Nonstationary modeling of neural population dynamics.

Abstract

A stochastic state point-process adaptive filter was used to track the temporal evolution of several simulated nonlinear dynamical systems. The estimated Laguerre coefficients and Laguerre poles were used to reconstruct the feedforward and feedback kernels in the system. Simulations showed that the proposed method could track the actual underlying changes of nonlinear kernels using spike input and spike output information alone. The estimated models also converge quickly to the actual models after abrupt step changes in kernels. The proposed method can be used to track the functional input-output properties of neural systems as a result of learning, changes in context, aging or other factors in the natural flow of behavioral events.