Neural population decoding in short-time windows

Authors:
Wenhao Zhang;Si Wu
Affiliations:
Institute of Neuroscience, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China,State Key Lab of Cognitive Neuroscience and Learning, Beijing Normal University ...;State Key Lab of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
Venue:
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2012

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Abstract

External information is encoded in spiking activities of neural population. The present study investigates the performance of population decoding in a short-time window. Two decoding strategies, namely, maximum likelihood inference and template-matching, are explored. We find that in a short-time window, two methods are not efficient and that their errors satisfy the Cauchy distributions. As expected, maximum likelihood inference outperforms template-matching asymptotically. However, in a very short time window, template-matching has smaller decoding errors than maximum likelihood inference. The implication of this result is discussed.