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From the entropy to the statistical structure of spike trains

Yun, G and Kontoyiannis, I and Bienenstock, E (2006) From the entropy to the statistical structure of spike trains. IEEE International Symposium on Information Theory - Proceedings. pp. 645-649.

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Abstract

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 for the existence of longterm structure in the data, and we construct a hypothesis test for whether the renewal process model is appropriate for these spike trains. Further, by applying the CTW algorithm we derive the maximum a posterior (MAP) tree model of our empirical data, and comment on the underlying structure it reveals. © 2006 IEEE.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div F > Signal Processing and Communications
Depositing User: Cron Job
Date Deposited: 08 Jan 2018 20:12
Last Modified: 27 Oct 2020 07:51
DOI: 10.1109/ISIT.2006.261864