From the entropy to the statistical structure of spike trains

— We use statistical estimates of the entropy rate of spike train data in order to make inferences about the underlying structure of the spike train itself. We first examine a number of different parametric and nonparametric estimators (some known and some new), including the “plug-in” method, several versions of Lempel-Ziv-based compression algorithms, a maximum likelihood estimator tailored to renewal processes, and the natural estimator derived from the Context-Tree Weighting method (CTW). The theoretical properties of these estimators are examined, several new theoretical results are developed, and all estimators are systematically applied to various types of synthetic data and under different conditions. Our main focus is on the performance of these entropy estimators on the (binary) spike trains of 28 neurons recorded simultaneously for a one-hour period from the primary motor and dorsal premotor cortices of a monkey. We show how the entropy estimates can be used to test f...

• Type: Journal
• Year: 2007
• Where: CORR
• Authors: Yun Gao, Ioannis Kontoyiannis, Elie Bienenstock
• Keywords: CORR 2007, Education, Estimators, Maximum Likelihood Estimator, Spike Trains