the neuronal structures supporting naming in TLE with other diseases, it will become possible to elucidate the common systems supporting naming. We aimed to evaluate the neuronal networks that support naming in TLE by using a machine learning algorithm intended to predict naming performance in subjects with medication refractory TLE using only the structural brain connectome reconstructed from diffusion tensor imaging. A connectome-based prediction framework was developed using network properties from anatomically defined brain regions across the entire brain, which were used in a multi-task machine learning algorithm followed by support vector regression. Nodal eigenvector centrality, a measure of regional network integration, predicted approximately 60% of the variance in naming. The nodes with the highest regression weight were bilaterally distributed among perilimbic sub-networks involving mainly the medial and lateral temporal lobe regions. In the context of emerging evidence regarding the role of large structural networks that support language processing, our results suggest intact naming relies on the integration of sub-networks, as opposed to being dependent on isolated brain areas. In the case of TLE, these sub-networks may be disproportionately indicative naming processes that are dependent semantic integration from memory and lexical retrieval, as opposed to multi-modal perception or motor speech production. Copyright © 2017. Published by Elsevier Inc.

  12. Noradrenergic control of gene expression and long-term neuronal adaptation evoked by learned vocalizations in songbirds.

    Directory of Open Access Journals (Sweden)

    Tarciso A F Velho

    Full Text Available Norepinephrine (NE is thought to play important roles in the consolidation and retrieval of long-term memories, but its role in the processing and memorization of complex acoustic signals used for vocal communication has yet to be determined. We have used a combination of gene expression analysis, electrophysiological recordings and pharmacological manipulations in zebra finches to examine the role of noradrenergic transmission in the brain's response to birdsong, a learned vocal behavior that shares important features with human speech. We show that noradrenergic transmission is required for both the expression of activity-dependent genes and the long-term maintenance of stimulus-specific electrophysiological adaptation that are induced in central auditory neurons by stimulation with birdsong. Specifically, we show that the caudomedial nidopallium (NCM, an area directly involved in the auditory processing and memorization of birdsong, receives strong noradrenergic innervation. Song-responsive neurons in this area express α-adrenergic receptors and are in close proximity to noradrenergic terminals. We further show that local α-adrenergic antagonism interferes with song-induced gene expression, without affecting spontaneous or evoked electrophysiological activity, thus dissociating the molecular and electrophysiological responses to song. Moreover, α-adrenergic antagonism disrupts the maintenance but not the acquisition of the adapted physiological state. We suggest that the noradrenergic system regulates long-term changes in song-responsive neurons by modulating the gene expression response that is associated with the electrophysiological activation triggered by song. We also suggest that this mechanism may be an important contributor to long-term auditory memories of learned vocalizations.

  13. The Stressed Female Brain: Neuronal activity in the prelimbic but not infralimbic region of the medial prefrontal cortex suppresses learning after acute stress

    Directory of Open Access Journals (Sweden)

    Lisa Y. Maeng

    2013-12-01

    Full Text Available Women are nearly twice as likely as men to suffer from anxiety and post-traumatic stress disorder (PTSD, indicating that many females are especially vulnerable to stressful life experience. A profound sex difference in the response to stress is also observed in laboratory animals. Acute exposure to an uncontrollable stressful event disrupts associative learning during classical eyeblink conditioning in female rats but enhances this same type of learning process in males. These sex differences in response to stress are dependent on neuronal activity in similar but also different brain regions. Neuronal activity in the basolateral nucleus of the amygdala (BLA is necessary in both males and females. However, neuronal activity in the medial prefrontal cortex (mPFC during the stressor is necessary to modify learning in females but not in males. The mPFC is often divided into its prelimbic (PL and infralimbic (IL subregions, which differ both in structure and function. Through its connections to the BLA, we hypothesized that neuronal activity within the PL, but not IL, during the stressor is necessary to suppress learning in females. To test this hypothesis, either the PL or IL of adult female rats was bilaterally inactivated with GABAA agonist muscimol during acute inescapable swim stress. 24h later, all subjects were trained with classical eyeblink conditioning. Though stressed, females without neuronal activity in the PL learned well. In contrast, females with IL inactivation during the stressor did not learn well, behaving similar to stressed vehicle-treated females. These data suggest that exposure to a stressful event critically engages the PL, but not IL, to disrupt associative learning in females. Together with previous studies, these data indicate that the PL communicates with the BLA to suppress learning after a stressful experience in females. This circuit may be similarly engaged in women who become cognitively impaired after stressful

  14. "Celebration of the Neurons": The Application of Brain Based Learning in Classroom Environment

    Science.gov (United States)

    Duman, Bilal

    2007-01-01

    The purpose of this study is to investigate approaches and techniques related to how brain based learning used in classroom atmosphere. This general purpose were answered following the questions: (1) What is the aim of brain based learning? (2) What are general approaches and techniques that brain based learning used? and (3) How should be used…

  15. The HOX genes are expressed, in vivo, in human tooth germs: in vitro cAMP exposure of dental pulp cells results in parallel HOX network activation and neuronal differentiation.

    Science.gov (United States)

    D'Antò, Vincenzo; Cantile, Monica; D'Armiento, Maria; Schiavo, Giulia; Spagnuolo, Gianrico; Terracciano, Luigi; Vecchione, Raffaela; Cillo, Clemente

    2006-03-01

    Homeobox-containing genes play a crucial role in odontogenesis. After the detection of Dlx and Msx genes in overlapping domains along maxillary and mandibular processes, a homeobox odontogenic code has been proposed to explain the interaction between different homeobox genes during dental lamina patterning. No role has so far been assigned to the Hox gene network in the homeobox odontogenic code due to studies on specific Hox genes and evolutionary considerations. Despite its involvement in early patterning during embryonal development, the HOX gene network, the most repeat-poor regions of the human genome, controls the phenotype identity of adult eukaryotic cells. Here, according to our results, the HOX gene network appears to be active in human tooth germs between 18 and 24 weeks of development. The immunohistochemical localization of specific HOX proteins mostly concerns the epithelial tooth germ compartment. Furthermore, only a few genes of the network are active in embryonal retromolar tissues, as well as in ectomesenchymal dental pulp cells (DPC) grown in vitro from adult human molar. Exposure of DPCs to cAMP induces the expression of from three to nine total HOX genes of the network in parallel with phenotype modifications with traits of neuronal differentiation. Our observations suggest that: (i) by combining its component genes, the HOX gene network determines the phenotype identity of epithelial and ectomesenchymal cells interacting in the generation of human tooth germ; (ii) cAMP treatment activates the HOX network and induces, in parallel, a neuronal-like phenotype in human primary ectomesenchymal dental pulp cells. 2005 Wiley-Liss, Inc.

  16. Age-dependent loss of cholinergic neurons in learning and memory-related brain regions and impaired learning in SAMP8 mice with trigeminal nerve damage

    Institute of Scientific and Technical Information of China (English)

    Yifan He; Jihong Zhu; Fang Huang; Liu Qin; Wenguo Fan; Hongwen He

    2014-01-01

    The tooth belongs to the trigeminal sensory pathway. Dental damage has been associated with impairments in the central nervous system that may be mediated by injury to the trigeminal nerve. In the present study, we investigated the effects of damage to the inferior alveolar nerve, an important peripheral nerve in the trigeminal sensory pathway, on learning and memory be-haviors and structural changes in related brain regions, in a mouse model of Alzheimer’s disease. Inferior alveolar nerve transection or sham surgery was performed in middle-aged (4-month-old) or elderly (7-month-old) senescence-accelerated mouse prone 8 (SAMP8) mice. When the middle-aged mice reached 8 months (middle-aged group 1) or 11 months (middle-aged group 2), and the elderly group reached 11 months, step-down passive avoidance and Y-maze tests of learn-ing and memory were performed, and the cholinergic system was examined in the hippocampus (Nissl staining and acetylcholinesterase histochemistry) and basal forebrain (choline acetyltrans-ferase immunohistochemistry). In the elderly group, animals that underwent nerve transection had fewer pyramidal neurons in the hippocampal CA1 and CA3 regions, fewer cholinergic ifbers in the CA1 and dentate gyrus, and fewer cholinergic neurons in the medial septal nucleus and vertical limb of the diagonal band, compared with sham-operated animals, as well as showing impairments in learning and memory. Conversely, no signiifcant differences in histology or be-havior were observed between middle-aged group 1 or group 2 transected mice and age-matched sham-operated mice. The present ifndings suggest that trigeminal nerve damage in old age, but not middle age, can induce degeneration of the septal-hippocampal cholinergic system and loss of hippocampal pyramidal neurons, and ultimately impair learning ability. Our results highlight the importance of active treatment of trigeminal nerve damage in elderly patients and those with Alzheimer’s disease, and

  17. Neural correlates of olfactory learning paradigms in an identified neuron in the honeybee brain.

    Science.gov (United States)

    Mauelshagen, J

    1993-02-01

    1. Sensitization and classical odor conditioning of the proboscis extension reflex were functionally analyzed by repeated intracellular recordings from a single identified neuron (PE1-neuron) in the central bee brain. This neuron belongs to the class of "extrinsic cells" arising from the pedunculus of the mushroom bodies and has extensive arborizations in the median and lateral protocerebrum. The recordings were performed on isolated bee heads. 2. Two different series of physiological experiments were carried out with the use of a similar temporal succession of stimuli as in previous behavioral experiments. In the first series, one group of animals was used for a single conditioning trial [conditioned stimulus (CS), carnation; unconditioned stimulus (US), sucrose solution to the antennae and proboscis), a second group was used for sensitization (sensitizing stimulus, sucrose solution to the antennae and/or proboscis), and the third group served as control (no sucrose stimulation). In the second series, a differential conditioning paradigm (paired odor CS+, carnation; unpaired odor CS-, orange blossom) was applied to test the associative nature of the conditioning effect. 3. The PE1-neuron showed a characteristic burstlike odor response before the training procedures. The treatments resulted in different spike-frequency modulations of this response, which were specific for the nonassociative and associative stimulus paradigms applied. During differential conditioning, there are dynamic up and down modulations of spike frequencies and of the DC potentials underlying the responses to the CS+. Overall, only transient changes in the minute range were observed. 4. The results of the sensitization procedures suggest two qualitatively different US pathways. The comparison between sensitization and one-trial conditioning shows differential effects of nonassociative and associative stimulus paradigms on the response behavior of the PE1-neuron. The results of the differential

  18. Free and membrane-bound ribosomes and polysomes in hippocampal neurons during a learning experiment.

    Science.gov (United States)

    Wenzel, J; David, H; Pohle, W; Marx, I; Matthies, H

    1975-01-24

    The ribosomes of the CA1 and CA3 pyramidal cells of hipocampus were investigated by morphometric methods after the acquisition of a shock-motivated brightness discrimination in rats. A significant increase in the total number of ribosomes was observed in CA1 cells of trained animals and in CA3 cells of both active controls and trained rats. A significant increase in membrane-bound ribosomes was obtained in CA1 and CA3 cells after training only. The results confirm the suggestion of an increased protein synthesis in hippocampal neurons during and after the acquisition of a brightness discrimination, as we have concluded from out previous investigations on the incorporation of labeled amino acids under identical experimental conditions. The results lead to the assumption that the protein synthesis in some neuronal cells may probably differ not only quantitatively, but also qualitatively in trained and untrained animals.

  19. Bayesian Ising approximation for learning dictionaries of multispike timing patterns in premotor neurons

    Science.gov (United States)

    Hernandez Lahme, Damian; Sober, Samuel; Nemenman, Ilya

    Important questions in computational neuroscience are whether, how much, and how information is encoded in the precise timing of neural action potentials. We recently demonstrated that, in the premotor cortex during vocal control in songbirds, spike timing is far more informative about upcoming behavior than is spike rate (Tang et al, 2014). However, identification of complete dictionaries that relate spike timing patterns with the controled behavior remains an elusive problem. Here we present a computational approach to deciphering such codes for individual neurons in the songbird premotor area RA, an analog of mammalian primary motor cortex. Specifically, we analyze which multispike patterns of neural activity predict features of the upcoming vocalization, and hence are important codewords. We use a recently introduced Bayesian Ising Approximation, which properly accounts for the fact that many codewords overlap and hence are not independent. Our results show which complex, temporally precise multispike combinations are used by individual neurons to control acoustic features of the produced song, and that these code words are different across individual neurons and across different acoustic features. This work was supported, in part, by JSMF Grant 220020321, NSF Grant 1208126, NIH Grant NS084844 and NIH Grant 1 R01 EB022872.

  20. Learning intrinsic excitability in medium spiny neurons [v2; ref status: indexed, http://f1000r.es/30b

    Directory of Open Access Journals (Sweden)

    Gabriele Scheler

    2014-02-01

    Full Text Available We present an unsupervised, local activation-dependent learning rule for intrinsic plasticity (IP which affects the composition of ion channel conductances for single neurons in a use-dependent way. We use a single-compartment conductance-based model for medium spiny striatal neurons in order to show the effects of parameterization of individual ion channels on the neuronal membrane potential-curent relationship (activation function. We show that parameter changes within the physiological ranges are sufficient to create an ensemble of neurons with significantly different activation functions. We emphasize that the effects of intrinsic neuronal modulation on spiking behavior require a distributed mode of synaptic input and can be eliminated by strongly correlated input. We show how modulation and adaptivity in ion channel conductances can be utilized to store patterns without an additional contribution by synaptic plasticity (SP. The adaptation of the spike response may result in either "positive" or "negative" pattern learning. However, read-out of stored information depends on a distributed pattern of synaptic activity to let intrinsic modulation determine spike response. We briefly discuss the implications of this conditional memory on learning and addiction.

  1. An Energy-Efficient and Scalable Deep Learning/Inference Processor With Tetra-Parallel MIMD Architecture for Big Data Applications.

    Science.gov (United States)

    Park, Seong-Wook; Park, Junyoung; Bong, Kyeongryeol; Shin, Dongjoo; Lee, Jinmook; Choi, Sungpill; Yoo, Hoi-Jun

    2015-12-01

    Deep Learning algorithm is widely used for various pattern recognition applications such as text recognition, object recognition and action recognition because of its best-in-class recognition accuracy compared to hand-crafted algorithm and shallow learning based algorithms. Long learning time caused by its complex structure, however, limits its usage only in high-cost servers or many-core GPU platforms so far. On the other hand, the demand on customized pattern recognition within personal devices will grow gradually as more deep learning applications will be developed. This paper presents a SoC implementation to enable deep learning applications to run with low cost platforms such as mobile or portable devices. Different from conventional works which have adopted massively-parallel architecture, this work adopts task-flexible architecture and exploits multiple parallelism to cover complex functions of convolutional deep belief network which is one of popular deep learning/inference algorithms. In this paper, we implement the most energy-efficient deep learning and inference processor for wearable system. The implemented 2.5 mm × 4.0 mm deep learning/inference processor is fabricated using 65 nm 8-metal CMOS technology for a battery-powered platform with real-time deep inference and deep learning operation. It consumes 185 mW average power, and 213.1 mW peak power at 200 MHz operating frequency and 1.2 V supply voltage. It achieves 411.3 GOPS peak performance and 1.93 TOPS/W energy efficiency, which is 2.07× higher than the state-of-the-art.

  2. Environmental enrichment protects spatial learning and hippocampal neurons from the long-lasting effects of protein malnutrition early in life.

    Science.gov (United States)

    Soares, Roberto O; Horiquini-Barbosa, Everton; Almeida, Sebastião S; Lachat, João-José

    2017-09-29

    As early protein malnutrition has a critically long-lasting impact on the hippocampal formation and its role in learning and memory, and environmental enrichment has demonstrated great success in ameliorating functional deficits, here we ask whether exposure to an enriched environment could be employed to prevent spatial memory impairment and neuroanatomical changes in the hippocampus of adult rats maintained on a protein deficient diet during brain development (P0-P35). To elucidate the protective effects of environmental enrichment, we used the Morris water task and neuroanatomical analysis to determine whether changes in spatial memory and number and size of CA1 neurons differed significantly among groups. Protein malnutrition and environmental enrichment during brain development had significant effects on the spatial memory and hippocampal anatomy of adult rats. Malnourished but non-enriched rats (MN) required more time to find the hidden platform than well-nourished but non-enriched rats (WN). Malnourished but enriched rats (ME) performed better than the MN and similarly to the WN rats. There was no difference between well-nourished but non-enriched and enriched rats (WE). Anatomically, fewer CA1 neurons were found in the hippocampus of MN rats than in those of WN rats. However, it was also observed that ME and WN rats retained a similar number of neurons. These results suggest that environmental enrichment during brain development alters cognitive task performance and hippocampal neuroanatomy in a manner that is neuroprotective against malnutrition-induced brain injury. These results could have significant implications for malnourished infants expected to be at risk of disturbed brain development. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. The GABAergic Anterior Paired Lateral Neurons Facilitate Olfactory Reversal Learning in "Drosophila"

    Science.gov (United States)

    Wu, Yanying; Ren, Qingzhong; Li, Hao; Guo, Aike

    2012-01-01

    Reversal learning has been widely used to probe the implementation of cognitive flexibility in the brain. Previous studies in monkeys identified an essential role of the orbitofrontal cortex (OFC) in reversal learning. However, the underlying circuits and molecular mechanisms are poorly understood. Here, we use the T-maze to investigate the neural…

  4. From Neurons to Brainpower: Cognitive Neuroscience and Brain-Based Learning

    Science.gov (United States)

    Phillips, Janet M.

    2005-01-01

    We have learned more about the brain in the past five years than the previous 100. Neuroimaging, lesion studies, and animal studies have revealed the intricate inner workings of the brain and learning. Synaptogenesis, pruning, sensitive periods, and plasticity have all become accepted concepts of cognitive neuroscience that are now being applied…

  5. Hebbian learning and predictive mirror neurons for actions, sensations and emotions

    NARCIS (Netherlands)

    Keysers, C.; Gazzola, Valeria

    2014-01-01

    Spike-timing-dependent plasticity is considered the neurophysiological basis of Hebbian learning and has been shown to be sensitive to both contingency and contiguity between pre- and postsynaptic activity. Here, we will examine how applying this Hebbian learning rule to a system of interconnected

  6. Scalable, incremental learning with MapReduce parallelization for cell detection in high-resolution 3D microscopy data

    KAUST Repository

    Sung, Chul; Woo, Jongwook; Goodman, Matthew; Huffman, Todd; Choe, Yoonsuck

    2013-01-01

    Accurate estimation of neuronal count and distribution is central to the understanding of the organization and layout of cortical maps in the brain, and changes in the cell population induced by brain disorders. High-throughput 3D microscopy

  7. Neuron class-specific requirements for Fragile X Mental Retardation Protein in critical period development of calcium signaling in learning and memory circuitry.

    Science.gov (United States)

    Doll, Caleb A; Broadie, Kendal

    2016-05-01

    Neural circuit optimization occurs through sensory activity-dependent mechanisms that refine synaptic connectivity and information processing during early-use developmental critical periods. Fragile X Mental Retardation Protein (FMRP), the gene product lost in Fragile X syndrome (FXS), acts as an activity sensor during critical period development, both as an RNA-binding translation regulator and channel-binding excitability regulator. Here, we employ a Drosophila FXS disease model to assay calcium signaling dynamics with a targeted transgenic GCaMP reporter during critical period development of the mushroom body (MB) learning/memory circuit. We find FMRP regulates depolarization-induced calcium signaling in a neuron-specific manner within this circuit, suppressing activity-dependent calcium transients in excitatory cholinergic MB input projection neurons and enhancing calcium signals in inhibitory GABAergic MB output neurons. Both changes are restricted to the developmental critical period and rectified at maturity. Importantly, conditional genetic (dfmr1) rescue of null mutants during the critical period corrects calcium signaling defects in both neuron classes, indicating a temporally restricted FMRP requirement. Likewise, conditional dfmr1 knockdown (RNAi) during the critical period replicates constitutive null mutant defects in both neuron classes, confirming cell-autonomous requirements for FMRP in developmental regulation of calcium signaling dynamics. Optogenetic stimulation during the critical period enhances depolarization-induced calcium signaling in both neuron classes, but this developmental change is eliminated in dfmr1 null mutants, indicating the activity-dependent regulation requires FMRP. These results show FMRP shapes neuron class-specific calcium signaling in excitatory vs. inhibitory neurons in developing learning/memory circuitry, and that FMRP mediates activity-dependent regulation of calcium signaling specifically during the early

  8. Effectiveness of telemedicine and distance learning applications for patients with chronic heart failure. A protocol for prospective parallel group non-randomised open label study

    OpenAIRE

    Vanagas, Giedrius; Umbrasienė, Jelena; Šlapikas, Rimvydas

    2012-01-01

    Introduction Chronic heart failure in Baltic Sea Region is responsible for more hospitalisations than all forms of cancer combined and is one of the leading causes of hospitalisations in elderly patients. Frequent hospitalisations, along with other direct and indirect costs, place financial burden on healthcare systems. We aim to test the hypothesis that telemedicine and distance learning applications is superior to the current standard of home care. Methods and analysis Prospective parallel ...

  9. Cannabinoid mitigation of neuronal morphological change important to development and learning: insight from a zebra finch model of psychopharmacology.

    Science.gov (United States)

    Soderstrom, Ken; Gilbert, Marcoita T

    2013-03-19

    Normal CNS development proceeds through late-postnatal stages of adolescent development. The activity-dependence of this development underscores the significance of CNS-active drug exposure prior to completion of brain maturation. Exogenous modulation of signaling important in regulating normal development is of particular concern. This mini-review presents a summary of the accumulated behavioral, physiological and biochemical evidence supporting such a key regulatory role for endocannabinoid signaling during late-postnatal CNS development. Our focus is on the data obtained using a unique zebra finch model of developmental psychopharmacology. This animal has allowed investigation of neuronal morphological effects essential to establishment and maintenance of neural circuitry, including processes related to synaptogenesis and dendritic spine dynamics. Altered neurophysiology that follows exogenous cannabinoid exposure during adolescent development has the potential to persistently alter cognition, learning and memory. Copyright © 2012 Elsevier Inc. All rights reserved.

  10. Attenuated Response to Methamphetamine Sensitization and Deficits in Motor Learning and Memory after Selective Deletion of [beta]-Catenin in Dopamine Neurons

    Science.gov (United States)

    Diaz-Ruiz, Oscar; Zhang, YaJun; Shan, Lufei; Malik, Nasir; Hoffman, Alexander F.; Ladenheim, Bruce; Cadet, Jean Lud; Lupica, Carl R.; Tagliaferro, Adriana; Brusco, Alicia; Backman, Cristina M.

    2012-01-01

    In the present study, we analyzed mice with a targeted deletion of [beta]-catenin in DA neurons (DA-[beta]cat KO mice) to address the functional significance of this molecule in the shaping of synaptic responses associated with motor learning and following exposure to drugs of abuse. Relative to controls, DA-[beta]cat KO mice showed significant…

  11. [Deep learning and neuronal networks in ophthalmology : Applications in the field of optical coherence tomography].

    Science.gov (United States)

    Treder, M; Eter, N

    2018-04-19

    Deep learning is increasingly becoming the focus of various imaging methods in medicine. Due to the large number of different imaging modalities, ophthalmology is particularly suitable for this field of application. This article gives a general overview on the topic of deep learning and its current applications in the field of optical coherence tomography. For the benefit of the reader it focuses on the clinical rather than the technical aspects.

  12. Supervised spike-timing-dependent plasticity: a spatiotemporal neuronal learning rule for function approximation and decisions.

    Science.gov (United States)

    Franosch, Jan-Moritz P; Urban, Sebastian; van Hemmen, J Leo

    2013-12-01

    How can an animal learn from experience? How can it train sensors, such as the auditory or tactile system, based on other sensory input such as the visual system? Supervised spike-timing-dependent plasticity (supervised STDP) is a possible answer. Supervised STDP trains one modality using input from another one as "supervisor." Quite complex time-dependent relationships between the senses can be learned. Here we prove that under very general conditions, supervised STDP converges to a stable configuration of synaptic weights leading to a reconstruction of primary sensory input.

  13. Parallel computing works

    Energy Technology Data Exchange (ETDEWEB)

    1991-10-23

    An account of the Caltech Concurrent Computation Program (C{sup 3}P), a five year project that focused on answering the question: Can parallel computers be used to do large-scale scientific computations '' As the title indicates, the question is answered in the affirmative, by implementing numerous scientific applications on real parallel computers and doing computations that produced new scientific results. In the process of doing so, C{sup 3}P helped design and build several new computers, designed and implemented basic system software, developed algorithms for frequently used mathematical computations on massively parallel machines, devised performance models and measured the performance of many computers, and created a high performance computing facility based exclusively on parallel computers. While the initial focus of C{sup 3}P was the hypercube architecture developed by C. Seitz, many of the methods developed and lessons learned have been applied successfully on other massively parallel architectures.

  14. Neuron-glia metabolic coupling and plasticity.

    Science.gov (United States)

    Magistretti, Pierre J

    2006-06-01

    The coupling between synaptic activity and glucose utilization (neurometabolic coupling) is a central physiological principle of brain function that has provided the basis for 2-deoxyglucose-based functional imaging with positron emission tomography (PET). Astrocytes play a central role in neurometabolic coupling, and the basic mechanism involves glutamate-stimulated aerobic glycolysis; the sodium-coupled reuptake of glutamate by astrocytes and the ensuing activation of the Na-K-ATPase triggers glucose uptake and processing via glycolysis, resulting in the release of lactate from astrocytes. Lactate can then contribute to the activity-dependent fuelling of the neuronal energy demands associated with synaptic transmission. An operational model, the 'astrocyte-neuron lactate shuttle', is supported experimentally by a large body of evidence, which provides a molecular and cellular basis for interpreting data obtained from functional brain imaging studies. In addition, this neuron-glia metabolic coupling undergoes plastic adaptations in parallel with adaptive mechanisms that characterize synaptic plasticity. Thus, distinct subregions of the hippocampus are metabolically active at different time points during spatial learning tasks, suggesting that a type of metabolic plasticity, involving by definition neuron-glia coupling, occurs during learning. In addition, marked variations in the expression of genes involved in glial glycogen metabolism are observed during the sleep-wake cycle, with in particular a marked induction of expression of the gene encoding for protein targeting to glycogen (PTG) following sleep deprivation. These data suggest that glial metabolic plasticity is likely to be concomitant with synaptic plasticity.

  15. Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral Inhibition.

    Directory of Open Access Journals (Sweden)

    Johannes Bill

    Full Text Available During the last decade, Bayesian probability theory has emerged as a framework in cognitive science and neuroscience for describing perception, reasoning and learning of mammals. However, our understanding of how probabilistic computations could be organized in the brain, and how the observed connectivity structure of cortical microcircuits supports these calculations, is rudimentary at best. In this study, we investigate statistical inference and self-organized learning in a spatially extended spiking network model, that accommodates both local competitive and large-scale associative aspects of neural information processing, under a unified Bayesian account. Specifically, we show how the spiking dynamics of a recurrent network with lateral excitation and local inhibition in response to distributed spiking input, can be understood as sampling from a variational posterior distribution of a well-defined implicit probabilistic model. This interpretation further permits a rigorous analytical treatment of experience-dependent plasticity on the network level. Using machine learning theory, we derive update rules for neuron and synapse parameters which equate with Hebbian synaptic and homeostatic intrinsic plasticity rules in a neural implementation. In computer simulations, we demonstrate that the interplay of these plasticity rules leads to the emergence of probabilistic local experts that form distributed assemblies of similarly tuned cells communicating through lateral excitatory connections. The resulting sparse distributed spike code of a well-adapted network carries compressed information on salient input features combined with prior experience on correlations among them. Our theory predicts that the emergence of such efficient representations benefits from network architectures in which the range of local inhibition matches the spatial extent of pyramidal cells that share common afferent input.

  16. Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral Inhibition

    Science.gov (United States)

    Bill, Johannes; Buesing, Lars; Habenschuss, Stefan; Nessler, Bernhard; Maass, Wolfgang; Legenstein, Robert

    2015-01-01

    During the last decade, Bayesian probability theory has emerged as a framework in cognitive science and neuroscience for describing perception, reasoning and learning of mammals. However, our understanding of how probabilistic computations could be organized in the brain, and how the observed connectivity structure of cortical microcircuits supports these calculations, is rudimentary at best. In this study, we investigate statistical inference and self-organized learning in a spatially extended spiking network model, that accommodates both local competitive and large-scale associative aspects of neural information processing, under a unified Bayesian account. Specifically, we show how the spiking dynamics of a recurrent network with lateral excitation and local inhibition in response to distributed spiking input, can be understood as sampling from a variational posterior distribution of a well-defined implicit probabilistic model. This interpretation further permits a rigorous analytical treatment of experience-dependent plasticity on the network level. Using machine learning theory, we derive update rules for neuron and synapse parameters which equate with Hebbian synaptic and homeostatic intrinsic plasticity rules in a neural implementation. In computer simulations, we demonstrate that the interplay of these plasticity rules leads to the emergence of probabilistic local experts that form distributed assemblies of similarly tuned cells communicating through lateral excitatory connections. The resulting sparse distributed spike code of a well-adapted network carries compressed information on salient input features combined with prior experience on correlations among them. Our theory predicts that the emergence of such efficient representations benefits from network architectures in which the range of local inhibition matches the spatial extent of pyramidal cells that share common afferent input. PMID:26284370

  17. A Mouse Model of Visual Perceptual Learning Reveals Alterations in Neuronal Coding and Dendritic Spine Density in the Visual Cortex.

    Science.gov (United States)

    Wang, Yan; Wu, Wei; Zhang, Xian; Hu, Xu; Li, Yue; Lou, Shihao; Ma, Xiao; An, Xu; Liu, Hui; Peng, Jing; Ma, Danyi; Zhou, Yifeng; Yang, Yupeng

    2016-01-01

    Visual perceptual learning (VPL) can improve spatial vision in normally sighted and visually impaired individuals. Although previous studies of humans and large animals have explored the neural basis of VPL, elucidation of the underlying cellular and molecular mechanisms remains a challenge. Owing to the advantages of molecular genetic and optogenetic manipulations, the mouse is a promising model for providing a mechanistic understanding of VPL. Here, we thoroughly evaluated the effects and properties of VPL on spatial vision in C57BL/6J mice using a two-alternative, forced-choice visual water task. Briefly, the mice underwent prolonged training at near the individual threshold of contrast or spatial frequency (SF) for pattern discrimination or visual detection for 35 consecutive days. Following training, the contrast-threshold trained mice showed an 87% improvement in contrast sensitivity (CS) and a 55% gain in visual acuity (VA). Similarly, the SF-threshold trained mice exhibited comparable and long-lasting improvements in VA and significant gains in CS over a wide range of SFs. Furthermore, learning largely transferred across eyes and stimulus orientations. Interestingly, learning could transfer from a pattern discrimination task to a visual detection task, but not vice versa. We validated that this VPL fully restored VA in adult amblyopic mice and old mice. Taken together, these data indicate that mice, as a species, exhibit reliable VPL. Intrinsic signal optical imaging revealed that mice with perceptual training had higher cut-off SFs in primary visual cortex (V1) than those without perceptual training. Moreover, perceptual training induced an increase in the dendritic spine density in layer 2/3 pyramidal neurons of V1. These results indicated functional and structural alterations in V1 during VPL. Overall, our VPL mouse model will provide a platform for investigating the neurobiological basis of VPL.

  18. A mouse model of visual perceptual learning reveals alterations in neuronal coding and dendritic spine density in the visual cortex

    Directory of Open Access Journals (Sweden)

    Yan eWang

    2016-03-01

    Full Text Available Visual perceptual learning (VPL can improve spatial vision in normally sighted and visually impaired individuals. Although previous studies of humans and large animals have explored the neural basis of VPL, elucidation of the underlying cellular and molecular mechanisms remains a challenge. Owing to the advantages of molecular genetic and optogenetic manipulations, the mouse is a promising model for providing a mechanistic understanding of VPL. Here, we thoroughly evaluated the effects and properties of VPL on spatial vision in C57BL/6J mice using a two-alternative, forced-choice visual water task. Briefly, the mice underwent prolonged training at near the individual threshold of contrast or spatial frequency (SF for pattern discrimination or visual detection for 35 consecutive days. Following training, the contrast-threshold trained mice showed an 87% improvement in contrast sensitivity (CS and a 55% gain in visual acuity (VA. Similarly, the SF-threshold trained mice exhibited comparable and long-lasting improvements in VA and significant gains in CS over a wide range of SFs. Furthermore, learning largely transferred across eyes and stimulus orientations. Interestingly, learning could transfer from a pattern discrimination task to a visual detection task, but not vice versa. We validated that this VPL fully restored VA in adult amblyopic mice and old mice. Taken together, these data indicate that mice, as a species, exhibit reliable VPL. Intrinsic signal optical imaging revealed that mice with perceptual training had higher cut-off SFs in primary visual cortex (V1 than those without perceptual training. Moreover, perceptual training induced an increase in the dendritic spine density in layer 2/3 pyramidal neurons of V1. These results indicated functional and structural alterations in V1 during VPL. Overall, our VPL mouse model will provide a platform for investigating the neurobiological basis of VPL.

  19. Localization of molecular correlates of memory consolidation to buccal ganglia mechanoafferent neurons after learning that food is inedible in Aplysia.

