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作者： Puggioni, Paolo University of Edinburgh

学位级别：doctor

The activity of cortical neurons in awake brains changes dynamically as a function of the behavioural and attentional state. The primary motor cortex (M1) plays a central role in regulating complex motor behaviors. Despite a growing knowledge on its connectivity and spiking pattern, little is known about intra-cellular mechanism and rhythms underlying motor-command generation. In the last decade, whole-cell recordings in awake animals has become a powerful tool for characterising both sub-and supra-threshold activity during behaviour. Seminal in vivo studies have shown that changes in input structure and sub-threshold regime determine spike output during behaviour (input-output transformations). In this thesis I make use of computational and experimental techniques to better understand (i) how the brain regulates the sub-threshold activity of the neurons during movement and (ii) how this reflects in their input-output transformation properties. In the first part of this work I present a novel probabilistic technique to infer input statistics from in-vivo voltage-clamp traces. This approach, based on Bayesian belief networks, outperforms current methods and allows an estimation of synaptic input (i) kinetic properties, (ii) frequency, and (iii) weight distribution. I first validate the model on simulated data, thus I apply it to voltage-clamp recordings of cerebellar interneurons in awake mice. I found that synaptic weight distributions have long tails, which on average do not change during movement. Interestingly, the increase in synaptic current observed during movement is a consequence of the increase in input frequency only. In the second part, I study how the brain regulates the activity of pyramidal neurons in the M1 of awake mice during movement. I performed whole-cell recordings of pyramidal neurons in layer 5B (L5B), which represent one of the main descending output channels from motor cortex. I found that slow large-amplitude membrane potential fluctuations

关键词： electrophysiology motor cortex neural coding Bayesian inference

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A history of spike trains as Point Processes in neural coding

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JOURNAL OF PHYSIOLOGY-PARIS 2010年第3-4期104卷 156-159页

作者： Pedro Segundo, Jose Univ Calif Los Angeles Dept Neurobiol Los Angeles CA 90095 USA Univ Republica Fac Ciencias Montevideo Uruguay

The history of viewing spike trains as point processes along time is that of the inference that spike trains participate in neural codings. A characteristic thought process, the "Conceptual Framework", present always and with sequential components, upholds the logic of this view. Early research in the 1920's introduced the view: experiments, though largely primitive, were designed and interpreted adequately and thus demonstrated convincingly this role. Modern research, starting around 1950, has contributed impressively. Essential was the mathematical Theory of Point Processes that permitted rigorous studies;computer implementation, though no substitute for clear thinking, was enormously helpful. Contributions, besides confirming the key inference, include wide-range train participation in neural codings, with many courses of action and formal implications involving neuronal performances individually and in networks, discharge forms, dynamic behaviors, critical Information and Communication issues, etc. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Spike trains Point Processes neural coding

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Learning Internal Representation of Visual Context in a neural coding Network

Learning Internal Representation of Visual Context in a Neur...

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20th International Conference on Artificial neural Networks

作者： Miao, Jun Zou, Baixian Qing, Laiyun Duan, Lijuan Fu, Yu Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Union Univ Coll Arts & Sci Beijing Dept Informat Sci & Technol Beijing 100083 Peoples R China Grad Univ Chinese Acad Sci Sch Informat Sci & Engn Beijing 100049 Peoples R China Univ Sci & Technol Beijing Coll Comp Sci & Technol Beijing 100124 Peoples R China Univ Surrey Dept Comp Surrey GU2 7XH England

ISBN: (纸本)9783642158186

Visual context plays a significant role in humans' gaze movement for target searching. How to transform the visual context into the internal representation of a brain-like neural network is an interesting issue. Population cell coding is a neural representation mechanism which was widely discovered in primates' visual neural system. This paper presents a biologically inspired neural network model which uses a population cell coding mechanism for visual context representation and target searching. Experimental results show that the population-cell-coding generally performs better than the single-cell-coding system.

