Information in the neuronal representation of individual stimuli in the primate temporal visual cortex

Edmund T. Rolls*, Alessandro Treves, Martin J. Tovee, Stefano Panzeri

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

92 Citations (Scopus)

Abstract

To analyze the information provided about individual visual stimuli in the responses of single neurons in the primate temporal lobe visual cortex, neuronal responses to a set of 65 visual stimuli were recorded in macaques performing a visual fixation task and analyzed using information theoretical measures. The population of neurons analyzed responded primarily to faces. The stimuli included 23 faces and 42 nonface images of real-world scenes, so that the function of this brain region could be analyzed when it was processing relatively natural scenes. It was found that for the majority of the neurons significant amounts of information were reflected about which of several of the 23 faces had been seen. Thus the representation was not local, for in a local representation almost all the information available can be obtained when the single stimulus to which the neuron responds best is shown. It is shown that the information available about any one stimulus depended on how different (for example, how many standard deviations) the response to that stimulus was from the average response to all stimuli. This was the case for responses below the average response as well as above. It is shown that the fraction of information carried by the low firing rates of a cell was large - much larger than that carried by the high firing rates. Part of the reason for this is that the probability distribution of different firing rates is biased toward low values (though with fewer very low values than would be predicted by an exponential distribution). Another factor is that the variability of the response is large at intermediate and high firing rates. Another finding is that at short sampling intervals (such as 20 ms) the neurons code information efficiently, by effectively acting as binary variables and behaving less noisily than would be expected of a Poisson process.

Original languageEnglish
Pages (from-to)309-333
Number of pages25
JournalJournal of Computational Neuroscience
Volume4
Issue number4
DOIs
Publication statusPublished - 8 Dec 1997
Externally publishedYes

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