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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2014年第AUG期8卷 102页

作者： Rosenbaum, Robert Tchumatchenko, Tatjana Moreno-Bote, Ruben Univ Notre Dame Dept Appl & Computat Math & Stat Notre Dame IN 46556 USA Ctr Neural Basis Cognit Pittsburgh PA USA Max Planck Inst Brain Res Dept Theory Neural Dynam Frankfurt Germany Parc Sanitari St Joan de Deu Res Unit Barcelona Spain Univ Barcelona Barcelona Spain Ctr Invest Biomed Red Salud Mental CIBERSAM Barcelona Spain

Correlated and synchronous activity in populations of neurons has been observed in many brain regions and has been shown to play a crucial role in cortical coding, attention, and network dynamics (Singer and Gray, 1995; Salinas and Sejnowski, 2001). Bird and Richardson (2014) study the interaction between long term plasticity, synaptic vesicle depletion at multiple release sites and presynaptic spiking correlations. The authors put forward a unified framework for the generation of pairwise correlations in recurrent networks and hypothesize that many different single model neurons, when coupled to a network, may generate the same pairwise correlation structures. In a theoretical work presented in this Research Topic, Finger and Koenig (2014) took an important step forward by showing that binding of features in natural images can be mediated by phase synchronization in a network of neural oscillators.

关键词： neuronal correlations neural synchrony neural coding spike train analysis neuronal networks noise correlation

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Bayesian Entropy Estimation for Countable Discrete Distributions

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JOURNAL OF MACHINE LEARNING RESEARCH 2014年 15卷 2833-2868页

作者： Archer, Evan Park, Il Memming Pillow, Jonathan W. Univ Texas Austin Ctr Perceptual Syst Austin TX 78712 USA Max Planck Inst Biol Cybernet D-72076 Tubingen Germany Univ Texas Austin Dept Psychol Div Stat & Sci Computat Neurobiol Sect Austin TX 78712 USA

We consider the problem of estimating Shannon's entropy H from discrete data, in cases where the number of possible symbols is unknown or even countably infinite. The Pitman-Yor process, a generalization of Dirichlet process, provides a tractable prior distribution over the space of countably infinite discrete distributions, and has found major applications in Bayesian non-parametric statistics and machine learning. Here we show that it provides a natural family of priors for Bayesian entropy estimation, due to the fact that moments of the induced posterior distribution over H can be computed analytically. We derive formulas for the posterior mean (Bayes' least squares estimate) and variance under Dirichlet and Pitman-Yor process priors. Moreover, we show that a fixed Dirichlet or Pitman-Yor process prior implies a narrow prior distribution over H, meaning the prior strongly determines the entropy estimate in the under-sampled regime. We derive a family of continuous measures for mixing Pitman-Yor processes to produce an approximately flat prior over H. We show that the resulting "Pitman-Yor Mixture" (PYM) entropy estimator is consistent for a large class of distributions. Finally, we explore the theoretical properties of the resulting estimator, and show that it performs well both in simulation and in application to real data.

关键词： entropy information theory Bayesian estimation Bayesian nonparametrics Dirichlet process Pitman-Yor process neural coding

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Motor cortical control of movement speed with implications for brain-machine interface control

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JOURNAL OF NEUROPHYSIOLOGY 2014年第2期112卷 411-429页

作者： Golub, Matthew D. Yu, Byron M. Schwartz, Andrew B. Chase, Steven M. Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA Carnegie Mellon Univ Ctr Neural Basis Cognit Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Biomed Engn Pittsburgh PA 15213 USA Univ Pittsburgh Dept Neurobiol Pittsburgh PA USA

Motor cortex plays a substantial role in driving movement, yet the details underlying this control remain unresolved. We analyzed the extent to which movement-related information could be extracted from single-trial motor cortical activity recorded while monkeys performed center-out reaching. Using information theoretic techniques, we found that single units carry relatively little speed-related information compared with direction-related information. This result is not mitigated at the population level: simultaneously recorded population activity predicted speed with significantly lower accuracy relative to direction predictions. Furthermore, a unit-dropping analysis revealed that speed accuracy would likely remain lower than direction accuracy, even given larger populations. These results suggest that the instantaneous details of single-trial movement speed are difficult to extract using commonly assumed coding schemes. This apparent paucity of speed information takes particular importance in the context of brain-machine interfaces (BMIs), which rely on extracting kinematic information from motor cortex. Previous studies have highlighted subjects' difficulties in holding a BMI cursor stable at targets. These studies, along with our finding of relatively little speed information in motor cortex, inspired a speed-dampening Kalman filter (SDKF) that automatically slows the cursor upon detecting changes in decoded movement direction. Effectively, SDKF enhances speed control by using prevalent directional signals, rather than requiring speed to be directly decoded from neural activity. SDKF improved success rates by a factor of 1.7 relative to a standard Kalman filter in a closed-loop BMI task requiring stable stops at targets. BMI systems enabling stable stops will be more effective and user-friendly when translated into clinical applications.

