Decoding spike train ensembles: tracking a moving stimulus
Rossoni, Enrico and Feng, Jianfeng. (2007) Decoding spike train ensembles: tracking a moving stimulus. BIOLOGICAL CYBERNETICS, 96 (1). pp. 99-112. ISSN 0340-1200Full text not available from this repository.
Official URL: http://dx.doi.org/10.1007/s00422-006-0106-4
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|
|Number of Pages:||14|
|Page Range:||pp. 99-112|
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