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. 1987 Dec 29;437(2):214-38.
doi: 10.1016/0006-8993(87)91638-6.

Time structure and stimulus dependence of precisely replicating patterns present in monkey cortical neuronal spike trains

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Time structure and stimulus dependence of precisely replicating patterns present in monkey cortical neuronal spike trains

R Lestienne et al. Brain Res. .

Abstract

Evidence is presented on the parameters that affect the occurrence of precisely replicating patterns of neural discharge present as 'hidden' patterns in individual neuronal discharge trains of the visual cortical cells of the rhesus monkey in response to precisely controlled stimuli described in our previous publication. Using the All-Interval analytical paradigm we demonstrate: (1) that precisely replicating patterns are present in numbers that cannot be generated through continuous, smoothly varying probability distributions of interspike intervals; (2) that the records contain very large numbers of precisely replicating patterns--doublets, triplets, quadruplets, quintuplets and hextuplets of pulses; (3) that triplet-antitriplet pairs and symmetrical quadruplets are also present in improbable numbers; (4) that different stimuli generate different triplets; (5) and that the first order decay constant of capacity to generate specific precise patterns is a direct function of the number of events making up the patterns and thus that a temporary memory of the occurrence of a pattern exists following the presentation of a stimulus. It is concluded that such patterns of pulses are almost certainly coded symbols related to visual information; that such symbols are sufficiently precise in their replication to permit them to be decoded through spatial summation mechanisms and finally that the ability to generate and the capacity to store such symbols are probably present in the brain as related and coordinated complexes of specific facilitated synapses. Some properties of a proposed model for the production and decoding of such patterns are presented and discussed as are specific mechanisms through which neural networks may implement such functions. Finally, existing and further experimental tests of the mechanisms proposed are outlined.

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