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中国神经科学学会第九届全国学术会议暨第五次会员代表大会

作者： Han-Yan GONG,Ying-Ying ZHANG,Pei-Ji LIANG,Pu-Ming ZHANG~* School of Biomedical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China

Objective It has been already known that stimulus information is represented with various rules in neuron spike trains called neural *** aim for understanding neural coding is to explore the distinct relationship between stimulus and the individual or ensemble neural *** between spike trains or neurons sometimes indicates certain neural coding rules in the visual *** relationship between spike timing correlation and pattern correlation is discussed,and their ability to represent stimulus features is compared to examine their coding strategies not only in individual neurons but also in *** Two kinds of stimuli,natural movies and checkerboard,are used to arouse firing activities in chicken retinal ganglion *** spike timing correlation and pattern correlation are calculated by cross-correlation function and Lempel-Ziv distance *** According to the correlation values,it is demonstrated that spike trains with similar spike patterns are not necessarily concerted in firing ***,spike pattern correlation values between individual neurons' responses reflect the difference of natural movies and checkerboard;neurons cooperate with each other with higher pattern correlation values which represent spatiotemporal correlations during response to natural *** timing does not reflect stimulus features as obvious as spike patterns,caused by their particular coding properties or physiological foundation. Conclusion Separating the pattern correlation out of traditional timing correlation concept uncover additional insight in neural coding.

关键词： neural coding spike pattern correlation spike timing correlation cross-correlation Lempel-Ziv distance

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Review: neural coding and the Basic Law of Psychophysics

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The Neuroscientist 2002年第2期8卷 111 - 121页

作者： Kenneth O. Johnson Steven S. Hsiao Takashi Yoshioka Krieger Mind/Brain Institute and Neuroscience Department Johns Hopkins University Baltimore Maryland Krieger Mind/Brain Institute and Neuroscience Department Johns Hopkins University Baltimore Maryland

There have been three main ideas about the basic law of psychophysics. In 1860, Fechner used Weber’s law to infer that the subjective sense of intensity is related to the physical intensity of a stimulus by a logarithmic function (the Weber-Fechner law). A hundred years later, Stevens refuted Fechner’s law by showing that direct reports of subjective intensity are related to the physical intensity of stimuli by a power law. MacKay soon showed, however, that the logarithmic and power laws are indistinguishable without examining the underlying neural mechanisms. Mountcastle and his colleagues did so, and, on the basis of transducer functions obeying power laws, inferred that subjective intensity must be related linearly to the neural coding measure on which it is based. In this review, we discuss these issues and we review a series of studies aimed at the neural mechanisms of a very complex form of subjective experience—the experience of roughness produced by a textured surface. The results, which are independent of any assumptions about the form of the psychophysical law, support the idea that the basic law of psychophysics is linearity between subjective experience and the neural activity on which it is based.

关键词： Texture neural coding Psychophysics Touch Neurophysiology Somatosensory

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Comparing neural coding of auditory in bat and vestibular in rat

Comparing neural coding of auditory in bat and vestibular in...

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中国神经科学学会第四次会员代表大会暨第八届全国学术会议

作者： Yong-Kui ZHANG Research Center for Learning Science School of Biological Sciences and Medical EngineeringSoutheast University

＜正＞This report summarize my experimental data on auditory and vestibular *** mammal,one inner ear has two functions:auditory and *** sensor and motor information mechanical are transformed by hair cells. Auditory neural code in lower frequency firing rate but processed with several nucleus pathway steps before reach auditory cortex,showing higher non-linear coding but keep tonotopic *** improve information processing, cortico-fugal feedback pathway modulating auditory neural activity of inferior colliculus in *** the other hand, vestibular neural coding in higher firing rate,showing linear coding and short downward reflex *** postnatal development data in rat vestibular afferent were present.