    Science.gov (United States)

    Levitan, David; Saada-Madar, Ravit; Teplinsky, Anastasiya; Susswein, Abraham J

    2012-10-15

    Training paradigms affecting Aplysia withdrawal reflexes cause changes in gene expression leading to long-term memory formation in primary mechanoafferents that initiate withdrawal. Similar mechanoafferents are also found in the buccal ganglia that control feeding behavior, raising the possibility that these mechanoafferents are a locus of memory formation after a training paradigm affecting feeding. Buccal ganglia mechanoafferent neurons expressed increases in mRNA expression for the transcription factor ApC/EBP, and for the growth factor sensorin-A, within the first 2 h after training with an inedible food. No increases in expression were detected in the rest of the buccal ganglia. Increased ApC/EBP expression was not elicited by food and feeding responses not causing long-term memory. Increased ApC/EBP expression was directly related to a measure of the efficacy of training in causing long-term memory, suggesting that ApC/EBP expression is necessary for the expression of aspects of long-term memory. In behaving animals, memory is expressed as a decrease in the likelihood to respond to food, and a decrease in the amplitude of protraction, the first phase of consummatory feeding behaviors. To determine how changes in the properties of mechanoafferents could cause learned changes in feeding behavior, synaptic contacts were mapped from the mechanoafferents to the B31/B32 neurons, which have a key role in initiating consummatory behaviors and also control protractions. Many mechanoafferents monosynaptically and polysynaptically connect with B31/B32. Monosynaptic connections were complex combinations of fast and slow excitation and/or inhibition. Changes in the response of B31/B32 to stimuli sensed by the mechanoafferent could underlie aspects of long-term memory expression.

  20. Parallel rendering

    Science.gov (United States)

    Crockett, Thomas W.

    1995-01-01

    This article provides a broad introduction to the subject of parallel rendering, encompassing both hardware and software systems. The focus is on the underlying concepts and the issues which arise in the design of parallel rendering algorithms and systems. We examine the different types of parallelism and how they can be applied in rendering applications. Concepts from parallel computing, such as data decomposition, task granularity, scalability, and load balancing, are considered in relation to the rendering problem. We also explore concepts from computer graphics, such as coherence and projection, which have a significant impact on the structure of parallel rendering algorithms. Our survey covers a number of practical considerations as well, including the choice of architectural platform, communication and memory requirements, and the problem of image assembly and display. We illustrate the discussion with numerous examples from the parallel rendering literature, representing most of the principal rendering methods currently used in computer graphics.

  1. The advantage of flexible neuronal tunings in neural network models for motor learning

    OpenAIRE

    Marongelli, Ellisha N.; Thoroughman, Kurt A.

    2013-01-01

    Human motor adaptation to novel environments is often modeled by a basis function network that transforms desired movement properties into estimated forces. This network employs a layer of nodes that have fixed broad tunings that generalize across the input domain. Learning is achieved by updating the weights of these nodes in response to training experience. This conventional model is unable to account for rapid flexibility observed in human spatial generalization during motor adaptation. Ho...

  2. Parallel computations

    CERN Document Server

    1982-01-01

    Parallel Computations focuses on parallel computation, with emphasis on algorithms used in a variety of numerical and physical applications and for many different types of parallel computers. Topics covered range from vectorization of fast Fourier transforms (FFTs) and of the incomplete Cholesky conjugate gradient (ICCG) algorithm on the Cray-1 to calculation of table lookups and piecewise functions. Single tridiagonal linear systems and vectorized computation of reactive flow are also discussed.Comprised of 13 chapters, this volume begins by classifying parallel computers and describing techn

  3. All-memristive neuromorphic computing with level-tuned neurons

    Science.gov (United States)

    Pantazi, Angeliki; Woźniak, Stanisław; Tuma, Tomas; Eleftheriou, Evangelos

    2016-09-01

    In the new era of cognitive computing, systems will be able to learn and interact with the environment in ways that will drastically enhance the capabilities of current processors, especially in extracting knowledge from vast amount of data obtained from many sources. Brain-inspired neuromorphic computing systems increasingly attract research interest as an alternative to the classical von Neumann processor architecture, mainly because of the coexistence of memory and processing units. In these systems, the basic components are neurons interconnected by synapses. The neurons, based on their nonlinear dynamics, generate spikes that provide the main communication mechanism. The computational tasks are distributed across the neural network, where synapses implement both the memory and the computational units, by means of learning mechanisms such as spike-timing-dependent plasticity. In this work, we present an all-memristive neuromorphic architecture comprising neurons and synapses realized by using the physical properties and state dynamics of phase-change memristors. The architecture employs a novel concept of interconnecting the neurons in the same layer, resulting in level-tuned neuronal characteristics that preferentially process input information. We demonstrate the proposed architecture in the tasks of unsupervised learning and detection of multiple temporal correlations in parallel input streams. The efficiency of the neuromorphic architecture along with the homogenous neuro-synaptic dynamics implemented with nanoscale phase-change memristors represent a significant step towards the development of ultrahigh-density neuromorphic co-processors.

  4. All-memristive neuromorphic computing with level-tuned neurons.

    Science.gov (United States)

    Pantazi, Angeliki; Woźniak, Stanisław; Tuma, Tomas; Eleftheriou, Evangelos

    2016-09-02

    In the new era of cognitive computing, systems will be able to learn and interact with the environment in ways that will drastically enhance the capabilities of current processors, especially in extracting knowledge from vast amount of data obtained from many sources. Brain-inspired neuromorphic computing systems increasingly attract research interest as an alternative to the classical von Neumann processor architecture, mainly because of the coexistence of memory and processing units. In these systems, the basic components are neurons interconnected by synapses. The neurons, based on their nonlinear dynamics, generate spikes that provide the main communication mechanism. The computational tasks are distributed across the neural network, where synapses implement both the memory and the computational units, by means of learning mechanisms such as spike-timing-dependent plasticity. In this work, we present an all-memristive neuromorphic architecture comprising neurons and synapses realized by using the physical properties and state dynamics of phase-change memristors. The architecture employs a novel concept of interconnecting the neurons in the same layer, resulting in level-tuned neuronal characteristics that preferentially process input information. We demonstrate the proposed architecture in the tasks of unsupervised learning and detection of multiple temporal correlations in parallel input streams. The efficiency of the neuromorphic architecture along with the homogenous neuro-synaptic dynamics implemented with nanoscale phase-change memristors represent a significant step towards the development of ultrahigh-density neuromorphic co-processors.

  5. Parallelization of learning problems by artificial neural networks. Application in external radiotherapy; Parallelisation de problemes d'apprentissage par des reseaux neuronaux artificiels. Application en radiotherapie externe

    Energy Technology Data Exchange (ETDEWEB)

    Sauget, M

    2007-12-15

    This research is about the application of neural networks used in the external radiotherapy domain. The goal is to elaborate a new evaluating system for the radiation dose distributions in heterogeneous environments. The al objective of this work is to build a complete tool kit to evaluate the optimal treatment planning. My st research point is about the conception of an incremental learning algorithm. The interest of my work is to combine different optimizations specialized in the function interpolation and to propose a new algorithm allowing to change the neural network architecture during the learning phase. This algorithm allows to minimise the al size of the neural network while keeping a good accuracy. The second part of my research is to parallelize the previous incremental learning algorithm. The goal of that work is to increase the speed of the learning step as well as the size of the learned dataset needed in a clinical case. For that, our incremental learning algorithm presents an original data decomposition with overlapping, together with a fault tolerance mechanism. My last research point is about a fast and accurate algorithm computing the radiation dose deposit in any heterogeneous environment. At the present time, the existing solutions used are not optimal. The fast solution are not accurate and do not give an optimal treatment planning. On the other hand, the accurate solutions are far too slow to be used in a clinical context. Our algorithm answers to this problem by bringing rapidity and accuracy. The concept is to use a neural network adequately learned together with a mechanism taking into account the environment changes. The advantages of this algorithm is to avoid the use of a complex physical code while keeping a good accuracy and reasonable computation times. (author)

  6. BDNF and Schizophrenia: from Neurodevelopment to Neuronal Plasticity, Learning and Memory.

    Directory of Open Access Journals (Sweden)

    Rodrigo eNieto

    2013-06-01

    Full Text Available Brain Derived Neurotrophic Factor (BDNF is a neurotrophin that has been related not only to neurodevelopment and neuroprotection, but also to synapse regulation, learning and memory. Research focused on the neurobiology of schizophrenia has emphasized the relevance of neurodevelompental and neurotoxicity-related elements in the pathogenesis of this disease. Research focused on the clinical features of schizophrenia in the past decades has emphasized the relevance of cognitive deficits of this illness, considered a core manifestation and an important predictor for functional outcome. Variations in neurotrophins such as BDNF may have a role as part of the molecular mechanisms underlying these processes, from the neurodevelopmental alterations to the molecular mechanisms of cognitive dysfunction in patients with schizophrenia.

  7. Active learning of neuron morphology for accurate automated tracing of neurites

    Science.gov (United States)

    Gala, Rohan; Chapeton, Julio; Jitesh, Jayant; Bhavsar, Chintan; Stepanyants, Armen

    2014-01-01

    Automating the process of neurite tracing from light microscopy stacks of images is essential for large-scale or high-throughput quantitative studies of neural circuits. While the general layout of labeled neurites can be captured by many automated tracing algorithms, it is often not possible to differentiate reliably between the processes belonging to different cells. The reason is that some neurites in the stack may appear broken due to imperfect labeling, while others may appear fused due to the limited resolution of optical microscopy. Trained neuroanatomists routinely resolve such topological ambiguities during manual tracing tasks by combining information about distances between branches, branch orientations, intensities, calibers, tortuosities, colors, as well as the presence of spines or boutons. Likewise, to evaluate different topological scenarios automatically, we developed a machine learning approach that combines many of the above mentioned features. A specifically designed confidence measure was used to actively train the algorithm during user-assisted tracing procedure. Active learning significantly reduces the training time and makes it possible to obtain less than 1% generalization error rates by providing few training examples. To evaluate the overall performance of the algorithm a number of image stacks were reconstructed automatically, as well as manually by several trained users, making it possible to compare the automated traces to the baseline inter-user variability. Several geometrical and topological features of the traces were selected for the comparisons. These features include the total trace length, the total numbers of branch and terminal points, the affinity of corresponding traces, and the distances between corresponding branch and terminal points. Our results show that when the density of labeled neurites is sufficiently low, automated traces are not significantly different from manual reconstructions obtained by trained users. PMID

  8. Active learning of neuron morphology for accurate automated tracing of neurites

    Directory of Open Access Journals (Sweden)

    Rohan eGala

    2014-05-01

    Full Text Available Automating the process of neurite tracing from light microscopy stacks of images is essential for large-scale or high-throughput quantitative studies of neural circuits. While the general layout of labeled neurites can be captured by many automated tracing algorithms, it is often not possible to differentiate reliably between the processes belonging to different cells. The reason is that some neurites in the stack may appear broken due to imperfect labeling, while others may appear fused due to the limited resolution of optical microscopy. Trained neuroanatomists routinely resolve such topological ambiguities during manual tracing tasks by combining information about distances between branches, branch orientations, intensities, calibers, tortuosities, colors, as well as the presence of spines or boutons. Likewise, to evaluate different topological scenarios automatically, we developed a machine learning approach that combines many of the above mentioned features. A specifically designed confidence measure was used to actively train the algorithm during user-assisted tracing procedure. Active learning significantly reduces the training time and makes it possible to obtain less than 1% generalization error rates by providing few training examples. To evaluate the overall performance of the algorithm a number of image stacks were reconstructed automatically, as well as manually by several trained users, making it possible to compare the automated traces to the baseline inter-user variability. Several geometrical and topological features of the traces were selected for the comparisons. These features include the total trace length, the total numbers of branch and terminal points, the affinity of corresponding traces, and the distances between corresponding branch and terminal points. Our results show that when the density of labeled neurites is sufficiently low, automated traces are not significantly different from manual reconstructions obtained by

  9. Parallel Computing for Brain Simulation.

    Science.gov (United States)

    Pastur-Romay, L A; Porto-Pazos, A B; Cedron, F; Pazos, A

    2017-01-01

    The human brain is the most complex system in the known universe, it is therefore one of the greatest mysteries. It provides human beings with extraordinary abilities. However, until now it has not been understood yet how and why most of these abilities are produced. For decades, researchers have been trying to make computers reproduce these abilities, focusing on both understanding the nervous system and, on processing data in a more efficient way than before. Their aim is to make computers process information similarly to the brain. Important technological developments and vast multidisciplinary projects have allowed creating the first simulation with a number of neurons similar to that of a human brain. This paper presents an up-to-date review about the main research projects that are trying to simulate and/or emulate the human brain. They employ different types of computational models using parallel computing: digital models, analog models and hybrid models. This review includes the current applications of these works, as well as future trends. It is focused on various works that look for advanced progress in Neuroscience and still others which seek new discoveries in Computer Science (neuromorphic hardware, machine learning techniques). Their most outstanding characteristics are summarized and the latest advances and future plans are presented. In addition, this review points out the importance of considering not only neurons: Computational models of the brain should also include glial cells, given the proven importance of astrocytes in information processing. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  10. A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models

    OpenAIRE

    Hanuschkin, A.; Ganguli, S.; Hahnloser, R. H. R.

    2013-01-01

    Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map-desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop...

  11. Parallel algorithms

    CERN Document Server

    Casanova, Henri; Robert, Yves

    2008-01-01

    ""…The authors of the present book, who have extensive credentials in both research and instruction in the area of parallelism, present a sound, principled treatment of parallel algorithms. … This book is very well written and extremely well designed from an instructional point of view. … The authors have created an instructive and fascinating text. The book will serve researchers as well as instructors who need a solid, readable text for a course on parallelism in computing. Indeed, for anyone who wants an understandable text from which to acquire a current, rigorous, and broad vi

  12. Lactation exposure to BDE-153 damages learning and memory, disrupts spontaneous behavior and induces hippocampus neuron death in adult rats.

    Science.gov (United States)

    Zhang, Hongmei; Li, Xin; Nie, Jisheng; Niu, Qiao

    2013-06-23

    -down test was significantly increased in the 10mg/kg BDE-153 group at 2 months after treatment (P<0.05), and the BDE-153-treated rats' swimming times and distances in the target quadrant were significantly decreased at 1 month and 2 months after treatment (P<0.05 or P<0.01). These parameters were also significantly increased in the opposite quadrant at 1 month after treatment (P<0.05 or P<0.01). The spontaneous behavior was significantly reduced in the treated groups compared to the controls (P<0.05 or P<0.01). The severity of neurobehavioral dysfunction was dependent on the exposure dose of BDE-153, and worsened with age. Under an optical microscope, the treated rats' neurons in the CA3 region of the hippocampus were observed to be reduced and disarranged, and the cell junctions were loosened and the intercellular spaces were enlarged. Under a transmission electron microscope, the cell nucleus was observed to shrink; the chromatin was condensed and gathered near the nuclear membrane, the Nissl bodies and other organelles in the perikaryon were reduced, and the vacuole was observed to degenerate and even disappear. Moreover, compared to the controls, the cell apoptosis rates were significantly increased in the 5 and 10mg/kg BDE-153 groups (P<0.05), and the LDH activity was significantly increased in the 10mg/kg BDE-153 groups (P<0.01). Lactation exposure to BDE-153 damaged adult rats' learning and memory abilities, disrupted their spontaneous behavior (hypoactivity) and induced hippocampus neuron apoptosis. Crown Copyright © 2013. Published by Elsevier B.V. All rights reserved.

  13. Neuroprotection, learning and memory improvement of a standardized extract from Renshen Shouwu against neuronal injury and vascular dementia in rats with brain ischemia.

    Science.gov (United States)

    Wan, Li; Cheng, Yufang; Luo, Zhanyuan; Guo, Haibiao; Zhao, Wenjing; Gu, Quanlin; Yang, Xu; Xu, Jiangping; Bei, Weijian; Guo, Jiao

    2015-05-13

    The Renshen Shouwu capsule (RSSW) is a patented Traditional Chinese Medicine (TCM), that has been proven to improve memory and is widely used in China to apoplexy syndrome and memory deficits. To investigate the neuroprotective and therapeutic effect of the Renshen Shouwu standardized extract (RSSW) on ischemic brain neuronal injury and impairment of learning and memory related to Vascular Dementia (VD) induced by a focal and global cerebral ischemia-reperfusion injury in rats. Using in vivo rat models of both focal ischemia/reperfusion (I/R) injuries induced by a middle cerebral artery occlusion (MCAO), and VD with transient global brain I/R neuronal injuries induced by a four-vessel occlusion (4-VO) in Sprague-Dawley (SD) rats, RSSW (50,100, and 200 mg kg(-1) body weights) and Egb761® (80 mg kg(-1)) were administered orally for 20 days (preventively 6 days+therapeutically 14 days) in 4-VO rats, and for 7 days (3 days preventively+4 days therapeutically) in MCAO rats. Learning and memory behavioral performance was assayed using a Morris water maze test including a place navigation trial and a spatial probe trial. Brain histochemical morphology and hippocampal neuron survival was quantified using microscope assay of a puffin brain/hippocampus slice with cresyl violet staining. MCAO ischemia/reperfusion caused infarct damage in rat brain tissue. 4-VO ischemia/reperfusion caused a hippocampal neuronal lesion and learning and memory deficits in rats. Administration of RSSW (50, 100, and 200mg/kg) or EGb761 significantly reduced the size of the insulted brain hemisphere lesion and improved the neurological behavior of MCAO rats. In addition, RSSW markedly reduced an increase in the brain infarct volume from an I/R-induced MCAO and reduced the cerebral water content in a dose-dependent way. Administration of RSSW also increased the pyramidal neuronal density in the hippocampus of surviving rats after transient global brain ischemia and improved the learning and memory

  14. The effect of a selective neuronal nitric oxide synthase inhibitor 3-bromo 7-nitroindazole on spatial learning and memory in rats.

    Science.gov (United States)

    Gocmez, Semil Selcen; Yazir, Yusufhan; Sahin, Deniz; Karadenizli, Sabriye; Utkan, Tijen

    2015-04-01

    Since the discovery of nitric oxide (NO) as a neuronal messenger, its way to modulate learning and memory functions is subject of intense research. NO is an intercellular messenger in the central nervous system and is formed on demand through the conversion of L-arginine to L-citrulline via the enzyme nitric oxide synthase (NOS). Neuronal form of nitric oxide synthase may play an important role in a wide range of physiological and pathological conditions. Therefore the aim of this study was to investigate the effects of chronic 3-bromo 7-nitroindazole (3-Br 7-NI), specific neuronal nitric oxide synthase (nNOS) inhibitor, administration on spatial learning and memory performance in rats using the Morris water maze (MWM) paradigm. Male rats received either 3-Br 7-NI (20mg/kg/day) or saline via intraperitoneal injection for 5days. Daily administration of the specific neuronal nitric oxide synthase (nNOS) inhibitor, 3-Br 7-NI impaired the acquisition of the MWM task. 3-Br 7-NI also impaired the probe trial. The MWM training was associated with a significant increase in the brain-derived neurotrophic factor (BDNF) mRNA expression in the hippocampus. BDNF mRNA expression in the hippocampus did not change after 3-Br 7-NI treatment. L-arginine significantly reversed behavioural parameters, and the effect of 3-Br 7-NI was found to be NO-dependent. There were no differences in locomotor activity and blood pressure in 3-Br 7-NI treated rats. Our results may suggest that nNOS plays a key role in spatial memory formation in rats. Copyright © 2015 Elsevier Inc. All rights reserved.

  15. Mesmerising mirror neurons.

    Science.gov (United States)

    Heyes, Cecilia

    2010-06-01

    Mirror neurons have been hailed as the key to understanding social cognition. I argue that three currents of thought-relating to evolution, atomism and telepathy-have magnified the perceived importance of mirror neurons. When they are understood to be a product of associative learning, rather than an adaptation for social cognition, mirror neurons are no longer mesmerising, but they continue to raise important questions about both the psychology of science and the neural bases of social cognition. Copyright 2010 Elsevier Inc. All rights reserved.

  16. The mirror neuron system.

    Science.gov (United States)

    Cattaneo, Luigi; Rizzolatti, Giacomo

    2009-05-01

    Mirror neurons are a class of neurons, originally discovered in the premotor cortex of monkeys, that discharge both when individuals perform a given motor act and when they observe others perform that same motor act. Ample evidence demonstrates the existence of a cortical network with the properties of mirror neurons (mirror system) in humans. The human mirror system is involved in understanding others' actions and their intentions behind them, and it underlies mechanisms of observational learning. Herein, we will discuss the clinical implications of the mirror system.

  17. Reproductive experience modified dendritic spines on cortical pyramidal neurons to enhance sensory perception and spatial learning in rats.

    Science.gov (United States)

    Chen, Jeng-Rung; Lim, Seh Hong; Chung, Sin-Cun; Lee, Yee-Fun; Wang, Yueh-Jan; Tseng, Guo-Fang; Wang, Tsyr-Jiuan

    2017-01-27

    Behavioral adaptations during motherhood are aimed at increasing reproductive success. Alterations of hormones during motherhood could trigger brain morphological changes to underlie behavioral alterations. Here we investigated whether motherhood changes a rat's sensory perception and spatial memory in conjunction with cortical neuronal structural changes. Female rats of different statuses, including virgin, pregnant, lactating, and primiparous rats were studied. Behavioral test showed that the lactating rats were most sensitive to heat, while rats with motherhood and reproduction experience outperformed virgin rats in a water maze task. By intracellular dye injection and computer-assisted 3-dimensional reconstruction, the dendritic arbors and spines of the layer III and V pyramidal neurons of the somatosensory cortex and CA1 hippocampal pyramidal neurons were revealed for closer analysis. The results showed that motherhood and reproductive experience increased dendritic spines but not arbors or the lengths of the layer III and V pyramidal neurons of the somatosensory cortex and CA1 hippocampal pyramidal neurons. In addition, lactating rats had a higher incidence of spines than pregnant or primiparous rats. The increase of dendritic spines was coupled with increased expression of the glutamatergic postsynaptic marker protein (PSD-95), especially in lactating rats. On the basis of the present results, it is concluded that motherhood enhanced rat sensory perception and spatial memory and was accompanied by increases in dendritic spines on output neurons of the somatosensory cortex and CA1 hippocampus. The effect was sustained for at least 6 weeks after the weaning of the pups.

  18. Gamma neurons mediate dopaminergic input during aversive olfactory memory formation in Drosophila.

    Science.gov (United States)

    Qin, Hongtao; Cressy, Michael; Li, Wanhe; Coravos, Jonathan S; Izzi, Stephanie A; Dubnau, Joshua

    2012-04-10

    Mushroom body (MB)-dependent olfactory learning in Drosophila provides a powerful model to investigate memory mechanisms. MBs integrate olfactory conditioned stimulus (CS) inputs with neuromodulatory reinforcement (unconditioned stimuli, US), which for aversive learning is thought to rely on dopaminergic (DA) signaling to DopR, a D1-like dopamine receptor expressed in MBs. A wealth of evidence suggests the conclusion that parallel and independent signaling occurs downstream of DopR within two MB neuron cell types, with each supporting half of memory performance. For instance, expression of the Rutabaga (Rut) adenylyl cyclase in γ neurons is sufficient to restore normal learning to rut mutants, whereas expression of Neurofibromatosis 1 (NF1) in α/β neurons is sufficient to rescue NF1 mutants. DopR mutations are the only case where memory performance is fully eliminated, consistent with the hypothesis that DopR receives the US inputs for both γ and α/β lobe traces. We demonstrate, however, that DopR expression in γ neurons is sufficient to fully support short- and long-term memory. We argue that DA-mediated CS-US association is formed in γ neurons followed by communication between γ and α/β neurons to drive consolidation. Copyright © 2012 Elsevier Ltd. All rights reserved.

  19. γ neurons mediate dopaminergic input during aversive olfactory memory formation in Drosophila

    Science.gov (United States)

    Qin, H.; Cressy, M.; Li, W.; Coravos, J.; Izzi, S.; Dubnau, J.

    2012-01-01

    SUMMARY Mushroom body (MB) dependent olfactory learning in Drosophila provides a powerful model to investigate memory mechanisms. MBs integrate olfactory conditioned stimuli (CS) inputs with neuromodulatory reinforcement (unconditioned stimuli, US) [1, 2], which for aversive learning is thought to rely on dopaminergic (DA) signaling [3–6] to DopR, a D1-like dopamine receptor expressed in MB [7, 8]. A wealth of evidence suggests the conclusion that parallel and independent signaling occurs downstream of DopR within two MB neuron cell types, with each supporting half of memory performance. For instance, expression of the rutabaga adenylyl cyclase (rut) in γ neurons is sufficient to restore normal learning to rut mutants [9] whereas expression of Neurofibromatosis I (NFI) in α/β neurons is sufficient to rescue NF1 mutants [10, 11]. DopR mutations are the only case where memory performance is fully eliminated [7], consistent with the hypothesis that DopR receives the US inputs for both γ and α/β lobe traces. We demonstrate, however, that DopR expression in γ neurons is sufficient to fully support short (STM) and long-term memory (LTM). We argue that DA-mediated CS-US association is formed in γ neurons followed by communication between γ and α/β neurons to drive consolidation. PMID:22425153

  20. Effects of Chinese herbal medicine Yinsiwei compound on spatial learning and memory ability and the ultrastructure of hippocampal neurons in a rat model of sporadic Alzheimer disease.

    Science.gov (United States)

    Diwu, Yong-chang; Tian, Jin-zhou; Shi, Jing

    2011-02-01

    To study the effects of Chinese herbal medicine Yinsiwei compound (YSW) on spatial learning and memory ability in rats with sporadic Alzheimer disease (SAD) and the ultrastructural basis of the hippocampal neurons. A rat model of SAD was established by intracerebroventricular injection of streptozotocin. The rats were divided into six groups: sham-operation group, model group, donepezil control group, and YSW low, medium and high dose groups. Drug interventions were started on the 21st day after modeling and each treatment group was given the corresponding drugs by gavage for two months. Meanwhile, the model group and the sham-operation group were given the same volume of distilled water by gavage once a day for two months. The Morris water maze was adopted to test spatial learning and memory ability of the rats. The place navigation test and the spatial probe test were conducted. The escape latency, total swimming distance and swimming time in the target quadrant of the rats were recorded. Also, the hippocampus tissues of rats were taken out and the ultrastructure of hippocampus neurons were observed by an electron microscope. In the place navigation test, compared with the model group, the mean escape latency and the total swimming distance of the donepezil group and the YSW low, medium and high dose groups were significantly shortened (Pmicroscope also confirmed the efficacy of the drug treatment. Chinese herbal medicine YSW compound can improve spatial learning and memory impairment of rats with SAD. The ultrastructural basis may be that it can protect the microtubule structures of hippocampal neurons and prevent nerve axons from being damaged.

  1. Target-Dependent Structural Changes Accompanying Long-Term Synaptic Facilitation in Aplysia Neurons

    Science.gov (United States)

    Glanzman, David L.; Kandel, Eric R.; Schacher, Samuel

    1990-08-01

    The mechanisms underlying structural changes that accompany learning and memory have been difficult to investigate in the intact nervous system. In order to make these changes more accessible for experimental analysis, dissociated cell culture and low-light-level video microscopy were used to examine Aplysia sensory neurons in the presence or absence of their target cells. Repeated applications of serotonin, a facilitating transmitter important in behavioral dishabituation and sensitization, produced growth of the sensory neurons that paralleled the long-term enhancement of synaptic strength. This growth required the presence of the postsynaptic motor neuron. Thus, both the structural changes and the synaptic facilitation of Aplysia sensorimotor synapses accompanying long-term behavioral sensitization can be produced in vitro by applying a single facilitating transmitter repeatedly. These structural changes depend on an interaction of the presynaptic neuron with an appropriate postsynaptic target.

  2. Toward Bulk Synchronous Parallel-Based Machine Learning Techniques for Anomaly Detection in High-Speed Big Data Networks

    Directory of Open Access Journals (Sweden)

    Kamran Siddique

    2017-09-01

    Full Text Available Anomaly detection systems, also known as intrusion detection systems (IDSs, continuously monitor network traffic aiming to identify malicious actions. Extensive research has been conducted to build efficient IDSs emphasizing two essential characteristics. The first is concerned with finding optimal feature selection, while another deals with employing robust classification schemes. However, the advent of big data concepts in anomaly detection domain and the appearance of sophisticated network attacks in the modern era require some fundamental methodological revisions to develop IDSs. Therefore, we first identify two more significant characteristics in addition to the ones mentioned above. These refer to the need for employing specialized big data processing frameworks and utilizing appropriate datasets for validating system’s performance, which is largely overlooked in existing studies. Afterwards, we set out to develop an anomaly detection system that comprehensively follows these four identified characteristics, i.e., the proposed system (i performs feature ranking and selection using information gain and automated branch-and-bound algorithms respectively; (ii employs logistic regression and extreme gradient boosting techniques for classification; (iii introduces bulk synchronous parallel processing to cater computational requirements of high-speed big data networks; and; (iv uses the Infromation Security Centre of Excellence, of the University of Brunswick real-time contemporary dataset for performance evaluation. We present experimental results that verify the efficacy of the proposed system.

  3. Parallel computation

    International Nuclear Information System (INIS)

    Jejcic, A.; Maillard, J.; Maurel, G.; Silva, J.; Wolff-Bacha, F.

    1997-01-01

    The work in the field of parallel processing has developed as research activities using several numerical Monte Carlo simulations related to basic or applied current problems of nuclear and particle physics. For the applications utilizing the GEANT code development or improvement works were done on parts simulating low energy physical phenomena like radiation, transport and interaction. The problem of actinide burning by means of accelerators was approached using a simulation with the GEANT code. A program of neutron tracking in the range of low energies up to the thermal region has been developed. It is coupled to the GEANT code and permits in a single pass the simulation of a hybrid reactor core receiving a proton burst. Other works in this field refers to simulations for nuclear medicine applications like, for instance, development of biological probes, evaluation and characterization of the gamma cameras (collimators, crystal thickness) as well as the method for dosimetric calculations. Particularly, these calculations are suited for a geometrical parallelization approach especially adapted to parallel machines of the TN310 type. Other works mentioned in the same field refer to simulation of the electron channelling in crystals and simulation of the beam-beam interaction effect in colliders. The GEANT code was also used to simulate the operation of germanium detectors designed for natural and artificial radioactivity monitoring of environment

  4. Presynaptic learning and memory with a persistent firing neuron and a habituating synapse: a model of short term persistent habituation.

    Science.gov (United States)

    Ramanathan, Kiruthika; Ning, Ning; Dhanasekar, Dhiviya; Li, Guoqi; Shi, Luping; Vadakkepat, Prahlad

    2012-08-01

    Our paper explores the interaction of persistent firing axonal and presynaptic processes in the generation of short term memory for habituation. We first propose a model of a sensory neuron whose axon is able to switch between passive conduction and persistent firing states, thereby triggering short term retention to the stimulus. Then we propose a model of a habituating synapse and explore all nine of the behavioral characteristics of short term habituation in a two neuron circuit. We couple the persistent firing neuron to the habituation synapse and investigate the behavior of short term retention of habituating response. Simulations show that, depending on the amount of synaptic resources, persistent firing either results in continued habituation or maintains the response, both leading to longer recovery times. The effectiveness of the model as an element in a bio-inspired memory system is discussed.

  5. Mirror neuron activation as a function of explicit learning: changes in mu-event-related power after learning novel responses to ideomotor compatible, partially compatible, and non-compatible stimuli.

    Science.gov (United States)

    Behmer, Lawrence P; Fournier, Lisa R

    2016-11-01

    Questions regarding the malleability of the mirror neuron system (MNS) continue to be debated. MNS activation has been reported when people observe another person performing biological goal-directed behaviors, such as grasping a cup. These findings support the importance of mapping goal-directed biological behavior onto one's motor repertoire as a means of understanding the actions of others. Still, other evidence supports the Associative Sequence Learning (ASL) model which predicts that the MNS responds to a variety of stimuli after sensorimotor learning, not simply biological behavior. MNS activity develops as a consequence of developing stimulus-response associations between a stimulus and its motor outcome. Findings from the ideomotor literature indicate that stimuli that are more ideomotor compatible with a response are accompanied by an increase in response activation compared to less compatible stimuli; however, non-compatible stimuli robustly activate a constituent response after sensorimotor learning. Here, we measured changes in the mu-rhythm, an EEG marker thought to index MNS activity, predicting that stimuli that differ along dimensions of ideomotor compatibility should show changes in mirror neuron activation as participants learn the respective stimulus-response associations. We observed robust mu-suppression for ideomotor-compatible hand actions and partially compatible dot animations prior to learning; however, compatible stimuli showed greater mu-suppression than partially or non-compatible stimuli after explicit learning. Additionally, non-compatible abstract stimuli exceeded baseline only after participants explicitly learned the motor responses associated with the stimuli. We conclude that the empirical differences between the biological and ASL accounts of the MNS can be explained by Ideomotor Theory. © 2016 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  6. The effects of short-term and long-term learning on the responses of lateral intraparietal neurons to visually presented objects.

    Science.gov (United States)

    Sigurdardottir, Heida M; Sheinberg, David L

    2015-07-01

    The lateral intraparietal area (LIP) is thought to play an important role in the guidance of where to look and pay attention. LIP can also respond selectively to differently shaped objects. We sought to understand to what extent short-term and long-term experience with visual orienting determines the responses of LIP to objects of different shapes. We taught monkeys to arbitrarily associate centrally presented objects of various shapes with orienting either toward or away from a preferred spatial location of a neuron. The training could last for less than a single day or for several months. We found that neural responses to objects are affected by such experience, but that the length of the learning period determines how this neural plasticity manifests. Short-term learning affects neural responses to objects, but these effects are only seen relatively late after visual onset; at this time, the responses to newly learned objects resemble those of familiar objects that share their meaning or arbitrary association. Long-term learning affects the earliest bottom-up responses to visual objects. These responses tend to be greater for objects that have been associated with looking toward, rather than away from, LIP neurons' preferred spatial locations. Responses to objects can nonetheless be distinct, although they have been similarly acted on in the past and will lead to the same orienting behavior in the future. Our results therefore indicate that a complete experience-driven override of LIP object responses may be difficult or impossible. We relate these results to behavioral work on visual attention.

