Graphical models for machine learning and digital communication
Graphical models for machine learning and digital communication
Dynamic programming, tree-width and computation on graphical models
Dynamic programming, tree-width and computation on graphical models
A Spike-Train Probability Model
Neural Computation
Dynamic programming and the graphical representation of error-correcting codes
IEEE Transactions on Information Theory
Efficient identification of assembly neurons within massively parallel spike trains
Computational Intelligence and Neuroscience - Special issue on signal processing for neural spike trains
An L1-regularized logistic model for detecting short-term neuronal interactions
Journal of Computational Neuroscience
Accelerated spike resampling for accurate multiple testing controls
Neural Computation
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Resampling methods are popular tools for exploring the statistical structure of neural spike trains. In many applications, it is desirable to have resamples that preserve certain non-Poisson properties, like refractory periods and bursting, and that are also robust to trial-to-trial variability. Pattern jitter is a resampling technique that accomplishes this by preserving the recent spiking history of all spikes and constraining resampled spikes to remain close to their original positions. The resampled spike times are maximally random up to these constraints. Dynamic programming is used to create an efficient resampling algorithm.