关键词： Visual context neural coding object search

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Time-varying vector Poisson processes with coincidences 40

Time-varying vector Poisson processes with coincidences

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Solo, Victor Godoy, Boris School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia

ISBN: (纸本)9781467369978

Three emerging applications are driving a renewed interest in vector point processes: neural coding, high frequency finance and genomics. This pressure has revealed a gross lack of models and system identification methods. In particular in at least the first two applications coincidences can occur i.e. more than one event can occur at the same time. Yet the models in common use exclude this possibility. In this paper we develop a class of time-varying vector Poisson models that allow coincident events and develop for the first time an hypothesis test for no coincidences. We show simulation results and an application to high frequency finance data. © 2015 IEEE.

关键词： finance genomics neural coding point process

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ProbabilisticinferenceindiscretespacescanbeimplementedintonetworksofLIFneurons

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2015年第JAN期9卷 13-13页

作者： Probst, Dimitri Petrovici, Mihai A. Bytschok, Ilja Bill, Johannes Pecevski, Dejan Schemmel, Johannes Meier, Karlheinz Heidelberg Univ Kirchhoff Inst Phys D-69120 Heidelberg Germany Graz Univ Technol Inst Theoret Comp Sci A-8010 Graz Austria

The means by which cortical neural networks are able to efficiently solve inference problems remains an open question in computational neuroscience. Recently, abstract models of Bayesian computation in neural circuits have been proposed, but they lack a mechanistic interpretation at the single-cell level. In this article, we describe a complete theoretical framework for building networks of leaky integrate-and-fire neurons that can sample from arbitrary probability distributions over binary random variables. We test our framework for a model inference task based on a psychophysical phenomenon (the Knill-Kersten optical illusion) and further assess its performance when applied to randomly generated distributions. As the local computations performed by the network strongly depend on the interaction between neurons, we compare several types of couplings mediated by either single synapses or interneuron chains. Due to its robustness to substrate imperfections such as parameter noise and background noise correlations, our model is particularly interesting for implementation on novel, neuro-inspired computing architectures, which can thereby serve as a fast, low-power substrate for solving real-world inference problems.

关键词： theoretical neuroscience neural coding computational neural models probabilistic models and methods Bayesian theory graphical models MCMC neuromorphic hardware

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A stimulus-dependent spike threshold is an optimal neural coder

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2015年第JUNE期9卷 61页

作者： Jones, Douglas L. Johnson, Erik C. Ratnam, Rama Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA Univ Illinois Beckman Inst Adv Sci & Technol Urbana IL 61801 USA Univ Illinois Coordinated Sci Lab Urbana IL 61801 USA Illinois Singapore Pte Ltd Adv Digital Sci Ctr Singapore Singapore

A neural code based on sequences of spikes can consume a significant portion of the brain's energy budget. Thus, energy considerations would dictate that spiking activity be kept as low as possible. However, a high spike-rate improves the coding and representation of signals in spike trains, particularly in sensory systems. These are competing demands, and selective pressure has presumably worked to optimize coding by apportioning a minimum number of spikes so as to maximize coding fidelity. The mechanisms by which a neuron generates spikes while maintaining a fidelity criterion are not known. Here, we show that a signal-dependent neural threshold, similar to a dynamic or adapting threshold, optimizes the trade-off between spike generation (encoding) and fidelity (decoding). The threshold mimics a post-synaptic membrane (a low-pass filter) and serves as an internal decoder. Further, it sets the average firing rate (the energy constraint). The decoding process provides an internal copy of the coding error to the spike-generator which emits a spike when the error equals or exceeds a spike threshold. When optimized, the trade-off leads to a deterministic spike firing-rule that generates optimally timed spikes so as to maximize fidelity. The optimal coder is derived in closed-form in the limit of high spike-rates, when the signal can be approximated as a piece-wise constant signal. The predicted spike-times are close to those obtained experimentally in the primary electrosensory afferent neurons of weakly electric fish (Apteronotus leptorhynchus) and pyramidal neurons from the somatosensory cortex of the rat. We suggest that KCNQ/Kv7 channels (underlying the M-current) are good candidates for the decoder. They are widely coupled to metabolic processes and do not inactivate. We conclude that the neural threshold is optimized to generate an energy-efficient and high-fidelity neural code.

