Paper Info
Title
Parameters of spike trains observed in a short time window.
Abstract
We study the estimation of statistical moments of interspike intervals based on observation of spike counts in many independent short time windows. This scenario corresponds to the situation in which a target neuron occurs. It receives information from many neurons and has to respond within a short time interval. The precision of the estimation procedures is examined. As the model for neuronal activity, two examples of stationary point processes are considered: renewal process and doubly stochastic Poisson process. Both moment and maximum likelihood estimators are investigated. Not only the mean but also the coefficient of variation is estimated. In accordance with our expectations, numerical studies confirm that the estimation of mean interspike interval is more reliable than the estimation of coefficient of variation. The error of estimation increases with increasing mean interspike interval, which is equivalent to decreasing the size of window (less events are observed in a window) and with decreasing the number of neurons (lower number of windows).
Year
DOI
Venue
2008
10.1162/neco.2007.01-07-442
Neural Computation
Keywords
Field
DocType
interspike interval,spike train,estimation procedure,lower number,short time interval,estimation increase,stochastic poisson process,mean interspike interval,independent short time windows,renewal process,stationary point process,point process,coefficient of variation,maximum likelihood estimate,neuronal activity,doubly stochastic poisson process
Renewal theory,Point process,Stochastic process,Stationary process,Stationary point,Cox process,Statistics,Mathematics,Method of moments (statistics),Estimator
Journal
Volume
Issue
ISSN
20
5
0899-7667
Citations 
PageRank 
References 
8
1.51
7
Authors
4
Name
Order
Citations
PageRank
Zbynek Pawlas1122.72
Lev B Klebanov2317.18
Martin Prokop381.51
Petr Lansky412515.94