Valuations for spike train prediction.
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 models of peer prediction, where it can be computed directly from cross-correlograms. Although Q does not have a straightforward probabilistic interpretation, Q is essentially given by Euclidean distance.
Center for Molecular and Behavioral Neuroscience, Rutgers, The State University of New Jersey, Newark, NJ 07102, U.S.A. vladimir@neurotheory.columbia.edu
Vladimir
V
Itskov
Carina
C
Curto
Kenneth D
KD
Harris
D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D006624:Hippocampus / Q000502:physiology; D006801:Humans; D015233:Models, Statistical; D009474:Neurons / Q000502:physiology*; D051381:Rats; D009435:Synaptic Transmission / Q000502:physiology; D013997:Time Factors