关键词： auditory vestibular neural coding computational neuroscience

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A new patterns of self-organization activity of brain: neural energy coding

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INFORMATION SCIENCES 2022年 608卷 1747-1755页

作者： Zheng, Jinchao Wang, Rubin Kong, Wanzeng Zhang, Jianhai Hangzhou Dianzi Univ Sch Comp Sci Hangzhou Peoples R China East China Univ Sci & Technol Inst Cognit Neurodynam Shanghai 200237 Peoples R China

According to the basic principles and methods of information theory, the operation way of neural coding is studied and analyzed by using the minimum mutual information and the maximum entropy principle. This paper describes how the principles of minimum mutual information and maximum entropy are used to evaluate the amount of information in neu-ral responses. Its main contribution is as follows: (1) that the expression of neural informa-tion is closely related to the utilization of neural energy, and it is found that the highly evolved nervous system strictly follows the two basic principles of economy and efficiency in energy consumption and utilization;(2) In order to verify the relationship between neu-ral information processing and energy utilization, this paper uses the concept of energy -efficiency ratio to measure the economy and high efficiency of the nervous system in term of energy utilization by using the maximum entropy principle;(3) The numerical results show that the energy consumed by the nervous system reflects not only the internal law of neural information conduction and processing, but also the self-organization structure of neural information coding. The results suggest that energy neural coding, a novel neural information processing method, can be used to understand how brain activity works. Such a coding pattern can not only be extended to research the large-scale neuroscience field, but also unify brain models at all levels by use of the energy theory. This will provide a sci-entific theoretical basis for the exploration of how the brain works and the computational principles of brain-like artificial intelligence.(c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： neural coding Information theory Minimum mutual information Maximum entropy Energy-efficiency ratio Energy utilization

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Correlation and neural information coding fidelity and efficiency

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NEUROCOMPUTING 1999年 26-7卷 163-168页

作者： Gruner, CM Johnson, DH Rice Univ Houston TX 77251 USA

Analysis of neural spike patterns shows that the firing patterns of neurons within an ensemble can be correlated. The question of how this interneuronal correlation affects neural coding is not well understood. We show that in a simulated neural population in which correlation is a result of convergent inputs, both coding ability and efficiency are \increased. coding efficiency is further increased when the population is composed of neurons with diverse responses rather than neurons with identical response characteristics. (C) 1999 Elsevier Science B.V. All rights reserved.

关键词： neural coding auditory system correlation information theory neural ensembles

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neural Population coding of Stimulus Features 22nd

Neural Population Coding of Stimulus Features

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22nd International Conference on neural Information Processing (ICONIP)

作者： Iclanzan, David Szilagyi, Laszlo Sapientia Hungarian Univ Transylvania Targu Mures Romania

ISBN: (纸本)9783319265612;9783319265605

While empirical evidence suggest that the brain can represent and operate on probability distributions, it is not clear how multivariate dependencies can be detected and represented by neural circuits. Based on previous work and the principle of entropy distillation, the paper introduces a massively parallel connectionist machine whose spiking behavior adapts to the statistical distribution of binary inputs. Experimental results confirm that the network is able to accurately capture the joint probability distribution of the inputs and it is able to represent even higher-order features lacking pairwise correlations.

关键词： neural coding Entropy distillation Higher-order features

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neural information coding of memory and forgetting in the hippocampus

Neural information coding of memory and forgetting in the hi...

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中国神经科学学会第十二届全国学术会议

作者： Jia-Min Xu Long-Nian Lin Key Laboratory of Brain Functional Genomics （Ministry of Education） Institute of Brain Functional Genomics School of Life Science East China Normal University

The hippocampus plays a crucial role in memory and forgetting as an important subcortical structure closely related to learning and memory in the brain. The hippocampal neuronal network consists mainly of excitatory pyramidal neurons and inhibitory interneurons. In order to decipher the activity profile and information coding mechanism of these neurons in memory and forgetting, we combine multi-channel invivo recording technique with optogenetics to investigate the significance of neuronal activity patterns in information coding by study the in vivo firing patterns of various excitatory and inhibitory neurons in the hippocampal CA1 area. The results show that the hippocampal CA1 pyramidal cells mainly act as place cells to participate in spatial information coding. When the animal enters a new environment, the response activity of place cells to a particular spatial location can occur very quickly, even when the animal passes through that particular location for the first time in some cases. Compared to the pyramidal neurons, the inhibitory CA1 interneurons often maintain a higher average firing rate, many of them show correlated firing activity with hippocampal field potential oscillations. We have observed theta-clock neuron, theta-driving neuron and rippledriving neuron in hippocampal CA1 area. Ripple-driving neuron can be further divided into two categories: type 1 ripple-driving neuron has only 1 spike within each ripple cycle,while type 2 ripple-driving neuron has 2-3 spikes. Since the frequency range of the ripple oscillation itself is 150-250 Hz, these ripple-driving neurons can have a firing rate of up to 400-500 Hz during the ripple period. Combined with optogenetics, we identified that some SST+ neurons are type 1 ripple-driving neurons and some PV+ neurons are type 2 ripple-driving neurons. The results have shown that different neuronal types in the hippocampal network have distinct in vivo firing patterns, which coordinate with each other to achieve mem

