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The Journal of Neurophysiology Vol. 81 No. 6 June 1999, pp. 3021-3033
Copyright ©1999 by the American Physiological Society
1National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892; and 2Institut des Neurosciences, Centre National de la Recherche Scientifique, Unité Mixte de Recherche 7624, Université Paris VI, 75005 Paris, France
Oram, M. W.,
M. C. Wiener,
R. Lestienne, and
B. J. Richmond.
Stochastic Nature of Precisely Timed Spike Patterns in Visual
System Neuronal Responses. J. Neurophysiol. 81: 3021-3033, 1999.
Stochastic nature of precisely timed spike patterns in visual system
neuronal responses. It is not clear how information related to
cognitive or psychological processes is carried by or represented in
the responses of single neurons. One provocative proposal is that
precisely timed spike patterns play a role in carrying such information. This would require that these spike patterns have the
potential for carrying information that would not be available from
other measures such as spike count or latency. We examined exactly
timed (1-ms precision) triplets and quadruplets of spikes in the
stimulus-elicited responses of lateral geniculate nucleus (LGN) and
primary visual cortex (V1) neurons of the awake fixating rhesus monkey.
Large numbers of these precisely timed spike patterns were found.
Information theoretical analysis showed that the precisely timed spike
patterns carried only information already available from spike count,
suggesting that the number of precisely timed spike patterns was
related to firing rate. We therefore examined statistical models
relating precisely timed spike patterns to response strength. Previous
statistical models use observed properties of neuronal responses such
as the peristimulus time histogram, interspike interval, and/or spike
count distributions to constrain the parameters of the model. We
examined a new stochastic model, which unlike previous models included
all three of these constraints and unlike previous models predicted the
numbers and types of observed precisely timed spike patterns. This
shows that the precise temporal structures of stimulus-elicited
responses in LGN and V1 can occur by chance. We show that any deviation
of the spike count distribution, no matter how small, from a Poisson
distribution necessarily changes the number of precisely timed spike
patterns expected in neural responses. Overall the results indicate
that the fine temporal structure of responses can only be interpreted once all the coarse temporal statistics of neural responses have been
taken into account.
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