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Maximum likelihood decoding of neuronal inputs from an interspike interval distribution
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Zhang, Xuejuan, You, Gongqiang, Chen, Tianping and Feng, Jianfeng (2009) Maximum likelihood decoding of neuronal inputs from an interspike interval distribution. Neural Computation, Vol.21 (No.11). pp. 30793105. ISSN 08997667
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Official URL: http://dx.doi.org/10.1162/neco.2009.0608807
Abstract
An expression for the probability distribution of the interspike interval
of a leaky integrateandfire (LIF) model neuron is rigorously derived,
based on recent theoretical developments in the theory of stochastic processes.
This enables us to find for the first time a way of developing
maximum likelihood estimates (MLE) of the input information (e.g., afferent
rate and variance) for an LIF neuron from a set of recorded spike
trains. Dynamic inputs to pools of LIF neurons both with and without
interactions are efficiently and reliably decoded by applying the MLE,
even within time windows as short as 25 msec.
Item Type:  Journal Item  

Subjects:  Q Science > QA Mathematics  
Divisions:  Faculty of Science > Centre for Scientific Computing Faculty of Science > Computer Science 

Library of Congress Subject Headings (LCSH):  Neurons  Mathematical models, Stochastic processes, Distribution (Probability theory)  
Journal or Publication Title:  Neural Computation  
Publisher:  MIT Press  
ISSN:  08997667  
Official Date:  November 2009  
Dates: 


Volume:  Vol.21  
Number:  No.11  
Page Range:  pp. 30793105  
Identification Number:  10.1162/neco.2009.0608807  
Status:  Peer Reviewed  
Access rights to Published version:  Open Access  
Funder:  Engineering and Physical Sciences Research Council (EPSRC), Guo jia zi ran ke xue ji jin wei yuan hui (China) [National Natural Science Foundation of China] (NSFC)  
Grant number:  EP/ E002331/1 (EPSRC), 10305007 (NNSFC), 10771155 (NNSFC)  
References:  Alili, L., Patie, P., & Pedersen, J. L. (2005). Representations of the first hitting time 

URI:  http://wrap.warwick.ac.uk/id/eprint/3039 
Data sourced from Thomson Reuters' Web of Knowledge
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