Abstract
We first show how to represent sharp posterior probability
distributions using real valued coefficients on broadly-tuned basis functions. Then
we show how the precise times of spikes can be used to convey the real-valued coefficients
on the basis functions quickly and accurately. Finally we describe a simple simulation in
which spiking neurons learn to model an image sequence by fitting a dynamic generative
model.
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In Advances in Neural Information Processing Systems
12, MIT Press, Cambridge, MA
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