Applying the multivariate time-rescaling theorem to neural population models

• Main Authors: Gerhard, Felipe (Author), Haslinger, Robert Heinz (Contributor), Pipa, Gordon (Author)
• Other Authors: Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences (Contributor)
• Format: Article
• Language: English
• Published: MIT Press, 2011-11-10T15:24:10Z.
• Subjects: Article
• Online Access: Get fulltext

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.
National Institutes of Health (U.S.) (NIH grant K25 NS052422-02)
Max Planck Society for the Advancement of Science
European Union (EU Grant PHOCUS, 240763)
European Union (FP7-ICT-2009-C)
Swiss National Science Foundation (grant number 200020-117975)
Stiftung Polytechnische Gesellschaft (Frankfurt am Main, Germany)