  7. Multiplexed Neurochemical Signaling by Neurons of the Ventral Tegmental Area

    Science.gov (United States)

    Barker, David J.; Root, David H.; Zhang, Shiliang; Morales, Marisela

    2016-01-01

    The ventral tegmental area (VTA) is an evolutionarily conserved structure that has roles in reward-seeking, safety-seeking, learning, motivation, and neuropsychiatric disorders such as addiction and depression. The involvement of the VTA in these various behaviors and disorders is paralleled by its diverse signaling mechanisms. Here we review recent advances in our understanding of neuronal diversity in the VTA with a focus on cell phenotypes that participate in ‘multiplexed’ neurotransmission involving distinct signaling mechanisms. First, we describe the cellular diversity within the VTA, including neurons capable of transmitting dopamine, glutamate or GABA as well as neurons capable of multiplexing combinations of these neurotransmitters. Next, we describe the complex synaptic architecture used by VTA neurons in order to accommodate the transmission of multiple transmitters. We specifically cover recent findings showing that VTA multiplexed neurotransmission may be mediated by either the segregation of dopamine and glutamate into distinct microdomains within a single axon or by the integration of glutamate and GABA into a single axon terminal. In addition, we discuss our current understanding of the functional role that these multiplexed signaling pathways have in the lateral habenula and the nucleus accumbens. Finally, we consider the putative roles of VTA multiplexed neurotransmission in synaptic plasticity and discuss how changes in VTA multiplexed neurons may relate to various psychopathologies including drug addiction and depression. PMID:26763116

  8. Neuronal Adaptations during Amygdala-Dependent Learning and Memory : Neuronale aanpassingen tijdens Amygdala-afhankelijk leren en geheugen

    NARCIS (Netherlands)

    B.S. Hosseini (Behdokht)

    2016-01-01

    textabstractThe amygdala, a structure deep in the temporal lobe of the brain, is an essential region for emotional and fearful processing. Neuronal coding in the lateral nucleus of the amygdala (LA) endows the brain with the ability to acquire enduring aversive associations, physically represented

  9. Chronic restraint stress promotes learning and memory impairment due to enhanced neuronal endoplasmic reticulum stress in the frontal cortex and hippocampus in male mice.

    Science.gov (United States)

    Huang, Rong-Rong; Hu, Wen; Yin, Yan-Yan; Wang, Yu-Chan; Li, Wei-Ping; Li, Wei-Zu

    2015-02-01

    Chronic stress has been implicated in many types of neurodegenerative diseases, such as Alzheimer's disease (AD). In our previous study, we demonstrated that chronic restraint stress (CRS) induced reactive oxygen species (ROS) overproduction and oxidative damage in the frontal cortex and hippocampus in mice. In the present study, we investigated the effects of CRS (over a period of 8 weeks) on learning and memory impairment and endoplasmic reticulum (ER) stress in the frontal cortex and hippocampus in male mice. The Morris water maze was used to investigate the effects of CRS on learning and memory impairment. Immunohistochemistry and immunoblot analysis were also used to determine the expression levels of protein kinase C α (PKCα), 78 kDa glucose-regulated protein (GRP78), C/EBP-homologous protein (CHOP) and mesencephalic astrocyte-derived neurotrophic factor (MANF). The results revealed that CRS significantly accelerated learning and memory impairment, and induced neuronal damage in the frontal cortex and hippocampus CA1 region. Moreover, CRS significantly increased the expression of PKCα, CHOP and MANF, and decreased that of GRP78 in the frontal cortex and hippocampus. Our data suggest that exposure to CRS (for 8 weeks) significantly accelerates learning and memory impairment, and the mechanisms involved may be related to ER stress in the frontal cortex and hippocampus.

  10. Parallel Lines

    Directory of Open Access Journals (Sweden)

    James G. Worner

    2017-05-01

    Full Text Available James Worner is an Australian-based writer and scholar currently pursuing a PhD at the University of Technology Sydney. His research seeks to expose masculinities lost in the shadow of Australia’s Anzac hegemony while exploring new opportunities for contemporary historiography. He is the recipient of the Doctoral Scholarship in Historical Consciousness at the university’s Australian Centre of Public History and will be hosted by the University of Bologna during 2017 on a doctoral research writing scholarship.   ‘Parallel Lines’ is one of a collection of stories, The Shapes of Us, exploring liminal spaces of modern life: class, gender, sexuality, race, religion and education. It looks at lives, like lines, that do not meet but which travel in proximity, simultaneously attracted and repelled. James’ short stories have been published in various journals and anthologies.

  11. BAF53b, a Neuron-Specific Nucleosome Remodeling Factor, Is Induced after Learning and Facilitates Long-Term Memory Consolidation.

    Science.gov (United States)

    Yoo, Miran; Choi, Kwang-Yeon; Kim, Jieun; Kim, Mujun; Shim, Jaehoon; Choi, Jun-Hyeok; Cho, Hye-Yeon; Oh, Jung-Pyo; Kim, Hyung-Su; Kaang, Bong-Kiun; Han, Jin-Hee

    2017-03-29

    Although epigenetic mechanisms of gene expression regulation have recently been implicated in memory consolidation and persistence, the role of nucleosome-remodeling is largely unexplored. Recent studies show that the functional loss of BAF53b, a postmitotic neuron-specific subunit of the BAF nucleosome-remodeling complex, results in the deficit of consolidation of hippocampus-dependent memory and cocaine-associated memory in the rodent brain. However, it is unclear whether BAF53b expression is regulated during memory formation and how BAF53b regulates fear memory in the amygdala, a key brain site for fear memory encoding and storage. To address these questions, we used viral vector approaches to either decrease or increase BAF53b function specifically in the lateral amygdala of adult mice in auditory fear conditioning paradigm. Knockdown of Baf53b before training disrupted long-term memory formation with no effect on short-term memory, basal synaptic transmission, and spine structures. We observed in our qPCR analysis that BAF53b was induced in the lateral amygdala neurons at the late consolidation phase after fear conditioning. Moreover, transient BAF53b overexpression led to persistently enhanced memory formation, which was accompanied by increase in thin-type spine density. Together, our results provide the evidence that BAF53b is induced after learning, and show that such increase of BAF53b level facilitates memory consolidation likely by regulating learning-related spine structural plasticity. SIGNIFICANCE STATEMENT Recent works in the rodent brain begin to link nucleosome remodeling-dependent epigenetic mechanism to memory consolidation. Here we show that BAF53b, an epigenetic factor involved in nucleosome remodeling, is induced in the lateral amygdala neurons at the late phase of consolidation after fear conditioning. Using specific gene knockdown or overexpression approaches, we identify the critical role of BAF53b in the lateral amygdala neurons for

  12. Spiking neurons in a hierarchical self-organizing map model can learn to develop spatial and temporal properties of entorhinal grid cells and hippocampal place cells.

    Directory of Open Access Journals (Sweden)

    Praveen K Pilly

    Full Text Available Medial entorhinal grid cells and hippocampal place cells provide neural correlates of spatial representation in the brain. A place cell typically fires whenever an animal is present in one or more spatial regions, or places, of an environment. A grid cell typically fires in multiple spatial regions that form a regular hexagonal grid structure extending throughout the environment. Different grid and place cells prefer spatially offset regions, with their firing fields increasing in size along the dorsoventral axes of the medial entorhinal cortex and hippocampus. The spacing between neighboring fields for a grid cell also increases along the dorsoventral axis. This article presents a neural model whose spiking neurons operate in a hierarchy of self-organizing maps, each obeying the same laws. This spiking GridPlaceMap model simulates how grid cells and place cells may develop. It responds to realistic rat navigational trajectories by learning grid cells with hexagonal grid firing fields of multiple spatial scales and place cells with one or more firing fields that match neurophysiological data about these cells and their development in juvenile rats. The place cells represent much larger spaces than the grid cells, which enable them to support navigational behaviors. Both self-organizing maps amplify and learn to categorize the most frequent and energetic co-occurrences of their inputs. The current results build upon a previous rate-based model of grid and place cell learning, and thus illustrate a general method for converting rate-based adaptive neural models, without the loss of any of their analog properties, into models whose cells obey spiking dynamics. New properties of the spiking GridPlaceMap model include the appearance of theta band modulation. The spiking model also opens a path for implementation in brain-emulating nanochips comprised of networks of noisy spiking neurons with multiple-level adaptive weights for controlling autonomous

  13. Real-time computing platform for spiking neurons (RT-spike).

    Science.gov (United States)

    Ros, Eduardo; Ortigosa, Eva M; Agís, Rodrigo; Carrillo, Richard; Arnold, Michael

    2006-07-01

    A computing platform is described for simulating arbitrary networks of spiking neurons in real time. A hybrid computing scheme is adopted that uses both software and hardware components to manage the tradeoff between flexibility and computational power; the neuron model is implemented in hardware and the network model and the learning are implemented in software. The incremental transition of the software components into hardware is supported. We focus on a spike response model (SRM) for a neuron where the synapses are modeled as input-driven conductances. The temporal dynamics of the synaptic integration process are modeled with a synaptic time constant that results in a gradual injection of charge. This type of model is computationally expensive and is not easily amenable to existing software-based event-driven approaches. As an alternative we have designed an efficient time-based computing architecture in hardware, where the different stages of the neuron model are processed in parallel. Further improvements occur by computing multiple neurons in parallel using multiple processing units. This design is tested using reconfigurable hardware and its scalability and performance evaluated. Our overall goal is to investigate biologically realistic models for the real-time control of robots operating within closed action-perception loops, and so we evaluate the performance of the system on simulating a model of the cerebellum where the emulation of the temporal dynamics of the synaptic integration process is important.

  14. Two Pairs of Mushroom Body Efferent Neurons Are Required for Appetitive Long-Term Memory Retrieval in Drosophila

    Directory of Open Access Journals (Sweden)

    Pierre-Yves Plaçais

    2013-11-01

    Full Text Available One of the challenges facing memory research is to combine network- and cellular-level descriptions of memory encoding. In this context, Drosophila offers the opportunity to decipher, down to single-cell resolution, memory-relevant circuits in connection with the mushroom bodies (MBs, prominent structures for olfactory learning and memory. Although the MB-afferent circuits involved in appetitive learning were recently described, the circuits underlying appetitive memory retrieval remain unknown. We identified two pairs of cholinergic neurons efferent from the MB α vertical lobes, named MB-V3, that are necessary for the retrieval of appetitive long-term memory (LTM. Furthermore, LTM retrieval was correlated to an enhanced response to the rewarded odor in these neurons. Strikingly, though, silencing the MB-V3 neurons did not affect short-term memory (STM retrieval. This finding supports a scheme of parallel appetitive STM and LTM processing.

  15. Developmental time windows for axon growth influence neuronal network topology.

    Science.gov (United States)

    Lim, Sol; Kaiser, Marcus

    2015-04-01

    Early brain connectivity development consists of multiple stages: birth of neurons, their migration and the subsequent growth of axons and dendrites. Each stage occurs within a certain period of time depending on types of neurons and cortical layers. Forming synapses between neurons either by growing axons starting at similar times for all neurons (much-overlapped time windows) or at different time points (less-overlapped) may affect the topological and spatial properties of neuronal networks. Here, we explore the extreme cases of axon formation during early development, either starting at the same time for all neurons (parallel, i.e., maximally overlapped time windows) or occurring for each neuron separately one neuron after another (serial, i.e., no overlaps in time windows). For both cases, the number of potential and established synapses remained comparable. Topological and spatial properties, however, differed: Neurons that started axon growth early on in serial growth achieved higher out-degrees, higher local efficiency and longer axon lengths while neurons demonstrated more homogeneous connectivity patterns for parallel growth. Second, connection probability decreased more rapidly with distance between neurons for parallel growth than for serial growth. Third, bidirectional connections were more numerous for parallel growth. Finally, we tested our predictions with C. elegans data. Together, this indicates that time windows for axon growth influence the topological and spatial properties of neuronal networks opening up the possibility to a posteriori estimate developmental mechanisms based on network properties of a developed network.

  16. The role of mirror neurons in language acquisition and evolution.

    Science.gov (United States)

    Behme, Christina

    2014-04-01

    I argue that Cook et al.'s attack of the genetic hypothesis of mirror neurons misses its target because the authors miss the point that genetics may specify how neurons may learn, not what they learn. Paying more attention to recent work linking mirror neurons to language acquisition and evolution would strengthen Cook et al.'s arguments against a rigid genetic hypothesis.

  17. Antarctic Exploration Parallels for Future Human Planetary Exploration: Science Operations Lessons Learned, Planning, and Equipment Capabilities for Long Range, Long Duration Traverses

    Science.gov (United States)

    Hoffman, Stephen J.

    2012-01-01

    The purpose for this workshop can be summed up by the question: Are there relevant analogs to planetary (meaning the Moon and Mars) to be found in polar exploration on Earth? The answer in my opinion is yes or else there would be no reason for this workshop. However, I think some background information would be useful to provide a context for my opinion on this matter. As all of you are probably aware, NASA has been set on a path that, in its current form, will eventually lead to putting human crews on the surface of the Moon and Mars for extended (months to years) in duration. For the past 50 V 60 years, starting not long after the end of World War II, exploration of the Antarctic has accumulated a significant body of experience that is highly analogous to our anticipated activities on the Moon and Mars. This relevant experience base includes: h Long duration (1 year and 2 year) continuous deployments by single crews, h Established a substantial outpost with a single deployment event to support these crews, h Carried out long distance (100 to 1000 kilometer) traverses, with and without intermediate support h Equipment and processes evolved based on lessons learned h International cooperative missions This is not a new or original thought; many people within NASA, including the most recent two NASA Administrators, have commented on the recognizable parallels between exploration in the Antarctic and on the Moon or Mars. But given that level of recognition, relatively little has been done, that I am aware of, to encourage these two exploration communities to collaborate in a significant way. [Slide 4] I will return to NASA s plans and the parallels with Antarctic traverses in a moment, but I want to spend a moment to explain the objective of this workshop and the anticipated products. We have two full days set aside for this workshop. This first day will be taken up with a series of presentations prepared by individuals with experience that extends back as far as the

  18. What More Has Been Learned? the Science of Early Childhood Development 15 Years after "Neurons to Neighborhoods"

    Science.gov (United States)

    Thompson, Ross A.

    2016-01-01

    The new Institute of Medicine/National Research Council report, "Transforming the Workforce for Children From Birth Through Age 8: A Unifying Foundation" (2015), begins with a summary of the science of early development and learning, with particular attention to discoveries during the past 15 years since the publication of "From…

  19. Role of PKA signaling in D2 receptor-expressing neurons in the core of the nucleus accumbens in aversive learning.

    Science.gov (United States)

    Yamaguchi, Takashi; Goto, Akihiro; Nakahara, Ichiro; Yawata, Satoshi; Hikida, Takatoshi; Matsuda, Michiyuki; Funabiki, Kazuo; Nakanishi, Shigetada

    2015-09-08

    The nucleus accumbens (NAc) serves as a key neural substrate for aversive learning and consists of two distinct subpopulations of medium-sized spiny neurons (MSNs). The MSNs of the direct pathway (dMSNs) and the indirect pathway (iMSNs) predominantly express dopamine (DA) D1 and D2 receptors, respectively, and are positively and negatively modulated by DA transmitters via Gs- and Gi-coupled cAMP-dependent protein kinase A (PKA) signaling cascades, respectively. In this investigation, we addressed how intracellular PKA signaling is involved in aversive learning in a cell type-specific manner. When the transmission of either dMSNs or iMSNs was unilaterally blocked by pathway-specific expression of transmission-blocking tetanus toxin, infusion of PKA inhibitors into the intact side of the NAc core abolished passive avoidance learning toward an electric shock in the indirect pathway-blocked mice, but not in the direct pathway-blocked mice. We then examined temporal changes in PKA activity in dMSNs and iMSNs in behaving mice by monitoring Förster resonance energy transfer responses of the PKA biosensor with the aid of microendoscopy. PKA activity was increased in iMSNs and decreased in dMSNs in both aversive memory formation and retrieval. Importantly, the increased PKA activity in iMSNs disappeared when aversive memory was prevented by keeping mice in the conditioning apparatus. Furthermore, the increase in PKA activity in iMSNs by aversive stimuli reflected facilitation of aversive memory retention. These results indicate that PKA signaling in iMSNs plays a critical role in both aversive memory formation and retention.

  20. Postconditioning with sevoflurane ameliorates spatial learning and memory deficit via attenuating endoplasmic reticulum stress induced neuron apoptosis in a rat model of hemorrhage shock and resuscitation.

    Science.gov (United States)

    Hu, Xianwen; Wang, Jingxian; Zhang, Li; Zhang, Qiquan; Duan, Xiaowen; Zhang, Ye

    2018-06-02

    Hemorrhage shock could initiate endoplasmic reticulum stress (ERS) and then induce neuronal apoptosis. The aim of this study was to investigate whether sevoflurane postconditioning could attenuate brain injury via suppressing apoptosis induced by ERS. Seventy male rats were randomized into five groups: sham, shock, low concentration (sevo1, 1.2%), middle concentration (sevo2, 2.4%) and high concentration (sevo3, 3.6%) of sevoflurane postconditioning. Hemorrhage shock was induced by removing 40% of the total blood volume during an interval of 30 min. 1h after the completion of bleeding, the animals were reinfused with shed blood during the ensuing 30 min. The spatial learning and memory ability of rats were measured by Morris water maze (MWM) test three days after the operation. Terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL) positive cells in the hippocampus CA1 region were assessed after the MWM test. The expression of C/EBP-homologousprotein (CHOP) and glucose-regulated protein 78 (GRP78) in the hippocampus were measured at 24h after reperfusion. We found that sevoflurane postconditioning with the concentrations of 2.4% and 3.6% significantly ameliorated the spatial learning and memory ability, decreased the TUNEL-positive cells, and reduced the GRP78 and CHOP expression compared with the shock group. These results suggested that sevoflurane postconditioning with the concentrations of 2.4% and 3.6% could ameliorate spatial learning and memory deficit after hemorrhage shock and resuscitation injury via suppressing apoptosis induced by ERS. Copyright © 2018. Published by Elsevier B.V.

  1. Motor Neurons

    DEFF Research Database (Denmark)

    Hounsgaard, Jorn

    2017-01-01

    Motor neurons translate synaptic input from widely distributed premotor networks into patterns of action potentials that orchestrate motor unit force and motor behavior. Intercalated between the CNS and muscles, motor neurons add to and adjust the final motor command. The identity and functional...... in in vitro preparations is far from complete. Nevertheless, a foundation has been provided for pursuing functional significance of intrinsic response properties in motoneurons in vivo during motor behavior at levels from molecules to systems....

  2. A Mouse Model of Visual Perceptual Learning Reveals Alterations in Neuronal Coding and Dendritic Spine Density in the Visual Cortex

    OpenAIRE

    Wang, Yan; Wu, Wei; Zhang, Xian; Hu, Xu; Li, Yue; Lou, Shihao; Ma, Xiao; An, Xu; Liu, Hui; Peng, Jing; Ma, Danyi; Zhou, Yifeng; Yang, Yupeng

    2016-01-01

    Visual perceptual learning (VPL) can improve spatial vision in normally sighted and visually impaired individuals. Although previous studies of humans and large animals have explored the neural basis of VPL, elucidation of the underlying cellular and molecular mechanisms remains a challenge. Owing to the advantages of molecular genetic and optogenetic manipulations, the mouse is a promising model for providing a mechanistic understanding of VPL. Here, we thoroughly evaluated the effects and p...

  3. Deep Learning Predicts Correlation between a Functional Signature of Higher Visual Areas and Sparse Firing of Neurons

    Directory of Open Access Journals (Sweden)

    Chengxu Zhuang

    2017-10-01

    Full Text Available Visual information in the visual cortex is processed in a hierarchical manner. Recent studies show that higher visual areas, such as V2, V3, and V4, respond more vigorously to images with naturalistic higher-order statistics than to images lacking them. This property is a functional signature of higher areas, as it is much weaker or even absent in the primary visual cortex (V1. However, the mechanism underlying this signature remains elusive. We studied this problem using computational models. In several typical hierarchical visual models including the AlexNet, VggNet, and SHMAX, this signature was found to be prominent in higher layers but much weaker in lower layers. By changing both the model structure and experimental settings, we found that the signature strongly correlated with sparse firing of units in higher layers but not with any other factors, including model structure, training algorithm (supervised or unsupervised, receptive field size, and property of training stimuli. The results suggest an important role of sparse neuronal activity underlying this special feature of higher visual areas.

  4. Exploiting Symmetry on Parallel Architectures.

    Science.gov (United States)

    Stiller, Lewis Benjamin

    1995-01-01

    This thesis describes techniques for the design of parallel programs that solve well-structured problems with inherent symmetry. Part I demonstrates the reduction of such problems to generalized matrix multiplication by a group-equivariant matrix. Fast techniques for this multiplication are described, including factorization, orbit decomposition, and Fourier transforms over finite groups. Our algorithms entail interaction between two symmetry groups: one arising at the software level from the problem's symmetry and the other arising at the hardware level from the processors' communication network. Part II illustrates the applicability of our symmetry -exploitation techniques by presenting a series of case studies of the design and implementation of parallel programs. First, a parallel program that solves chess endgames by factorization of an associated dihedral group-equivariant matrix is described. This code runs faster than previous serial programs, and discovered it a number of results. Second, parallel algorithms for Fourier transforms for finite groups are developed, and preliminary parallel implementations for group transforms of dihedral and of symmetric groups are described. Applications in learning, vision, pattern recognition, and statistics are proposed. Third, parallel implementations solving several computational science problems are described, including the direct n-body problem, convolutions arising from molecular biology, and some communication primitives such as broadcast and reduce. Some of our implementations ran orders of magnitude faster than previous techniques, and were used in the investigation of various physical phenomena.

  5. Optimizing NEURON Simulation Environment Using Remote Memory Access with Recursive Doubling on Distributed Memory Systems

    OpenAIRE

    Shehzad, Danish; Bozkuş, Zeki

    2016-01-01

    Increase in complexity of neuronal network models escalated the efforts to make NEURON simulation environment efficient. The computational neuroscientists divided the equations into subnets amongst multiple processors for achieving better hardware performance. On parallel machines for neuronal networks, interprocessor spikes exchange consumes large section of overall simulation time. In NEURON for communication between processors Message Passing Interface (MPI) is used. MPI_Allgather collecti...

  6. Hebbian learning in a model with dynamic rate-coded neurons: an alternative to the generative model approach for learning receptive fields from natural scenes.

    Science.gov (United States)

    Hamker, Fred H; Wiltschut, Jan

    2007-09-01

    Most computational models of coding are based on a generative model according to which the feedback signal aims to reconstruct the visual scene as close as possible. We here explore an alternative model of feedback. It is derived from studies of attention and thus, probably more flexible with respect to attentive processing in higher brain areas. According to this model, feedback implements a gain increase of the feedforward signal. We use a dynamic model with presynaptic inhibition and Hebbian learning to simultaneously learn feedforward and feedback weights. The weights converge to localized, oriented, and bandpass filters similar as the ones found in V1. Due to presynaptic inhibition the model predicts the organization of receptive fields within the feedforward pathway, whereas feedback primarily serves to tune early visual processing according to the needs of the task.

  7. [Mirror neurons].

    Science.gov (United States)

    Rubia Vila, Francisco José

    2011-01-01

    Mirror neurons were recently discovered in frontal brain areas of the monkey. They are activated when the animal makes a specific movement, but also when the animal observes the same movement in another animal. Some of them also respond to the emotional expression of other animals of the same species. These mirror neurons have also been found in humans. They respond to or "reflect" actions of other individuals in the brain and are thought to represent the basis for imitation and empathy and hence the neurobiological substrate for "theory of mind", the potential origin of language and the so-called moral instinct.

  8. Parallel Programming with Intel Parallel Studio XE

    CERN Document Server

    Blair-Chappell , Stephen

    2012-01-01

    Optimize code for multi-core processors with Intel's Parallel Studio Parallel programming is rapidly becoming a "must-know" skill for developers. Yet, where to start? This teach-yourself tutorial is an ideal starting point for developers who already know Windows C and C++ and are eager to add parallelism to their code. With a focus on applying tools, techniques, and language extensions to implement parallelism, this essential resource teaches you how to write programs for multicore and leverage the power of multicore in your programs. Sharing hands-on case studies and real-world examples, the

  9. Habituation: a non-associative learning rule design for spiking neurons and an autonomous mobile robots implementation

    International Nuclear Information System (INIS)

    Cyr, André; Boukadoum, Mounir

    2013-01-01

    This paper presents a novel bio-inspired habituation function for robots under control by an artificial spiking neural network. This non-associative learning rule is modelled at the synaptic level and validated through robotic behaviours in reaction to different stimuli patterns in a dynamical virtual 3D world. Habituation is minimally represented to show an attenuated response after exposure to and perception of persistent external stimuli. Based on current neurosciences research, the originality of this rule includes modulated response to variable frequencies of the captured stimuli. Filtering out repetitive data from the natural habituation mechanism has been demonstrated to be a key factor in the attention phenomenon, and inserting such a rule operating at multiple temporal dimensions of stimuli increases a robot's adaptive behaviours by ignoring broader contextual irrelevant information. (paper)

  10. Habituation: a non-associative learning rule design for spiking neurons and an autonomous mobile robots implementation.

    Science.gov (United States)

    Cyr, André; Boukadoum, Mounir

    2013-03-01

    This paper presents a novel bio-inspired habituation function for robots under control by an artificial spiking neural network. This non-associative learning rule is modelled at the synaptic level and validated through robotic behaviours in reaction to different stimuli patterns in a dynamical virtual 3D world. Habituation is minimally represented to show an attenuated response after exposure to and perception of persistent external stimuli. Based on current neurosciences research, the originality of this rule includes modulated response to variable frequencies of the captured stimuli. Filtering out repetitive data from the natural habituation mechanism has been demonstrated to be a key factor in the attention phenomenon, and inserting such a rule operating at multiple temporal dimensions of stimuli increases a robot's adaptive behaviours by ignoring broader contextual irrelevant information.

  11. Mirror neurons: From origin to function

    OpenAIRE

    Cook, R; Bird, G; Catmur, C; Press, C; Heyes, C

    2014-01-01

    This article argues that mirror neurons originate in sensorimotor associative learning and therefore a new approach is needed to investigate their functions. Mirror neurons were discovered about 20 years ago in the monkey brain, and there is now evidence that they are also present in the human brain. The intriguing feature of many mirror neurons is that they fire not only when the animal is performing an action, such as grasping an object using a power grip, but also when the animal passively...

  12. Parallel Education and Defining the Fourth Sector.

    Science.gov (United States)

    Chessell, Diana

    1996-01-01

    Parallel to the primary, secondary, postsecondary, and adult/community education sectors is education not associated with formal programs--learning in arts and cultural sites. The emergence of cultural and educational tourism is an opportunity for adult/community education to define itself by extending lifelong learning opportunities into parallel…

  13. The mirror-neuron system.

    Science.gov (United States)

    Rizzolatti, Giacomo; Craighero, Laila

    2004-01-01

    A category of stimuli of great importance for primates, humans in particular, is that formed by actions done by other individuals. If we want to survive, we must understand the actions of others. Furthermore, without action understanding, social organization is impossible. In the case of humans, there is another faculty that depends on the observation of others' actions: imitation learning. Unlike most species, we are able to learn by imitation, and this faculty is at the basis of human culture. In this review we present data on a neurophysiological mechanism--the mirror-neuron mechanism--that appears to play a fundamental role in both action understanding and imitation. We describe first the functional properties of mirror neurons in monkeys. We review next the characteristics of the mirror-neuron system in humans. We stress, in particular, those properties specific to the human mirror-neuron system that might explain the human capacity to learn by imitation. We conclude by discussing the relationship between the mirror-neuron system and language.

  14. Practical parallel computing

    CERN Document Server

    Morse, H Stephen

    1994-01-01

    Practical Parallel Computing provides information pertinent to the fundamental aspects of high-performance parallel processing. This book discusses the development of parallel applications on a variety of equipment.Organized into three parts encompassing 12 chapters, this book begins with an overview of the technology trends that converge to favor massively parallel hardware over traditional mainframes and vector machines. This text then gives a tutorial introduction to parallel hardware architectures. Other chapters provide worked-out examples of programs using several parallel languages. Thi

  15. Parallel sorting algorithms

    CERN Document Server

    Akl, Selim G

    1985-01-01

    Parallel Sorting Algorithms explains how to use parallel algorithms to sort a sequence of items on a variety of parallel computers. The book reviews the sorting problem, the parallel models of computation, parallel algorithms, and the lower bounds on the parallel sorting problems. The text also presents twenty different algorithms, such as linear arrays, mesh-connected computers, cube-connected computers. Another example where algorithm can be applied is on the shared-memory SIMD (single instruction stream multiple data stream) computers in which the whole sequence to be sorted can fit in the

  16. Introduction to parallel programming

    CERN Document Server

    Brawer, Steven

    1989-01-01

    Introduction to Parallel Programming focuses on the techniques, processes, methodologies, and approaches involved in parallel programming. The book first offers information on Fortran, hardware and operating system models, and processes, shared memory, and simple parallel programs. Discussions focus on processes and processors, joining processes, shared memory, time-sharing with multiple processors, hardware, loops, passing arguments in function/subroutine calls, program structure, and arithmetic expressions. The text then elaborates on basic parallel programming techniques, barriers and race

  17. Parallel computing works!

    CERN Document Server

    Fox, Geoffrey C; Messina, Guiseppe C

    2014-01-01

    A clear illustration of how parallel computers can be successfully appliedto large-scale scientific computations. This book demonstrates how avariety of applications in physics, biology, mathematics and other scienceswere implemented on real parallel computers to produce new scientificresults. It investigates issues of fine-grained parallelism relevant forfuture supercomputers with particular emphasis on hypercube architecture. The authors describe how they used an experimental approach to configuredifferent massively parallel machines, design and implement basic systemsoftware, and develop

  18. Neurons other than motor neurons in motor neuron disease.

    Science.gov (United States)

    Ruffoli, Riccardo; Biagioni, Francesca; Busceti, Carla L; Gaglione, Anderson; Ryskalin, Larisa; Gambardella, Stefano; Frati, Alessandro; Fornai, Francesco

    2017-11-01

    Amyotrophic lateral sclerosis (ALS) is typically defined by a loss of motor neurons in the central nervous system. Accordingly, morphological analysis for decades considered motor neurons (in the cortex, brainstem and spinal cord) as the neuronal population selectively involved in ALS. Similarly, this was considered the pathological marker to score disease severity ex vivo both in patients and experimental models. However, the concept of non-autonomous motor neuron death was used recently to indicate the need for additional cell types to produce motor neuron death in ALS. This means that motor neuron loss occurs only when they are connected with other cell types. This concept originally emphasized the need for resident glia as well as non-resident inflammatory cells. Nowadays, the additional role of neurons other than motor neurons emerged in the scenario to induce non-autonomous motor neuron death. In fact, in ALS neurons diverse from motor neurons are involved. These cells play multiple roles in ALS: (i) they participate in the chain of events to produce motor neuron loss; (ii) they may even degenerate more than and before motor neurons. In the present manuscript evidence about multi-neuronal involvement in ALS patients and experimental models is discussed. Specific sub-classes of neurons in the whole spinal cord are reported either to degenerate or to trigger neuronal degeneration, thus portraying ALS as a whole spinal cord disorder rather than a disease affecting motor neurons solely. This is associated with a novel concept in motor neuron disease which recruits abnormal mechanisms of cell to cell communication.

  19. The origin and function of mirror neurons: the missing link.

    Science.gov (United States)

    Lingnau, Angelika; Caramazza, Alfonso

    2014-04-01

    We argue, by analogy to the neural organization of the object recognition system, that demonstration of modulation of mirror neurons by associative learning does not imply absence of genetic adaptation. Innate connectivity defines the types of processes mirror neurons can participate in while allowing for extensive local plasticity. However, the proper function of these neurons remains to be worked out.

  20. Parallel Atomistic Simulations

    Energy Technology Data Exchange (ETDEWEB)

    HEFFELFINGER,GRANT S.

    2000-01-18

    Algorithms developed to enable the use of atomistic molecular simulation methods with parallel computers are reviewed. Methods appropriate for bonded as well as non-bonded (and charged) interactions are included. While strategies for obtaining parallel molecular simulations have been developed for the full variety of atomistic simulation methods, molecular dynamics and Monte Carlo have received the most attention. Three main types of parallel molecular dynamics simulations have been developed, the replicated data decomposition, the spatial decomposition, and the force decomposition. For Monte Carlo simulations, parallel algorithms have been developed which can be divided into two categories, those which require a modified Markov chain and those which do not. Parallel algorithms developed for other simulation methods such as Gibbs ensemble Monte Carlo, grand canonical molecular dynamics, and Monte Carlo methods for protein structure determination are also reviewed and issues such as how to measure parallel efficiency, especially in the case of parallel Monte Carlo algorithms with modified Markov chains are discussed.

