Neural Networks - Special issue on organisation of computation in brain-like systems
Differences in spiking patterns among cortical neurons
Neural Computation
Information geometry on hierarchy of probability distributions
IEEE Transactions on Information Theory
Estimating Spiking Irregularities Under Changing Environments
Neural Computation
Capacity of a single spiking neuron channel
Neural Computation
Estimating instantaneous irregularity of neuronal firing
Neural Computation
Information geometry of interspike intervals in spiking neurons with refractories
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
A mathematical theory of energy efficient neural computation and communication
IEEE Transactions on Information Theory - Special issue on information theory in molecular biology and neuroscience
A characterization of the time-rescaled gamma process as a model for spike trains
Journal of Computational Neuroscience
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An information geometrical method is developed for characterizing or classifying neurons in cortical areas, whose spike rates fluctuate in time. Under the assumption that the interspike intervals of a spike sequence of a neuron obey a gamma process with a time-variant spike rate and a fixed shape parameter, we formulate the problem of characterization as a semiparametric statistical estimation, where the spike rate is a nuisance parameter. We derive optimal criteria from the information geometrical viewpoint when certain assumptions are added to the formulation, and we show that some existing measures, such as the coefficient of variation and the local variation, are expressed as estimators of certain functions under the same assumptions.