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Spiking Boltzmann Machines

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If you have a question about this talk, please contact Philip Sterne.

In this journal club we will cover the following paper: Hinton and Brown (2000) – Spiking Boltzmann Machines

Abstract:

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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.

This talk is part of the Machine Learning Journal Club series.

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