Linear-Nonlinear-Poisson Neuron Networks Perform Bayesian Inference On Boltzmann Machines

Yuanlong Shao

Invalid Date (modified: Jan 16, 2013) ICLR 2013 conference submission readers: everyone
  • Decision: conferencePoster-iclr2013-workshop
  • Abstract: One conjecture in both deep learning and classical connectionist viewpoint is that the biological brain implements certain kinds of deep networks as its back-end. However, to our knowledge, a detailed correspondence has not yet been set up, which is important if we want to bridge between neuroscience and machine learning. Recent researches emphasized the biological plausibility of Linear-Nonlinear-Poisson (LNP) neuron model. We show that with neurally plausible settings, the whole network is capable of representing any Boltzmann machine and performing a semi-stochastic Bayesian inference algorithm lying between Gibbs sampling and variational inference.
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