Classification of stationary neuronal activity according to its information rate
| Autor: | Lubomir Kostal, Petr Lánský |
|---|---|
| Rok vydání: | 2006 |
| Předmět: |
Neurons
Time Factors Markov chain business.industry Entropy Spike train Models Neurological Autocorrelation Neuroscience (miscellaneous) Action Potentials Signal Processing Computer-Assisted Pattern recognition Code rate Markov model Markov Chains Differential entropy Neural Pathways Statistics Animals Entropy (information theory) Renewal theory Artificial intelligence business Mathematics |
| Zdroj: | Network: Computation in Neural Systems. 17:193-210 |
| ISSN: | 1361-6536 0954-898X |
| DOI: | 10.1080/09548980600594165 |
| Popis: | We propose a measure of the information rate of a single stationary neuronal activity with respect to the state of null information. The measure is based on the Kullback-Leibler distance between two interspike interval distributions. The selected activity is compared with the Poisson model with the same mean firing frequency. We show that the approach is related to the notion of specific information and that the method allows us to judge the relative encoding efficiency. Two classes of neuronal activity models are classified according to their information rate: the renewal process models and the first-order Markov chain models. It has been proven that information can be transmitted changing neither the spike rate nor the coefficient of variation and that the increase in serial correlation does not necessarily increase the information gain. We employ the simple, but powerful, Vasicek's estimator of differential entropy to illustrate an application on the experimental data coming from olfactory sensory neurons of rats. |
| Databáze: | OpenAIRE |
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