Efficient information transfer by Poisson neurons
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...
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AIMS Press
2015-12-01
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Series: | Mathematical Biosciences and Engineering |
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Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2016004 |
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author | Lubomir Kostal Shigeru Shinomoto |
author_facet | Lubomir Kostal Shigeru Shinomoto |
author_sort | Lubomir Kostal |
collection | DOAJ |
description | 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. |
format | Article |
id | doaj-art-9a4b31f2b1c14a759d6b763a79069ae6 |
institution | Kabale University |
issn | 1551-0018 |
language | English |
publishDate | 2015-12-01 |
publisher | AIMS Press |
record_format | Article |
series | Mathematical Biosciences and Engineering |
spelling | doaj-art-9a4b31f2b1c14a759d6b763a79069ae62025-01-24T02:35:23ZengAIMS PressMathematical Biosciences and Engineering1551-00182015-12-0113350952010.3934/mbe.2016004Efficient information transfer by Poisson neuronsLubomir Kostal0Shigeru Shinomoto1Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14220 Prague 4Department of Physics, Graduate School of Science, Kyoto University, Sakyo-ku, Kyoto 606-8502Recently, 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.https://www.aimspress.com/article/doi/10.3934/mbe.2016004poisson neuroninformation capacitymetabolic costdecoding error. |
spellingShingle | Lubomir Kostal Shigeru Shinomoto Efficient information transfer by Poisson neurons Mathematical Biosciences and Engineering poisson neuron information capacity metabolic cost decoding error. |
title | Efficient information transfer by Poisson neurons |
title_full | Efficient information transfer by Poisson neurons |
title_fullStr | Efficient information transfer by Poisson neurons |
title_full_unstemmed | Efficient information transfer by Poisson neurons |
title_short | Efficient information transfer by Poisson neurons |
title_sort | efficient information transfer by poisson neurons |
topic | poisson neuron information capacity metabolic cost decoding error. |
url | https://www.aimspress.com/article/doi/10.3934/mbe.2016004 |
work_keys_str_mv | AT lubomirkostal efficientinformationtransferbypoissonneurons AT shigerushinomoto efficientinformationtransferbypoissonneurons |