%0 Generic %1 shao2012linearnonlinearpoisson %A Shao, Louis Yuanlong %D 2012 %K Linear-Nonlinear-Poisson Networks Neural models probabilistic %T Linear-Nonlinear-Poisson Neuron Networks Perform Bayesian Inference On Boltzmann Machines %U http://arxiv.org/abs/1210.8442 %X 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.

@misc{shao2012linearnonlinearpoisson, 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.}, added-at = {2013-05-27T13:18:31.000+0200}, author = {Shao, Louis Yuanlong}, biburl = {https://www.bibsonomy.org/bibtex/246b4bfb1a7fe5eb90b3ae0435ceac683/sidyr}, description = {Linear-Nonlinear-Poisson Neuron Networks Perform Bayesian Inference On Boltzmann Machines}, interhash = {094ce765ba90893be28110b529d2ad4a}, intrahash = {46b4bfb1a7fe5eb90b3ae0435ceac683}, keywords = {Linear-Nonlinear-Poisson Networks Neural models probabilistic}, note = {cite arxiv:1210.8442Comment: Submitted to International Conference of Learning Representation (ICLR) 2013}, timestamp = {2013-05-27T13:18:31.000+0200}, title = {Linear-Nonlinear-Poisson Neuron Networks Perform Bayesian Inference On Boltzmann Machines}, url = {http://arxiv.org/abs/1210.8442}, year = 2012 }