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548 – Contributed Oral Poster Presentations: Section on Nonparametric Statistics
Encoding Neurons' Communication: A Statistical Approach to Analyze Interactive Neural Spike Trains
Ruiwen Zhang
SAS
Feng Liu
The University of North Carolina at Chapel Hill
Neuroscience experiments and neural spike train data have special features that present novel and exciting challenges for statistical researches. Several standard statistical procedures, widely used in other fields of science have found their way into mainstream application in neuroscience data analysis. Given the firing times of an ensemble of neurons, an integrate several inputs and fire model is introduced based on the conditional intensity function approach. This is different from the existing methods where the intensity function is approximated by discretization with the sampling intervals chosen arbitrary. In this paper, we model the log conditional intensity function directly by employing a polynomial spline function for the target or response spike train and a tensor product of splines for the peer or predictor spike trains. The parameters are defined by those used in constructing the polynomial splines, and they will be estimated by the maximum likelihood method. The statistical properties of this procedure will be evaluated using a simulated experiment. Our model captures the underlying spontaneous firing of the target as well as the stimulus inputs from its peers, and both in continuous time.