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

Part of Advances in Neural Information Processing Systems 27 (NIPS 2014)

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Florian Stimberg, Andreas Ruttor, Manfred Opper


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.