Extracting state transition dynamics from multiple spike trains using hidden Markov models with correlated poisson distribution.

  • Katahira K
  • Nishikawa J
  • Okanoya K
 et al. 
  • 34


    Mendeley users who have this article in their library.
  • N/A


    Citations of this article.


Neural activity is nonstationary 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, an independent Poisson model has 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. The VB could automatically determine the appropriate number of hidden states and correlation types while avoiding the overlearning problem. We developed an efficient algorithm for computing posteriors using the recursive relationship of a multivariate Poisson distribution. We demonstrated the performance of our method on synthetic data and real spike trains recorded from a songbird.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in


  • Kentaro Katahira

  • Jun Nishikawa

  • Kazuo Okanoya

  • Masato Okada

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free