关键词： motor control neural coding brain-machine interface

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

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NEUROSCIENCE LETTERS 2014年 581卷 80-84页

作者： Strelnikov, Kuzma CNRS CERCO Fac Med Rangueil UMR 5549 F-31052 Toulouse France

It was shown recently in neuroimaging that spatial differentiation of brain activity provides novel information about brain function. This confirms the integrative organisation of brain activity, but given present technical limitations of neuroimaging approaches, the exact role of integrative activity remains unclear. We trained a neural network to integrate information using random numbers' so as to imitate the "centre-periphery" pattern of brain activity in neuroimaging. Only the hierarchical organisation of the network permitted the learning of fast and reliable integration. We presented images to the trained network and, by spatial differentiation of the network activity, obtained virtual spaces with the presented images. Thus, our study established the necessity of the hierarchical organisation of neural networks for integration and demonstrated that the role of neural integration in the brain may be to create virtual spaces with internal representations of the objects. (C) 2014 Elsevier Ireland Ltd. All rights reserved.

关键词： Integrative neural coding Internal representations Brain activity

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Processing and Transmission of Confidence in Recurrent neural Hierarchies

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neural PROCESSING LETTERS 2014年第1期40卷 75-91页

作者： Gepperth, Alexander ENSTA ParisTech F-91762 Palaiseau France INRIA FLOWERS INRIA Bordeaux Sud Ouest FLOWERS Res Team F-33405 Talence France

This article addresses the construction of hierarchies from dynamic attractor networks. We claim that such networks, e.g., dynamic neural fields (DNFs), contain a data model which is encoded in their lateral connections, and which describes typical properties of afferent inputs. This allows to infer the most likely interpretation of inputs, robustly expressed through the position of the attractor state. The principal problem resides in the fact that positions of attractor states alone do not reflect the quality of match between input and data model, termed decision confidence. In hierarchies, this inevitably leads to final decisions which are not Bayes-optimal when inputs exhibit different degrees of ambiguity or conflict, since the resulting differences in confidence will be ignored by downstream layers. We demonstrate a solution to this problem by showing that a correctly parametrized DNF layer can encode decision confidence into the latency of the attractor state in a well-defined way. Conversely, we show that input stimuli gain competitive advantages w.r.t. each other as a function of their relative latency, thus allowing downstream layers to decode attractor latency in an equally well-defined way. Putting these encoding and decoding mechanisms together, we construct a three-stage hierarchy of DNF layers and show that the top-level layer can take Bayes-optimal decisions when the decisions in the lowest hierarchy levels have variable degrees of confidence. In the discussion, we generalize these findings, suggesting a novel possibility to represent and manipulate probabilistic information in recurrent networks without any need for log-encoding, just using the biologically well-founded effect of response latency as an additional coding dimension.

关键词： Recurrent neural networks neural coding Bayesian inference Response latency

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SEROTONIN MODULATES ELECTROSENSORY PROCESSING AND BEHAVIOR VIA 5-HT2-LIKE RECEPTORS

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NEUROSCIENCE 2014年 271卷 108-118页

作者： Larson, E. A. Metzen, M. G. Chacron, M. J. McGill Univ Dept Physiol Montreal PQ Canada McGill Univ Dept Phys Montreal PQ Canada

Efficient sensory processing of the environment is a critical function for any organism to survive and is accomplished by having neurons adapt their responses to stimuli based on behavioral context in part through neuromodulators such as serotonin (5-HT). We have recently shown that one critical function of the serotonergic system in weakly electric fish is to enhance sensory pyramidal neuron responses within the electrosensory lateral line lobe (ELL) to stimuli caused by same sex conspecifics, thereby enhancing their perception. This enhancement is accomplished by making pyramidal neurons more excitable through downregulation of potassium channels. However, the nature of the 5-HT receptors that mediate this effect is not known. Here we show that the 5-HT2 receptor antagonist ketanserin (ket) can effectively block the effects of 5-HT on pyramidal neuron excitability in vitro. Indeed, 5-HT application subsequent to ket application did not cause any significant changes in neuron excitability and responses to current injection. We further show that ket applied in vivo can block the effects of 5-HT on behavioral responses. Thus, our results strongly suggest that the previously observed effects of 5-HT on sensory processing within ELL and their consequences for behavior are mediated by 5-HT2 receptors. (C) 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

关键词： neural coding weakly electric fish neuromodulation neuroethology

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coding and Transformations in the Olfactory System

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ANNUAL REVIEW OF NEUROSCIENCE, VOL 37 2014年第1期37卷 363-385页

作者： Uchida, Naoshige Poo, Cindy Haddad, Rafi Harvard Univ Ctr Brain Sci Dept Mol & Cellular Biol Cambridge MA 02138 USA Champalimaud Ctr Unknown Champalimaud Neurosci Programme P-1400038 Lisbon Portugal Bar Ilan Univ Gonda Brain Res Ctr IL-52900 Ramat Gan Israel

Howis sensory information represented in the brain? Along-standing debate in neural coding is whether and how timing of spikes conveys information to downstream neurons. Although we know that neurons in the olfactory bulb (OB) exhibit rich temporal dynamics, the functional relevance of temporal coding remains hotly debated. Recent recording experiments in awake behaving animals have elucidated highly organized temporal structures of activity in the OB. In addition, the analysis of neural circuits in the piriform cortex (PC) demonstrated the importance of not only OB afferent inputs but also intrinsic PC neural circuits in shaping odor responses. Furthermore, new experiments involving stimulation of the OB with specific temporal patterns allowed for testing the relevance of temporal codes. Together, these studies suggest that the relative timing of neuronal activity in the OB conveys odor information and that neural circuits in the PC possess various mechanisms to decode temporal patterns of OB input.