  1. Massively parallel evolutionary computation on GPGPUs

    CERN Document Server

    Tsutsui, Shigeyoshi

    2013-01-01

    Evolutionary algorithms (EAs) are metaheuristics that learn from natural collective behavior and are applied to solve optimization problems in domains such as scheduling, engineering, bioinformatics, and finance. Such applications demand acceptable solutions with high-speed execution using finite computational resources. Therefore, there have been many attempts to develop platforms for running parallel EAs using multicore machines, massively parallel cluster machines, or grid computing environments. Recent advances in general-purpose computing on graphics processing units (GPGPU) have opened u

  2. Drosophila Learn Opposing Components of a Compound Food Stimulus

    Science.gov (United States)

    Das, Gaurav; Klappenbach, Martín; Vrontou, Eleftheria; Perisse, Emmanuel; Clark, Christopher M.; Burke, Christopher J.; Waddell, Scott

    2014-01-01

    Summary Dopaminergic neurons provide value signals in mammals and insects [1–3]. During Drosophila olfactory learning, distinct subsets of dopaminergic neurons appear to assign either positive or negative value to odor representations in mushroom body neurons [4–9]. However, it is not known how flies evaluate substances that have mixed valence. Here we show that flies form short-lived aversive olfactory memories when trained with odors and sugars that are contaminated with the common insect repellent DEET. This DEET-aversive learning required the MB-MP1 dopaminergic neurons that are also required for shock learning [7]. Moreover, differential conditioning with DEET versus shock suggests that formation of these distinct aversive olfactory memories relies on a common negatively reinforcing dopaminergic mechanism. Surprisingly, as time passed after training, the behavior of DEET-sugar-trained flies reversed from conditioned odor avoidance into odor approach. In addition, flies that were compromised for reward learning exhibited a more robust and longer-lived aversive-DEET memory. These data demonstrate that flies independently process the DEET and sugar components to form parallel aversive and appetitive olfactory memories, with distinct kinetics, that compete to guide learned behavior. PMID:25042590

  3. Mirror neurons: from origin to function.

    Science.gov (United States)

    Cook, Richard; Bird, Geoffrey; Catmur, Caroline; Press, Clare; Heyes, Cecilia

    2014-04-01

    This article argues that mirror neurons originate in sensorimotor associative learning and therefore a new approach is needed to investigate their functions. Mirror neurons were discovered about 20 years ago in the monkey brain, and there is now evidence that they are also present in the human brain. The intriguing feature of many mirror neurons is that they fire not only when the animal is performing an action, such as grasping an object using a power grip, but also when the animal passively observes a similar action performed by another agent. It is widely believed that mirror neurons are a genetic adaptation for action understanding; that they were designed by evolution to fulfill a specific socio-cognitive function. In contrast, we argue that mirror neurons are forged by domain-general processes of associative learning in the course of individual development, and, although they may have psychological functions, they do not necessarily have a specific evolutionary purpose or adaptive function. The evidence supporting this view shows that (1) mirror neurons do not consistently encode action "goals"; (2) the contingency- and context-sensitive nature of associative learning explains the full range of mirror neuron properties; (3) human infants receive enough sensorimotor experience to support associative learning of mirror neurons ("wealth of the stimulus"); and (4) mirror neurons can be changed in radical ways by sensorimotor training. The associative account implies that reliable information about the function of mirror neurons can be obtained only by research based on developmental history, system-level theory, and careful experimentation.

  4. Neuron Learning to Network Organization.

    Science.gov (United States)

    1983-12-20

    Inrhcrt )l lintaion ’.lclti Itl, dc% chop’. aind cstcird’. to all \\. vial ceills inl area 17 1t tile .irrirrai is lcarcd. and bchal c’. frciv Ii a...detecting cells in visual cortex", Biol. Cybernetics 19, 1-18 (1975). Perez, R. L. Glass and R. J. Shaler, **Development of specificity in the cat

  5. Learning-related brain hemispheric dominance in sleeping songbirds.

    Science.gov (United States)

    Moorman, Sanne; Gobes, Sharon M H; van de Kamp, Ferdinand C; Zandbergen, Matthijs A; Bolhuis, Johan J

    2015-03-12

    There are striking behavioural and neural parallels between the acquisition of speech in humans and song learning in songbirds. In humans, language-related brain activation is mostly lateralised to the left hemisphere. During language acquisition in humans, brain hemispheric lateralisation develops as language proficiency increases. Sleep is important for the formation of long-term memory, in humans as well as in other animals, including songbirds. Here, we measured neuronal activation (as the expression pattern of the immediate early gene ZENK) during sleep in juvenile zebra finch males that were still learning their songs from a tutor. We found that during sleep, there was learning-dependent lateralisation of spontaneous neuronal activation in the caudomedial nidopallium (NCM), a secondary auditory brain region that is involved in tutor song memory, while there was right hemisphere dominance of neuronal activation in HVC (used as a proper name), a premotor nucleus that is involved in song production and sensorimotor learning. Specifically, in the NCM, birds that imitated their tutors well were left dominant, while poor imitators were right dominant, similar to language-proficiency related lateralisation in humans. Given the avian-human parallels, lateralised neural activation during sleep may also be important for speech and language acquisition in human infants.

  6. Learning-related brain hemispheric dominance in sleeping songbirds

    Science.gov (United States)

    Moorman, Sanne; Gobes, Sharon M. H.; van de Kamp, Ferdinand C.; Zandbergen, Matthijs A.; Bolhuis, Johan J.

    2015-01-01

    There are striking behavioural and neural parallels between the acquisition of speech in humans and song learning in songbirds. In humans, language-related brain activation is mostly lateralised to the left hemisphere. During language acquisition in humans, brain hemispheric lateralisation develops as language proficiency increases. Sleep is important for the formation of long-term memory, in humans as well as in other animals, including songbirds. Here, we measured neuronal activation (as the expression pattern of the immediate early gene ZENK) during sleep in juvenile zebra finch males that were still learning their songs from a tutor. We found that during sleep, there was learning-dependent lateralisation of spontaneous neuronal activation in the caudomedial nidopallium (NCM), a secondary auditory brain region that is involved in tutor song memory, while there was right hemisphere dominance of neuronal activation in HVC (used as a proper name), a premotor nucleus that is involved in song production and sensorimotor learning. Specifically, in the NCM, birds that imitated their tutors well were left dominant, while poor imitators were right dominant, similar to language-proficiency related lateralisation in humans. Given the avian-human parallels, lateralised neural activation during sleep may also be important for speech and language acquisition in human infants. PMID:25761654

  7. Parallelization in Modern C++

    CERN Multimedia

    CERN. Geneva

    2016-01-01

    The traditionally used and well established parallel programming models OpenMP and MPI are both targeting lower level parallelism and are meant to be as language agnostic as possible. For a long time, those models were the only widely available portable options for developing parallel C++ applications beyond using plain threads. This has strongly limited the optimization capabilities of compilers, has inhibited extensibility and genericity, and has restricted the use of those models together with other, modern higher level abstractions introduced by the C++11 and C++14 standards. The recent revival of interest in the industry and wider community for the C++ language has also spurred a remarkable amount of standardization proposals and technical specifications being developed. Those efforts however have so far failed to build a vision on how to seamlessly integrate various types of parallelism, such as iterative parallel execution, task-based parallelism, asynchronous many-task execution flows, continuation s...

  8. Parallelism in matrix computations

    CERN Document Server

    Gallopoulos, Efstratios; Sameh, Ahmed H

    2016-01-01

    This book is primarily intended as a research monograph that could also be used in graduate courses for the design of parallel algorithms in matrix computations. It assumes general but not extensive knowledge of numerical linear algebra, parallel architectures, and parallel programming paradigms. The book consists of four parts: (I) Basics; (II) Dense and Special Matrix Computations; (III) Sparse Matrix Computations; and (IV) Matrix functions and characteristics. Part I deals with parallel programming paradigms and fundamental kernels, including reordering schemes for sparse matrices. Part II is devoted to dense matrix computations such as parallel algorithms for solving linear systems, linear least squares, the symmetric algebraic eigenvalue problem, and the singular-value decomposition. It also deals with the development of parallel algorithms for special linear systems such as banded ,Vandermonde ,Toeplitz ,and block Toeplitz systems. Part III addresses sparse matrix computations: (a) the development of pa...

  9. A parallel buffer tree

    DEFF Research Database (Denmark)

    Sitchinava, Nodar; Zeh, Norbert

    2012-01-01

    We present the parallel buffer tree, a parallel external memory (PEM) data structure for batched search problems. This data structure is a non-trivial extension of Arge's sequential buffer tree to a private-cache multiprocessor environment and reduces the number of I/O operations by the number of...... in the optimal OhOf(psortN + K/PB) parallel I/O complexity, where K is the size of the output reported in the process and psortN is the parallel I/O complexity of sorting N elements using P processors....

  10. Parallel MR imaging.

    Science.gov (United States)

    Deshmane, Anagha; Gulani, Vikas; Griswold, Mark A; Seiberlich, Nicole

    2012-07-01

    Parallel imaging is a robust method for accelerating the acquisition of magnetic resonance imaging (MRI) data, and has made possible many new applications of MR imaging. Parallel imaging works by acquiring a reduced amount of k-space data with an array of receiver coils. These undersampled data can be acquired more quickly, but the undersampling leads to aliased images. One of several parallel imaging algorithms can then be used to reconstruct artifact-free images from either the aliased images (SENSE-type reconstruction) or from the undersampled data (GRAPPA-type reconstruction). The advantages of parallel imaging in a clinical setting include faster image acquisition, which can be used, for instance, to shorten breath-hold times resulting in fewer motion-corrupted examinations. In this article the basic concepts behind parallel imaging are introduced. The relationship between undersampling and aliasing is discussed and two commonly used parallel imaging methods, SENSE and GRAPPA, are explained in detail. Examples of artifacts arising from parallel imaging are shown and ways to detect and mitigate these artifacts are described. Finally, several current applications of parallel imaging are presented and recent advancements and promising research in parallel imaging are briefly reviewed. Copyright © 2012 Wiley Periodicals, Inc.

  11. Parallel Algorithms and Patterns

    Energy Technology Data Exchange (ETDEWEB)

    Robey, Robert W. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-06-16

    This is a powerpoint presentation on parallel algorithms and patterns. A parallel algorithm is a well-defined, step-by-step computational procedure that emphasizes concurrency to solve a problem. Examples of problems include: Sorting, searching, optimization, matrix operations. A parallel pattern is a computational step in a sequence of independent, potentially concurrent operations that occurs in diverse scenarios with some frequency. Examples are: Reductions, prefix scans, ghost cell updates. We only touch on parallel patterns in this presentation. It really deserves its own detailed discussion which Gabe Rockefeller would like to develop.

  12. Application Portable Parallel Library

    Science.gov (United States)

    Cole, Gary L.; Blech, Richard A.; Quealy, Angela; Townsend, Scott

    1995-01-01

    Application Portable Parallel Library (APPL) computer program is subroutine-based message-passing software library intended to provide consistent interface to variety of multiprocessor computers on market today. Minimizes effort needed to move application program from one computer to another. User develops application program once and then easily moves application program from parallel computer on which created to another parallel computer. ("Parallel computer" also include heterogeneous collection of networked computers). Written in C language with one FORTRAN 77 subroutine for UNIX-based computers and callable from application programs written in C language or FORTRAN 77.

  13. Where do mirror neurons come from?

    Science.gov (United States)

    Heyes, Cecilia

    2010-03-01

    Debates about the evolution of the 'mirror neuron system' imply that it is an adaptation for action understanding. Alternatively, mirror neurons may be a byproduct of associative learning. Here I argue that the adaptation and associative hypotheses both offer plausible accounts of the origin of mirror neurons, but the associative hypothesis has three advantages. First, it provides a straightforward, testable explanation for the differences between monkeys and humans that have led some researchers to question the existence of a mirror neuron system. Second, it is consistent with emerging evidence that mirror neurons contribute to a range of social cognitive functions, but do not play a dominant, specialised role in action understanding. Finally, the associative hypothesis is supported by recent data showing that, even in adulthood, the mirror neuron system can be transformed by sensorimotor learning. The associative account implies that mirror neurons come from sensorimotor experience, and that much of this experience is obtained through interaction with others. Therefore, if the associative account is correct, the mirror neuron system is a product, as well as a process, of social interaction. (c) 2009 Elsevier Ltd. All rights reserved.

  14. Short- and long-term memory in Drosophila require cAMP signaling in distinct neuron types.

    Science.gov (United States)

    Blum, Allison L; Li, Wanhe; Cressy, Mike; Dubnau, Josh

    2009-08-25

    A common feature of memory and its underlying synaptic plasticity is that each can be dissected into short-lived forms involving modification or trafficking of existing proteins and long-term forms that require new gene expression. An underlying assumption of this cellular view of memory consolidation is that these different mechanisms occur within a single neuron. At the neuroanatomical level, however, different temporal stages of memory can engage distinct neural circuits, a notion that has not been conceptually integrated with the cellular view. Here, we investigated this issue in the context of aversive Pavlovian olfactory memory in Drosophila. Previous studies have demonstrated a central role for cAMP signaling in the mushroom body (MB). The Ca(2+)-responsive adenylyl cyclase RUTABAGA is believed to be a coincidence detector in gamma neurons, one of the three principle classes of MB Kenyon cells. We were able to separately restore short-term or long-term memory to a rutabaga mutant with expression of rutabaga in different subsets of MB neurons. Our findings suggest a model in which the learning experience initiates two parallel associations: a short-lived trace in MB gamma neurons, and a long-lived trace in alpha/beta neurons.

  15. Parallel discrete event simulation

    NARCIS (Netherlands)

    Overeinder, B.J.; Hertzberger, L.O.; Sloot, P.M.A.; Withagen, W.J.

    1991-01-01

    In simulating applications for execution on specific computing systems, the simulation performance figures must be known in a short period of time. One basic approach to the problem of reducing the required simulation time is the exploitation of parallelism. However, in parallelizing the simulation

  16. Parallel reservoir simulator computations

    International Nuclear Information System (INIS)

    Hemanth-Kumar, K.; Young, L.C.

    1995-01-01

    The adaptation of a reservoir simulator for parallel computations is described. The simulator was originally designed for vector processors. It performs approximately 99% of its calculations in vector/parallel mode and relative to scalar calculations it achieves speedups of 65 and 81 for black oil and EOS simulations, respectively on the CRAY C-90

  17. Totally parallel multilevel algorithms

    Science.gov (United States)

    Frederickson, Paul O.

    1988-01-01

    Four totally parallel algorithms for the solution of a sparse linear system have common characteristics which become quite apparent when they are implemented on a highly parallel hypercube such as the CM2. These four algorithms are Parallel Superconvergent Multigrid (PSMG) of Frederickson and McBryan, Robust Multigrid (RMG) of Hackbusch, the FFT based Spectral Algorithm, and Parallel Cyclic Reduction. In fact, all four can be formulated as particular cases of the same totally parallel multilevel algorithm, which are referred to as TPMA. In certain cases the spectral radius of TPMA is zero, and it is recognized to be a direct algorithm. In many other cases the spectral radius, although not zero, is small enough that a single iteration per timestep keeps the local error within the required tolerance.

  18. Massively parallel mathematical sieves

    Energy Technology Data Exchange (ETDEWEB)

    Montry, G.R.

    1989-01-01

    The Sieve of Eratosthenes is a well-known algorithm for finding all prime numbers in a given subset of integers. A parallel version of the Sieve is described that produces computational speedups over 800 on a hypercube with 1,024 processing elements for problems of fixed size. Computational speedups as high as 980 are achieved when the problem size per processor is fixed. The method of parallelization generalizes to other sieves and will be efficient on any ensemble architecture. We investigate two highly parallel sieves using scattered decomposition and compare their performance on a hypercube multiprocessor. A comparison of different parallelization techniques for the sieve illustrates the trade-offs necessary in the design and implementation of massively parallel algorithms for large ensemble computers.

  19. Fitting neuron models to spike trains

    Directory of Open Access Journals (Sweden)

    Cyrille eRossant

    2011-02-01

    Full Text Available Computational modeling is increasingly used to understand the function of neural circuitsin systems neuroscience.These studies require models of individual neurons with realisticinput-output properties.Recently, it was found that spiking models can accurately predict theprecisely timed spike trains produced by cortical neurons in response tosomatically injected currents,if properly fitted. This requires fitting techniques that are efficientand flexible enough to easily test different candidate models.We present a generic solution, based on the Brian simulator(a neural network simulator in Python, which allowsthe user to define and fit arbitrary neuron models to electrophysiological recordings.It relies on vectorization and parallel computing techniques toachieve efficiency.We demonstrate its use on neural recordings in the barrel cortex andin the auditory brainstem, and confirm that simple adaptive spiking modelscan accurately predict the response of cortical neurons. Finally, we show how a complexmulticompartmental model can be reduced to a simple effective spiking model.

  20. Spiking Neurons for Analysis of Patterns

    Science.gov (United States)

    Huntsberger, Terrance

    2008-01-01

    Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern-analysis and pattern-recognition computational systems. These neurons are represented by a mathematical model denoted the state-variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets. Spiking neurons imitate biological neurons more closely than do the neurons of traditional artificial neural networks. A spiking neuron includes a central cell body (soma) surrounded by a tree-like interconnection network (dendrites). Spiking neurons are so named because they generate trains of output pulses (spikes) in response to inputs received from sensors or from other neurons. They gain their speed advantage over traditional neural networks by using the timing of individual spikes for computation, whereas traditional artificial neurons use averages of activity levels over time. Moreover, spiking neurons use the delays inherent in dendritic processing in order to efficiently encode the information content of incoming signals. Because traditional artificial neurons fail to capture this encoding, they have less processing capability, and so it is necessary to use more gates when implementing traditional artificial neurons in electronic circuitry. Such higher-order functions as dynamic tasking are effected by use of pools (collections) of spiking neurons interconnected by spike-transmitting fibers. The SVM includes adaptive thresholds and submodels of transport of ions (in imitation of such transport in biological

  1. Central auditory neurons have composite receptive fields.

    Science.gov (United States)

    Kozlov, Andrei S; Gentner, Timothy Q

    2016-02-02

    High-level neurons processing complex, behaviorally relevant signals are sensitive to conjunctions of features. Characterizing the receptive fields of such neurons is difficult with standard statistical tools, however, and the principles governing their organization remain poorly understood. Here, we demonstrate multiple distinct receptive-field features in individual high-level auditory neurons in a songbird, European starling, in response to natural vocal signals (songs). We then show that receptive fields with similar characteristics can be reproduced by an unsupervised neural network trained to represent starling songs with a single learning rule that enforces sparseness and divisive normalization. We conclude that central auditory neurons have composite receptive fields that can arise through a combination of sparseness and normalization in neural circuits. Our results, along with descriptions of random, discontinuous receptive fields in the central olfactory neurons in mammals and insects, suggest general principles of neural computation across sensory systems and animal classes.

  2. A smart-pixel holographic competitive learning network

    Science.gov (United States)

    Slagle, Timothy Michael

    Neural networks are adaptive classifiers which modify their decision boundaries based on feedback from externally- or internally-generated error signals. Optics is an attractive technology for neural network implementation because it offers the possibility of parallel, nearly instantaneous computation of the weighted neuron inputs by the propagation of light through the optical system. Using current optical device technology, system performance levels of 3 × 1011 connection updates per second can be achieved. This thesis presents an architecture for an optical competitive learning network which offers advantages over previous optical implementations, including smart-pixel-based optical neurons, phase- conjugate self-alignment of a single neuron plane, and high-density, parallel-access weight storage, interconnection, and learning in a volume hologram. The competitive learning algorithm with modifications for optical implementation is described, and algorithm simulations are performed for an example problem. The optical competitive learning architecture is then introduced. The optical system is simulated using the ``beamprop'' algorithm at the level of light propagating through the system components, and results showing competitive learning operation in agreement with the algorithm simulations are presented. The optical competitive learning requires a non-linear, non-local ``winner-take-all'' (WTA) neuron function. Custom-designed smart-pixel WTA neuron arrays were fabricated using CMOS VLSI/liquid crystal technology. Results of laboratory tests of the WTA arrays' switching characteristics, time response, and uniformity are then presented. The system uses a phase-conjugate mirror to write the self-aligning interconnection weight holograms, and energy gain is required from the reflection to minimize erasure of the existing weights. An experimental system for characterizing the PCM response is described. Useful gains of 20 were obtained with a polarization

  3. Algorithms for parallel computers

    International Nuclear Information System (INIS)

    Churchhouse, R.F.

    1985-01-01

    Until relatively recently almost all the algorithms for use on computers had been designed on the (usually unstated) assumption that they were to be run on single processor, serial machines. With the introduction of vector processors, array processors and interconnected systems of mainframes, minis and micros, however, various forms of parallelism have become available. The advantage of parallelism is that it offers increased overall processing speed but it also raises some fundamental questions, including: (i) which, if any, of the existing 'serial' algorithms can be adapted for use in the parallel mode. (ii) How close to optimal can such adapted algorithms be and, where relevant, what are the convergence criteria. (iii) How can we design new algorithms specifically for parallel systems. (iv) For multi-processor systems how can we handle the software aspects of the interprocessor communications. Aspects of these questions illustrated by examples are considered in these lectures. (orig.)

  4. Parallelism and array processing

    International Nuclear Information System (INIS)

    Zacharov, V.

    1983-01-01

    Modern computing, as well as the historical development of computing, has been dominated by sequential monoprocessing. Yet there is the alternative of parallelism, where several processes may be in concurrent execution. This alternative is discussed in a series of lectures, in which the main developments involving parallelism are considered, both from the standpoint of computing systems and that of applications that can exploit such systems. The lectures seek to discuss parallelism in a historical context, and to identify all the main aspects of concurrency in computation right up to the present time. Included will be consideration of the important question as to what use parallelism might be in the field of data processing. (orig.)

  5. Neural Parallel Engine: A toolbox for massively parallel neural signal processing.

    Science.gov (United States)

    Tam, Wing-Kin; Yang, Zhi

    2018-05-01

    Large-scale neural recordings provide detailed information on neuronal activities and can help elicit the underlying neural mechanisms of the brain. However, the computational burden is also formidable when we try to process the huge data stream generated by such recordings. In this study, we report the development of Neural Parallel Engine (NPE), a toolbox for massively parallel neural signal processing on graphical processing units (GPUs). It offers a selection of the most commonly used routines in neural signal processing such as spike detection and spike sorting, including advanced algorithms such as exponential-component-power-component (EC-PC) spike detection and binary pursuit spike sorting. We also propose a new method for detecting peaks in parallel through a parallel compact operation. Our toolbox is able to offer a 5× to 110× speedup compared with its CPU counterparts depending on the algorithms. A user-friendly MATLAB interface is provided to allow easy integration of the toolbox into existing workflows. Previous efforts on GPU neural signal processing only focus on a few rudimentary algorithms, are not well-optimized and often do not provide a user-friendly programming interface to fit into existing workflows. There is a strong need for a comprehensive toolbox for massively parallel neural signal processing. A new toolbox for massively parallel neural signal processing has been created. It can offer significant speedup in processing signals from large-scale recordings up to thousands of channels. Copyright © 2018 Elsevier B.V. All rights reserved.

  6. Mirror neuron system: basic findings and clinical applications.

    Science.gov (United States)

    Iacoboni, Marco; Mazziotta, John C

    2007-09-01

    In primates, ventral premotor and rostral inferior parietal neurons fire during the execution of hand and mouth actions. Some cells (called mirror neurons) also fire when hand and mouth actions are just observed. Mirror neurons provide a simple neural mechanism for understanding the actions of others. In humans, posterior inferior frontal and rostral inferior parietal areas have mirror properties. These human areas are relevant to imitative learning and social behavior. Indeed, the socially isolating condition of autism is associated with a deficit in mirror neuron areas. Strategies inspired by mirror neuron research recently have been used in the treatment of autism and in motor rehabilitation after stroke.

  7. Adaptive gain modulation in V1 explains contextual modifications during bisection learning.

    Directory of Open Access Journals (Sweden)

    Roland Schäfer

    2009-12-01

    Full Text Available The neuronal processing of visual stimuli in primary visual cortex (V1 can be modified by perceptual training. Training in bisection discrimination, for instance, changes the contextual interactions in V1 elicited by parallel lines. Before training, two parallel lines inhibit their individual V1-responses. After bisection training, inhibition turns into non-symmetric excitation while performing the bisection task. Yet, the receptive field of the V1 neurons evaluated by a single line does not change during task performance. We present a model of recurrent processing in V1 where the neuronal gain can be modulated by a global attentional signal. Perceptual learning mainly consists in strengthening this attentional signal, leading to a more effective gain modulation. The model reproduces both the psychophysical results on bisection learning and the modified contextual interactions observed in V1 during task performance. It makes several predictions, for instance that imagery training should improve the performance, or that a slight stimulus wiggling can strongly affect the representation in V1 while performing the task. We conclude that strengthening a top-down induced gain increase can explain perceptual learning, and that this top-down signal can modify lateral interactions within V1, without significantly changing the classical receptive field of V1 neurons.

  8. Parallel magnetic resonance imaging

    International Nuclear Information System (INIS)

    Larkman, David J; Nunes, Rita G

    2007-01-01

    Parallel imaging has been the single biggest innovation in magnetic resonance imaging in the last decade. The use of multiple receiver coils to augment the time consuming Fourier encoding has reduced acquisition times significantly. This increase in speed comes at a time when other approaches to acquisition time reduction were reaching engineering and human limits. A brief summary of spatial encoding in MRI is followed by an introduction to the problem parallel imaging is designed to solve. There are a large number of parallel reconstruction algorithms; this article reviews a cross-section, SENSE, SMASH, g-SMASH and GRAPPA, selected to demonstrate the different approaches. Theoretical (the g-factor) and practical (coil design) limits to acquisition speed are reviewed. The practical implementation of parallel imaging is also discussed, in particular coil calibration. How to recognize potential failure modes and their associated artefacts are shown. Well-established applications including angiography, cardiac imaging and applications using echo planar imaging are reviewed and we discuss what makes a good application for parallel imaging. Finally, active research areas where parallel imaging is being used to improve data quality by repairing artefacted images are also reviewed. (invited topical review)

  9. Mirror neurons: functions, mechanisms and models.

    Science.gov (United States)

    Oztop, Erhan; Kawato, Mitsuo; Arbib, Michael A

    2013-04-12

    Mirror neurons for manipulation fire both when the animal manipulates an object in a specific way and when it sees another animal (or the experimenter) perform an action that is more or less similar. Such neurons were originally found in macaque monkeys, in the ventral premotor cortex, area F5 and later also in the inferior parietal lobule. Recent neuroimaging data indicate that the adult human brain is endowed with a "mirror neuron system," putatively containing mirror neurons and other neurons, for matching the observation and execution of actions. Mirror neurons may serve action recognition in monkeys as well as humans, whereas their putative role in imitation and language may be realized in human but not in monkey. This article shows the important role of computational models in providing sufficient and causal explanations for the observed phenomena involving mirror systems and the learning processes which form them, and underlines the need for additional circuitry to lift up the monkey mirror neuron circuit to sustain the posited cognitive functions attributed to the human mirror neuron system. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  10. The STAPL Parallel Graph Library

    KAUST Repository

    Harshvardhan,; Fidel, Adam; Amato, Nancy M.; Rauchwerger, Lawrence

    2013-01-01

    This paper describes the stapl Parallel Graph Library, a high-level framework that abstracts the user from data-distribution and parallelism details and allows them to concentrate on parallel graph algorithm development. It includes a customizable

  11. Development of rat telencephalic neurons after prenatal x-irradiation

    International Nuclear Information System (INIS)

    Norton, S.

    1979-01-01

    Telencephalic neurons of rats, irradiated at day 15 of gestation with 125 R, develop synaptic connections on dendrites during maturation which appear to be normal spines in Golgi-stained light microscope preparations. At six weeks of postnatal age both control and irradiated rats have spiny dendritic processes on cortical pyramidal cells and caudate Golgi type II neurons. However, when the rats are 6 months old the irradiated rats have more neurons with beaded dendritic processes that lack spines or neurons and are likely to be degenerating neurons. The apparently normal development of the neurons followed by degeneration in the irradiated rat has a parallel in previous reports of the delayed hyperactivity which develops in rats irradiated on the fifteenth gestational day

  12. Neurons on the couch.

    Science.gov (United States)

    Marić, Nadja P; Jašović-Gašić, Miroslava

    2010-12-01

    A hundred years after psychoanalysis was introduced, neuroscience has taken a giant step forward. It seems nowadays that effects of psychotherapy could be monitored and measured by state-of-the art brain imaging techniques. Today, the psychotherapy is considered as a strategic and purposeful environmental influence intended to enhance learning. Since gene expression is regulated by environmental influences throughout life and these processes create brain architecture and influence the strength of synaptic connections, psychotherapy (as a kind of learning) should be explored in the context of aforementioned paradigm. In other words, when placing a client on the couch, therapist actually placed client's neuronal network; while listening and talking, expressing and analyzing, experiencing transference and counter transference, therapist tends to stabilize synaptic connections and influence dendritic growth by regulating gene-transcriptional activity. Therefore, we strongly believe that, in the near future, an increasing knowledge on cellular and molecular interactions and mechanisms of action of different psycho- and pharmaco-therapeutic procedures will enable us to tailor a sophisticated therapeutic approach toward a person, by combining major therapeutic strategies in psychiatry on the basis of rational goals and evidence-based therapeutic expectations.

  13. Massively parallel multicanonical simulations

    Science.gov (United States)

    Gross, Jonathan; Zierenberg, Johannes; Weigel, Martin; Janke, Wolfhard

    2018-03-01

    Generalized-ensemble Monte Carlo simulations such as the multicanonical method and similar techniques are among the most efficient approaches for simulations of systems undergoing discontinuous phase transitions or with rugged free-energy landscapes. As Markov chain methods, they are inherently serial computationally. It was demonstrated recently, however, that a combination of independent simulations that communicate weight updates at variable intervals allows for the efficient utilization of parallel computational resources for multicanonical simulations. Implementing this approach for the many-thread architecture provided by current generations of graphics processing units (GPUs), we show how it can be efficiently employed with of the order of 104 parallel walkers and beyond, thus constituting a versatile tool for Monte Carlo simulations in the era of massively parallel computing. We provide the fully documented source code for the approach applied to the paradigmatic example of the two-dimensional Ising model as starting point and reference for practitioners in the field.

  14. SPINning parallel systems software

    International Nuclear Information System (INIS)

    Matlin, O.S.; Lusk, E.; McCune, W.

    2002-01-01

    We describe our experiences in using Spin to verify parts of the Multi Purpose Daemon (MPD) parallel process management system. MPD is a distributed collection of processes connected by Unix network sockets. MPD is dynamic processes and connections among them are created and destroyed as MPD is initialized, runs user processes, recovers from faults, and terminates. This dynamic nature is easily expressible in the Spin/Promela framework but poses performance and scalability challenges. We present here the results of expressing some of the parallel algorithms of MPD and executing both simulation and verification runs with Spin

  15. Parallel programming with Python

    CERN Document Server

    Palach, Jan

    2014-01-01

    A fast, easy-to-follow and clear tutorial to help you develop Parallel computing systems using Python. Along with explaining the fundamentals, the book will also introduce you to slightly advanced concepts and will help you in implementing these techniques in the real world. If you are an experienced Python programmer and are willing to utilize the available computing resources by parallelizing applications in a simple way, then this book is for you. You are required to have a basic knowledge of Python development to get the most of this book.

  16. Neuronal Migration Disorders

    Science.gov (United States)

    ... Understanding Sleep The Life and Death of a Neuron Genes At Work In The Brain Order Publications ... birth defects caused by the abnormal migration of neurons in the developing brain and nervous system. In ...

  17. Motor Neuron Diseases

    Science.gov (United States)

    ... and other neurodegenerative diseases to better understand the function of neurons and other support cells and identify candidate therapeutic ... and other neurodegenerative diseases to better understand the function of neurons and other support cells and identify candidate therapeutic ...

  18. New technologies for examining the role of neuronal ensembles in drug addiction and fear.

    Science.gov (United States)

    Cruz, Fabio C; Koya, Eisuke; Guez-Barber, Danielle H; Bossert, Jennifer M; Lupica, Carl R; Shaham, Yavin; Hope, Bruce T

    2013-11-01

    Correlational data suggest that learned associations are encoded within neuronal ensembles. However, it has been difficult to prove that neuronal ensembles mediate learned behaviours because traditional pharmacological and lesion methods, and even newer cell type-specific methods, affect both activated and non-activated neurons. In addition, previous studies on synaptic and molecular alterations induced by learning did not distinguish between behaviourally activated and non-activated neurons. Here, we describe three new approaches--Daun02 inactivation, FACS sorting of activated neurons and Fos-GFP transgenic rats--that have been used to selectively target and study activated neuronal ensembles in models of conditioned drug effects and relapse. We also describe two new tools--Fos-tTA transgenic mice and inactivation of CREB-overexpressing neurons--that have been used to study the role of neuronal ensembles in conditioned fear.

  19. New technologies for examining neuronal ensembles in drug addiction and fear

    Science.gov (United States)

    Cruz, Fabio C.; Koya, Eisuke; Guez-Barber, Danielle H.; Bossert, Jennifer M.; Lupica, Carl R.; Shaham, Yavin; Hope, Bruce T.

