THE EFFECT OF INTERSPIKE INTERVAL STATISTICS ON THE INFORMATION GAIN UNDER THE RATE CODING HYPOTHESIS
THE EFFECT OF INTERSPIKE INTERVAL STATISTICS ON THE INFORMATION GAIN UNDER THE RATE CODING HYPOTHESIS
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DOI：
10.3934/mbe.2014.11.63
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发表时间：
2014-02-01
影响因子：
2.6
通讯作者：
Kostal, Lubomir
中科院分区：
文献类型：
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作者：
Koyama, Shinsuke;Kostal, Lubomir
研究了在恒定的平均放电频率下，可变神经元放电频率在理论上能获得多少信息的问题。我们使用了基于Kullback-Leibler发散的信息统计概念，并假设速率调制的更新过程是尖峰序列的模型。我们证明，如果放电率变化足够小和足够慢(相对于平均尖峰间隔)，信息增益可以用Fisher信息来表示。此外，在某些假设下，伽马分布的尖峰间隔提供了可能的最小信息增益。该方法在几种不同的神经元活动统计模型上进行了说明和讨论。
The question, how much information can be theoretically gained from variable neuronal firing rate with respect to constant average firing rate is investigated. We employ the statistical concept of information based on the Kullback-Leibler divergence, and assume rate-modulated renewal processes as a model of spike trains. We show that if the firing rate variation is sufficiently small and slow (with respect to the mean interspike interval), the information gain can be expressed by the Fisher information. Furthermore, under certain assumptions, the smallest possible information gain is provided by gamma-distributed interspike intervals. The methodology is illustrated and discussed on several different statistical models of neuronal activity.