Valuations for spike train prediction.

@article{Itskov2008, abstract = {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.}, address = {Center for Molecular and Behavioral Neuroscience, Rutgers, The State University of New Jersey, Newark, NJ 07102, U.S.A. vladimir@neurotheory.columbia.edu}, author = {Itskov, V. and Curto, C. and Harris, K. D. }, citeulike-article-id = {2730016}, doi = {10.1162/neco.2007.3179}, issn = {0899-7667}, journal = {Neural computation}, month = {March}, number = {3}, pages = {644--667}, posted-at = {2008-04-28 14:42:12}, priority = {2}, title = {Valuations for spike train prediction.}, url = {http://dx.doi.org/10.1162/neco.2007.3179}, volume = {20}, year = {2008} }

TY - JOUR ID - Itskov2008 L3 - citeulike-article-id:2730016 TI - Valuations for spike train prediction. JF - Neural computation VL - 20 IS - 3 SP - 644 EP - 667 AD - Center for Molecular and Behavioral Neuroscience, Rutgers, The State University of New Jersey, Newark, NJ 07102, U.S.A. vladimir@neurotheory.columbia.edu SN - 0899-7667 N2 - 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. AU - Itskov, V AU - Curto, C AU - Harris, KD PY - 2008/03// UR - http://dx.doi.org/10.1162/neco.2007.3179 ER -