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The Information Capacity of Nerve Cells Using a Frequency Code

机译:使用频率码的神经细胞的信息容量

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摘要

Approximate equations are derived for the amount of information a nerve cell or group of nerve cells can transmit about a stimulus of a given duration using a frequency code (i.e., assuming the mean frequency of nerve impulses measures the intensity of a maintained stimulus). The equations take into account the variability of successive interspike intervals, and any serial correlations between successive intervals, but do not require detailed assumptions about the mechanism of impulse initiation. The errors involved in using these approximations are evaluated for neurons which discharge either completely regularly, completely at random (Poisson process) or show a particular type of intermediate variability (gamma distribution model). The errors become negligibly small as the stimulus duration or the number of functionally similar nerve cells increases. The conditions for applying these equations to experimental data are discussed. The application of these equations should help considerably in eliminating the enormous discrepancies between some earlier estimates for the information processing capabilities of single nerve cells and systems of nerve cells.
机译:使用频率代码(即假设神经冲动的平均频率测量维持的刺激强度),得出神经元或一组神经细胞可以在给定持续时间的刺激周围传递的信息量的近似方程式。这些方程式考虑到了连续尖峰间隔的可变性以及连续间隔之间的任何序列相关性,但是不需要关于脉冲引发机制的详细假设。对于使用神经网络的神经元,评估使用这些近似值所涉及的误差,这些神经元要么完全规则地释放,要么完全随机释放(泊松过程),要么显示出特定类型的中间变异性(伽玛分布模型)。随着刺激持续时间或功能相似神经细胞数量的增加,误差变得可以忽略不计。讨论了将这些方程式应用于实验数据的条件。这些方程的应用应在很大程度上有助于消除一些早期估计的单个神经细胞和神经细胞系统的信息处理能力之间的巨大差异。

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