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This entails estimating information theoretic quantities from neural spike train data. This paper highlights two issues that may arise: non-parametric entropy ...
ABSTRACT. Information theory provides an attractive framework for attacking the neural coding problem. This entails estimating information the-.
(PDF) Some statistical issues in estimating information in neural ...
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This entails estimating information theoretic quantities from neural spike train data. This paper highlights two issues that may arise: non-parametric entropy ...
[PDF] Some statistical issues in estimating information in neural ...
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An overview of issues that may arise: non-parametric entropy estimation and non-stationarity is given and some of the progress that has been made.
Spike times in each raster plot are aggregrated into 5 millisecond bins, then counted and displayed in the corresponding PSTH, in units of spikes per second.
There are two basic problems in the statistical analysis of neural data. The "encoding" problem concerns how information is encoded in neural spike trains ...
This article reviews in three parts recent developments of the statistical analysis of discharge patterns of spike trains recorded from individual nerve cells.
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Which spike train distance is most suitable for distinguishing rate and temporal coding?
What is neural spike train analysis?
While representing neuronal spiking through a predictive statistical model is only a limited aspect of neural computation, it is a fundamental first step in ...
This article contains two main theoretical results on neural spike train models, using the counting or point process on the real line as a model for.
The information from spike trains was estimated by calculating the entropy associated with the various temporal patterns of spike discharge, using Shannon's ...