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Extracting State Transition Dynamics from Multiple Spike Trains with Correlated Poisson HMM

Neural Information Processing SystemsApr-6-2023, 14:31:28 GMT

Neural activity is non-stationary and varies across time. Hidden Markov Models (HMMs) have been used to track the state transition among quasi-stationary discrete neural states. Within this context, independent Poisson models have been used for the output distribution of HMMs; hence, the model is incapable of tracking the change in correlation without modulating the firing rate. To achieve this, we applied a multivariate Poisson distribution with correlation terms for the output distribution of HMMs. We formulated a Variational Bayes (VB) inference for the model.

correlated poisson hmm, extracting state transition dynamic, multiple spike train, (2 more...)

Neural Information Processing Systems

Technology: Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Undirected Networks > Markov Models (1.00)

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