Spiking Boltzmann Machines Geoffrey E. Hinton and Andrew D. Brown
Gatsby Computational Neuroscience Unit
University College London
In Advances in Neural Information Processing Systems 12, MIT Press, Cambridge, MA
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.