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Mathematical Biosciences and Engineering

2016, Volume 13, Issue 3: 509-520. doi: 10.3934/mbe.2016004
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Efficient information transfer by Poisson neurons

  • 1.
    Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14220 Prague 4
  • 2.
    Department of Physics, Graduate School of Science, Kyoto University, Sakyo-ku, Kyoto 606-8502
  • Received: 01 March 2015 Accepted: 29 June 2018 Published: 01 January 2016

  • MSC : Primary: 62B10, 62P10; Secondary: 60G55.

  • Recently, it has been suggested that certain neurons with Poissonianspiking statistics may communicate by discontinuously switchingbetween two levels of firing intensity. Such a situation resembles inmany ways the optimal information transmission protocol for thecontinuous-time Poisson channel known from information theory. In thiscontribution we employ the classical information-theoretic results toanalyze the efficiency of such a transmission from differentperspectives, emphasising the neurobiological viewpoint. We addressboth the ultimate limits, in terms of the information capacity undermetabolic cost constraints, and the achievable bounds on performanceat rates below capacity with fixed decoding error probability. Indoing so we discuss optimal values of experimentally measurablequantities that can be compared with the actual neuronal recordings ina future effort.

    Citation: Lubomir Kostal, Shigeru Shinomoto. Efficient information transfer by Poisson neurons[J]. Mathematical Biosciences and Engineering, 2016, 13(3): 509-520. doi: 10.3934/mbe.2016004

    Related Papers:

  • Recently, it has been suggested that certain neurons with Poissonianspiking statistics may communicate by discontinuously switchingbetween two levels of firing intensity. Such a situation resembles inmany ways the optimal information transmission protocol for thecontinuous-time Poisson channel known from information theory. In thiscontribution we employ the classical information-theoretic results toanalyze the efficiency of such a transmission from differentperspectives, emphasising the neurobiological viewpoint. We addressboth the ultimate limits, in terms of the information capacity undermetabolic cost constraints, and the achievable bounds on performanceat rates below capacity with fixed decoding error probability. Indoing so we discuss optimal values of experimentally measurablequantities that can be compared with the actual neuronal recordings ina future effort.


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  • © 2016 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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