Literature DB >> 8895888

Population coding by cell assemblies--what it really is in the brain.

Y Sakurai1.   

Abstract

One of the central questions in neuroscience concerns the basic code for information processing in the brain. Much experimental evidence and theoretical consideration have suggested that single-neuron coding is no longer a tenable hypothesis. The present review explains why population neuronal coding is valid and discusses how it is carried out in the brain. The main context is experimental access to real features of the coding in working brains as deduced from experimental research. Several recent studies recording neuronal activities from behaving animals have shown that ensemble activity of neurons represents specific information, indicating the reality of population coding by many neurons. The key concept which can integrate the experimental evidence is the 'cell assembly', i.e., overlapped populations of neurons with flexible functional connections within and among the populations. Correlated activity among the neurons constructs the functional connection. In order to see features of the cell-assembly coding, two main properties of cell assemblies in processing several different kinds of information must be investigated, that is, the overlapping of neurons and the dynamics of synaptic connections. This manner of coding can provide both the experimental and theoretical framework to detect the real dynamic features of information processing by the brain.

Mesh:

Year:  1996        PMID: 8895888     DOI: 10.1016/0168-0102(96)01075-9

Source DB:  PubMed          Journal:  Neurosci Res        ISSN: 0168-0102            Impact factor:   3.304


  16 in total

1.  Sequential rearrangements of the ensemble activity of putamen neurons in the monkey brain as a correlate of continuous behavior.

Authors:  S V Afanas'ev; B F Tolkunov; N B Rogatskaya; A A Orlov; E V Filatova
Journal:  Neurosci Behav Physiol       Date:  2004-03

2.  Interneuronal frontohippocampal interactions in cats trained to choose on the basis of reinforcement quality.

Authors:  G Kh Merzhanova; E E Dolbakyan; V N Khokhlova
Journal:  Neurosci Behav Physiol       Date:  2004-07

3.  Organization of frontohippocampal neuronal networks in cats in different types of directed behavior.

Authors:  G Kh Merzhanova; E E Dolbakyan; V N Khokhlova
Journal:  Neurosci Behav Physiol       Date:  2005-07

4.  Dynamic synchrony of firing in the monkey prefrontal cortex during working-memory tasks.

Authors:  Yoshio Sakurai; Susumu Takahashi
Journal:  J Neurosci       Date:  2006-10-04       Impact factor: 6.167

5.  Limitations to Estimating Mutual Information in Large Neural Populations.

Authors:  Jan Mölter; Geoffrey J Goodhill
Journal:  Entropy (Basel)       Date:  2020-04-24       Impact factor: 2.524

6.  State-space analysis of time-varying higher-order spike correlation for multiple neural spike train data.

Authors:  Hideaki Shimazaki; Shun-Ichi Amari; Emery N Brown; Sonja Grün
Journal:  PLoS Comput Biol       Date:  2012-03-08       Impact factor: 4.475

7.  Changes in regional activity are accompanied with changes in inter-regional connectivity during 4 weeks motor learning.

Authors:  Liangsuo Ma; Binquan Wang; Shalini Narayana; Eliot Hazeltine; Xiying Chen; Donald A Robin; Peter T Fox; Jinhu Xiong
Journal:  Brain Res       Date:  2010-01-04       Impact factor: 3.252

8.  Target-, limb-, and context-dependent neural activity in the cingulate and supplementary motor areas of the monkey.

Authors:  M D Crutcher; G S Russo; S Ye; D A Backus
Journal:  Exp Brain Res       Date:  2004-07-29       Impact factor: 1.972

9.  Spatially invariant computations in stereoscopic vision.

Authors:  Michel Vidal-Naquet; Sergei Gepshtein
Journal:  Front Comput Neurosci       Date:  2012-07-16       Impact factor: 2.380

10.  Multiplexed Population Coding of Stimulus Properties by Leech Mechanosensory Cells.

Authors:  Friederice Pirschel; Jutta Kretzberg
Journal:  J Neurosci       Date:  2016-03-30       Impact factor: 6.167

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