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