    2015-01-01

    Correlational data suggest that learned associations are encoded within neuronal ensembles. However, it has been difficult to prove that neuronal ensembles mediate learned behaviours because traditional pharmacological and lesion methods, and even newer cell type-specific methods, affect both activated and non-activated neurons. Additionally, previous studies on synaptic and molecular alterations induced by learning did not distinguish between behaviourally activated and non-activated neurons. Here, we describe three new approaches—Daun02 inactivation, FACS sorting of activated neurons and c-fos-GFP transgenic rats — that have been used to selectively target and study activated neuronal ensembles in models of conditioned drug effects and relapse. We also describe two new tools — c-fos-tTA mice and inactivation of CREB-overexpressing neurons — that have been used to study the role of neuronal ensembles in conditioned fear. PMID:24088811

  20. Expressing Parallelism with ROOT

    Energy Technology Data Exchange (ETDEWEB)

    Piparo, D. [CERN; Tejedor, E. [CERN; Guiraud, E. [CERN; Ganis, G. [CERN; Mato, P. [CERN; Moneta, L. [CERN; Valls Pla, X. [CERN; Canal, P. [Fermilab

    2017-11-22

    The need for processing the ever-increasing amount of data generated by the LHC experiments in a more efficient way has motivated ROOT to further develop its support for parallelism. Such support is being tackled both for shared-memory and distributed-memory environments. The incarnations of the aforementioned parallelism are multi-threading, multi-processing and cluster-wide executions. In the area of multi-threading, we discuss the new implicit parallelism and related interfaces, as well as the new building blocks to safely operate with ROOT objects in a multi-threaded environment. Regarding multi-processing, we review the new MultiProc framework, comparing it with similar tools (e.g. multiprocessing module in Python). Finally, as an alternative to PROOF for cluster-wide executions, we introduce the efforts on integrating ROOT with state-of-the-art distributed data processing technologies like Spark, both in terms of programming model and runtime design (with EOS as one of the main components). For all the levels of parallelism, we discuss, based on real-life examples and measurements, how our proposals can increase the productivity of scientists.

  1. Expressing Parallelism with ROOT

    Science.gov (United States)

    Piparo, D.; Tejedor, E.; Guiraud, E.; Ganis, G.; Mato, P.; Moneta, L.; Valls Pla, X.; Canal, P.

    2017-10-01

    The need for processing the ever-increasing amount of data generated by the LHC experiments in a more efficient way has motivated ROOT to further develop its support for parallelism. Such support is being tackled both for shared-memory and distributed-memory environments. The incarnations of the aforementioned parallelism are multi-threading, multi-processing and cluster-wide executions. In the area of multi-threading, we discuss the new implicit parallelism and related interfaces, as well as the new building blocks to safely operate with ROOT objects in a multi-threaded environment. Regarding multi-processing, we review the new MultiProc framework, comparing it with similar tools (e.g. multiprocessing module in Python). Finally, as an alternative to PROOF for cluster-wide executions, we introduce the efforts on integrating ROOT with state-of-the-art distributed data processing technologies like Spark, both in terms of programming model and runtime design (with EOS as one of the main components). For all the levels of parallelism, we discuss, based on real-life examples and measurements, how our proposals can increase the productivity of scientists.

  2. Parallel Fast Legendre Transform

    NARCIS (Netherlands)

    Alves de Inda, M.; Bisseling, R.H.; Maslen, D.K.

    1998-01-01

    We discuss a parallel implementation of a fast algorithm for the discrete polynomial Legendre transform We give an introduction to the DriscollHealy algorithm using polynomial arithmetic and present experimental results on the eciency and accuracy of our implementation The algorithms were

  3. Practical parallel programming

    CERN Document Server

    Bauer, Barr E

    2014-01-01

    This is the book that will teach programmers to write faster, more efficient code for parallel processors. The reader is introduced to a vast array of procedures and paradigms on which actual coding may be based. Examples and real-life simulations using these devices are presented in C and FORTRAN.

  4. Parallel hierarchical radiosity rendering

    Energy Technology Data Exchange (ETDEWEB)

    Carter, Michael [Iowa State Univ., Ames, IA (United States)

    1993-07-01

    In this dissertation, the step-by-step development of a scalable parallel hierarchical radiosity renderer is documented. First, a new look is taken at the traditional radiosity equation, and a new form is presented in which the matrix of linear system coefficients is transformed into a symmetric matrix, thereby simplifying the problem and enabling a new solution technique to be applied. Next, the state-of-the-art hierarchical radiosity methods are examined for their suitability to parallel implementation, and scalability. Significant enhancements are also discovered which both improve their theoretical foundations and improve the images they generate. The resultant hierarchical radiosity algorithm is then examined for sources of parallelism, and for an architectural mapping. Several architectural mappings are discussed. A few key algorithmic changes are suggested during the process of making the algorithm parallel. Next, the performance, efficiency, and scalability of the algorithm are analyzed. The dissertation closes with a discussion of several ideas which have the potential to further enhance the hierarchical radiosity method, or provide an entirely new forum for the application of hierarchical methods.

  5. Parallel universes beguile science

    CERN Multimedia

    2007-01-01

    A staple of mind-bending science fiction, the possibility of multiple universes has long intrigued hard-nosed physicists, mathematicians and cosmologists too. We may not be able -- as least not yet -- to prove they exist, many serious scientists say, but there are plenty of reasons to think that parallel dimensions are more than figments of eggheaded imagination.

  6. Parallel k-means++

    Energy Technology Data Exchange (ETDEWEB)

    2017-04-04

    A parallelization of the k-means++ seed selection algorithm on three distinct hardware platforms: GPU, multicore CPU, and multithreaded architecture. K-means++ was developed by David Arthur and Sergei Vassilvitskii in 2007 as an extension of the k-means data clustering technique. These algorithms allow people to cluster multidimensional data, by attempting to minimize the mean distance of data points within a cluster. K-means++ improved upon traditional k-means by using a more intelligent approach to selecting the initial seeds for the clustering process. While k-means++ has become a popular alternative to traditional k-means clustering, little work has been done to parallelize this technique. We have developed original C++ code for parallelizing the algorithm on three unique hardware architectures: GPU using NVidia's CUDA/Thrust framework, multicore CPU using OpenMP, and the Cray XMT multithreaded architecture. By parallelizing the process for these platforms, we are able to perform k-means++ clustering much more quickly than it could be done before.

  7. Parallel plate detectors

    International Nuclear Information System (INIS)

    Gardes, D.; Volkov, P.

    1981-01-01

    A 5x3cm 2 (timing only) and a 15x5cm 2 (timing and position) parallel plate avalanche counters (PPAC) are considered. The theory of operation and timing resolution is given. The measurement set-up and the curves of experimental results illustrate the possibilities of the two counters [fr

  8. Parallel hierarchical global illumination

    Energy Technology Data Exchange (ETDEWEB)

    Snell, Quinn O. [Iowa State Univ., Ames, IA (United States)

    1997-10-08

    Solving the global illumination problem is equivalent to determining the intensity of every wavelength of light in all directions at every point in a given scene. The complexity of the problem has led researchers to use approximation methods for solving the problem on serial computers. Rather than using an approximation method, such as backward ray tracing or radiosity, the authors have chosen to solve the Rendering Equation by direct simulation of light transport from the light sources. This paper presents an algorithm that solves the Rendering Equation to any desired accuracy, and can be run in parallel on distributed memory or shared memory computer systems with excellent scaling properties. It appears superior in both speed and physical correctness to recent published methods involving bidirectional ray tracing or hybrid treatments of diffuse and specular surfaces. Like progressive radiosity methods, it dynamically refines the geometry decomposition where required, but does so without the excessive storage requirements for ray histories. The algorithm, called Photon, produces a scene which converges to the global illumination solution. This amounts to a huge task for a 1997-vintage serial computer, but using the power of a parallel supercomputer significantly reduces the time required to generate a solution. Currently, Photon can be run on most parallel environments from a shared memory multiprocessor to a parallel supercomputer, as well as on clusters of heterogeneous workstations.

  9. Neuromorphic function learning with carbon nanotube based synapses

    International Nuclear Information System (INIS)

    Gacem, Karim; Filoramo, Arianna; Derycke, Vincent; Retrouvey, Jean-Marie; Chabi, Djaafar; Zhao, Weisheng; Klein, Jacques-Olivier

    2013-01-01

    The principle of using nanoscale memory devices as artificial synapses in neuromorphic circuits is recognized as a promising way to build ground-breaking circuit architectures tolerant to defects and variability. Yet, actual experimental demonstrations of the neural network type of circuits based on non-conventional/non-CMOS memory devices and displaying function learning capabilities remain very scarce. We show here that carbon-nanotube-based memory elements can be used as artificial synapses, combined with conventional neurons and trained to perform functions through the application of a supervised learning algorithm. The same ensemble of eight devices can notably be trained multiple times to code successively any three-input linearly separable Boolean logic function despite device-to-device variability. This work thus represents one of the very few demonstrations of actual function learning with synapses based on nanoscale building blocks. The potential of such an approach for the parallel learning of multiple and more complex functions is also evaluated. (paper)

  10. 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

  11. A Topological Model for Parallel Algorithm Design

    Science.gov (United States)

    1991-09-01

    effort should be directed to planning, requirements analysis, specification and design, with 20% invested into the actual coding, and then the final 40...be olle more language to learn. And by investing the effort into improving the utility of ai, existing language instead of creating a new one, this...193) it abandons the notion of a process as a fundemental concept of parallel program design and that it facilitates program derivation by rigorously

  12. Modulation of neuronal network activity with ghrelin

    NARCIS (Netherlands)

    Stoyanova, Irina; Rutten, Wim; le Feber, Jakob

    2012-01-01

    Ghrelin is a neuropeptide regulating multiple physiological processes, including high brain functions such as learning and memory formation. However, the effect of ghrelin on network activity patterns and developments has not been studied yet. Therefore, we used dissociated cortical neurons plated

  13. Parallel grid population

    Science.gov (United States)

    Wald, Ingo; Ize, Santiago

    2015-07-28

    Parallel population of a grid with a plurality of objects using a plurality of processors. One example embodiment is a method for parallel population of a grid with a plurality of objects using a plurality of processors. The method includes a first act of dividing a grid into n distinct grid portions, where n is the number of processors available for populating the grid. The method also includes acts of dividing a plurality of objects into n distinct sets of objects, assigning a distinct set of objects to each processor such that each processor determines by which distinct grid portion(s) each object in its distinct set of objects is at least partially bounded, and assigning a distinct grid portion to each processor such that each processor populates its distinct grid portion with any objects that were previously determined to be at least partially bounded by its distinct grid portion.

  14. Ultrascalable petaflop parallel supercomputer

    Science.gov (United States)

    Blumrich, Matthias A [Ridgefield, CT; Chen, Dong [Croton On Hudson, NY; Chiu, George [Cross River, NY; Cipolla, Thomas M [Katonah, NY; Coteus, Paul W [Yorktown Heights, NY; Gara, Alan G [Mount Kisco, NY; Giampapa, Mark E [Irvington, NY; Hall, Shawn [Pleasantville, NY; Haring, Rudolf A [Cortlandt Manor, NY; Heidelberger, Philip [Cortlandt Manor, NY; Kopcsay, Gerard V [Yorktown Heights, NY; Ohmacht, Martin [Yorktown Heights, NY; Salapura, Valentina [Chappaqua, NY; Sugavanam, Krishnan [Mahopac, NY; Takken, Todd [Brewster, NY

    2010-07-20

    A massively parallel supercomputer of petaOPS-scale includes node architectures based upon System-On-a-Chip technology, where each processing node comprises a single Application Specific Integrated Circuit (ASIC) having up to four processing elements. The ASIC nodes are interconnected by multiple independent networks that optimally maximize the throughput of packet communications between nodes with minimal latency. The multiple networks may include three high-speed networks for parallel algorithm message passing including a Torus, collective network, and a Global Asynchronous network that provides global barrier and notification functions. These multiple independent networks may be collaboratively or independently utilized according to the needs or phases of an algorithm for optimizing algorithm processing performance. The use of a DMA engine is provided to facilitate message passing among the nodes without the expenditure of processing resources at the node.

  15. More parallel please

    DEFF Research Database (Denmark)

    Gregersen, Frans; Josephson, Olle; Kristoffersen, Gjert

    of departure that English may be used in parallel with the various local, in this case Nordic, languages. As such, the book integrates the challenge of internationalization faced by any university with the wish to improve quality in research, education and administration based on the local language......Abstract [en] More parallel, please is the result of the work of an Inter-Nordic group of experts on language policy financed by the Nordic Council of Ministers 2014-17. The book presents all that is needed to plan, practice and revise a university language policy which takes as its point......(s). There are three layers in the text: First, you may read the extremely brief version of the in total 11 recommendations for best practice. Second, you may acquaint yourself with the extended version of the recommendations and finally, you may study the reasoning behind each of them. At the end of the text, we give...

  16. PARALLEL MOVING MECHANICAL SYSTEMS

    Directory of Open Access Journals (Sweden)

    Florian Ion Tiberius Petrescu

    2014-09-01

    Full Text Available Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 Moving mechanical systems parallel structures are solid, fast, and accurate. Between parallel systems it is to be noticed Stewart platforms, as the oldest systems, fast, solid and precise. The work outlines a few main elements of Stewart platforms. Begin with the geometry platform, kinematic elements of it, and presented then and a few items of dynamics. Dynamic primary element on it means the determination mechanism kinetic energy of the entire Stewart platforms. It is then in a record tail cinematic mobile by a method dot matrix of rotation. If a structural mottoelement consists of two moving elements which translates relative, drive train and especially dynamic it is more convenient to represent the mottoelement as a single moving components. We have thus seven moving parts (the six motoelements or feet to which is added mobile platform 7 and one fixed.

  17. Xyce parallel electronic simulator.

    Energy Technology Data Exchange (ETDEWEB)

    Keiter, Eric R; Mei, Ting; Russo, Thomas V.; Rankin, Eric Lamont; Schiek, Richard Louis; Thornquist, Heidi K.; Fixel, Deborah A.; Coffey, Todd S; Pawlowski, Roger P; Santarelli, Keith R.

    2010-05-01

    This document is a reference guide to the Xyce Parallel Electronic Simulator, and is a companion document to the Xyce Users Guide. The focus of this document is (to the extent possible) exhaustively list device parameters, solver options, parser options, and other usage details of Xyce. This document is not intended to be a tutorial. Users who are new to circuit simulation are better served by the Xyce Users Guide.

  18. Stability of parallel flows

    CERN Document Server

    Betchov, R

    2012-01-01

    Stability of Parallel Flows provides information pertinent to hydrodynamical stability. This book explores the stability problems that occur in various fields, including electronics, mechanics, oceanography, administration, economics, as well as naval and aeronautical engineering. Organized into two parts encompassing 10 chapters, this book starts with an overview of the general equations of a two-dimensional incompressible flow. This text then explores the stability of a laminar boundary layer and presents the equation of the inviscid approximation. Other chapters present the general equation

  19. Algorithmically specialized parallel computers

    CERN Document Server

    Snyder, Lawrence; Gannon, Dennis B

    1985-01-01

    Algorithmically Specialized Parallel Computers focuses on the concept and characteristics of an algorithmically specialized computer.This book discusses the algorithmically specialized computers, algorithmic specialization using VLSI, and innovative architectures. The architectures and algorithms for digital signal, speech, and image processing and specialized architectures for numerical computations are also elaborated. Other topics include the model for analyzing generalized inter-processor, pipelined architecture for search tree maintenance, and specialized computer organization for raster

  20. Plastic changes to dendritic spines on layer V pyramidal neurons are involved in the rectifying role of the prefrontal cortex during the fast period of motor learning.

    Science.gov (United States)

    González-Tapia, David; Martínez-Torres, Nestor I; Hernández-González, Marisela; Guevara, Miguel Angel; González-Burgos, Ignacio

    2016-02-01

    The prefrontal cortex participates in the rectification of information related to motor activity that favors motor learning. Dendritic spine plasticity is involved in the modifications of motor patterns that underlie both motor activity and motor learning. To study this association in more detail, adult male rats were trained over six days in an acrobatic motor learning paradigm and they were subjected to a behavioral evaluation on each day of training. Also, a Golgi-based morphological study was carried out to determine the spine density and the proportion of the different spine types. In the learning paradigm, the number of errors diminished as motor training progressed. Concomitantly, spine density increased on days 1 and 3 of training, particularly reflecting an increase in the proportion of thin (day 1), stubby (day 1) and branched (days 1, 2 and 5) spines. Conversely, mushroom spines were less prevalent than in the control rats on days 5 and 6, as were stubby spines on day 6, together suggesting that this plasticity might enhance motor learning. The increase in stubby spines on day 1 suggests a regulation of excitability related to the changes in synaptic input to the prefrontal cortex. The plasticity to thin spines observed during the first 3 days of training could be related to the active rectification induced by the information relayed to the prefrontal cortex -as the behavioral findings indeed showed-, which in turn could be linked to the lower proportion of mushroom and stubby spines seen in the last days of training. Copyright © 2015 Elsevier B.V. All rights reserved.

  1. Noise and neuronal populations conspire to encode simple waveforms reliably

    Science.gov (United States)

    Parnas, B. R.

    1996-01-01

    Sensory systems rely on populations of neurons to encode information transduced at the periphery into meaningful patterns of neuronal population activity. This transduction occurs in the presence of intrinsic neuronal noise. This is fortunate. The presence of noise allows more reliable encoding of the temporal structure present in the stimulus than would be possible in a noise-free environment. Simulations with a parallel model of signal processing at the auditory periphery have been used to explore the effects of noise and a neuronal population on the encoding of signal information. The results show that, for a given set of neuronal modeling parameters and stimulus amplitude, there is an optimal amount of noise for stimulus encoding with maximum fidelity.

  2. New Reflections on Mirror Neuron Research, the Tower of Babel, and Intercultural Education

    Science.gov (United States)

    Westbrook, Timothy Paul

    2015-01-01

    Studies of the human mirror neuron system demonstrate how mental mimicking of one's social environment affects learning. The mirror neuron system also has implications for intercultural encounters. This article explores the common ground between the mirror neuron system and theological principles from the Tower of Babel narrative and applies them…

  3. Mechanisms underlying the social enhancement of vocal learning in songbirds.

    Science.gov (United States)

    Chen, Yining; Matheson, Laura E; Sakata, Jon T

    2016-06-14

    Social processes profoundly influence speech and language acquisition. Despite the importance of social influences, little is known about how social interactions modulate vocal learning. Like humans, songbirds learn their vocalizations during development, and they provide an excellent opportunity to reveal mechanisms of social influences on vocal learning. Using yoked experimental designs, we demonstrate that social interactions with adult tutors for as little as 1 d significantly enhanced vocal learning. Social influences on attention to song seemed central to the social enhancement of learning because socially tutored birds were more attentive to the tutor's songs than passively tutored birds, and because variation in attentiveness and in the social modulation of attention significantly predicted variation in vocal learning. Attention to song was influenced by both the nature and amount of tutor song: Pupils paid more attention to songs that tutors directed at them and to tutors that produced fewer songs. Tutors altered their song structure when directing songs at pupils in a manner that resembled how humans alter their vocalizations when speaking to infants, that was distinct from how tutors changed their songs when singing to females, and that could influence attention and learning. Furthermore, social interactions that rapidly enhanced learning increased the activity of noradrenergic and dopaminergic midbrain neurons. These data highlight striking parallels between humans and songbirds in the social modulation of vocal learning and suggest that social influences on attention and midbrain circuitry could represent shared mechanisms underlying the social modulation of vocal learning.

  4. Precise synaptic efficacy alignment suggests potentiation dominated learning

    Directory of Open Access Journals (Sweden)

    Christoph eHartmann

    2016-01-01

    Full Text Available Recent evidence suggests that parallel synapses from the same axonal branch onto the same dendritic branch have almost identical strength. It has been proposed that this alignment is only possible through learning rules that integrate activity over long time spans. However, learning mechanisms such as spike-timing-dependent plasticity (STDP are commonly assumed to be temporally local. Here, we propose that the combination of temporally local STDP and a multiplicative synaptic normalization mechanism is sufficient to explain the alignment of parallel synapses.To address this issue, we introduce three increasingly complex models: First, we model the idealized interaction of STDP and synaptic normalization in a single neuron as a simple stochastic process and derive analytically that the alignment effect can be described by a so-called Kesten process. From this we can derive that synaptic efficacy alignment requires potentiation-dominated learning regimes. We verify these conditions in a single-neuron model with independent spiking activities but more realistic synapses. As expected, we only observe synaptic efficacy alignment for long-term potentiation-biased STDP. Finally, we explore how well the findings transfer to recurrent neural networks where the learning mechanisms interact with the correlated activity of the network. We find that due to the self-reinforcing correlations in recurrent circuits under STDP, alignment occurs for both long-term potentiation- and depression-biased STDP, because the learning will be potentiation dominated in both cases due to the potentiating events induced by correlated activity. This is in line with recent results demonstrating a dominance of potentiation over depression during waking and normalization during sleep. This leads us to predict that individual spine pairs will be more similar in the morning than they are after sleep depriviation.In conclusion, we show that synaptic normalization in conjunction with

  5. Large-scale modelling of neuronal systems

    International Nuclear Information System (INIS)

    Castellani, G.; Verondini, E.; Giampieri, E.; Bersani, F.; Remondini, D.; Milanesi, L.; Zironi, I.

    2009-01-01

    The brain is, without any doubt, the most, complex system of the human body. Its complexity is also due to the extremely high number of neurons, as well as the huge number of synapses connecting them. Each neuron is capable to perform complex tasks, like learning and memorizing a large class of patterns. The simulation of large neuronal systems is challenging for both technological and computational reasons, and can open new perspectives for the comprehension of brain functioning. A well-known and widely accepted model of bidirectional synaptic plasticity, the BCM model, is stated by a differential equation approach based on bistability and selectivity properties. We have modified the BCM model extending it from a single-neuron to a whole-network model. This new model is capable to generate interesting network topologies starting from a small number of local parameters, describing the interaction between incoming and outgoing links from each neuron. We have characterized this model in terms of complex network theory, showing how this, learning rule can be a support For network generation.

  6. Memristors Empower Spiking Neurons With Stochasticity

    KAUST Repository

    Al-Shedivat, Maruan

    2015-06-01

    Recent theoretical studies have shown that probabilistic spiking can be interpreted as learning and inference in cortical microcircuits. This interpretation creates new opportunities for building neuromorphic systems driven by probabilistic learning algorithms. However, such systems must have two crucial features: 1) the neurons should follow a specific behavioral model, and 2) stochastic spiking should be implemented efficiently for it to be scalable. This paper proposes a memristor-based stochastically spiking neuron that fulfills these requirements. First, the analytical model of the memristor is enhanced so it can capture the behavioral stochasticity consistent with experimentally observed phenomena. The switching behavior of the memristor model is demonstrated to be akin to the firing of the stochastic spike response neuron model, the primary building block for probabilistic algorithms in spiking neural networks. Furthermore, the paper proposes a neural soma circuit that utilizes the intrinsic nondeterminism of memristive switching for efficient spike generation. The simulations and analysis of the behavior of a single stochastic neuron and a winner-take-all network built of such neurons and trained on handwritten digits confirm that the circuit can be used for building probabilistic sampling and pattern adaptation machinery in spiking networks. The findings constitute an important step towards scalable and efficient probabilistic neuromorphic platforms. © 2011 IEEE.

  7. SOFTWARE FOR DESIGNING PARALLEL APPLICATIONS

    Directory of Open Access Journals (Sweden)

    M. K. Bouza

    2017-01-01

    Full Text Available The object of research is the tools to support the development of parallel programs in C/C ++. The methods and software which automates the process of designing parallel applications are proposed.

  8. Parallel External Memory Graph Algorithms

    DEFF Research Database (Denmark)

    Arge, Lars Allan; Goodrich, Michael T.; Sitchinava, Nodari

    2010-01-01

    In this paper, we study parallel I/O efficient graph algorithms in the Parallel External Memory (PEM) model, one o f the private-cache chip multiprocessor (CMP) models. We study the fundamental problem of list ranking which leads to efficient solutions to problems on trees, such as computing lowest...... an optimal speedup of ¿(P) in parallel I/O complexity and parallel computation time, compared to the single-processor external memory counterparts....

  9. Failure of Neuronal Maturation in Alzheimer Disease Dentate Gyrus

    Science.gov (United States)

    Li, Bin; Yamamori, Hidenaga; Tatebayashi, Yoshitaka; Shafit-Zagardo, Bridget; Tanimukai, Hitoshi; Chen, She; Iqbal, Khalid; Grundke-Iqbal, Inge

    2011-01-01

    The dentate gyrus, an important anatomic structure of the hippocampal formation, is one of the major areas in which neurogenesis takes place in the adult mammalian brain. Neurogenesis in the dentate gyrus is thought to play an important role in hippocampus-dependent learning and memory. Neurogenesis has been reported to be increased in the dentate gyrus of patients with Alzheimer disease, but it is not known whether the newly generated neurons differentiate into mature neurons. In this study, the expression of the mature neuronal marker high molecular weight microtubule-associated protein (MAP) isoforms MAP2a and b was found to be dramatically decreased in Alzheimer disease dentate gyrus, as determined by immunohistochemistry and in situ hybridization. The total MAP2, including expression of the immature neuronal marker, the MAP2c isoform, was less affected. These findings suggest that newly generated neurons in Alzheimer disease dentate gyrus do not become mature neurons, although neuroproliferation is increased. PMID:18091557

  10. Automatically tracking neurons in a moving and deforming brain.

    Directory of Open Access Journals (Sweden)

    Jeffrey P Nguyen

    2017-05-01

    Full Text Available Advances in optical neuroimaging techniques now allow neural activity to be recorded with cellular resolution in awake and behaving animals. Brain motion in these recordings pose a unique challenge. The location of individual neurons must be tracked in 3D over time to accurately extract single neuron activity traces. Recordings from small invertebrates like C. elegans are especially challenging because they undergo very large brain motion and deformation during animal movement. Here we present an automated computer vision pipeline to reliably track populations of neurons with single neuron resolution in the brain of a freely moving C. elegans undergoing large motion and deformation. 3D volumetric fluorescent images of the animal's brain are straightened, aligned and registered, and the locations of neurons in the images are found via segmentation. Each neuron is then assigned an identity using a new time-independent machine-learning approach we call Neuron Registration Vector Encoding. In this approach, non-rigid point-set registration is used to match each segmented neuron in each volume with a set of reference volumes taken from throughout the recording. The way each neuron matches with the references defines a feature vector which is clustered to assign an identity to each neuron in each volume. Finally, thin-plate spline interpolation is used to correct errors in segmentation and check consistency of assigned identities. The Neuron Registration Vector Encoding approach proposed here is uniquely well suited for tracking neurons in brains undergoing large deformations. When applied to whole-brain calcium imaging recordings in freely moving C. elegans, this analysis pipeline located 156 neurons for the duration of an 8 minute recording and consistently found more neurons more quickly than manual or semi-automated approaches.

  11. Parallel inter channel interaction mechanisms

    International Nuclear Information System (INIS)

    Jovic, V.; Afgan, N.; Jovic, L.

    1995-01-01

    Parallel channels interactions are examined. For experimental researches of nonstationary regimes flow in three parallel vertical channels results of phenomenon analysis and mechanisms of parallel channel interaction for adiabatic condition of one-phase fluid and two-phase mixture flow are shown. (author)

  12. Massively Parallel QCD

    International Nuclear Information System (INIS)

    Soltz, R; Vranas, P; Blumrich, M; Chen, D; Gara, A; Giampap, M; Heidelberger, P; Salapura, V; Sexton, J; Bhanot, G

    2007-01-01

    The theory of the strong nuclear force, Quantum Chromodynamics (QCD), can be numerically simulated from first principles on massively-parallel supercomputers using the method of Lattice Gauge Theory. We describe the special programming requirements of lattice QCD (LQCD) as well as the optimal supercomputer hardware architectures that it suggests. We demonstrate these methods on the BlueGene massively-parallel supercomputer and argue that LQCD and the BlueGene architecture are a natural match. This can be traced to the simple fact that LQCD is a regular lattice discretization of space into lattice sites while the BlueGene supercomputer is a discretization of space into compute nodes, and that both are constrained by requirements of locality. This simple relation is both technologically important and theoretically intriguing. The main result of this paper is the speedup of LQCD using up to 131,072 CPUs on the largest BlueGene/L supercomputer. The speedup is perfect with sustained performance of about 20% of peak. This corresponds to a maximum of 70.5 sustained TFlop/s. At these speeds LQCD and BlueGene are poised to produce the next generation of strong interaction physics theoretical results

  13. A Parallel Butterfly Algorithm

    KAUST Repository

    Poulson, Jack; Demanet, Laurent; Maxwell, Nicholas; Ying, Lexing

    2014-01-01

    The butterfly algorithm is a fast algorithm which approximately evaluates a discrete analogue of the integral transform (Equation Presented.) at large numbers of target points when the kernel, K(x, y), is approximately low-rank when restricted to subdomains satisfying a certain simple geometric condition. In d dimensions with O(Nd) quasi-uniformly distributed source and target points, when each appropriate submatrix of K is approximately rank-r, the running time of the algorithm is at most O(r2Nd logN). A parallelization of the butterfly algorithm is introduced which, assuming a message latency of α and per-process inverse bandwidth of β, executes in at most (Equation Presented.) time using p processes. This parallel algorithm was then instantiated in the form of the open-source DistButterfly library for the special case where K(x, y) = exp(iΦ(x, y)), where Φ(x, y) is a black-box, sufficiently smooth, real-valued phase function. Experiments on Blue Gene/Q demonstrate impressive strong-scaling results for important classes of phase functions. Using quasi-uniform sources, hyperbolic Radon transforms, and an analogue of a three-dimensional generalized Radon transform were, respectively, observed to strong-scale from 1-node/16-cores up to 1024-nodes/16,384-cores with greater than 90% and 82% efficiency, respectively. © 2014 Society for Industrial and Applied Mathematics.

  14. A Parallel Butterfly Algorithm

    KAUST Repository

    Poulson, Jack

    2014-02-04

    The butterfly algorithm is a fast algorithm which approximately evaluates a discrete analogue of the integral transform (Equation Presented.) at large numbers of target points when the kernel, K(x, y), is approximately low-rank when restricted to subdomains satisfying a certain simple geometric condition. In d dimensions with O(Nd) quasi-uniformly distributed source and target points, when each appropriate submatrix of K is approximately rank-r, the running time of the algorithm is at most O(r2Nd logN). A parallelization of the butterfly algorithm is introduced which, assuming a message latency of α and per-process inverse bandwidth of β, executes in at most (Equation Presented.) time using p processes. This parallel algorithm was then instantiated in the form of the open-source DistButterfly library for the special case where K(x, y) = exp(iΦ(x, y)), where Φ(x, y) is a black-box, sufficiently smooth, real-valued phase function. Experiments on Blue Gene/Q demonstrate impressive strong-scaling results for important classes of phase functions. Using quasi-uniform sources, hyperbolic Radon transforms, and an analogue of a three-dimensional generalized Radon transform were, respectively, observed to strong-scale from 1-node/16-cores up to 1024-nodes/16,384-cores with greater than 90% and 82% efficiency, respectively. © 2014 Society for Industrial and Applied Mathematics.

  15. Fast parallel event reconstruction

    CERN Multimedia

    CERN. Geneva

    2010-01-01

    On-line processing of large data volumes produced in modern HEP experiments requires using maximum capabilities of modern and future many-core CPU and GPU architectures.One of such powerful feature is a SIMD instruction set, which allows packing several data items in one register and to operate on all of them, thus achievingmore operations per clock cycle. Motivated by the idea of using the SIMD unit ofmodern processors, the KF based track fit has been adapted for parallelism, including memory optimization, numerical analysis, vectorization with inline operator overloading, and optimization using SDKs. The speed of the algorithm has been increased in 120000 times with 0.1 ms/track, running in parallel on 16 SPEs of a Cell Blade computer.  Running on a Nehalem CPU with 8 cores it shows the processing speed of 52 ns/track using the Intel Threading Building Blocks. The same KF algorithm running on an Nvidia GTX 280 in the CUDA frameworkprovi...

  16. Neurons in primary motor cortex engaged during action observation.

    Science.gov (United States)

    Dushanova, Juliana; Donoghue, John

    2010-01-01

    Neurons in higher cortical areas appear to become active during action observation, either by mirroring observed actions (termed mirror neurons) or by eliciting mental rehearsal of observed motor acts. We report the existence of neurons in the primary motor cortex (M1), an area that is generally considered to initiate and guide movement performance, responding to viewed actions. Multielectrode recordings in monkeys performing or observing a well-learned step-tracking task showed that approximately half of the M1 neurons that were active when monkeys performed the task were also active when they observed the action being performed by a human. These 'view' neurons were spatially intermingled with 'do' neurons, which are active only during movement performance. Simultaneously recorded 'view' neurons comprised two groups: approximately 38% retained the same preferred direction (PD) and timing during performance and viewing, and the remainder (62%) changed their PDs and time lag during viewing as compared with performance. Nevertheless, population activity during viewing was sufficient to predict the direction and trajectory of viewed movements as action unfolded, although less accurately than during performance. 'View' neurons became less active and contained poorer representations of action when only subcomponents of the task were being viewed. M1 'view' neurons thus appear to reflect aspects of a learned movement when observed in others, and form part of a broadly engaged set of cortical areas routinely responding to learned behaviors. These findings suggest that viewing a learned action elicits replay of aspects of M1 activity needed to perform the observed action, and could additionally reflect processing related to understanding, learning or mentally rehearsing action.

