Metabolic cost of neuronal information in an empirical stimulus-response model

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dc.contributor.authorKostal, L.
dc.contributor.authorLansky, P.
dc.contributor.authorMcDonnell, M.
dc.date.issued2013
dc.descriptionPublished online: 7 March 2013
dc.description.abstractThe 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.
dc.description.statementofresponsibilityLubomir Kostal, Petr Lansky, Mark D. McDonnell
dc.identifier.citationBiological Cybernetics, 2013; 107(3):355-365
dc.identifier.doi10.1007/s00422-013-0554-6
dc.identifier.issn0340-1200
dc.identifier.issn1432-0770
dc.identifier.orcidMcDonnell, M. [0000-0002-7009-3869]
dc.identifier.urihttp://hdl.handle.net/2440/106632
dc.language.isoen
dc.publisherSpringer-Verlag
dc.relation.granthttp://purl.org/au-research/grants/arc/DP1093425
dc.rights© Springer-Verlag Berlin Heidelberg 2013
dc.source.urihttps://doi.org/10.1007/s00422-013-0554-6
dc.subjectInformation capacity; metabolic cost; stimulus-response curve
dc.titleMetabolic cost of neuronal information in an empirical stimulus-response model
dc.typeJournal article
pubs.publication-statusPublished

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