Literature DB >> 21340601

Mutual information and redundancy in spontaneous communication between cortical neurons.

J Szczepanski1, M Arnold, E Wajnryb, J M Amigó, M V Sanchez-Vives.   

Abstract

An important question in neural information processing is how neurons cooperate to transmit information. To study this question, we resort to the concept of redundancy in the information transmitted by a group of neurons and, at the same time, we introduce a novel concept for measuring cooperation between pairs of neurons called relative mutual information (RMI). Specifically, we studied these two parameters for spike trains generated by neighboring neurons from the primary visual cortex in the awake, freely moving rat. The spike trains studied here were spontaneously generated in the cortical network, in the absence of visual stimulation. Under these conditions, our analysis revealed that while the value of RMI oscillated slightly around an average value, the redundancy exhibited a behavior characterized by a higher variability. We conjecture that this combination of approximately constant RMI and greater variable redundancy makes information transmission more resistant to noise disturbances. Furthermore, the redundancy values suggest that neurons can cooperate in a flexible way during information transmission. This mostly occurs via a leading neuron with higher transmission rate or, less frequently, through the information rate of the whole group being higher than the sum of the individual information rates-in other words in a synergetic manner. The proposed method applies not only to the stationary, but also to locally stationary neural signals.

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Year:  2011        PMID: 21340601     DOI: 10.1007/s00422-011-0425-y

Source DB:  PubMed          Journal:  Biol Cybern        ISSN: 0340-1200            Impact factor:   2.086


  4 in total

1.  Evaluation of Non-Uniform Image Quality Caused by Anode Heel Effect in Digital Radiography Using Mutual Information.

Authors:  Ming-Chung Chou
Journal:  Entropy (Basel)       Date:  2021-04-25       Impact factor: 2.524

2.  Estimating the amount of information conveyed by a population of neurons.

Authors:  Marshall Crumiller; Bruce Knight; Yunguo Yu; Ehud Kaplan
Journal:  Front Neurosci       Date:  2011-07-15       Impact factor: 4.677

3.  Mutual information against correlations in binary communication channels.

Authors:  Agnieszka Pregowska; Janusz Szczepanski; Eligiusz Wajnryb
Journal:  BMC Neurosci       Date:  2015-05-19       Impact factor: 3.288

4.  Estimating temporal causal interaction between spike trains with permutation and transfer entropy.

Authors:  Zhaohui Li; Xiaoli Li
Journal:  PLoS One       Date:  2013-08-05       Impact factor: 3.240

  4 in total

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