Stochastic simulation
A Spike-Train Probability Model
Neural Computation
Modern Applied Statistics with S
Modern Applied Statistics with S
Estimating a state-space model from point process observations
Neural Computation
Dynamic analysis of neural encoding by point process adaptive filtering
Neural Computation
Dynamic Analyses of Information Encoding in Neural Ensembles
Neural Computation
Estimating Spiking Irregularities Under Changing Environments
Neural Computation
Discovery, visualization and performance analysis of enterprise workflow
Computational Statistics & Data Analysis
Valuations for spike train prediction
Neural Computation
Multi-flow attacks against network flow watermarking schemes
SS'08 Proceedings of the 17th conference on Security symposium
Maximally reliable markov chains under energy constraints
Neural Computation
Estimating instantaneous irregularity of neuronal firing
Neural Computation
Direct estimation of inhomogeneous markov interval models of spike trains
Neural Computation
Automatic spike sorting using tuning information
Neural Computation
The computational structure of spike trains
Neural Computation
Information theory and neural information processing
IEEE Transactions on Information Theory - Special issue on information theory in molecular biology and neuroscience
A dynamical point process model of auditory nerve spiking in response to complex sounds
Journal of Computational Neuroscience
Time series analysis of hybrid neurophysiological data and application of mutual information
Journal of Computational Neuroscience
Detection of bursts in extracellular spike trains using hidden semi-Markov point process models
Journal of Computational Neuroscience
Journal of Computational Neuroscience
Detection of hidden structures in nonstationary spike trains
Neural Computation
Applying the multivariate time-rescaling theorem to neural population models
Neural Computation
Computing confidence intervals for point process models
Neural Computation
An information-geometric framework for statistical inferences in the neural spike train space
Journal of Computational Neuroscience
Improved similarity measures for small sets of spike trains
Neural Computation
Optimizing time histograms for non-poissonian spike trains
Neural Computation
Journal of Computational Neuroscience
Mapping of visual receptive fields by tomographic reconstruction
Neural Computation
Time-sensitive web image ranking and retrieval via dynamic multi-task regression
Proceedings of the sixth ACM international conference on Web search and data mining
Information transmission using non-poisson regular firing
Neural Computation
Journal of Computational Neuroscience
Firing-rate models capture essential response dynamics of LGN relay cells
Journal of Computational Neuroscience
Likelihood methods for point processes with refractoriness
Neural Computation
An overview of bayesian methods for neural spike train analysis
Computational Intelligence and Neuroscience - Special issue on Modeling and Analysis of Neural Spike Trains
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Measuring agreement between a statistical model and a spike train data series, that is, evaluating goodness of fit, is crucial for establishing the model's validity prior to using it to make inferences about a particular neural system. Assessing goodness-of-fit is a challenging problem for point process neural spike train models, especially for histogram-based models such as perstimulus time histograms (PSTH) and rate functions estimated by spike train smoothing. The time-rescaling theorem is a well-known result in probability theory, which states that any point process with an integrable conditional intensity function may be transformed into a Poisson process with unit rate. We describe how the theorem may be used to develop goodness-of-fit tests for both parametric and histogram-based point process models of neural spike trains. We apply these tests in two examples: a comparison of PSTH, inhomogeneous Poisson, and inhomogeneous Markov interval models of neural spike trains from the supplementary eye field of a macque monkey and a comparison of temporal and spatial smoothers, inhomogeneous Poisson, inhomogeneous gamma, and inhomogeneous inverse gaussian models of rat hippocampal place cell spiking activity. To help make the logic behind the time-rescaling theorem more accessible to researchers in neuroscience, we present a proof using only elementary probability theory arguments. We also show how the theorem may be used to simulate a general point process model of a spike train. Our paradigm makes it possible to compare parametric and histogram-based neural spike train models directly. These results suggest that the time-rescaling theorem can be a valuable tool for neural spike train data analysis.