Estimating a state-space model from point process observations
Neural Computation
Estimation of entropy and mutual information
Neural Computation
Dynamic analysis of neural encoding by point process adaptive filtering
Neural Computation
A Spike-Train Probability Model
Neural Computation
Introduction to Linear Regression Analysis, Solutions Manual (Wiley Series in Probability and Statistics)
Generation of Synthetic Spike Trains with Defined Pairwise Correlations
Neural Computation
Valuations for spike train prediction
Neural Computation
Generating spike trains with specified correlation coefficients
Neural Computation
Sequential optimal design of neurophysiology experiments
Neural Computation
Estimating instantaneous irregularity of neuronal firing
Neural Computation
Direct estimation of inhomogeneous markov interval models of spike trains
Neural Computation
A new look at state-space models for neural data
Journal of Computational Neuroscience
Journal of Computational Neuroscience
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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.