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Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data
Florian Stimberg · Andreas Ruttor · Manfred Opper

Thu Dec 11 07:10 AM -- 07:35 AM (PST) @ Level 2, room 210

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.

Author Information

Florian Stimberg (TU Berlin)
Andreas Ruttor (TU Berlin)
Manfred Opper (TU Berlin)

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