Neural Population Coding Is Optimized by Discrete Tuning Curves
Neural Population Coding Is Optimized by Discrete Tuning Curves
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DOI：
10.1103/physrevlett.103.138101
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发表时间：
2009-09-25
影响因子：
8.6
通讯作者：
McDonnell, Mark D.
中科院分区：
文献类型：
--
作者：
Nikitin, Alexander P.;Stocks, Nigel G.;McDonnell, Mark D.
获得使泊松神经元或泊松神经元群体的互信息最大化的 S 形调谐曲线。发现最佳调谐曲线具有离散结构，导致输入信号量化。随着编码窗口长度的变化，量化级别的数量经历阶段转变的层次。我们假设，以哺乳动物听觉系统为例，神经群体中亚群结构的存在与最佳神经编码一致。
The sigmoidal tuning curve that maximizes the mutual information for a Poisson neuron, or population of Poisson neurons, is obtained. The optimal tuning curve is found to have a discrete structure that results in a quantization of the input signal. The number of quantization levels undergoes a hierarchy of phase transitions as the length of the coding window is varied. We postulate, using the mammalian auditory system as an example, that the presence of a subpopulation structure within a neural population is consistent with an optimal neural code.