  17. Separate groups of dopamine neurons innervate caudate head and tail encoding flexible and stable value memories

    Directory of Open Access Journals (Sweden)

    Hyoung F Kim

    2014-10-01

    Full Text Available Dopamine neurons are thought to be critical for reward value-based learning by modifying synaptic transmissions in the striatum. Yet, different regions of the striatum seem to guide different kinds of learning. Do dopamine neurons contribute to the regional differences of the striatum in learning? As a first step to answer this question, we examined whether the head and tail of the caudate nucleus of the monkey (Macaca mulatta receive inputs from the same or different dopamine neurons. We chose these caudate regions because we previously showed that caudate head neurons learn values of visual objects quickly and flexibly, whereas caudate tail neurons learn object values slowly but retain them stably. Here we confirmed the functional difference by recording single neuronal activity while the monkey performed the flexible and stable value tasks, and then injected retrograde tracers in the functional domains of caudate head and tail. The projecting dopaminergic neurons were identified using tyrosine hydroxylase immunohistochemistry. We found that two groups of dopamine neurons in the substantia nigra pars compacta project largely separately to the caudate head and tail. These groups of dopamine neurons were mostly separated topographically: head-projecting neurons were located in the rostral-ventral-medial region, while tail-projecting neurons were located in the caudal-dorsal-lateral regions of the substantia nigra. Furthermore, they showed different morphological features: tail-projecting neurons were larger and less circular than head-projecting neurons. Our data raise the possibility that different groups of dopamine neurons selectively guide learning of flexible (short-term and stable (long-term memories of object values.

  18. Kappe neurons, a novel population of olfactory sensory neurons

    OpenAIRE

    Ahuja, Gaurav; Nia, Shahrzad Bozorg; Zapilko, Veronika; Shiriagin, Vladimir; Kowatschew, Daniel; Oka, Yuichiro; Korsching, Sigrun I.

    2014-01-01

    Perception of olfactory stimuli is mediated by distinct populations of olfactory sensory neurons, each with a characteristic set of morphological as well as functional parameters. Beyond two large populations of ciliated and microvillous neurons, a third population, crypt neurons, has been identified in teleost and cartilaginous fishes. We report here a novel, fourth olfactory sensory neuron population in zebrafish, which we named kappe neurons for their characteristic shape. Kappe neurons ar...

  19. Mathematical Abstraction: Constructing Concept of Parallel Coordinates

    Science.gov (United States)

    Nurhasanah, F.; Kusumah, Y. S.; Sabandar, J.; Suryadi, D.

    2017-09-01

    Mathematical abstraction is an important process in teaching and learning mathematics so pre-service mathematics teachers need to understand and experience this process. One of the theoretical-methodological frameworks for studying this process is Abstraction in Context (AiC). Based on this framework, abstraction process comprises of observable epistemic actions, Recognition, Building-With, Construction, and Consolidation called as RBC + C model. This study investigates and analyzes how pre-service mathematics teachers constructed and consolidated concept of Parallel Coordinates in a group discussion. It uses AiC framework for analyzing mathematical abstraction of a group of pre-service teachers consisted of four students in learning Parallel Coordinates concepts. The data were collected through video recording, students’ worksheet, test, and field notes. The result shows that the students’ prior knowledge related to concept of the Cartesian coordinate has significant role in the process of constructing Parallel Coordinates concept as a new knowledge. The consolidation process is influenced by the social interaction between group members. The abstraction process taken place in this group were dominated by empirical abstraction that emphasizes on the aspect of identifying characteristic of manipulated or imagined object during the process of recognizing and building-with.

  20. Parallel Computing in SCALE

    International Nuclear Information System (INIS)

    DeHart, Mark D.; Williams, Mark L.; Bowman, Stephen M.

    2010-01-01

    The SCALE computational architecture has remained basically the same since its inception 30 years ago, although constituent modules and capabilities have changed significantly. This SCALE concept was intended to provide a framework whereby independent codes can be linked to provide a more comprehensive capability than possible with the individual programs - allowing flexibility to address a wide variety of applications. However, the current system was designed originally for mainframe computers with a single CPU and with significantly less memory than today's personal computers. It has been recognized that the present SCALE computation system could be restructured to take advantage of modern hardware and software capabilities, while retaining many of the modular features of the present system. Preliminary work is being done to define specifications and capabilities for a more advanced computational architecture. This paper describes the state of current SCALE development activities and plans for future development. With the release of SCALE 6.1 in 2010, a new phase of evolutionary development will be available to SCALE users within the TRITON and NEWT modules. The SCALE (Standardized Computer Analyses for Licensing Evaluation) code system developed by Oak Ridge National Laboratory (ORNL) provides a comprehensive and integrated package of codes and nuclear data for a wide range of applications in criticality safety, reactor physics, shielding, isotopic depletion and decay, and sensitivity/uncertainty (S/U) analysis. Over the last three years, since the release of version 5.1 in 2006, several important new codes have been introduced within SCALE, and significant advances applied to existing codes. Many of these new features became available with the release of SCALE 6.0 in early 2009. However, beginning with SCALE 6.1, a first generation of parallel computing is being introduced. In addition to near-term improvements, a plan for longer term SCALE enhancement

  1. Parallel Polarization State Generation.

    Science.gov (United States)

    She, Alan; Capasso, Federico

    2016-05-17

    The control of polarization, an essential property of light, is of wide scientific and technological interest. The general problem of generating arbitrary time-varying states of polarization (SOP) has always been mathematically formulated by a series of linear transformations, i.e. a product of matrices, imposing a serial architecture. Here we show a parallel architecture described by a sum of matrices. The theory is experimentally demonstrated by modulating spatially-separated polarization components of a laser using a digital micromirror device that are subsequently beam combined. This method greatly expands the parameter space for engineering devices that control polarization. Consequently, performance characteristics, such as speed, stability, and spectral range, are entirely dictated by the technologies of optical intensity modulation, including absorption, reflection, emission, and scattering. This opens up important prospects for polarization state generation (PSG) with unique performance characteristics with applications in spectroscopic ellipsometry, spectropolarimetry, communications, imaging, and security.

  2. NEURON and Python.

    Science.gov (United States)

    Hines, Michael L; Davison, Andrew P; Muller, Eilif

    2009-01-01

    The NEURON simulation program now allows Python to be used, alone or in combination with NEURON's traditional Hoc interpreter. Adding Python to NEURON has the immediate benefit of making available a very extensive suite of analysis tools written for engineering and science. It also catalyzes NEURON software development by offering users a modern programming tool that is recognized for its flexibility and power to create and maintain complex programs. At the same time, nothing is lost because all existing models written in Hoc, including graphical user interface tools, continue to work without change and are also available within the Python context. An example of the benefits of Python availability is the use of the xml module in implementing NEURON's Import3D and CellBuild tools to read MorphML and NeuroML model specifications.

  3. Parallel imaging microfluidic cytometer.

    Science.gov (United States)

    Ehrlich, Daniel J; McKenna, Brian K; Evans, James G; Belkina, Anna C; Denis, Gerald V; Sherr, David H; Cheung, Man Ching

    2011-01-01

    By adding an additional degree of freedom from multichannel flow, the parallel microfluidic cytometer (PMC) combines some of the best features of fluorescence-activated flow cytometry (FCM) and microscope-based high-content screening (HCS). The PMC (i) lends itself to fast processing of large numbers of samples, (ii) adds a 1D imaging capability for intracellular localization assays (HCS), (iii) has a high rare-cell sensitivity, and (iv) has an unusual capability for time-synchronized sampling. An inability to practically handle large sample numbers has restricted applications of conventional flow cytometers and microscopes in combinatorial cell assays, network biology, and drug discovery. The PMC promises to relieve a bottleneck in these previously constrained applications. The PMC may also be a powerful tool for finding rare primary cells in the clinic. The multichannel architecture of current PMC prototypes allows 384 unique samples for a cell-based screen to be read out in ∼6-10 min, about 30 times the speed of most current FCM systems. In 1D intracellular imaging, the PMC can obtain protein localization using HCS marker strategies at many times for the sample throughput of charge-coupled device (CCD)-based microscopes or CCD-based single-channel flow cytometers. The PMC also permits the signal integration time to be varied over a larger range than is practical in conventional flow cytometers. The signal-to-noise advantages are useful, for example, in counting rare positive cells in the most difficult early stages of genome-wide screening. We review the status of parallel microfluidic cytometry and discuss some of the directions the new technology may take. Copyright © 2011 Elsevier Inc. All rights reserved.

  4. Synchrony detection and amplification by silicon neurons with STDP synapses.

    Science.gov (United States)

    Bofill-i-petit, Adria; Murray, Alan F

    2004-09-01

    Spike-timing dependent synaptic plasticity (STDP) is a form of plasticity driven by precise spike-timing differences between presynaptic and postsynaptic spikes. Thus, the learning rules underlying STDP are suitable for learning neuronal temporal phenomena such as spike-timing synchrony. It is well known that weight-independent STDP creates unstable learning processes resulting in balanced bimodal weight distributions. In this paper, we present a neuromorphic analog very large scale integration (VLSI) circuit that contains a feedforward network of silicon neurons with STDP synapses. The learning rule implemented can be tuned to have a moderate level of weight dependence. This helps stabilise the learning process and still generates binary weight distributions. From on-chip learning experiments we show that the chip can detect and amplify hierarchical spike-timing synchrony structures embedded in noisy spike trains. The weight distributions of the network emerging from learning are bimodal.

  5. Spinal cord: motor neuron diseases.

    Science.gov (United States)

    Rezania, Kourosh; Roos, Raymond P

    2013-02-01

    Spinal cord motor neuron diseases affect lower motor neurons in the ventral horn. This article focuses on the most common spinal cord motor neuron disease, amyotrophic lateral sclerosis, which also affects upper motor neurons. Also discussed are other motor neuron diseases that only affect the lower motor neurons. Despite the identification of several genes associated with familial amyotrophic lateral sclerosis, the pathogenesis of this complex disease remains elusive. Copyright © 2013 Elsevier Inc. All rights reserved.

  6. Voltage imaging to understand connections and functions of neuronal circuits

    Science.gov (United States)

    Antic, Srdjan D.; Empson, Ruth M.

    2016-01-01

    Understanding of the cellular mechanisms underlying brain functions such as cognition and emotions requires monitoring of membrane voltage at the cellular, circuit, and system levels. Seminal voltage-sensitive dye and calcium-sensitive dye imaging studies have demonstrated parallel detection of electrical activity across populations of interconnected neurons in a variety of preparations. A game-changing advance made in recent years has been the conceptualization and development of optogenetic tools, including genetically encoded indicators of voltage (GEVIs) or calcium (GECIs) and genetically encoded light-gated ion channels (actuators, e.g., channelrhodopsin2). Compared with low-molecular-weight calcium and voltage indicators (dyes), the optogenetic imaging approaches are 1) cell type specific, 2) less invasive, 3) able to relate activity and anatomy, and 4) facilitate long-term recordings of individual cells' activities over weeks, thereby allowing direct monitoring of the emergence of learned behaviors and underlying circuit mechanisms. We highlight the potential of novel approaches based on GEVIs and compare those to calcium imaging approaches. We also discuss how novel approaches based on GEVIs (and GECIs) coupled with genetically encoded actuators will promote progress in our knowledge of brain circuits and systems. PMID:27075539

  7. Neuron-glia metabolic coupling and plasticity.

    Science.gov (United States)

    Magistretti, Pierre J

    2011-04-01

    The focus of the current research projects in my laboratory revolves around the question of metabolic plasticity of neuron-glia coupling. Our hypothesis is that behavioural conditions, such as for example learning or the sleep-wake cycle, in which synaptic plasticity is well documented, or during specific pathological conditions, are accompanied by changes in the regulation of energy metabolism of astrocytes. We have indeed observed that the 'metabolic profile' of astrocytes is modified during the sleep-wake cycle and during conditions mimicking neuroinflammation in the presence or absence of amyloid-β. The effect of amyloid-β on energy metabolism is dependent on its state of aggregation and on internalization of the peptide by astrocytes. Distinct patterns of metabolic activity could be observed during the learning and recall phases in a spatial learning task. Gene expression analysis in activated areas, notably hippocampous and retrosplenial cortex, demonstrated that the expression levels of several genes implicated in astrocyte-neuron metabolic coupling are enhanced by learning. Regarding metabolic plasticity during the sleep-wake cycle, we have observed that the level of expression of a panel of selected genes, which we know are key for neuron-glia metabolic coupling, is modulated by sleep deprivation.

  8. About Parallel Programming: Paradigms, Parallel Execution and Collaborative Systems

    Directory of Open Access Journals (Sweden)

    Loredana MOCEAN

    2009-01-01

    Full Text Available In the last years, there were made efforts for delineation of a stabile and unitary frame, where the problems of logical parallel processing must find solutions at least at the level of imperative languages. The results obtained by now are not at the level of the made efforts. This paper wants to be a little contribution at these efforts. We propose an overview in parallel programming, parallel execution and collaborative systems.

  9. Neuronal-glial trafficking

    International Nuclear Information System (INIS)

    Bachelard, H.S.

    2001-01-01

    Full text: The name 'glia' originates from the Greek word for glue, because astro glia (or astrocytes) were thought only to provide an anatomical framework for the electrically-excitable neurones. However, awareness that astrocytes perform vital roles in protecting the neurones, which they surround, emerged from evidence that they act as neuroprotective K + -sinks, and that they remove potentially toxic extracellular glutamate from the vicinity of the neurones. The astrocytes convert the glutamate to non-toxic glutamine which is returned to the neurones and used to replenish transmitter glutamate. This 'glutamate-glutamine cycle' (established in the 1960s by Berl and his colleagues) also contributes to protecting the neurones against a build-up of toxic ammonia. Glial cells also supply the neurones with components for free-radical scavenging glutathione. Recent studies have revealed that glial cells play a more positive interactive role in furnishing the neurones with fuels. Studies using radioactive 14 C, 13 C-MRS and 15 N-GCMS have revealed that glia produce alanine, lactate and proline for consumption by neurones, with increased formation of neurotransmitter glutamate. On neuronal activation the release of NH 4 + and glutamate from the neurones stimulates glucose uptake and glycolysis in the glia to produce more alanine, which can be regarded as an 'alanine-glutamate cycle' Use of 14 C-labelled precursors provided early evidence that neurotransmitter GABA may be partly derived from glial glutamine, and this has been confirmed recently in vivo by MRS isotopomer analysis of the GABA and glutamine labelled from 13 C-acetate. Relative rates of intermediary metabolism in glia and neurones can be calculated using a combination of [1- 13 C] glucose and [1,2- 13 C] acetate. When glutamate is released by neurones there is a net neuronal loss of TCA intermediates which have to be replenished. Part of this is derived from carboxylation of pyruvate, (pyruvate carboxylase

  10. An FPGA-Based Massively Parallel Neuromorphic Cortex Simulator.

    Science.gov (United States)

    Wang, Runchun M; Thakur, Chetan S; van Schaik, André

    2018-01-01

    This paper presents a massively parallel and scalable neuromorphic cortex simulator designed for simulating large and structurally connected spiking neural networks, such as complex models of various areas of the cortex. The main novelty of this work is the abstraction of a neuromorphic architecture into clusters represented by minicolumns and hypercolumns, analogously to the fundamental structural units observed in neurobiology. Without this approach, simulating large-scale fully connected networks needs prohibitively large memory to store look-up tables for point-to-point connections. Instead, we use a novel architecture, based on the structural connectivity in the neocortex, such that all the required parameters and connections can be stored in on-chip memory. The cortex simulator can be easily reconfigured for simulating different neural networks without any change in hardware structure by programming the memory. A hierarchical communication scheme allows one neuron to have a fan-out of up to 200 k neurons. As a proof-of-concept, an implementation on one Altera Stratix V FPGA was able to simulate 20 million to 2.6 billion leaky-integrate-and-fire (LIF) neurons in real time. We verified the system by emulating a simplified auditory cortex (with 100 million neurons). This cortex simulator achieved a low power dissipation of 1.62 μW per neuron. With the advent of commercially available FPGA boards, our system offers an accessible and scalable tool for the design, real-time simulation, and analysis of large-scale spiking neural networks.

  11. Confounding the origin and function of mirror neurons.

    Science.gov (United States)

    Rizzolatti, Giacomo

    2014-04-01

    Cook et al. argue that mirror neurons originate in sensorimotor associative learning and that their function is determined by their origin. Both these claims are hard to accept. It is here suggested that a major role in the origin of the mirror mechanism is played by top-down connections rather than by associative learning.

  12. Neuronal regulation of homeostasis by nutrient sensing.

    Science.gov (United States)

    Lam, Tony K T

    2010-04-01

    In type 2 diabetes and obesity, the homeostatic control of glucose and energy balance is impaired, leading to hyperglycemia and hyperphagia. Recent studies indicate that nutrient-sensing mechanisms in the body activate negative-feedback systems to regulate energy and glucose homeostasis through a neuronal network. Direct metabolic signaling within the intestine activates gut-brain and gut-brain-liver axes to regulate energy and glucose homeostasis, respectively. In parallel, direct metabolism of nutrients within the hypothalamus regulates food intake and blood glucose levels. These findings highlight the importance of the central nervous system in mediating the ability of nutrient sensing to maintain homeostasis. Futhermore, they provide a physiological and neuronal framework by which enhancing or restoring nutrient sensing in the intestine and the brain could normalize energy and glucose homeostasis in diabetes and obesity.

  13. An FPGA-based silicon neuronal network with selectable excitability silicon neurons

    Directory of Open Access Journals (Sweden)

    Jing eLi

    2012-12-01

    Full Text Available This paper presents a digital silicon neuronal network which simulates the nerve system in creatures and has the ability to execute intelligent tasks, such as associative memory. Two essential elements, the mathematical-structure-based digital spiking silicon neuron (DSSN and the transmitter release based silicon synapse, allow the network to show rich dynamic behaviors and are computationally efficient for hardware implementation. We adopt mixed pipeline and parallel structure and shift operations to design a sufficient large and complex network without excessive hardware resource cost. The network with $256$ full-connected neurons is built on a Digilent Atlys board equipped with a Xilinx Spartan-6 LX45 FPGA. Besides, a memory control block and USB control block are designed to accomplish the task of data communication between the network and the host PC. This paper also describes the mechanism of associative memory performed in the silicon neuronal network. The network is capable of retrieving stored patterns if the inputs contain enough information of them. The retrieving probability increases with the similarity between the input and the stored pattern increasing. Synchronization of neurons is observed when the successful stored pattern retrieval occurs.

  14. Parallel Framework for Cooperative Processes

    Directory of Open Access Journals (Sweden)

    Mitică Craus

    2005-01-01

    Full Text Available This paper describes the work of an object oriented framework designed to be used in the parallelization of a set of related algorithms. The idea behind the system we are describing is to have a re-usable framework for running several sequential algorithms in a parallel environment. The algorithms that the framework can be used with have several things in common: they have to run in cycles and the work should be possible to be split between several "processing units". The parallel framework uses the message-passing communication paradigm and is organized as a master-slave system. Two applications are presented: an Ant Colony Optimization (ACO parallel algorithm for the Travelling Salesman Problem (TSP and an Image Processing (IP parallel algorithm for the Symmetrical Neighborhood Filter (SNF. The implementations of these applications by means of the parallel framework prove to have good performances: approximatively linear speedup and low communication cost.

  15. Single neuron computation

    CERN Document Server

    McKenna, Thomas M; Zornetzer, Steven F

    1992-01-01

    This book contains twenty-two original contributions that provide a comprehensive overview of computational approaches to understanding a single neuron structure. The focus on cellular-level processes is twofold. From a computational neuroscience perspective, a thorough understanding of the information processing performed by single neurons leads to an understanding of circuit- and systems-level activity. From the standpoint of artificial neural networks (ANNs), a single real neuron is as complex an operational unit as an entire ANN, and formalizing the complex computations performed by real n

  16. Parallel Monte Carlo reactor neutronics

    International Nuclear Information System (INIS)

    Blomquist, R.N.; Brown, F.B.

    1994-01-01

    The issues affecting implementation of parallel algorithms for large-scale engineering Monte Carlo neutron transport simulations are discussed. For nuclear reactor calculations, these include load balancing, recoding effort, reproducibility, domain decomposition techniques, I/O minimization, and strategies for different parallel architectures. Two codes were parallelized and tested for performance. The architectures employed include SIMD, MIMD-distributed memory, and workstation network with uneven interactive load. Speedups linear with the number of nodes were achieved

  17. Signals and Circuits in the Purkinje Neuron

    Directory of Open Access Journals (Sweden)

    Ze'ev R Abrams

    2011-09-01

    Full Text Available Purkinje neurons in the cerebellum have over 100,000 inputs organized in an orthogonal geometry, and a single output channel. As the sole output of the cerebellar cortex layer, their complex firing pattern has been associated with motor control and learning. As such they have been extensively modeled and measured using tools ranging from electrophysiology and neuroanatomy, to dynamic systems and artificial intelligence methods. However, there is an alternative approach to analyze and describe the neuronal output of these cells using concepts from Electrical Engineering, particularly signal processing and digital/analog circuits. By viewing the Purkinje neuron as an unknown circuit to be reverse-engineered, we can use the tools that provide the foundations of today’s integrated circuits and communication systems to analyze the Purkinje system at the circuit level. We use Fourier transforms to analyze and isolate the inherent frequency modes in the Purkinje neuron and define 3 unique frequency ranges associated with the cells’ output. Comparing the Purkinje neuron to a signal generator that can be externally modulated adds an entire level of complexity to the functional role of these neurons both in terms of data analysis and information processing, relying on Fourier analysis methods in place of statistical ones. We also re-describe some of the recent literature in the field, using the nomenclature of signal processing. Furthermore, by comparing the experimental data of the past decade with basic electronic circuitry, we can resolve the outstanding controversy in the field, by recognizing that the Purkinje neuron can act as a multivibrator circuit.

  18. Anti-parallel triplexes

    DEFF Research Database (Denmark)

    Kosbar, Tamer R.; Sofan, Mamdouh A.; Waly, Mohamed A.

    2015-01-01

    about 6.1 °C when the TFO strand was modified with Z and the Watson-Crick strand with adenine-LNA (AL). The molecular modeling results showed that, in case of nucleobases Y and Z a hydrogen bond (1.69 and 1.72 Å, respectively) was formed between the protonated 3-aminopropyn-1-yl chain and one...... of the phosphate groups in Watson-Crick strand. Also, it was shown that the nucleobase Y made a good stacking and binding with the other nucleobases in the TFO and Watson-Crick duplex, respectively. In contrast, the nucleobase Z with LNA moiety was forced to twist out of plane of Watson-Crick base pair which......The phosphoramidites of DNA monomers of 7-(3-aminopropyn-1-yl)-8-aza-7-deazaadenine (Y) and 7-(3-aminopropyn-1-yl)-8-aza-7-deazaadenine LNA (Z) are synthesized, and the thermal stability at pH 7.2 and 8.2 of anti-parallel triplexes modified with these two monomers is determined. When, the anti...

  19. Parallel consensual neural networks.

    Science.gov (United States)

    Benediktsson, J A; Sveinsson, J R; Ersoy, O K; Swain, P H

    1997-01-01

    A new type of a neural-network architecture, the parallel consensual neural network (PCNN), is introduced and applied in classification/data fusion of multisource remote sensing and geographic data. The PCNN architecture is based on statistical consensus theory and involves using stage neural networks with transformed input data. The input data are transformed several times and the different transformed data are used as if they were independent inputs. The independent inputs are first classified using the stage neural networks. The output responses from the stage networks are then weighted and combined to make a consensual decision. In this paper, optimization methods are used in order to weight the outputs from the stage networks. Two approaches are proposed to compute the data transforms for the PCNN, one for binary data and another for analog data. The analog approach uses wavelet packets. The experimental results obtained with the proposed approach show that the PCNN outperforms both a conjugate-gradient backpropagation neural network and conventional statistical methods in terms of overall classification accuracy of test data.

  20. A Parallel Particle Swarm Optimizer

    National Research Council Canada - National Science Library

    Schutte, J. F; Fregly, B .J; Haftka, R. T; George, A. D

    2003-01-01

    .... Motivated by a computationally demanding biomechanical system identification problem, we introduce a parallel implementation of a stochastic population based global optimizer, the Particle Swarm...

  1. Patterns for Parallel Software Design

    CERN Document Server

    Ortega-Arjona, Jorge Luis

    2010-01-01

    Essential reading to understand patterns for parallel programming Software patterns have revolutionized the way we think about how software is designed, built, and documented, and the design of parallel software requires you to consider other particular design aspects and special skills. From clusters to supercomputers, success heavily depends on the design skills of software developers. Patterns for Parallel Software Design presents a pattern-oriented software architecture approach to parallel software design. This approach is not a design method in the classic sense, but a new way of managin

  2. Seeing or moving in parallel

    DEFF Research Database (Denmark)

    Christensen, Mark Schram; Ehrsson, H Henrik; Nielsen, Jens Bo

    2013-01-01

    a different network, involving bilateral dorsal premotor cortex (PMd), primary motor cortex, and SMA, was more active when subjects viewed parallel movements while performing either symmetrical or parallel movements. Correlations between behavioral instability and brain activity were present in right lateral...... adduction-abduction movements symmetrically or in parallel with real-time congruent or incongruent visual feedback of the movements. One network, consisting of bilateral superior and middle frontal gyrus and supplementary motor area (SMA), was more active when subjects performed parallel movements, whereas...

  3. Altered learning, memory, and social behavior in type 1 taste receptor subunit 3 knock-out mice are associated with neuronal dysfunction.

    Science.gov (United States)

    Martin, Bronwen; Wang, Rui; Cong, Wei-Na; Daimon, Caitlin M; Wu, Wells W; Ni, Bin; Becker, Kevin G; Lehrmann, Elin; Wood, William H; Zhang, Yongqing; Etienne, Harmonie; van Gastel, Jaana; Azmi, Abdelkrim; Janssens, Jonathan; Maudsley, Stuart

    2017-07-07

    The type 1 taste receptor member 3 (T1R3) is a G protein-coupled receptor involved in sweet-taste perception. Besides the tongue, the T1R3 receptor is highly expressed in brain areas implicated in cognition, including the hippocampus and cortex. As cognitive decline is often preceded by significant metabolic or endocrinological dysfunctions regulated by the sweet-taste perception system, we hypothesized that a disruption of the sweet-taste perception in the brain could have a key role in the development of cognitive dysfunction. To assess the importance of the sweet-taste receptors in the brain, we conducted transcriptomic and proteomic analyses of cortical and hippocampal tissues isolated from T1R3 knock-out (T1R3KO) mice. The effect of an impaired sweet-taste perception system on cognition functions were examined by analyzing synaptic integrity and performing animal behavior on T1R3KO mice. Although T1R3KO mice did not present a metabolically disrupted phenotype, bioinformatic interpretation of the high-dimensionality data indicated a strong neurodegenerative signature associated with significant alterations in pathways involved in neuritogenesis, dendritic growth, and synaptogenesis. Furthermore, a significantly reduced dendritic spine density was observed in T1R3KO mice together with alterations in learning and memory functions as well as sociability deficits. Taken together our data suggest that the sweet-taste receptor system plays an important neurotrophic role in the extralingual central nervous tissue that underpins synaptic function, memory acquisition, and social behavior. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

  4. Learning

    Directory of Open Access Journals (Sweden)

    Mohsen Laabidi

    2014-01-01

    Full Text Available Nowadays learning technologies transformed educational systems with impressive progress of Information and Communication Technologies (ICT. Furthermore, when these technologies are available, affordable and accessible, they represent more than a transformation for people with disabilities. They represent real opportunities with access to an inclusive education and help to overcome the obstacles they met in classical educational systems. In this paper, we will cover basic concepts of e-accessibility, universal design and assistive technologies, with a special focus on accessible e-learning systems. Then, we will present recent research works conducted in our research Laboratory LaTICE toward the development of an accessible online learning environment for persons with disabilities from the design and specification step to the implementation. We will present, in particular, the accessible version “MoodleAcc+” of the well known e-learning platform Moodle as well as new elaborated generic models and a range of tools for authoring and evaluating accessible educational content.

  5. Learning, memory, and the role of neural network architecture.

    Directory of Open Access Journals (Sweden)

    Ann M Hermundstad

    2011-06-01

    Full Text Available The performance of information processing systems, from artificial neural networks to natural neuronal ensembles, depends heavily on the underlying system architecture. In this study, we compare the performance of parallel and layered network architectures during sequential tasks that require both acquisition and retention of information, thereby identifying tradeoffs between learning and memory processes. During the task of supervised, sequential function approximation, networks produce and adapt representations of external information. Performance is evaluated by statistically analyzing the error in these representations while varying the initial network state, the structure of the external information, and the time given to learn the information. We link performance to complexity in network architecture by characterizing local error landscape curvature. We find that variations in error landscape structure give rise to tradeoffs in performance; these include the ability of the network to maximize accuracy versus minimize inaccuracy and produce specific versus generalizable representations of information. Parallel networks generate smooth error landscapes with deep, narrow minima, enabling them to find highly specific representations given sufficient time. While accurate, however, these representations are difficult to generalize. In contrast, layered networks generate rough error landscapes with a variety of local minima, allowing them to quickly find coarse representations. Although less accurate, these representations are easily adaptable. The presence of measurable performance tradeoffs in both layered and parallel networks has implications for understanding the behavior of a wide variety of natural and artificial learning systems.

  6. The Widrow-Hoff algorithm for McCulloch-Pitts type neurons.

    Science.gov (United States)

    Hui, S; Zak, S H

    1994-01-01

    We analyze the convergence properties of the Widrow-Hoff delta rule applied to McCulloch-Pitts type neurons. We give sufficiency conditions under which the learning parameters converge and conditions under which the learning parameters diverge. In particular, we analyze how the learning rate affects the convergence of the learning parameters.

  7. 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

  8. 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.

  9. Neuron-glia metabolic coupling and plasticity

    OpenAIRE

    Magistretti PJ

    2011-01-01

    Abstract The focus of the current research projects in my laboratory revolves around the question of metabolic plasticity of neuron glia coupling. Our hypothesis is that behavioural conditions such as for example learning or the sleep wake cycle in which synaptic plasticity is well documented or during specific pathological conditions are accompanied by changes in the regulation of energy metabolism of astrocytes. We have indeed observed that the 'metabolic profile' of astrocytes is modified...

  10. [Infantile autism and mirror neurons].

    Science.gov (United States)

    Cornelio-Nieto, J O

    2009-02-27

    Infantile autism is a disorder that is characterised by alterations affecting reciprocal social interactions, abnormal verbal and non-verbal communication, poor imaginative activity and a restricted repertoire of activities and interests. The causes of autism remain unknown, but there are a number of different approaches that attempt to explain the neurobiological causes of the syndrome. A recent theory that has been considered is that of a dysfunction in the mirror neuron system (MNS). The MNS is a neuronal complex, originally described in monkeys and also found in humans, that is related with our movements and which offers specific responses to the movements and intended movements of other subjects. This system is believed to underlie processes of imitation and our capacity to learn by imitation. It is also thought to play a role in language acquisition, in expressing the emotions, in understanding what is happening to others and in empathy. Because these functions are altered in children with autism, it has been suggested that there is some dysfunction present in the MNS of those with autism. Dysfunction of the MNS could account for the symptoms that are observed in children with autism.

  11. NeuronBank: a tool for cataloging neuronal circuitry

    Directory of Open Access Journals (Sweden)

    Paul S Katz

    2010-04-01

    Full Text Available The basic unit of any nervous system is the neuron. Therefore, understanding the operation of nervous systems ultimately requires an inventory of their constituent neurons and synaptic connectivity, which form neural circuits. The presence of uniquely identifiable neurons or classes of neurons in many invertebrates has facilitated the construction of cellular-level connectivity diagrams that can be generalized across individuals within a species. Homologous neurons can also be recognized across species. Here we describe NeuronBank.org, a web-based tool that we are developing for cataloging, searching, and analyzing neuronal circuitry within and across species. Information from a single species is represented in an individual branch of NeuronBank. Users can search within a branch or perform queries across branches to look for similarities in neuronal circuits across species. The branches allow for an extensible ontology so that additional characteristics can be added as knowledge grows. Each entry in NeuronBank generates a unique accession ID, allowing it to be easily cited. There is also an automatic link to a Wiki page allowing an encyclopedic explanation of the entry. All of the 44 previously published neurons plus one previously unpublished neuron from the mollusc, Tritonia diomedea, have been entered into a branch of NeuronBank as have 4 previously published neurons from the mollusc, Melibe leonina. The ability to organize information about neuronal circuits will make this information more accessible, ultimately aiding research on these important models.