关键词： neural coding olfaction pattern recognition

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On parsing the neural code in the prefrontal cortex of primates using principal dynamic modes

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JOURNAL OF COMPUTATIONAL NEUROSCIENCE 2014年第3期36卷 321-337页

作者： Marmarelis, V. Z. Shin, D. C. Song, D. Hampson, R. E. Deadwyler, S. A. Berger, T. W. Univ So Calif Dept Biomed Engn & Biomed Simulat Resource BMSR Los Angeles CA 90089 USA Univ So Calif Ctr Neural Engn Dept Biomed Engn Program Neurosci Los Angeles CA 90089 USA Wake Forest Univ Hlth Sci Dept Physiol & Pharmacol Winston Salem NC 27157 USA

Nonlinear modeling of multi-input multi-output (MIMO) neuronal systems using Principal Dynamic Modes (PDMs) provides a novel method for analyzing the functional connectivity between neuronal groups. This paper presents the PDM-based modeling methodology and initial results from actual multi-unit recordings in the prefrontal cortex of non-human primates. We used the PDMs to analyze the dynamic transformations of spike train activity from Layer 2 (input) to Layer 5 (output) of the prefrontal cortex in primates performing a Delayed-Match-to-Sample task. The PDM-based models reduce the complexity of representing large-scale neural MIMO systems that involve large numbers of neurons, and also offer the prospect of improved biological/physiological interpretation of the obtained models. PDM analysis of neuronal connectivity in this system revealed "input-output channels of communication" corresponding to specific bands of neural rhythms that quantify the relative importance of these frequency-specific PDMs across a variety of different tasks. We found that behavioral performance during the Delayed-Match-to-Sample task (correct vs. incorrect outcome) was associated with differential activation of frequency-specific PDMs in the prefrontal cortex.

关键词： Multi-input/multi-output (MIMO) neuronal systems Prefrontal cortex Dynamic nonlinear modeling Principal dynamic modes Modeling of neural systems neural coding Volterra-Wiener modeling

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Touch is a team effort: interplay of submodalities in cutaneous sensibility

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TRENDS IN NEUROSCIENCES 2014年第12期37卷 689-697页

作者： Saal, Hennes P. Bensmaia, Sliman J. Univ Chicago Dept Organismal Biol & Anat Chicago IL 60637 USA Univ Chicago Comm Computat Neurosci Chicago IL 60637 USA

Traditionally, different classes of cutaneous mechanoreceptive afferents are ascribed different and largely non-overlapping functional roles (for example texture or motion) stemming from their different response properties. This functional segregation is thought to be reflected in cortex, where each neuron receives input from a single submodality. We summarize work that challenges this notion. First, while it is Possible to design artificial stimuli that preferentially excite a single afferent class, most natural stimuli excite all afferents and most tactile percepts are shaped by multiple submodalities. Second, closer inspection of cortical responses reveals that most neurons receive convergent input from multiple afferent classes. We argue that cortical neurons should be grouped based on their function rather than on their submodality composition.

关键词： somatosensory perception neural coding

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Fast inference in generalized linear models via expected log-likelihoods

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JOURNAL OF COMPUTATIONAL NEUROSCIENCE 2014年第2期36卷 215-234页

作者： Ramirez, Alexandro D. Paninski, Liam Weill Cornell Med Coll New York NY USA Columbia Univ Dept Stat Ctr Theoret Neurosci Grossman Ctr Stat Mind New York NY USA

Generalized linear models play an essential role in a wide variety of statistical applications. This paper discusses an approximation of the likelihood in these models that can greatly facilitate computation. The basic idea is to replace a sum that appears in the exact log-likelihood by an expectation over the model covariates;the resulting "expected log-likelihood" can in many cases be computed significantly faster than the exact log-likelihood. In many neuroscience experiments the distribution over model covariates is controlled by the experimenter and the expected log-likelihood approximation becomes particularly useful;for example, estimators based on maximizing this expected log-likelihood (or a penalized version thereof) can often be obtained with orders of magnitude computational savings compared to the exact maximum likelihood estimators. A risk analysis establishes that these maximum EL estimators often come with little cost in accuracy (and in some cases even improved accuracy) compared to standard maximum likelihood estimates. Finally, we find that these methods can significantly decrease the computation time of marginal likelihood calculations for model selection and of Markov chain Monte Carlo methods for sampling from the posterior parameter distribution. We illustrate our results by applying these methods to a computationally-challenging dataset of neural spike trains obtained via large-scale multi-electrode recordings in the primate retina.

关键词： neural coding Generalized linear models Computationally efficient algorithms Expected log-likelihood

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