The EPFL Latsis Symposium 2003 on Neural Coding and Modeling
Barry Richmond, NIMH
Decoding spike trains instant-by-instant using order statistics
and mixture of Poissons models
Spike trains are generated in time, and presumably also interpreted as
they unfold. Recent work suggests that in several areas of the monkey
brain, individual spike times carry information because they reflect
underlying rate variation. Constructing a model Given this stochastic
structure order statistics provides a means to decode spike trains
instant by instant, as spikes arrive or do not. Order statistics are
time-consuming to compute in the general case. Our data from neurons
in V1 are well-fit by a mixture of Poisson processes. In this special
case, decoding is substantially faster. In these data, spike timing
contributed information beyond that available from spike count
throughout the trial. At the end of the trial, a decoder based on the
mixture of Poissons model correctly decoded about three times as many
trials as expected by chance, compared to about twice as many as
expected by chance using spike count only. If our model perfectly
describedthe spike trains, and enough data were available to estimate
model parameters, this Bayesian decoder would be optimal. For 4/5 of
the sets of stimulus-elicited responses, the observed spike trainswere
consistent with the mixture of Poissons model. Most of the error in
estimating stimulus probabilities is due to not havingenough data to
specify the parameters of the model rather than to misspecification of
the model itself.