Acoustics, Speech, and Signal Processing, IEEE International Conference on (2009)
Apr. 19, 2009 to Apr. 24, 2009
Hideaki Shimazaki , Theoretical Neuroscience Group, RIKEN Brain Science Institute, Wako-shi, Saitama, Japan
Shun-ichi Amari , Theoretical Neuroscience Group, RIKEN Brain Science Institute, Wako-shi, Saitama, Japan
Emery N. Brown , Anesthesia and Critical Care, Massachusetts General Hospital, Boston, MA, USA
Sonja Grun , Theoretical Neuroscience Group, RIKEN Brain Science Institute, Wako-shi, Saitama, Japan
A state-space method for simultaneously estimating time-dependent rate and higher-order correlation underlying parallel spike sequences is proposed. Discretized parallel spike sequences are modeled by a conditionally independent multivariate Bernoulli process using a log-linear link function, which contains a state of higher-order interaction factors. A nonlinear recursive filtering formula is derived from a log-quadratic approximation to the posterior distribution of the state. Together with a fixed-interval smoothing algorithm, time-dependent log-linear parameters are estimated. The smoothed estimates are optimized via EM-algorithm such that their prior covariance matrix maximizes the expected complete data log-likelihood. In addition, we perform model selection on the hierarchical log-linear state-space models to avoid over-fitting. Application of the method to simultaneously recorded neuronal spike sequences is expected to contribute to uncover dynamic cooperative activities of neurons in relation to behavior.
S. Amari, E. N. Brown, H. Shimazaki and S. Grun, "State-space analysis on time-varying correlations in parallel spike sequences," Acoustics, Speech, and Signal Processing, IEEE International Conference on(ICASSP), Taipei, Taiwan, 2009, pp. 3501-3504.