  12. Refinement of learned skilled movement representation in motor cortex deep output layer

    Science.gov (United States)

    Li, Qian; Ko, Ho; Qian, Zhong-Ming; Yan, Leo Y. C.; Chan, Danny C. W.; Arbuthnott, Gordon; Ke, Ya; Yung, Wing-Ho

    2017-01-01

    The mechanisms underlying the emergence of learned motor skill representation in primary motor cortex (M1) are not well understood. Specifically, how motor representation in the deep output layer 5b (L5b) is shaped by motor learning remains virtually unknown. In rats undergoing motor skill training, we detect a subpopulation of task-recruited L5b neurons that not only become more movement-encoding, but their activities are also more structured and temporally aligned to motor execution with a timescale of refinement in tens-of-milliseconds. Field potentials evoked at L5b in vivo exhibit persistent long-term potentiation (LTP) that parallels motor performance. Intracortical dopamine denervation impairs motor learning, and disrupts the LTP profile as well as the emergent neurodynamical properties of task-recruited L5b neurons. Thus, dopamine-dependent recruitment of L5b neuronal ensembles via synaptic reorganization may allow the motor cortex to generate more temporally structured, movement-encoding output signal from M1 to downstream circuitry that drives increased uniformity and precision of movement during motor learning. PMID:28598433

  13. Candidate glutamatergic neurons in the visual system of Drosophila.

    Directory of Open Access Journals (Sweden)

    Shamprasad Varija Raghu

    Full Text Available The visual system of Drosophila contains approximately 60,000 neurons that are organized in parallel, retinotopically arranged columns. A large number of these neurons have been characterized in great anatomical detail. However, studies providing direct evidence for synaptic signaling and the neurotransmitter used by individual neurons are relatively sparse. Here we present a first layout of neurons in the Drosophila visual system that likely release glutamate as their major neurotransmitter. We identified 33 different types of neurons of the lamina, medulla, lobula and lobula plate. Based on the previous Golgi-staining analysis, the identified neurons are further classified into 16 major subgroups representing lamina monopolar (L, transmedullary (Tm, transmedullary Y (TmY, Y, medulla intrinsic (Mi, Mt, Pm, Dm, Mi Am, bushy T (T, translobula plate (Tlp, lobula intrinsic (Lcn, Lt, Li, lobula plate tangential (LPTCs and lobula plate intrinsic (LPi cell types. In addition, we found 11 cell types that were not described by the previous Golgi analysis. This classification of candidate glutamatergic neurons fosters the future neurogenetic dissection of information processing in circuits of the fly visual system.

  14. Growth of large patterned arrays of neurons using plasma methods

    International Nuclear Information System (INIS)

    Brown, I G; Bjornstad, K A; Blakely, E A; Galvin, J E; Monteiro, O R; Sangyuenyongpipat, S

    2003-01-01

    To understand how large systems of neurons communicate, we need to develop, among other things, methods for growing patterned networks of large numbers of neurons. Success with this challenge will be important to our understanding of how the brain works, as well as to the development of novel kinds of computer architecture that may parallel the organization of the brain. We have investigated the use of metal ion implantation using a vacuum-arc ion source, and plasma deposition with a filtered vacuum-arc system, as a means of forming regions of selective neuronal attachment on surfaces. Lithographic patterns created by the treating surface with ion species that enhance or inhibit neuronal cell attachment allow subsequent proliferation and/or differentiation of the neurons to form desired patterned neural arrays. In the work described here, we used glass microscope slides as substrates, and some of the experiments made use of simple masks to form patterns of ion beam or plasma deposition treated regions. PC-12 rat neurons were then cultured on the treated substrates coated with Type I Collagen, and the growth and differentiation was monitored. Particularly good selective growth was obtained using plasma deposition of diamond-like carbon films of about one hundred Angstroms thickness. Neuron proliferation and the elaboration of dendrites and axons after the addition of nerve growth factor both showed excellent contrast, with prolific growth and differentiation on the treated surfaces and very low growth on the untreated surfaces

  15. Growth of large patterned arrays of neurons using plasma methods

    Energy Technology Data Exchange (ETDEWEB)

    Brown, I G; Bjornstad, K A; Blakely, E A; Galvin, J E; Monteiro, O R; Sangyuenyongpipat, S [Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States)

    2003-05-01

    To understand how large systems of neurons communicate, we need to develop, among other things, methods for growing patterned networks of large numbers of neurons. Success with this challenge will be important to our understanding of how the brain works, as well as to the development of novel kinds of computer architecture that may parallel the organization of the brain. We have investigated the use of metal ion implantation using a vacuum-arc ion source, and plasma deposition with a filtered vacuum-arc system, as a means of forming regions of selective neuronal attachment on surfaces. Lithographic patterns created by the treating surface with ion species that enhance or inhibit neuronal cell attachment allow subsequent proliferation and/or differentiation of the neurons to form desired patterned neural arrays. In the work described here, we used glass microscope slides as substrates, and some of the experiments made use of simple masks to form patterns of ion beam or plasma deposition treated regions. PC-12 rat neurons were then cultured on the treated substrates coated with Type I Collagen, and the growth and differentiation was monitored. Particularly good selective growth was obtained using plasma deposition of diamond-like carbon films of about one hundred Angstroms thickness. Neuron proliferation and the elaboration of dendrites and axons after the addition of nerve growth factor both showed excellent contrast, with prolific growth and differentiation on the treated surfaces and very low growth on the untreated surfaces.

  16. Pattern formation and firing synchronization in networks of map neurons

    International Nuclear Information System (INIS)

    Wang Qingyun; Duan Zhisheng; Huang Lin; Chen Guanrong; Lu Qishao

    2007-01-01

    Patterns and collective phenomena such as firing synchronization are studied in networks of nonhomogeneous oscillatory neurons and mixtures of oscillatory and excitable neurons, with dynamics of each neuron described by a two-dimensional (2D) Rulkov map neuron. It is shown that as the coupling strength is increased, typical patterns emerge spatially, which propagate through the networks in the form of beautiful target waves or parallel ones depending on the size of networks. Furthermore, we investigate the transitions of firing synchronization characterized by the rate of firing when the coupling strength is increased. It is found that there exists an intermediate coupling strength; firing synchronization is minimal simultaneously irrespective of the size of networks. For further increasing the coupling strength, synchronization is enhanced. Since noise is inevitable in real neurons, we also investigate the effects of white noise on firing synchronization for different networks. For the networks of oscillatory neurons, it is shown that firing synchronization decreases when the noise level increases. For the missed networks, firing synchronization is robust under the noise conditions considered in this paper. Results presented in this paper should prove to be valuable for understanding the properties of collective dynamics in real neuronal networks

  17. PARALLEL IMPORT: REALITY FOR RUSSIA

    Directory of Open Access Journals (Sweden)

    Т. А. Сухопарова

    2014-01-01

    Full Text Available Problem of parallel import is urgent question at now. Parallel import legalization in Russia is expedient. Such statement based on opposite experts opinion analysis. At the same time it’s necessary to negative consequences consider of this decision and to apply remedies to its minimization.Purchase on Elibrary.ru > Buy now

  18. Energy-efficient neuron, synapse and STDP integrated circuits.

    Science.gov (United States)

    Cruz-Albrecht, Jose M; Yung, Michael W; Srinivasa, Narayan

    2012-06-01

    Ultra-low energy biologically-inspired neuron and synapse integrated circuits are presented. The synapse includes a spike timing dependent plasticity (STDP) learning rule circuit. These circuits have been designed, fabricated and tested using a 90 nm CMOS process. Experimental measurements demonstrate proper operation. The neuron and the synapse with STDP circuits have an energy consumption of around 0.4 pJ per spike and synaptic operation respectively.

  19. The Galley Parallel File System

    Science.gov (United States)

    Nieuwejaar, Nils; Kotz, David

    1996-01-01

    Most current multiprocessor file systems are designed to use multiple disks in parallel, using the high aggregate bandwidth to meet the growing I/0 requirements of parallel scientific applications. Many multiprocessor file systems provide applications with a conventional Unix-like interface, allowing the application to access multiple disks transparently. This interface conceals the parallelism within the file system, increasing the ease of programmability, but making it difficult or impossible for sophisticated programmers and libraries to use knowledge about their I/O needs to exploit that parallelism. In addition to providing an insufficient interface, most current multiprocessor file systems are optimized for a different workload than they are being asked to support. We introduce Galley, a new parallel file system that is intended to efficiently support realistic scientific multiprocessor workloads. We discuss Galley's file structure and application interface, as well as the performance advantages offered by that interface.

  20. Parallelization of the FLAPW method

    International Nuclear Information System (INIS)

    Canning, A.; Mannstadt, W.; Freeman, A.J.

    1999-01-01

    The FLAPW (full-potential linearized-augmented plane-wave) method is one of the most accurate first-principles methods for determining electronic and magnetic properties of crystals and surfaces. Until the present work, the FLAPW method has been limited to systems of less than about one hundred atoms due to a lack of an efficient parallel implementation to exploit the power and memory of parallel computers. In this work we present an efficient parallelization of the method by division among the processors of the plane-wave components for each state. The code is also optimized for RISC (reduced instruction set computer) architectures, such as those found on most parallel computers, making full use of BLAS (basic linear algebra subprograms) wherever possible. Scaling results are presented for systems of up to 686 silicon atoms and 343 palladium atoms per unit cell, running on up to 512 processors on a CRAY T3E parallel computer

  1. Parallelization of the FLAPW method

    Science.gov (United States)

    Canning, A.; Mannstadt, W.; Freeman, A. J.

    2000-08-01

    The FLAPW (full-potential linearized-augmented plane-wave) method is one of the most accurate first-principles methods for determining structural, electronic and magnetic properties of crystals and surfaces. Until the present work, the FLAPW method has been limited to systems of less than about a hundred atoms due to the lack of an efficient parallel implementation to exploit the power and memory of parallel computers. In this work, we present an efficient parallelization of the method by division among the processors of the plane-wave components for each state. The code is also optimized for RISC (reduced instruction set computer) architectures, such as those found on most parallel computers, making full use of BLAS (basic linear algebra subprograms) wherever possible. Scaling results are presented for systems of up to 686 silicon atoms and 343 palladium atoms per unit cell, running on up to 512 processors on a CRAY T3E parallel supercomputer.

  2. Two Parallel Pathways Assign Opposing Odor Valences during Drosophila Memory Formation

    Directory of Open Access Journals (Sweden)

    Daisuke Yamazaki

    2018-02-01

    Full Text Available During olfactory associative learning in Drosophila, odors activate specific subsets of intrinsic mushroom body (MB neurons. Coincident exposure to either rewards or punishments is thought to activate extrinsic dopaminergic neurons, which modulate synaptic connections between odor-encoding MB neurons and MB output neurons to alter behaviors. However, here we identify two classes of intrinsic MB γ neurons based on cAMP response element (CRE-dependent expression, γCRE-p and γCRE-n, which encode aversive and appetitive valences. γCRE-p and γCRE-n neurons act antagonistically to maintain neutral valences for neutral odors. Activation or inhibition of either cell type upsets this balance, toggling odor preferences to either positive or negative values. The mushroom body output neurons, MBON-γ5β′2a/β′2mp and MBON-γ2α′1, mediate the actions of γCRE-p and γCRE-n neurons. Our data indicate that MB neurons encode valence information, as well as odor information, and this information is integrated through a process involving MBONs to regulate learning and memory.

  3. Auditory stimuli elicit hippocampal neuronal responses during sleep

    Directory of Open Access Journals (Sweden)

    Ekaterina eVinnik

    2012-06-01

    Full Text Available To investigate how hippocampal neurons code behaviorally salient stimuli, we recorded from neurons in the CA1 region of hippocampus in rats while they learned to associate the presence of sound with water reward. Rats learned to alternate between two reward ports at which, in 50 percent of the trials, sound stimuli were presented followed by water reward after a 3-second delay. Sound at the water port predicted subsequent reward delivery in 100 percent of the trials and the absence of sound predicted reward omission. During this task, 40% of recorded neurons fired differently according to which of the 2 reward ports the rat was visiting. A smaller fraction of neurons demonstrated onset response to sound/nosepoke (19% and reward delivery (24%. When the sounds were played during passive wakefulness, 8% of neurons responded with short latency onset responses; 25% of neurons responded to sounds when they were played during sleep. Based on the current findings and the results of previous experiments we propose the existence of two types of hippocampal neuronal responses to sounds: sound-onset responses with very short latency and longer-lasting sound-specific responses that are likely to be present when the animal is actively engaged in the task. During sleep the short-latency responses in hippocampus are intermingled with sustained activity which in the current experiment was detected for 1-2 seconds.

  4. Is Monte Carlo embarrassingly parallel?

    Energy Technology Data Exchange (ETDEWEB)

    Hoogenboom, J. E. [Delft Univ. of Technology, Mekelweg 15, 2629 JB Delft (Netherlands); Delft Nuclear Consultancy, IJsselzoom 2, 2902 LB Capelle aan den IJssel (Netherlands)

    2012-07-01

    Monte Carlo is often stated as being embarrassingly parallel. However, running a Monte Carlo calculation, especially a reactor criticality calculation, in parallel using tens of processors shows a serious limitation in speedup and the execution time may even increase beyond a certain number of processors. In this paper the main causes of the loss of efficiency when using many processors are analyzed using a simple Monte Carlo program for criticality. The basic mechanism for parallel execution is MPI. One of the bottlenecks turn out to be the rendez-vous points in the parallel calculation used for synchronization and exchange of data between processors. This happens at least at the end of each cycle for fission source generation in order to collect the full fission source distribution for the next cycle and to estimate the effective multiplication factor, which is not only part of the requested results, but also input to the next cycle for population control. Basic improvements to overcome this limitation are suggested and tested. Also other time losses in the parallel calculation are identified. Moreover, the threading mechanism, which allows the parallel execution of tasks based on shared memory using OpenMP, is analyzed in detail. Recommendations are given to get the maximum efficiency out of a parallel Monte Carlo calculation. (authors)

  5. Is Monte Carlo embarrassingly parallel?

    International Nuclear Information System (INIS)

    Hoogenboom, J. E.

    2012-01-01

    Monte Carlo is often stated as being embarrassingly parallel. However, running a Monte Carlo calculation, especially a reactor criticality calculation, in parallel using tens of processors shows a serious limitation in speedup and the execution time may even increase beyond a certain number of processors. In this paper the main causes of the loss of efficiency when using many processors are analyzed using a simple Monte Carlo program for criticality. The basic mechanism for parallel execution is MPI. One of the bottlenecks turn out to be the rendez-vous points in the parallel calculation used for synchronization and exchange of data between processors. This happens at least at the end of each cycle for fission source generation in order to collect the full fission source distribution for the next cycle and to estimate the effective multiplication factor, which is not only part of the requested results, but also input to the next cycle for population control. Basic improvements to overcome this limitation are suggested and tested. Also other time losses in the parallel calculation are identified. Moreover, the threading mechanism, which allows the parallel execution of tasks based on shared memory using OpenMP, is analyzed in detail. Recommendations are given to get the maximum efficiency out of a parallel Monte Carlo calculation. (authors)

  6. Parallel integer sorting with medium and fine-scale parallelism

    Science.gov (United States)

    Dagum, Leonardo

    1993-01-01

    Two new parallel integer sorting algorithms, queue-sort and barrel-sort, are presented and analyzed in detail. These algorithms do not have optimal parallel complexity, yet they show very good performance in practice. Queue-sort designed for fine-scale parallel architectures which allow the queueing of multiple messages to the same destination. Barrel-sort is designed for medium-scale parallel architectures with a high message passing overhead. The performance results from the implementation of queue-sort on a Connection Machine CM-2 and barrel-sort on a 128 processor iPSC/860 are given. The two implementations are found to be comparable in performance but not as good as a fully vectorized bucket sort on the Cray YMP.

  7. Template based parallel checkpointing in a massively parallel computer system

    Science.gov (United States)

    Archer, Charles Jens [Rochester, MN; Inglett, Todd Alan [Rochester, MN

    2009-01-13

    A method and apparatus for a template based parallel checkpoint save for a massively parallel super computer system using a parallel variation of the rsync protocol, and network broadcast. In preferred embodiments, the checkpoint data for each node is compared to a template checkpoint file that resides in the storage and that was previously produced. Embodiments herein greatly decrease the amount of data that must be transmitted and stored for faster checkpointing and increased efficiency of the computer system. Embodiments are directed to a parallel computer system with nodes arranged in a cluster with a high speed interconnect that can perform broadcast communication. The checkpoint contains a set of actual small data blocks with their corresponding checksums from all nodes in the system. The data blocks may be compressed using conventional non-lossy data compression algorithms to further reduce the overall checkpoint size.

  8. Worker flexibility in a parallel dual resource constrained job shop

    NARCIS (Netherlands)

    Yue, H.; Slomp, J.; Molleman, E.; van der Zee, D.J.

    2008-01-01

    In this paper we investigate cross-training policies in a dual resource constraint (DRC) parallel job shop where new part types are frequently introduced into the system. Each new part type introduction induces the need for workers to go through a learning curve. A cross-training policy relates to

  9. Parallel education: what is it?

    OpenAIRE

    Amos, Michelle Peta

    2017-01-01

    In the history of education it has long been discussed that single-sex and coeducation are the two models of education present in schools. With the introduction of parallel schools over the last 15 years, there has been very little research into this 'new model'. Many people do not understand what it means for a school to be parallel or they confuse a parallel model with co-education, due to the presence of both boys and girls within the one institution. Therefore, the main obj...

  10. Balanced, parallel operation of flashlamps

    International Nuclear Information System (INIS)

    Carder, B.M.; Merritt, B.T.

    1979-01-01

    A new energy store, the Compensated Pulsed Alternator (CPA), promises to be a cost effective substitute for capacitors to drive flashlamps that pump large Nd:glass lasers. Because the CPA is large and discrete, it will be necessary that it drive many parallel flashlamp circuits, presenting a problem in equal current distribution. Current division to +- 20% between parallel flashlamps has been achieved, but this is marginal for laser pumping. A method is presented here that provides equal current sharing to about 1%, and it includes fused protection against short circuit faults. The method was tested with eight parallel circuits, including both open-circuit and short-circuit fault tests

  11. Kappe neurons, a novel population of olfactory sensory neurons.

    Science.gov (United States)

    Ahuja, Gaurav; Bozorg Nia, Shahrzad; Zapilko, Veronika; Shiriagin, Vladimir; Kowatschew, Daniel; Oka, Yuichiro; Korsching, Sigrun I

    2014-02-10

    Perception of olfactory stimuli is mediated by distinct populations of olfactory sensory neurons, each with a characteristic set of morphological as well as functional parameters. Beyond two large populations of ciliated and microvillous neurons, a third population, crypt neurons, has been identified in teleost and cartilaginous fishes. We report here a novel, fourth olfactory sensory neuron population in zebrafish, which we named kappe neurons for their characteristic shape. Kappe neurons are identified by their Go-like immunoreactivity, and show a distinct spatial distribution within the olfactory epithelium, similar to, but significantly different from that of crypt neurons. Furthermore, kappe neurons project to a single identified target glomerulus within the olfactory bulb, mdg5 of the mediodorsal cluster, whereas crypt neurons are known to project exclusively to the mdg2 glomerulus. Kappe neurons are negative for established markers of ciliated, microvillous and crypt neurons, but appear to have microvilli. Kappe neurons constitute the fourth type of olfactory sensory neurons reported in teleost fishes and their existence suggests that encoding of olfactory stimuli may require a higher complexity than hitherto assumed already in the peripheral olfactory system.

  12. Toxoplasma gondii Actively Inhibits Neuronal Function in Chronically Infected Mice

    Science.gov (United States)

    Haroon, Fahad; Händel, Ulrike; Angenstein, Frank; Goldschmidt, Jürgen; Kreutzmann, Peter; Lison, Holger; Fischer, Klaus-Dieter; Scheich, Henning; Wetzel, Wolfram; Schlüter, Dirk; Budinger, Eike

    2012-01-01

    Upon infection with the obligate intracellular parasite Toxoplasma gondii, fast replicating tachyzoites infect a broad spectrum of host cells including neurons. Under the pressure of the immune response, tachyzoites convert into slow-replicating bradyzoites, which persist as cysts in neurons. Currently, it is unclear whether T. gondii alters the functional activity of neurons, which may contribute to altered behaviour of T. gondii–infected mice and men. In the present study we demonstrate that upon oral infection with T. gondii cysts, chronically infected BALB/c mice lost over time their natural fear against cat urine which was paralleled by the persistence of the parasite in brain regions affecting behaviour and odor perception. Detailed immunohistochemistry showed that in infected neurons not only parasitic cysts but also the host cell cytoplasm and some axons stained positive for Toxoplasma antigen suggesting that parasitic proteins might directly interfere with neuronal function. In fact, in vitro live cell calcium (Ca2+) imaging studies revealed that tachyzoites actively manipulated Ca2+ signalling upon glutamate stimulation leading either to hyper- or hypo-responsive neurons. Experiments with the endoplasmatic reticulum Ca2+ uptake inhibitor thapsigargin indicate that tachyzoites deplete Ca2+ stores in the endoplasmatic reticulum. Furthermore in vivo studies revealed that the activity-dependent uptake of the potassium analogue thallium was reduced in cyst harbouring neurons indicating their functional impairment. The percentage of non-functional neurons increased over time In conclusion, both bradyzoites and tachyzoites functionally silence infected neurons, which may significantly contribute to the altered behaviour of the host. PMID:22530040

  13. Toxoplasma gondii actively inhibits neuronal function in chronically infected mice.

    Directory of Open Access Journals (Sweden)

    Fahad Haroon

    Full Text Available Upon infection with the obligate intracellular parasite Toxoplasma gondii, fast replicating tachyzoites infect a broad spectrum of host cells including neurons. Under the pressure of the immune response, tachyzoites convert into slow-replicating bradyzoites, which persist as cysts in neurons. Currently, it is unclear whether T. gondii alters the functional activity of neurons, which may contribute to altered behaviour of T. gondii-infected mice and men. In the present study we demonstrate that upon oral infection with T. gondii cysts, chronically infected BALB/c mice lost over time their natural fear against cat urine which was paralleled by the persistence of the parasite in brain regions affecting behaviour and odor perception. Detailed immunohistochemistry showed that in infected neurons not only parasitic cysts but also the host cell cytoplasm and some axons stained positive for Toxoplasma antigen suggesting that parasitic proteins might directly interfere with neuronal function. In fact, in vitro live cell calcium (Ca(2+ imaging studies revealed that tachyzoites actively manipulated Ca(2+ signalling upon glutamate stimulation leading either to hyper- or hypo-responsive neurons. Experiments with the endoplasmatic reticulum Ca(2+ uptake inhibitor thapsigargin indicate that tachyzoites deplete Ca(2+ stores in the endoplasmatic reticulum. Furthermore in vivo studies revealed that the activity-dependent uptake of the potassium analogue thallium was reduced in cyst harbouring neurons indicating their functional impairment. The percentage of non-functional neurons increased over time In conclusion, both bradyzoites and tachyzoites functionally silence infected neurons, which may significantly contribute to the altered behaviour of the host.

  14. Stochastic neuron models

    CERN Document Server

    Greenwood, Priscilla E

    2016-01-01

    This book describes a large number of open problems in the theory of stochastic neural systems, with the aim of enticing probabilists to work on them. This includes problems arising from stochastic models of individual neurons as well as those arising from stochastic models of the activities of small and large networks of interconnected neurons. The necessary neuroscience background to these problems is outlined within the text, so readers can grasp the context in which they arise. This book will be useful for graduate students and instructors providing material and references for applying probability to stochastic neuron modeling. Methods and results are presented, but the emphasis is on questions where additional stochastic analysis may contribute neuroscience insight. An extensive bibliography is included. Dr. Priscilla E. Greenwood is a Professor Emerita in the Department of Mathematics at the University of British Columbia. Dr. Lawrence M. Ward is a Professor in the Department of Psychology and the Brain...

  15. Control of neuropeptide expression by parallel activity-dependent pathways in caenorhabditis elegans

    DEFF Research Database (Denmark)

    Rojo Romanos, Teresa; Petersen, Jakob Gramstrup; Pocock, Roger

    2017-01-01

    Monitoring of neuronal activity within circuits facilitates integrated responses and rapid changes in behavior. We have identified a system in Caenorhabditis elegans where neuropeptide expression is dependent on the ability of the BAG neurons to sense carbon dioxide. In C. Elegans, CO 2 sensing...... is predominantly coordinated by the BAG-expressed receptor-type guanylate cyclase GCY-9. GCY-9 binding to CO 2 causes accumulation of cyclic GMP and opening of the cGMP-gated TAX-2/TAX-4 cation channels; provoking an integrated downstream cascade that enables C. Elegans to avoid high CO 2. Here we show that c...... that expression of flp-19::GFP is controlled in parallel to GCY-9 by the activity-dependent transcription factor CREB (CRH-1) and the cAMP-dependent protein kinase (KIN-2) signaling pathway. We therefore show that two parallel pathways regulate neuropeptide gene expression in the BAG sensory neurons: the ability...

  16. Workspace Analysis for Parallel Robot

    Directory of Open Access Journals (Sweden)

    Ying Sun

    2013-05-01

    Full Text Available As a completely new-type of robot, the parallel robot possesses a lot of advantages that the serial robot does not, such as high rigidity, great load-carrying capacity, small error, high precision, small self-weight/load ratio, good dynamic behavior and easy control, hence its range is extended in using domain. In order to find workspace of parallel mechanism, the numerical boundary-searching algorithm based on the reverse solution of kinematics and limitation of link length has been introduced. This paper analyses position workspace, orientation workspace of parallel robot of the six degrees of freedom. The result shows: It is a main means to increase and decrease its workspace to change the length of branch of parallel mechanism; The radius of the movement platform has no effect on the size of workspace, but will change position of workspace.

  17. "Feeling" Series and Parallel Resistances.

    Science.gov (United States)

    Morse, Robert A.

    1993-01-01

    Equipped with drinking straws and stirring straws, a teacher can help students understand how resistances in electric circuits combine in series and in parallel. Follow-up suggestions are provided. (ZWH)

  18. Parallel encoders for pixel detectors

    International Nuclear Information System (INIS)

    Nikityuk, N.M.

    1991-01-01

    A new method of fast encoding and determining the multiplicity and coordinates of fired pixels is described. A specific example construction of parallel encodes and MCC for n=49 and t=2 is given. 16 refs.; 6 figs.; 2 tabs

  19. Massively Parallel Finite Element Programming

    KAUST Repository

    Heister, Timo

    2010-01-01

    Today\\'s large finite element simulations require parallel algorithms to scale on clusters with thousands or tens of thousands of processor cores. We present data structures and algorithms to take advantage of the power of high performance computers in generic finite element codes. Existing generic finite element libraries often restrict the parallelization to parallel linear algebra routines. This is a limiting factor when solving on more than a few hundreds of cores. We describe routines for distributed storage of all major components coupled with efficient, scalable algorithms. We give an overview of our effort to enable the modern and generic finite element library deal.II to take advantage of the power of large clusters. In particular, we describe the construction of a distributed mesh and develop algorithms to fully parallelize the finite element calculation. Numerical results demonstrate good scalability. © 2010 Springer-Verlag.

  20. Event monitoring of parallel computations

    Directory of Open Access Journals (Sweden)

    Gruzlikov Alexander M.

    2015-06-01

    Full Text Available The paper considers the monitoring of parallel computations for detection of abnormal events. It is assumed that computations are organized according to an event model, and monitoring is based on specific test sequences

  1. Massively Parallel Finite Element Programming

    KAUST Repository

    Heister, Timo; Kronbichler, Martin; Bangerth, Wolfgang

    2010-01-01

    Today's large finite element simulations require parallel algorithms to scale on clusters with thousands or tens of thousands of processor cores. We present data structures and algorithms to take advantage of the power of high performance computers in generic finite element codes. Existing generic finite element libraries often restrict the parallelization to parallel linear algebra routines. This is a limiting factor when solving on more than a few hundreds of cores. We describe routines for distributed storage of all major components coupled with efficient, scalable algorithms. We give an overview of our effort to enable the modern and generic finite element library deal.II to take advantage of the power of large clusters. In particular, we describe the construction of a distributed mesh and develop algorithms to fully parallelize the finite element calculation. Numerical results demonstrate good scalability. © 2010 Springer-Verlag.

  2. The STAPL Parallel Graph Library

    KAUST Repository

    Harshvardhan,

    2013-01-01

    This paper describes the stapl Parallel Graph Library, a high-level framework that abstracts the user from data-distribution and parallelism details and allows them to concentrate on parallel graph algorithm development. It includes a customizable distributed graph container and a collection of commonly used parallel graph algorithms. The library introduces pGraph pViews that separate algorithm design from the container implementation. It supports three graph processing algorithmic paradigms, level-synchronous, asynchronous and coarse-grained, and provides common graph algorithms based on them. Experimental results demonstrate improved scalability in performance and data size over existing graph libraries on more than 16,000 cores and on internet-scale graphs containing over 16 billion vertices and 250 billion edges. © Springer-Verlag Berlin Heidelberg 2013.

  3. Astrocytic actions on extrasynaptic neuronal currents

    Directory of Open Access Journals (Sweden)

    Balazs ePal

    2015-12-01

    Full Text Available In the last few decades, knowledge about astrocytic functions has significantly increased. It was demonstrated that astrocytes are not passive elements of the central nervous system, but active partners of neurons. There is a growing body of knowledge about the calcium excitability of astrocytes, the actions of different gliotransmitters and their release mechanisms, as well as the participation of astrocytes in the regulation of synaptic functions and their contribution to synaptic plasticity. However, astrocytic functions are even more complex than being a partner of the 'tripartite synapse', as they can influence extrasynaptic neuronal currents either by releasing substances or regulating ambient neurotransmitter levels. Several types of currents or changes of membrane potential with different kinetics and via different mechanisms can be elicited by astrocytic activity. Astrocyte-dependent phasic or tonic, inward or outward currents were described in several brain areas. Such currents, together with the synaptic actions of astrocytes, can contribute to neuromodulatory mechanisms, neurosensory and –secretory processes, cortical oscillatory activity, memory and learning or overall neuronal excitability. This mini-review is an attempt to give a brief summary of astrocyte-dependent extrasynaptic neuronal currents and their possible functional significance.

  4. Writing parallel programs that work

    CERN Multimedia

    CERN. Geneva

    2012-01-01

    Serial algorithms typically run inefficiently on parallel machines. This may sound like an obvious statement, but it is the root cause of why parallel programming is considered to be difficult. The current state of the computer industry is still that almost all programs in existence are serial. This talk will describe the techniques used in the Intel Parallel Studio to provide a developer with the tools necessary to understand the behaviors and limitations of the existing serial programs. Once the limitations are known the developer can refactor the algorithms and reanalyze the resulting programs with the tools in the Intel Parallel Studio to create parallel programs that work. About the speaker Paul Petersen is a Sr. Principal Engineer in the Software and Solutions Group (SSG) at Intel. He received a Ph.D. degree in Computer Science from the University of Illinois in 1993. After UIUC, he was employed at Kuck and Associates, Inc. (KAI) working on auto-parallelizing compiler (KAP), and was involved in th...

  5. Parallel algorithms for continuum dynamics

    International Nuclear Information System (INIS)

    Hicks, D.L.; Liebrock, L.M.

    1987-01-01

    Simply porting existing parallel programs to a new parallel processor may not achieve the full speedup possible; to achieve the maximum efficiency may require redesigning the parallel algorithms for the specific architecture. The authors discuss here parallel algorithms that were developed first for the HEP processor and then ported to the CRAY X-MP/4, the ELXSI/10, and the Intel iPSC/32. Focus is mainly on the most recent parallel processing results produced, i.e., those on the Intel Hypercube. The applications are simulations of continuum dynamics in which the momentum and stress gradients are important. Examples of these are inertial confinement fusion experiments, severe breaks in the coolant system of a reactor, weapons physics, shock-wave physics. Speedup efficiencies on the Intel iPSC Hypercube are very sensitive to the ratio of communication to computation. Great care must be taken in designing algorithms for this machine to avoid global communication. This is much more critical on the iPSC than it was on the three previous parallel processors

  6. 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.

  7. Neuronal Migration and Neuronal Migration Disorder in Cerebral Cortex

    OpenAIRE

    SUN, Xue-Zhi; TAKAHASHI, Sentaro; GUI, Chun; ZHANG, Rui; KOGA, Kazuo; NOUYE, Minoru; MURATA, Yoshiharu

    2002-01-01

    Neuronal cell migration is one of the most significant features during cortical development. After final mitosis, neurons migrate from the ventricular zone into the cortical plate, and then establish neuronal lamina and settle onto the outermost layer, forming an "inside-out" gradient of maturation. Neuronal migration is guided by radial glial fibers and also needs proper receptors, ligands, and other unknown extracellular factors, requests local signaling (e.g. some emitted by the Cajal-Retz...

  8. Learning and structure of neuronal networks

    Indian Academy of Sciences (India)

    structures, protein–protein interaction networks, social interactions, the Internet, and so on can be described by complex networks [1–5]. Recent developments in the understanding of complex networks has led to deeper insights about their origin and other properties [1–5]. One common realization that emerges from these ...

  9. ANNarchy: a code generation approach to neural simulations on parallel hardware

    Science.gov (United States)

    Vitay, Julien; Dinkelbach, Helge Ü.; Hamker, Fred H.

    2015-01-01

    Many modern neural simulators focus on the simulation of networks of spiking neurons on parallel hardware. Another important framework in computational neuroscience, rate-coded neural networks, is mostly difficult or impossible to implement using these simulators. We present here the ANNarchy (Artificial Neural Networks architect) neural simulator, which allows to easily define and simulate rate-coded and spiking networks, as well as combinations of both. The interface in Python has been designed to be close to the PyNN interface, while the definition of neuron and synapse models can be specified using an equation-oriented mathematical description similar to the Brian neural simulator. This information is used to generate C++ code that will efficiently perform the simulation on the chosen parallel hardware (multi-core system or graphical processing unit). Several numerical methods are available to transform ordinary differential equations into an efficient C++code. We compare the parallel performance of the simulator to existing solutions. PMID:26283957

  10. Changes in Appetitive Associative Strength Modulates Nucleus Accumbens, But Not Orbitofrontal Cortex Neuronal Ensemble Excitability.

