Applying the multivariate time-rescaling theorem to neural population models
Author(s)
Gerhard, Felipe; Haslinger, Robert Heinz; Pipa, Gordon
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Statistical models of neural activity are integral to modern neuroscience. Recently interest has grown in modeling the spiking activity of populations of simultaneously recorded neurons to study the effects of correlations and functional connectivity on neural information processing. However, any statistical model must be validated by an appropriate goodness-of-fit test. Kolmogorov-Smirnov tests based on the time-rescaling theorem have proven to be useful for evaluating point-process-based statistical models of single-neuron spike trains. Here we discuss the extension of the time-rescaling theorem to the multivariate (neural population) case. We show that even in the presence of strong correlations between spike trains, models that neglect couplings between neurons can be erroneously passed by the univariate time-rescaling test. We present the multivariate version of the time-rescaling theorem and provide a practical step-by-step procedure for applying it to testing the sufficiency of neural population models. Using several simple analytically tractable models and more complex simulated and real data sets, we demonstrate that important features of the population activity can be detected only using the multivariate extension of the test.
Date issued
2011-05Department
Massachusetts Institute of Technology. Department of Brain and Cognitive SciencesJournal
Neural Computation
Publisher
MIT Press
Citation
Gerhard, Felipe, Robert Haslinger, and Gordon Pipa. “Applying the Multivariate Time-Rescaling Theorem to Neural Population Models.” Neural Computation 23 (2011): 1452-1483. © 2011 Massachusetts Institute of Technology.
Version: Final published version
ISSN
0899-7667
1530-888X