Rossoni, Enrico and Feng, Jianfeng. (2007) Decoding spike train ensembles: tracking a moving stimulus. BIOLOGICAL CYBERNETICS, 96 (1). pp. 99-112. ISSN 0340-1200

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Abstract

We consider the issue of how to read out the information from nonstationary spike train ensembles. Based on the theory of censored data in statistics, we propose a 'censored' maximum-likelihood estimator (CMLE) for decoding the input in an unbiased way when the spike activity is observed over time windows of finite length. Compared with a rate-based, moment estimator, the CMLE is proved consistently more efficient, particularly with nonstationary inputs. Using our approach, we show that a dynamical input to a group of neurons can be inferred accurately and with high temporal resolution (50 ms) using as few as about one spike per neuron within each decoding window. By applying our theoretical results to a population coding setting, we then demonstrate that a spiking neural network can encode spatial information in such a way to allow fast and precise tracking of a moving target.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions:	Faculty of Science > Centre for Scientific Computing Faculty of Science > Computer Science
Journal or Publication Title:	BIOLOGICAL CYBERNETICS
Publisher:	SPRINGER
ISSN:	0340-1200
Date:	January 2007
Volume:	96
Number:	1
Number of Pages:	14
Page Range:	pp. 99-112
Identification Number:	10.1007/s00422-006-0106-4
Publication Status:	Published
URI:	http://wrap.warwick.ac.uk/id/eprint/32405

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