Applying the Multivariate Time-Rescaling Theorem to Neural Population Models

Autor(en): Gerhard, Felipe
Haslinger, Robert
Pipa, Gordon 
Stichwörter: Computer Science; Computer Science, Artificial Intelligence; CONNECTIVITY; ENSEMBLES; EXCESS; FIELD; FRAMEWORK; GENERATION; INFORMATION; Neurosciences; Neurosciences & Neurology; SPIKE TRAINS; STATISTICAL-MODELS; TRIAL
Erscheinungsdatum: 2011
Herausgeber: MIT PRESS
Journal: NEURAL COMPUTATION
Volumen: 23
Ausgabe: 6
Startseite: 1452
Seitenende: 1483
Zusammenfassung: 
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.
ISSN: 08997667
DOI: 10.1162/NECO_a_00126

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