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Valuations for Spike Train Prediction

vladimir{at}neurotheory.columbia.edukdharris@andromeda.rutgers.edu Center for Molecular and Behavioral Neuroscience, Rutgers, The State University of New Jersey, Newark, NJ 07102, U.S.A.

Carina Curto

ccurto{at}rutgers.edu Center for Molecular and Behavioral Neuroscience, Rutgers, The State University of New Jersey, Newark, NJ 07102, U.S.A.

Kenneth D. Harris

kdharris{at}andromeda.rutgers.edu Center for Molecular and Behavioral Neuroscience, Rutgers, The State University of New Jersey,Newark, NJ 07102, U.S.A.

The ultimate product of an electrophysiology experiment is often a decision on which biological hypothesis or model best explains the observed data. We outline a paradigm designed for comparison of different models, which we refer to as spike train prediction. A key ingredient of this paradigm is a prediction quality valuation that estimates how close a predicted conditional intensity function is to an actual observed spike train. Although a valuation based on log likelihood (L) is most natural, it has various complications in this context. We propose that a quadratic valuation (Q) can be used as an alternative to L. Q shares some important theoretical properties with L, including consistency, and the two valuations perform similarly on simulated and experimental data. Moreover, Q is more robust than L, and optimization with Q can dramatically improve computational efficiency. We illustrate the utility of Q for comparing modelsof peer prediction, where it can be computed directly from cross-correlograms. Although Qdoes not have a straightforward probabilistic interpretation, Q is essentially given by Euclidean distance.

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