Neural Computation
Estimating a state-space model from point process observations
Neural Computation
Differences in spiking patterns among cortical neurons
Neural Computation
A Spike-Train Probability Model
Neural Computation
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
A Method for Selecting the Bin Size of a Time Histogram
Neural Computation
Estimating instantaneous irregularity of neuronal firing
Neural Computation
Kernel bandwidth optimization in spike rate estimation
Journal of Computational Neuroscience
Comparison of brain---computer interface decoding algorithms in open-loop and closed-loop control
Journal of Computational Neuroscience
Optimizing time histograms for non-poissonian spike trains
Neural Computation
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In many cortical areas, neural spike trains do not follow a Poisson process. In this study, we investigate a possible benefit of non-Poisson spiking for information transmission by studying the minimal rate fluctuation that can be detected by a Bayesian estimator. The idea is that an inhomogeneous Poisson process may make it difficult for downstream decoders to resolve subtle changes in rate fluctuation, but by using a more regular non-Poisson process, the nervous system can make rate fluctuations easier to detect. We evaluate the degree to which regular firing reduces the rate fluctuation detection threshold. We find that the threshold for detection is reduced in proportion to the coefficient of variation of interspike intervals.