To achieve this, we applied a multivariate Poisson distribution with correlation terms for the output distribution of HMMs. We formulated a Variational Bayes ( ...
Sep 1, 2010 · We developed an efficient algorithm for computing posteriors using the recursive relationship of a multivariate Poisson distribution. We ...
Neural activity is non-stationary and varies across time. Hidden Markov Models. (HMMs) have been used to track the state transition among quasi-stationary ...
Sep 1, 2010 · We developed an efficient algorithm for computing posteriors using the recursive relationship of a multivariate Poisson distribution. We ...
Missing: HMM. | Show results with:HMM.
Extracting State Transition Dynamics from Multiple Spike Trains with Correlated Poisson HMM ... HMM with third-order correlation applied to simulated spike train ...
An efficient algorithm for computing posteriors using the recursive relationship of a multivariate Poisson distribution with correlation terms for the ...
Extracting state transition dynamics from multiple spike trains with correlated Poisson HMM. K Katahira, J Nishikawa, K Okanoya, M Okada. Advances in neural ...
Abstract— To extract state transition from multi- ple spike train based on correlation changes, we intro- duce hidden Markov models with multivariate Poisson.
... multi-spike trains based on point process model and ... Extracting state transition dynamics from multiple spike trains with correlated Poisson HMM.
Extracting State Transition Dynamics from Multiple Spike Trains Using Hidden Markov Models with Correlated Poisson Distribution ... HMM for sequence alignment ...