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We consider the problem of inferring time-varying Granger causal interactions among multiple simultaneously recorded spike trains from a neuronal ensemble. We present a dynamic Granger causality measure with sparsity and adaptivity features for point process observations, and estimate it recursively. We develop a statistical inference framework based on asymptotic analysis of deviance, and perform...
We consider the problem of sparse adaptive neuronal system identification, where the goal is to estimate the sparse time-varying neuronal model parameters in an online fashion from neural spiking observations. We develop two adaptive filters based on greedy estimation techniques and regularized log-likelihood maximization. We apply the proposed algorithms to simulated spiking data as well as experimentally...
We consider the problem of estimating the sparse time-varying parameter vectors of a point process model in an online fashion, where the observations and inputs respectively consist of binary and continuous time series. We construct a novel objective function by incorporating a forgetting factor mechanism into the point process log-likelihood to enforce adaptivity and employ -regularization...
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