Modern Applied Statistics with S
Modern Applied Statistics with S
Discovery, visualization and performance analysis of enterprise workflow
Computational Statistics & Data Analysis
Spike train decoding without spike sorting
Neural Computation
Continuous speech recognition with sparse coding
Computer Speech and Language
A rate and history-preserving resampling algorithm for neural spike trains
Neural Computation
Direct estimation of inhomogeneous markov interval models of spike trains
Neural Computation
Reconstruction of sensory stimuli encoded with integrate-and-fire neurons with random thresholds
EURASIP Journal on Advances in Signal Processing - Special issue on statistical signal processing in neuroscience
Nonconvergence in logistic and poisson models for neural spiking
Neural Computation
Journal of Computational Neuroscience
Local field potentials indicate network state and account for neuronal response variability
Journal of Computational Neuroscience
Applying the multivariate time-rescaling theorem to neural population models
Neural Computation
An information-geometric framework for statistical inferences in the neural spike train space
Journal of Computational Neuroscience
On the relation between encoding and decoding of neuronal spikes
Neural Computation
Statistical properties of superimposed stationary spike trains
Journal of Computational Neuroscience
Information transmission using non-poisson regular firing
Neural Computation
Journal of Computational Neuroscience
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Poisson processes usually provide adequate descriptions of the irregularity in neuron spike times after pooling the data across large numbers of trials, as is done in constructing the peristimulus time histogram. When probabilities are needed to describe the behavior of neurons within individual trials, however, Poisson process models are often inadequate. In principle, an explicit formula gives the probability density of a single spike train in great generality, but without additional assumptions, the firing-rate intensity function appearing in that formula cannot be estimated. We propose a simple solution to this problem, which is to assume that the time at which a neuron fires is determined probabilistically by, and only by, two quantities: the experimental clock time and the elapsed time since the previous spike. We show that this model can be fitted with standard methods and software and that it may used successfully to fit neuronal data.