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2014 NeurIPS NeurIPS 2014

Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data

Abstract

We introduce a model where the rate of an inhomogeneous Poisson process is modified by a Chinese restaurant process. Applying a MCMC sampler to this model allows us to do posterior Bayesian inference about the number of states in Poisson-like data. Our sampler is shown to get accurate results for synthetic data and we apply it to V1 neuron spike data to find discrete firing rate states depending on the orientation of a stimulus.

🌉 Interdisciplinary Bridge - Artificial Intelligence and Healthcare & Medicine

Authors

Florian Stimberg, Andreas Ruttor, Manfred Opper

Topics

Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning Machine Learning > Optimization & Theory > Bayesian Inference Healthcare & Medicine > Research > Biosignal Processing Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling Machine Learning > Optimization & Theory > Statistics Artificial Intelligence > Bayesian & Probabilistic > Bayesian Inference Machine Learning > Bayesian & Probabilistic > Markov Chain Monte Carlo Machine Learning > Bayesian & Probabilistic > Nonparametric Bayesian

Keywords

nonparametric bayesian bayesian inference markov chain monte carlo posterior inference mcmc sampling chinese restaurant process poisson process firing rate neural spike data neural spike

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