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Type: Journal article
Title: Metabolic cost of neuronal information in an empirical stimulus-response model
Author: Kostal, L.
Lansky, P.
McDonnell, M.
Citation: Biological Cybernetics: communication and control in organisms and automata, 2013; 107(3):355-365
Publisher: Springer-Verlag
Issue Date: 2013
ISSN: 0340-1200
Statement of
Lubomir Kostal, Petr Lansky, Mark D. McDonnell
Abstract: The limits on maximum information that can be transferred by single neurons may help us to understand how sensory and other information is being processed in the brain. According to the efficient-coding hypothesis (Barlow, Sensory Comunication, MIT press, Cambridge, 1961), neurons are adapted to the statistical properties of the signals to which they are exposed. In this paper we employ methods of information theory to calculate, both exactly (numerically) and approximately, the ultimate limits on reliable information transmission for an empirical neuronal model. We couple information transfer with the metabolic cost of neuronal activity and determine the optimal information-to-metabolic cost ratios. We find that the optimal input distribution is discrete with only six points of support, both with and without a metabolic constraint. However, we also find that many different input distributions achieve mutual information close to capacity, which implies that the precise structure of the capacity-achieving input is of lesser importance than the value of capacity.
Keywords: Information capacity; metabolic cost; stimulus-response curve
Description: Published online: 7 March 2013
Rights: © Springer-Verlag Berlin Heidelberg 2013
DOI: 10.1007/s00422-013-0554-6
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Electrical and Electronic Engineering publications

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