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Mathematical Biosciences and Engineering
Article . 2016 . Peer-reviewed
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Mathematical Biosciences and Engineering
Article
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zbMATH Open
Article . 2016
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Efficient information transfer by Poisson neurons

Authors: Lubomir Kostal; Shigeru Shinomoto;

Efficient information transfer by Poisson neurons

Abstract

Recently, it has been suggested that certain neurons with Poissonian spiking statistics may communicate by discontinuously switching between two levels of firing intensity. Such a situation resembles in many ways the optimal information transmission protocol for the continuous-time Poisson channel known from information theory. In this contribution we employ the classical information-theoretic results to analyze the efficiency of such a transmission from different perspectives, emphasising the neurobiological viewpoint. We address both the ultimate limits, in terms of the information capacity under metabolic cost constraints, and the achievable bounds on performance at rates below capacity with fixed decoding error probability. In doing so we discuss optimal values of experimentally measurable quantities that can be compared with the actual neuronal recordings in a future effort.

Related Organizations
Keywords

Neurons, poisson neuron, Poisson neuron, Models, Neurological, Statistical aspects of information-theoretic topics, information capacity, Synaptic Transmission, Applications of statistics to biology and medical sciences; meta analysis, metabolic cost, QA1-939, decoding error., Point processes (e.g., Poisson, Cox, Hawkes processes), decoding error, TP248.13-248.65, Mathematics, Biotechnology, Probability

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    popularity
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Average
Average
Average
gold