关键词： hippocampus memory forgetting neural coding oscillations

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Optimal stimulus coding by neural populations using rate codes

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JOURNAL OF COMPUTATIONAL NEUROSCIENCE 2004年第2期16卷 129-138页

作者： Johnson, DH Ray, W Rice Univ Dept Elect & Comp Engn Houston TX 77251 USA

We create a framework based on Fisher information for determining the most effective population coding scheme for representing a continuous-valued stimulus attribute over its entire range. Using this scheme, we derive optimal single- and multi-neuron rate codes for homogeneous populations using several statistical models frequently used to describe neural data. We show that each neuron's discharge rate should increase quadratically with the stimulus and that statistically independent neural outputs provides optimal coding. Only cooperative populations can achieve this condition in an informationally effective way.

关键词： neural coding neural populations information theory Fisher information

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Synergistic coding by Cortical neural Ensembles

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IEEE TRANSACTIONS ON INFORMATION THEORY 2010年第2期56卷 875-889页

作者： Aghagolzadeh, Mehdi Eldawlatly, Seif Oweiss, Karim Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA Michigan State Univ Neurosci Program E Lansing MI 48824 USA

An essential step towards understanding how the brain orchestrates information processing at the cellular and population levels is to simultaneously observe the spiking activity of cortical neurons that mediate perception, learning, and motor processing. In this paper, we formulate an information theoretic approach to determine whether cooperation among neurons may constitute a governing mechanism of information processing when encoding external covariates. Specifically, we show that conditional independence between neuronal outputs may not provide an optimal encoding strategy when the firing probability of a neuron depends on the history of firing of other neurons connected to it. Rather, cooperation among neurons can provide a "message-passing" mechanism that preserves most of the information in the covariates under specific constraints governing their connectivity structure. Using a biologically plausible statistical learning model, we demonstrate the performance of the proposed approach in synergistically encoding a motor task using a subset of neurons drawn randomly from a large population. We demonstrate its superiority in approximating the joint density of the population from limited data compared to a statistically independent model and a pairwise maximum entropy (MaxEnt) model.

关键词： Cortical networks graph theory maximum entropy (MaxEnt) minimum entropy distance (MinED) neural coding spike trains

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The Geometry of Information coding in Correlated neural Populations

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ANNUAL REVIEW OF NEUROSCIENCE, VOL 44, 2021 2021年第1期44卷 403-424页

作者： da Silveira, Rava Azeredo Rieke, Fred Ecole Normale Super Dept Phys F-75005 Paris France Univ Paris Sorbonne Univ Univ Paris Sci & Lettres PSL CNRSLab Phys ENS F-75006 Paris France Inst Mol & Clin Ophthalmol Basel CH-4031 Basel Switzerland Univ Basel Fac Sci CH-4056 Basel Switzerland Univ Washington Dept Physiol & Biophys Seattle WA 98195 USA

Neurons in the brain represent information in their collective activity. The fidelity of this neural population code depends on whether and how variability in the response of one neuron is shared with other neurons. Two decades of studies have investigated the influence of these noise correlations on the properties of neural coding. We provide an overview of the theoretical developments on the topic. Using simple, qualitative, and general arguments, we discuss, categorize, and relate the various published results. We emphasize the relevance of the fine structure of noise correlation, and we present a new approach to the issue. Throughout this review, we emphasize a geometrical picture of how noise correlations impact the neural code.

关键词： neural coding neural computation correlations

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