Spikes: exploring the neural code
Spikes: exploring the neural code
Estimating a state-space model from point process observations
Neural Computation
Differences in spiking patterns among cortical neurons
Neural Computation
Estimating Spiking Irregularities Under Changing Environments
Neural Computation
A Method for Selecting the Bin Size of a Time Histogram
Neural Computation
Estimating instantaneous irregularity of neuronal firing
Neural Computation
A characterization of the time-rescaled gamma process as a model for spike trains
Journal of Computational Neuroscience
Feature extraction from spike trains with Bayesian binning: `Latency is where the signal starts'
Journal of Computational Neuroscience
Kernel bandwidth optimization in spike rate estimation
Journal of Computational Neuroscience
Random bin for analyzing neuron spike trains
Computational Intelligence and Neuroscience - Special issue on Computational Intelligence in Biomedical Science and Engineering
Information transmission using non-poisson regular firing
Neural Computation
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The time histogram is a fundamental tool for representing the inhomogeneous density of event occurrences such as neuronal firings. The shape of a histogram critically depends on the size of the bins that partition the time axis. In most neurophysiological studies, however, researchers have arbitrarily selected the bin size when analyzing fluctuations in neuronal activity. A rigorous method for selecting the appropriate bin size was recently derived so that the mean integrated squared error between the time histogram and the unknown underlying rate is minimized (Shimazaki & Shinomoto, 2007). This derivation assumes that spikes are independently drawn from a given rate. However, in practice, biological neurons express non-Poissonian features in their firing patterns, such that the spike occurrence depends on the preceding spikes, which inevitably deteriorate the optimization. In this letter, we revise the method for selecting the bin size by considering the possible non-Poissonian features. Improvement in the goodness of fit of the time histogram is assessed and confirmed by numerically simulated non-Poissonian spike trains derived from the given fluctuating rate. For some experimental data, the revised algorithm transforms the shape of the time histogram from the Poissonian optimization method.