关键词： neural coding coding fidelity energy-efficient coding dynamic threshold spike-timing source coding spike-threshold

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An Application Specific Instruction Set Processor (ASIP) for Adaptive Filters in neural Prosthetics

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015年第5期12卷 1034-1047页

作者： Xin, Yao Li, Will X. Y. Zhang, Zhaorui Cheung, Ray C. C. Song, Dong Berger, Theodore W. City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Univ So Calif Ctr Neural Engn Dept Biomed Engn Los Angeles CA 90089 USA

neural coding is an essential process for neuroprosthetic design, in which adaptive filters have been widely utilized. In a practical application, it is needed to switch between different filters, which could be based on continuous observations or point process, when the neuron models, conditions, or system requirements have changed. As candidates of coding chip for neural prostheses, low-power general purpose processors are not computationally efficient especially for large scale neural population coding. Application specific integrated circuits (ASICs) do not have flexibility to switch between different adaptive filters while the cost for design and fabrication is formidable. In this research work, we explore an application specific instruction set processor (ASIP) for adaptive filters in neural decoding activity. The proposed architecture focuses on efficient computation for the most time-consuming matrix/vector operations among commonly used adaptive filters, being able to provide both flexibility and throughput. Evaluation and implementation results are provided to demonstrate that the proposed ASIP design is area-efficient while being competitive to commercial CPUs in computational performance.

关键词： Neuroprosthetics neural coding adaptive filter application specific instruction set processor field-programmable gate array

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Synchronized Response to Grayscale Image Inputs in Chaotic Cellular neural Network

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JOURNAL OF ROBOTICS NETWORKING AND ARTIFICIAL LIFE 2015年第1期2卷 26-29页

作者： Fujiwara, Masayuki Yamaguchi, Akihiro Kubo, Masao Fukuoka Inst Technol Dept Informat & Syst Engn Higashi Ku Fukuoka 8110116 Japan Natl Def Acad Japan Dept Comp Sci Yokosuka Kanagawa 2398686 Japan

In this study, the synchronized response in the chaotic cellular neural network for a grayscale visual stimulus was investigated in the viewpoint of neural coding. Simple gradation patterns were used as visual stimuli and the synchronized response was analyzed by the correlation of the spike firing times. As a result, synchronized responses were observed for the neurons that had similar input values and formed chaotic cell assemblies. Each assembly was distinguished from the others in terms of cross-correlation.

关键词： chaotic synchronization neural coding spike response model visual segmentation

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Time-varying vector Poisson processes with coincidences

Time-varying vector Poisson processes with coincidences

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： V. Solo B. Godoy Sch. of Electr. Eng. & Telecommun. Univ. of New South Wales Sydney NSW Australia

ISBN: (纸本)9781467369985

关键词： finance genomics neural coding point process point operation Genomics Coincidence financing Poisson process Hypothesis testing time varying

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Synergy, redundancy, and multivariate information measures: an experimentalist's perspective

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JOURNAL OF COMPUTATIONAL NEUROSCIENCE 2014年第2期36卷 119-140页

作者： Timme, Nicholas Alford, Wesley Flecker, Benjamin Beggs, John M. Indiana Univ Dept Phys Bloomington IN 47405 USA

Information theory has long been used to quantify interactions between two variables. With the rise of complex systems research, multivariate information measures have been increasingly used to investigate interactions between groups of three or more variables, often with an emphasis on so called synergistic and redundant interactions. While bivariate information measures are commonly agreed upon, the multivariate information measures in use today have been developed by many different groups, and differ in subtle, yet significant ways. Here, we will review these multivariate information measures with special emphasis paid to their relationship to synergy and redundancy, as well as examine the differences between these measures by applying them to several simple model systems. In addition to these systems, we will illustrate the usefulness of the information measures by analyzing neural spiking data from a dissociated culture through early stages of its development. Our aim is that this work will aid other researchers as they seek the best multivariate information measure for their specific research goals and system. Finally, we have made software available online which allows the user to calculate all of the information measures discussedwithin this paper.

关键词： Information theory Multivariate information measures Complex systems neural coding Dissociated neuronal cultures Multielectrode array

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