Acoustics, Speech, and Signal Processing, IEEE International Conference on (2009)
Apr. 19, 2009 to Apr. 24, 2009
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
Hideaki Shimazaki , Theoretical Neuroscience Group, RIKEN Brain Science Institute, Wako-shi, Saitama, Japan
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.
Shun-ichi Amari, Emery N. Brown, Hideaki Shimazaki, Sonja Grun, "State-space analysis on time-varying correlations in parallel spike sequences", Acoustics, Speech, and Signal Processing, IEEE International Conference on, vol. 00, no. , pp. 3501-3504, 2009, doi:10.1109/ICASSP.2009.4960380