    Science.gov (United States)

    Ziminski, Joseph J; Hessler, Sabine; Margetts-Smith, Gabriella; Sieburg, Meike C; Crombag, Hans S; Koya, Eisuke

    2017-03-22

    Cues that predict the availability of food rewards influence motivational states and elicit food-seeking behaviors. If a cue no longer predicts food availability, then animals may adapt accordingly by inhibiting food-seeking responses. Sparsely activated sets of neurons, coined "neuronal ensembles," have been shown to encode the strength of reward-cue associations. Although alterations in intrinsic excitability have been shown to underlie many learning and memory processes, little is known about these properties specifically on cue-activated neuronal ensembles. We examined the activation patterns of cue-activated orbitofrontal cortex (OFC) and nucleus accumbens (NAc) shell ensembles using wild-type and Fos-GFP mice, which express green fluorescent protein (GFP) in activated neurons, after appetitive conditioning with sucrose and extinction learning. We also investigated the neuronal excitability of recently activated, GFP+ neurons in these brain areas using whole-cell electrophysiology in brain slices. Exposure to a sucrose cue elicited activation of neurons in both the NAc shell and OFC. In the NAc shell, but not the OFC, these activated GFP+ neurons were more excitable than surrounding GFP- neurons. After extinction, the number of neurons activated in both areas was reduced and activated ensembles in neither area exhibited altered excitability. These data suggest that learning-induced alterations in the intrinsic excitability of neuronal ensembles is regulated dynamically across different brain areas. Furthermore, we show that changes in associative strength modulate the excitability profile of activated ensembles in the NAc shell. SIGNIFICANCE STATEMENT Sparsely distributed sets of neurons called "neuronal ensembles" encode learned associations about food and cues predictive of its availability. Widespread changes in neuronal excitability have been observed in limbic brain areas after associative learning, but little is known about the excitability changes that

  11. Towards deep learning with segregated dendrites.

    Science.gov (United States)

    Guerguiev, Jordan; Lillicrap, Timothy P; Richards, Blake A

    2017-12-05

    Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the neocortex optimizes cost functions. Like neocortical pyramidal neurons, neurons in our model receive sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, neurons in different layers of the network can coordinate synaptic weight updates. As a result, the network learns to categorize images better than a single layer network. Furthermore, we show that our algorithm takes advantage of multilayer architectures to identify useful higher-order representations-the hallmark of deep learning. This work demonstrates that deep learning can be achieved using segregated dendritic compartments, which may help to explain the morphology of neocortical pyramidal neurons.

  12. Democratic population decisions result in robust policy-gradient learning: a parametric study with GPU simulations.

    Directory of Open Access Journals (Sweden)

    Paul Richmond

    2011-05-01

    Full Text Available High performance computing on the Graphics Processing Unit (GPU is an emerging field driven by the promise of high computational power at a low cost. However, GPU programming is a non-trivial task and moreover architectural limitations raise the question of whether investing effort in this direction may be worthwhile. In this work, we use GPU programming to simulate a two-layer network of Integrate-and-Fire neurons with varying degrees of recurrent connectivity and investigate its ability to learn a simplified navigation task using a policy-gradient learning rule stemming from Reinforcement Learning. The purpose of this paper is twofold. First, we want to support the use of GPUs in the field of Computational Neuroscience. Second, using GPU computing power, we investigate the conditions under which the said architecture and learning rule demonstrate best performance. Our work indicates that networks featuring strong Mexican-Hat-shaped recurrent connections in the top layer, where decision making is governed by the formation of a stable activity bump in the neural population (a "non-democratic" mechanism, achieve mediocre learning results at best. In absence of recurrent connections, where all neurons "vote" independently ("democratic" for a decision via population vector readout, the task is generally learned better and more robustly. Our study would have been extremely difficult on a desktop computer without the use of GPU programming. We present the routines developed for this purpose and show that a speed improvement of 5x up to 42x is provided versus optimised Python code. The higher speed is achieved when we exploit the parallelism of the GPU in the search of learning parameters. This suggests that efficient GPU programming can significantly reduce the time needed for simulating networks of spiking neurons, particularly when multiple parameter configurations are investigated.

  13. Endpoint-based parallel data processing in a parallel active messaging interface of a parallel computer

    Science.gov (United States)

    Archer, Charles J.; Blocksome, Michael A.; Ratterman, Joseph D.; Smith, Brian E.

    2014-08-12

    Endpoint-based parallel data processing in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI composed of data communications endpoints, each endpoint including a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task, the compute nodes coupled for data communications through the PAMI, including establishing a data communications geometry, the geometry specifying, for tasks representing processes of execution of the parallel application, a set of endpoints that are used in collective operations of the PAMI including a plurality of endpoints for one of the tasks; receiving in endpoints of the geometry an instruction for a collective operation; and executing the instruction for a collective operation through the endpoints in dependence upon the geometry, including dividing data communications operations among the plurality of endpoints for one of the tasks.

  14. Spontaneous neuronal activity as a self-organized critical phenomenon

    Science.gov (United States)

    de Arcangelis, L.; Herrmann, H. J.

    2013-01-01

    Neuronal avalanches are a novel mode of activity in neuronal networks, experimentally found in vitro and in vivo, and exhibit a robust critical behaviour. Avalanche activity can be modelled within the self-organized criticality framework, including threshold firing, refractory period and activity-dependent synaptic plasticity. The size and duration distributions confirm that the system acts in a critical state, whose scaling behaviour is very robust. Next, we discuss the temporal organization of neuronal avalanches. This is given by the alternation between states of high and low activity, named up and down states, leading to a balance between excitation and inhibition controlled by a single parameter. During these periods both the single neuron state and the network excitability level, keeping memory of past activity, are tuned by homeostatic mechanisms. Finally, we verify if a system with no characteristic response can ever learn in a controlled and reproducible way. Learning in the model occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. Learning is a truly collective process and the learning dynamics exhibits universal features. Even complex rules can be learned provided that the plastic adaptation is sufficiently slow.

  15. Neuronal nets in robotics

    International Nuclear Information System (INIS)

    Jimenez Sanchez, Raul

    1999-01-01

    The paper gives a generic idea of the solutions that the neuronal nets contribute to the robotics. The advantages and the inconveniences are exposed that have regarding the conventional techniques. It also describe the more excellent applications as the pursuit of trajectories, the positioning based on images, the force control or of the mobile robots management, among others

  16. Metastable states in the hierarchical Dyson model drive parallel processing in the hierarchical Hopfield network

    International Nuclear Information System (INIS)

    Agliari, Elena; Barra, Adriano; Guerra, Francesco; Galluzzi, Andrea; Tantari, Daniele; Tavani, Flavia

    2015-01-01

    In this paper, we introduce and investigate the statistical mechanics of hierarchical neural networks. First, we approach these systems à la Mattis, by thinking of the Dyson model as a single-pattern hierarchical neural network. We also discuss the stability of different retrievable states as predicted by the related self-consistencies obtained both from a mean-field bound and from a bound that bypasses the mean-field limitation. The latter is worked out by properly reabsorbing the magnetization fluctuations related to higher levels of the hierarchy into effective fields for the lower levels. Remarkably, mixing Amit's ansatz technique for selecting candidate-retrievable states with the interpolation procedure for solving for the free energy of these states, we prove that, due to gauge symmetry, the Dyson model accomplishes both serial and parallel processing. We extend this scenario to multiple stored patterns by implementing the Hebb prescription for learning within the couplings. This results in Hopfield-like networks constrained on a hierarchical topology, for which, by restricting to the low-storage regime where the number of patterns grows at its most logarithmical with the amount of neurons, we prove the existence of the thermodynamic limit for the free energy, and we give an explicit expression of its mean-field bound and of its related improved bound. We studied the resulting self-consistencies for the Mattis magnetizations, which act as order parameters, are studied and the stability of solutions is analyzed to get a picture of the overall retrieval capabilities of the system according to both mean-field and non-mean-field scenarios. Our main finding is that embedding the Hebbian rule on a hierarchical topology allows the network to accomplish both serial and parallel processing. By tuning the level of fast noise affecting it or triggering the decay of the interactions with the distance among neurons, the system may switch from sequential retrieval to

  17. Sweet Taste and Nutrient Value Subdivide Rewarding Dopaminergic Neurons in Drosophila

    OpenAIRE

    Huetteroth, Wolf; Perisse, Emmanuel; Lin, Suewei; Klappenbach, Mart?n; Burke, Christopher; Waddell, Scott

    2015-01-01

    Dopaminergic neurons provide reward learning signals in mammals and insects. Recent work in Drosophila has demonstrated that water-reinforcing dopaminergic neurons are different to those for nutritious sugars. Here, we tested whether the sweet taste and nutrient properties of sugar reinforcement further subdivide the fly reward system. We found that dopaminergic neurons expressing the OAMB octopamine receptor specifically convey the short-term reinforcing effects of sweet taste. These dopamin...

  18. CNF1 Improves Astrocytic Ability to Support Neuronal Growth and Differentiation In vitro

    OpenAIRE

    Malchiodi-Albedi, Fiorella; Paradisi, Silvia; Di Nottia, Michela; Simone, Daiana; Travaglione, Sara; Falzano, Loredana; Guidotti, Marco; Frank, Claudio; Cutarelli, Alessandro; Fabbri, Alessia; Fiorentini, Carla

    2012-01-01

    Modulation of cerebral Rho GTPases activity in mice brain by intracerebral administration of Cytotoxic Necrotizing Factor 1 (CNF1) leads to enhanced neurotransmission and synaptic plasticity and improves learning and memory. To gain more insight into the interactions between CNF1 and neuronal cells, we used primary neuronal and astrocytic cultures from rat embryonic brain to study CNF1 effects on neuronal differentiation, focusing on dendritic tree growth and synapse formation, which are stri...

  19. Parallel Implicit Algorithms for CFD

    Science.gov (United States)

    Keyes, David E.

    1998-01-01

    The main goal of this project was efficient distributed parallel and workstation cluster implementations of Newton-Krylov-Schwarz (NKS) solvers for implicit Computational Fluid Dynamics (CFD.) "Newton" refers to a quadratically convergent nonlinear iteration using gradient information based on the true residual, "Krylov" to an inner linear iteration that accesses the Jacobian matrix only through highly parallelizable sparse matrix-vector products, and "Schwarz" to a domain decomposition form of preconditioning the inner Krylov iterations with primarily neighbor-only exchange of data between the processors. Prior experience has established that Newton-Krylov methods are competitive solvers in the CFD context and that Krylov-Schwarz methods port well to distributed memory computers. The combination of the techniques into Newton-Krylov-Schwarz was implemented on 2D and 3D unstructured Euler codes on the parallel testbeds that used to be at LaRC and on several other parallel computers operated by other agencies or made available by the vendors. Early implementations were made directly in Massively Parallel Integration (MPI) with parallel solvers we adapted from legacy NASA codes and enhanced for full NKS functionality. Later implementations were made in the framework of the PETSC library from Argonne National Laboratory, which now includes pseudo-transient continuation Newton-Krylov-Schwarz solver capability (as a result of demands we made upon PETSC during our early porting experiences). A secondary project pursued with funding from this contract was parallel implicit solvers in acoustics, specifically in the Helmholtz formulation. A 2D acoustic inverse problem has been solved in parallel within the PETSC framework.

  20. Second derivative parallel block backward differentiation type ...

    African Journals Online (AJOL)

    Second derivative parallel block backward differentiation type formulas for Stiff ODEs. ... Log in or Register to get access to full text downloads. ... and the methods are inherently parallel and can be distributed over parallel processors. They are ...

  1. A Parallel Approach to Fractal Image Compression

    OpenAIRE

    Lubomir Dedera

    2004-01-01

    The paper deals with a parallel approach to coding and decoding algorithms in fractal image compressionand presents experimental results comparing sequential and parallel algorithms from the point of view of achieved bothcoding and decoding time and effectiveness of parallelization.

  2. Unpacking the cognitive map: the parallel map theory of hippocampal function.

    Science.gov (United States)

    Jacobs, Lucia F; Schenk, Françoise

    2003-04-01

    In the parallel map theory, the hippocampus encodes space with 2 mapping systems. The bearing map is constructed primarily in the dentate gyrus from directional cues such as stimulus gradients. The sketch map is constructed within the hippocampus proper from positional cues. The integrated map emerges when data from the bearing and sketch maps are combined. Because the component maps work in parallel, the impairment of one can reveal residual learning by the other. Such parallel function may explain paradoxes of spatial learning, such as learning after partial hippocampal lesions, taxonomic and sex differences in spatial learning, and the function of hippocampal neurogenesis. By integrating evidence from physiology to phylogeny, the parallel map theory offers a unified explanation for hippocampal function.

  3. Production and survival of projection neurons in a forebrain vocal center of adult male canaries

    International Nuclear Information System (INIS)

    Kirn, J.R.; Alvarez-Buylla, A.; Nottebohm, F.

    1991-01-01

    Neurons are produced in the adult canary telencephalon. Many of these cells are incorporated into the high vocal center (nucleus HVC), which participates in the control of learned song. In the present work, 3H-thymidine and fluorogold were employed to follow the differentiation and survival of HVC neurons born in adulthood. We found that many HVC neurons born in September grow long axons to the robust nucleus of the archistriatum (nucleus RA) and thus become part of the efferent pathway for song control. Many of these new neurons have already established their connections with RA by 30 d after their birth. By 240 d, 75-80% of the September-born HVC neurons project to RA. Most of these new projection neurons survive at least 8 months. The longevity of HVC neurons born in September suggests that these cells remain part of the vocal control circuit long enough to participate in the yearly renewal of the song repertoire

  4. Vasculo-Neuronal Coupling: Retrograde Vascular Communication to Brain Neurons.

    Science.gov (United States)

    Kim, Ki Jung; Ramiro Diaz, Juan; Iddings, Jennifer A; Filosa, Jessica A

    2016-12-14

    Continuous cerebral blood flow is essential for neuronal survival, but whether vascular tone influences resting neuronal function is not known. Using a multidisciplinary approach in both rat and mice brain slices, we determined whether flow/pressure-evoked increases or decreases in parenchymal arteriole vascular tone, which result in arteriole constriction and dilation, respectively, altered resting cortical pyramidal neuron activity. We present evidence for intercellular communication in the brain involving a flow of information from vessel to astrocyte to neuron, a direction opposite to that of classic neurovascular coupling and referred to here as vasculo-neuronal coupling (VNC). Flow/pressure increases within parenchymal arterioles increased vascular tone and simultaneously decreased resting pyramidal neuron firing activity. On the other hand, flow/pressure decreases evoke parenchymal arteriole dilation and increased resting pyramidal neuron firing activity. In GLAST-CreERT2; R26-lsl-GCaMP3 mice, we demonstrate that increased parenchymal arteriole tone significantly increased intracellular calcium in perivascular astrocyte processes, the onset of astrocyte calcium changes preceded the inhibition of cortical pyramidal neuronal firing activity. During increases in parenchymal arteriole tone, the pyramidal neuron response was unaffected by blockers of nitric oxide, GABA A , glutamate, or ecto-ATPase. However, VNC was abrogated by TRPV4 channel, GABA B , as well as an adenosine A 1 receptor blocker. Differently to pyramidal neuron responses, increases in flow/pressure within parenchymal arterioles increased the firing activity of a subtype of interneuron. Together, these data suggest that VNC is a complex constitutive active process that enables neurons to efficiently adjust their resting activity according to brain perfusion levels, thus safeguarding cellular homeostasis by preventing mismatches between energy supply and demand. We present evidence for vessel-to-neuron

  5. An FPGA-Based Massively Parallel Neuromorphic Cortex Simulator

    Directory of Open Access Journals (Sweden)

    Runchun M. Wang

    2018-04-01

    Full Text Available This paper presents a massively parallel and scalable neuromorphic cortex simulator designed for simulating large and structurally connected spiking neural networks, such as complex models of various areas of the cortex. The main novelty of this work is the abstraction of a neuromorphic architecture into clusters represented by minicolumns and hypercolumns, analogously to the fundamental structural units observed in neurobiology. Without this approach, simulating large-scale fully connected networks needs prohibitively large memory to store look-up tables for point-to-point connections. Instead, we use a novel architecture, based on the structural connectivity in the neocortex, such that all the required parameters and connections can be stored in on-chip memory. The cortex simulator can be easily reconfigured for simulating different neural networks without any change in hardware structure by programming the memory. A hierarchical communication scheme allows one neuron to have a fan-out of up to 200 k neurons. As a proof-of-concept, an implementation on one Altera Stratix V FPGA was able to simulate 20 million to 2.6 billion leaky-integrate-and-fire (LIF neurons in real time. We verified the system by emulating a simplified auditory cortex (with 100 million neurons. This cortex simulator achieved a low power dissipation of 1.62 μW per neuron. With the advent of commercially available FPGA boards, our system offers an accessible and scalable tool for the design, real-time simulation, and analysis of large-scale spiking neural networks.

  6. Parallel fabrication of macroporous scaffolds.

    Science.gov (United States)

    Dobos, Andrew; Grandhi, Taraka Sai Pavan; Godeshala, Sudhakar; Meldrum, Deirdre R; Rege, Kaushal

    2018-07-01

    Scaffolds generated from naturally occurring and synthetic polymers have been investigated in several applications because of their biocompatibility and tunable chemo-mechanical properties. Existing methods for generation of 3D polymeric scaffolds typically cannot be parallelized, suffer from low throughputs, and do not allow for quick and easy removal of the fragile structures that are formed. Current molds used in hydrogel and scaffold fabrication using solvent casting and porogen leaching are often single-use and do not facilitate 3D scaffold formation in parallel. Here, we describe a simple device and related approaches for the parallel fabrication of macroporous scaffolds. This approach was employed for the generation of macroporous and non-macroporous materials in parallel, in higher throughput and allowed for easy retrieval of these 3D scaffolds once formed. In addition, macroporous scaffolds with interconnected as well as non-interconnected pores were generated, and the versatility of this approach was employed for the generation of 3D scaffolds from diverse materials including an aminoglycoside-derived cationic hydrogel ("Amikagel"), poly(lactic-co-glycolic acid) or PLGA, and collagen. Macroporous scaffolds generated using the device were investigated for plasmid DNA binding and cell loading, indicating the use of this approach for developing materials for different applications in biotechnology. Our results demonstrate that the device-based approach is a simple technology for generating scaffolds in parallel, which can enhance the toolbox of current fabrication techniques. © 2018 Wiley Periodicals, Inc.

  7. Parallel plasma fluid turbulence calculations

    International Nuclear Information System (INIS)

    Leboeuf, J.N.; Carreras, B.A.; Charlton, L.A.; Drake, J.B.; Lynch, V.E.; Newman, D.E.; Sidikman, K.L.; Spong, D.A.

    1994-01-01

    The study of plasma turbulence and transport is a complex problem of critical importance for fusion-relevant plasmas. To this day, the fluid treatment of plasma dynamics is the best approach to realistic physics at the high resolution required for certain experimentally relevant calculations. Core and edge turbulence in a magnetic fusion device have been modeled using state-of-the-art, nonlinear, three-dimensional, initial-value fluid and gyrofluid codes. Parallel implementation of these models on diverse platforms--vector parallel (National Energy Research Supercomputer Center's CRAY Y-MP C90), massively parallel (Intel Paragon XP/S 35), and serial parallel (clusters of high-performance workstations using the Parallel Virtual Machine protocol)--offers a variety of paths to high resolution and significant improvements in real-time efficiency, each with its own advantages. The largest and most efficient calculations have been performed at the 200 Mword memory limit on the C90 in dedicated mode, where an overlap of 12 to 13 out of a maximum of 16 processors has been achieved with a gyrofluid model of core fluctuations. The richness of the physics captured by these calculations is commensurate with the increased resolution and efficiency and is limited only by the ingenuity brought to the analysis of the massive amounts of data generated

  8. Evaluating parallel optimization on transputers

    Directory of Open Access Journals (Sweden)

    A.G. Chalmers

    2003-12-01

    Full Text Available The faster processing power of modern computers and the development of efficient algorithms have made it possible for operations researchers to tackle a much wider range of problems than ever before. Further improvements in processing speed can be achieved utilising relatively inexpensive transputers to process components of an algorithm in parallel. The Davidon-Fletcher-Powell method is one of the most successful and widely used optimisation algorithms for unconstrained problems. This paper examines the algorithm and identifies the components that can be processed in parallel. The results of some experiments with these components are presented which indicates under what conditions parallel processing with an inexpensive configuration is likely to be faster than the traditional sequential implementations. The performance of the whole algorithm with its parallel components is then compared with the original sequential algorithm. The implementation serves to illustrate the practicalities of speeding up typical OR algorithms in terms of difficulty, effort and cost. The results give an indication of the savings in time a given parallel implementation can be expected to yield.

  9. Pattern-Driven Automatic Parallelization

    Directory of Open Access Journals (Sweden)

    Christoph W. Kessler

    1996-01-01

    Full Text Available This article describes a knowledge-based system for automatic parallelization of a wide class of sequential numerical codes operating on vectors and dense matrices, and for execution on distributed memory message-passing multiprocessors. Its main feature is a fast and powerful pattern recognition tool that locally identifies frequently occurring computations and programming concepts in the source code. This tool also works for dusty deck codes that have been "encrypted" by former machine-specific code transformations. Successful pattern recognition guides sophisticated code transformations including local algorithm replacement such that the parallelized code need not emerge from the sequential program structure by just parallelizing the loops. It allows access to an expert's knowledge on useful parallel algorithms, available machine-specific library routines, and powerful program transformations. The partially restored program semantics also supports local array alignment, distribution, and redistribution, and allows for faster and more exact prediction of the performance of the parallelized target code than is usually possible.

  10. Enhancement of synchronized activity between hippocampal CA1 neurons during initial storage of associative fear memory.

    Science.gov (United States)

    Liu, Yu-Zhang; Wang, Yao; Shen, Weida; Wang, Zhiru

    2017-08-01

    Learning and memory storage requires neuronal plasticity induced in the hippocampus and other related brain areas, and this process is thought to rely on synchronized activity in neural networks. We used paired whole-cell recording in vivo to examine the synchronized activity that was induced in hippocampal CA1 neurons by associative fear learning. We found that both membrane potential synchronization and spike synchronization of CA1 neurons could be transiently enhanced after task learning, as observed on day 1 but not day 5. On day 1 after learning, CA1 neurons showed a decrease in firing threshold and rise times of suprathreshold membrane potential changes as well as an increase in spontaneous firing rates, possibly contributing to the enhancement of spike synchronization. The transient enhancement of CA1 neuronal synchronization may play important roles in the induction of neuronal plasticity for initial storage and consolidation of associative memory. The hippocampus is critical for memory acquisition and consolidation. This function requires activity- and experience-induced neuronal plasticity. It is known that neuronal plasticity is largely dependent on synchronized activity. As has been well characterized, repetitive correlated activity of presynaptic and postsynaptic neurons can lead to long-term modifications at their synapses. Studies on network activity have also suggested that memory processing in the hippocampus may involve learning-induced changes of neuronal synchronization, as observed in vivo between hippocampal CA3 and CA1 networks as well as between the rhinal cortex and the hippocampus. However, further investigation of learning-induced synchronized activity in the hippocampus is needed for a full understanding of hippocampal memory processing. In this study, by performing paired whole-cell recording in vivo on CA1 pyramidal cells (PCs) in anaesthetized adult rats, we examined CA1 neuronal synchronization before and after associative fear

  11. [Development of intellect, emotion, and intentions, and their neuronal systems].

    Science.gov (United States)

    Segawa, Masaya

    2008-09-01

    Intellect, emotion and intentions, the major components of the human mentality, are neurologically correlated to memory and sensorimotor integration, the neuronal system consisting of the amygdale and hypothalamus, and motivation and learning, respectively. Development of these neuronal processes was evaluated by correlating the pathophysiologies of idiopathic developmental neuropsychiatric disorders and developmental courses of sleep parameters, sleep-wake rhythm (SWR), and locomotion. The memory system and sensory pathways develop by the 9th gestational months. Habituation or dorsal bundle extinction (DBE) develop after the 34th gestational week. In the first 4 months after birth, DBE is consolidated and fine tuning of the primary sensory cortex and its neuronal connection to the unimodal sensory association area along with functional lateralization of the cortex are accomplished. After 4 months, restriction of atonia in the REM stage enables the integrative function of the brain and induces synaptogenesis of the cortex around 6 months and locomotion in late infancy by activating the dopaminergic (DA) neurons induces synaptogenesis of the frontal cortex. Locomotion in early infancy involves functional specialization of the cortex and in childhood with development of biphasic SWR activation of the areas of the prefrontal cortex. Development of emotions reflects in the development of personal communication and the arousal function of the hypothalamus. The former is shown in the mother-child relationship in the first 4 months, in communication with adults and playmates in late infancy to early childhood, and in development of social relationships with sympathy by the early school age with functional maturation of the orbitofrontal cortex. The latter is demonstrated in the secretion of melatonin during night time by 4 months, in the circadian rhythm of body temperature by 8 months, and in the secretion of the growth hormone by 4-5 years with synchronization to the

  12. Neuronal survival in the brain: neuron type-specific mechanisms

    DEFF Research Database (Denmark)

    Pfisterer, Ulrich Gottfried; Khodosevich, Konstantin

    2017-01-01

    Neurogenic regions of mammalian brain produce many more neurons that will eventually survive and reach a mature stage. Developmental cell death affects both embryonically produced immature neurons and those immature neurons that are generated in regions of adult neurogenesis. Removal of substantial...... numbers of neurons that are not yet completely integrated into the local circuits helps to ensure that maturation and homeostatic function of neuronal networks in the brain proceed correctly. External signals from brain microenvironment together with intrinsic signaling pathways determine whether...... for survival in a certain brain region. This review focuses on how immature neurons survive during normal and impaired brain development, both in the embryonic/neonatal brain and in brain regions associated with adult neurogenesis, and emphasizes neuron type-specific mechanisms that help to survive for various...

  13. The Age of Enlightenment: Evolving Opportunities in Brain Research Through Optical Manipulation of Neuronal Activity

    OpenAIRE

    Jerome, Jason; Heck, Detlef H.

    2011-01-01

    Optical manipulation of neuronal activity has rapidly developed into the most powerful and widely used approach to study mechanisms related to neuronal connectivity over a range of scales. Since the early use of single site uncaging to map network connectivity, rapid technological development of light modulation techniques has added important new options, such as fast scanning photostimulation, massively parallel control of light stimuli, holographic uncaging, and two-photon stimulation techn...

  14. Parallel artificial liquid membrane extraction

    DEFF Research Database (Denmark)

    Gjelstad, Astrid; Rasmussen, Knut Einar; Parmer, Marthe Petrine

    2013-01-01

    This paper reports development of a new approach towards analytical liquid-liquid-liquid membrane extraction termed parallel artificial liquid membrane extraction. A donor plate and acceptor plate create a sandwich, in which each sample (human plasma) and acceptor solution is separated by an arti......This paper reports development of a new approach towards analytical liquid-liquid-liquid membrane extraction termed parallel artificial liquid membrane extraction. A donor plate and acceptor plate create a sandwich, in which each sample (human plasma) and acceptor solution is separated...... by an artificial liquid membrane. Parallel artificial liquid membrane extraction is a modification of hollow-fiber liquid-phase microextraction, where the hollow fibers are replaced by flat membranes in a 96-well plate format....

  15. Parallel algorithms for mapping pipelined and parallel computations

    Science.gov (United States)

    Nicol, David M.

    1988-01-01

    Many computational problems in image processing, signal processing, and scientific computing are naturally structured for either pipelined or parallel computation. When mapping such problems onto a parallel architecture it is often necessary to aggregate an obvious problem decomposition. Even in this context the general mapping problem is known to be computationally intractable, but recent advances have been made in identifying classes of problems and architectures for which optimal solutions can be found in polynomial time. Among these, the mapping of pipelined or parallel computations onto linear array, shared memory, and host-satellite systems figures prominently. This paper extends that work first by showing how to improve existing serial mapping algorithms. These improvements have significantly lower time and space complexities: in one case a published O(nm sup 3) time algorithm for mapping m modules onto n processors is reduced to an O(nm log m) time complexity, and its space requirements reduced from O(nm sup 2) to O(m). Run time complexity is further reduced with parallel mapping algorithms based on these improvements, which run on the architecture for which they create the mappings.

  16. Cellular automata a parallel model

    CERN Document Server

    Mazoyer, J

    1999-01-01

    Cellular automata can be viewed both as computational models and modelling systems of real processes. This volume emphasises the first aspect. In articles written by leading researchers, sophisticated massive parallel algorithms (firing squad, life, Fischer's primes recognition) are treated. Their computational power and the specific complexity classes they determine are surveyed, while some recent results in relation to chaos from a new dynamic systems point of view are also presented. Audience: This book will be of interest to specialists of theoretical computer science and the parallelism challenge.

  17. Repeated Blockade of NMDA Receptors during Adolescence Impairs Reversal Learning and Disrupts GABAergic Interneurons in Rat Medial Prefrontal Cortex

    Directory of Open Access Journals (Sweden)

    Jitao eLi

    2016-03-01

    Full Text Available Adolescence is of particular significance to schizophrenia, since psychosis onset typically occurs in this critical period. Based on the N-methyl-D-aspartate (NMDA receptor hypofunction hypothesis of schizophrenia, in this study, we investigated whether and how repeated NMDA receptor blockade during adolescence would affect GABAergic interneurons in rat medial prefrontal cortex (mPFC and mPFC-mediated cognitive functions. Specifically, adolescent rats were subjected to intraperitoneal administration of MK-801 (0.1, 0.2, 0.4 mg/kg, a non-competitive NMDA receptor antagonist, for 14 days and then tested for reference memory and reversal learning in the water maze. The density of parvabumin (PV-, calbindin (CB- and calretinin (CR-positive neurons in mPFC were analyzed at either 24 hours or 7 days after drug cessation. We found that MK-801 treatment delayed reversal learning in the water maze without affecting initial acquisition. Strikingly, MK-801 treatment also significantly reduced the density of PV+ and CB+ neurons, and this effect persisted for 7 days after drug cessation at the dose of 0.2 mg/kg. We further demonstrated that the reduction in PV+ and CB+ neuron densities was ascribed to a downregulation of the expression levels of PV and CB, but not to neuronal death. These results parallel the behavioral and neuropathological changes of schizophrenia and provide evidence that adolescent NMDA receptors antagonism offers a useful tool for unraveling the etiology of the disease.

  18. Neuronal synchrony: peculiarity and generality.

    Science.gov (United States)

    Nowotny, Thomas; Huerta, Ramon; Rabinovich, Mikhail I

    2008-09-01

    Synchronization in neuronal systems is a new and intriguing application of dynamical systems theory. Why are neuronal systems different as a subject for synchronization? (1) Neurons in themselves are multidimensional nonlinear systems that are able to exhibit a wide variety of different activity patterns. Their "dynamical repertoire" includes regular or chaotic spiking, regular or chaotic bursting, multistability, and complex transient regimes. (2) Usually, neuronal oscillations are the result of the cooperative activity of many synaptically connected neurons (a neuronal circuit). Thus, it is necessary to consider synchronization between different neuronal circuits as well. (3) The synapses that implement the coupling between neurons are also dynamical elements and their intrinsic dynamics influences the process of synchronization or entrainment significantly. In this review we will focus on four new problems: (i) the synchronization in minimal neuronal networks with plastic synapses (synchronization with activity dependent coupling), (ii) synchronization of bursts that are generated by a group of nonsymmetrically coupled inhibitory neurons (heteroclinic synchronization), (iii) the coordination of activities of two coupled neuronal networks (partial synchronization of small composite structures), and (iv) coarse grained synchronization in larger systems (synchronization on a mesoscopic scale). (c) 2008 American Institute of Physics.

  19. Replicating receptive fields of simple and complex cells in primary visual cortex in a neuronal network model with temporal and population sparseness and reliability.

    Science.gov (United States)

    Tanaka, Takuma; Aoyagi, Toshio; Kaneko, Takeshi

    2012-10-01

    We propose a new principle for replicating receptive field properties of neurons in the primary visual cortex. We derive a learning rule for a feedforward network, which maintains a low firing rate for the output neurons (resulting in temporal sparseness) and allows only a small subset of the neurons in the network to fire at any given time (resulting in population sparseness). Our learning rule also sets the firing rates of the output neurons at each time step to near-maximum or near-minimum levels, resulting in neuronal reliability. The learning rule is simple enough to be written in spatially and temporally local forms. After the learning stage is performed using input image patches of natural scenes, output neurons in the model network are found to exhibit simple-cell-like receptive field properties. When the output of these simple-cell-like neurons are input to another model layer using the same learning rule, the second-layer output neurons after learning become less sensitive to the phase of gratings than the simple-cell-like input neurons. In particular, some of the second-layer output neurons become completely phase invariant, owing to the convergence of the connections from first-layer neurons with similar orientation selectivity to second-layer neurons in the model network. We examine the parameter dependencies of the receptive field properties of the model neurons after learning and discuss their biological implications. We also show that the localized learning rule is consistent with experimental results concerning neuronal plasticity and can replicate the receptive fields of simple and complex cells.

  20. How attention can create synaptic tags for the learning of working memories in sequential tasks

    OpenAIRE

    Rombouts, Jaldert O; Bohte, Sander M; Roelfsema, Pieter R

    2015-01-01

    htmlabstractIntelligence is our ability to learn appropriate responses to new stimuli and situations. Neurons in association cortex are thought to be essential for this ability. During learning these neurons become tuned to relevant features and start to represent them with persistent activity during memory delays. This learning process is not well understood. Here we develop a biologically plausible learning scheme that explains how trial-and-error learning induces neuronal selectivity and w...