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Nonparametric estimation of characteristics of the interspike interval distribution
BMC Neuroscience volume 16, Article number: P131 (2015)
We address the problem of non-parametric estimation of the probability density function as a description of the probability distribution of noncorrelated interspike intervals (ISI) in records of neuronal activity. We also continue our previous effort [1, 2] to propose alternative estimators of the variability measures. Kernel density estimators are probably the most frequently used non-parametric estimators of the probability distribution. However, there are also other non-parametric approaches. We focus on non-parametric methods based on a principle of extrema of the Fisher information. Specifically, we focus on the maximum penalized likelihood estimation of the probability density function proposed by Good and Gaskins , which can be understood as a kernel estimator with a particular kernel function . Other non-parametric approach we would like to address is the spline interpolation proposed by Huber  which can uniquely estimate the ISI distribution.
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Eggermont PPB, LaRiccia VN: Maximum Penalized Likelihood Estimation: Volume I: Density Estimation. Springer. 2001
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This work was supported by the Czech Science Foundation (GACR) grants 15-06991S (Ondrej Pokora) and 15-08066S (Lubomir Kostal).
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Pokora, O., Kostal, L. Nonparametric estimation of characteristics of the interspike interval distribution. BMC Neurosci 16, P131 (2015). https://0-doi-org.brum.beds.ac.uk/10.1186/1471-2202-16-S1-P131
- Probability Density Function
- Kernel Function
- Kernel Density
- Fisher Information
- Density Estimator