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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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