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• W2013060654 abstract "Changes in membrane potential of a neuron are, under certain experimental conditions, manifested by spike discharges. The number of spikes recorded from the neuron within a given period represents the information which the neuron conveys; and the message, thus transmitted, is carried by the pattern of spikes and the intervals between spikes. This can be modeled by a stochastic point process. This article reviews in three parts recent developments of the statistical analysis of discharge patterns of spike trains recorded from individual nerve cells, and suggests several methods for identifying certain types of spike trains. The first part reviews those stochastic processes Y(t) used as models for the membrane potential changes. The occurence time of a spike is defined as a first-passage time of Y(t) upcrossing a threshold value. These upcrossing times form a point process describing the occurences of spikes. Evidently, the occurence rate depends on the underlying assumption of Y(t). Presently, a majority of the point process models adopt the renewal-process assumptions. The second part deals with statistical inferences on the models and the validation methods. The research in this area is scarce; the inference on renewal processes is based mostly on Cox and Lewis's book The Statistical Analysis of Series of Events. We have used the contiguity theory due to Le Cam [30] to derive several asymptotically optimal statistical procedures. The last part discusses some popular statistical techniques employed and some areas needed for further investigation." @default.
• W2013060654 created "2016-06-24" @default.
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• W2013060654 date "1978-01-01" @default.
• W2013060654 modified "2023-09-27" @default.
• W2013060654 title "On statistical methods in neuronal spike-train analysis" @default.
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• W2013060654 doi "https://doi.org/10.1016/0025-5564(78)90015-9" @default.
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