| | |
| | |
Stat |
Members: 3643 Articles: 2'487'895 Articles rated: 2609
29 March 2024 |
|
| | | |
|
Article overview
| |
|
From the entropy to the statistical structure of spike train | Yun Gao
; Ioannis Kontoyiannis
; Elie Bienenstock
; | Date: |
22 Oct 2007 | 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 long-term
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. | Source: | arXiv, 0710.4117 | Services: | Forum | Review | PDF | Favorites |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser claudebot
|
| |
|
|
|
| News, job offers and information for researchers and scientists:
| |