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A Model of Predictive Coding based on Spike Timing |
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@Article{,
author = {Dana H. Ballard and Rajesh P.N. Rao and Zuohua Zhang},
title = {A Model of Predictive Coding based on Spike Timing},
journal = {Natioal Resource Lab},
year = {1999},
abstract={
Several decades of research have made many advances towards the goal of interpreting
the neural spike train but a comprehensive understanding remains elusive. This
paper pursues this goal in the context of a new class of models termed predictive
models. Predictive models characterize the cortex as a memory whose parameters
can be used to predict its input. This allows the input to be economically coded as
a residual difference between itself and the prediction. Such models have recently
had considerable success in modeling features of visual cortex. This paper shows
that the predictive coding model can be extended to a lower level of detail that
includes individual spikes as primitives. This is a significant improvement in per
spicuity compared to the firing rate variables used by most current models. The
specific model we describe exploits the use of coincidence of spike arrival times and
the fact that neural representations can be distributed over large numbers of cells.
}
}