Functional diversity among sensory neurons from efficient coding principles
Functional diversity among sensory neurons from efficient coding principles
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DOI：
10.1371/journal.pcbi.1007476
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发表时间：
2019-11-01
影响因子：
4.3
通讯作者：
Sompolinsky, Haim
中科院分区：
文献类型：
--
作者：
Gjorgjieva, Julijana;Meister, Markus;Sompolinsky, Haim
大脑通过特殊的受体细胞和相关的感觉回路处理外部刺激。在许多感觉系统中，神经元群分为“开”细胞和“关”细胞，即表示感觉变量增加和减少的细胞。这发生在从蠕虫到人类的大脑中，也发生在对温度、气味、光和声音的感知中。本文运用信息论分析了“路径分割”可能带来的好处。我们推导出最有效的通路分裂为ON和OFF神经元，并预测每种神经元类型的响应范围作为噪声和刺激统计的函数。我们的理论提供了对这种普遍存在的神经组织现象的见解，并建议在不同的感觉系统中进行新的实验。在许多感觉系统中，神经信号是由异质神经元群的协调反应编码的。这种多样性给信息处理带来了什么计算上的好处？我们推导了一个有效的编码框架，假设神经元已经进化到在自然刺激统计和代谢限制下最优地交流信号。在考虑非线性和现实噪声的基础上，研究了同一感官变量的最优群体编码，采用了两种方法：最大化刺激和反应之间的相互信息，以及最小化响应的最优线性解码器所产生的误差。我们的理论适用于通常观察到的感觉神经元分裂成信号刺激增加或减少的ON和OFF，以及相同类型的单调增加的反应群体，ON。根据最优度量，我们对如何最优地将种群划分为on和OFF，以及如何在给定现实刺激分布和噪声的情况下分配单个神经元的放电阈值做出了不同的预测，这些预测符合实验观察到的某些偏差。
Author summary The brain processes external stimuli through special receptor cells and associated sensory circuits. In many sensory systems the population of neurons splits into ON and OFF cells, namely cells that signal an increase vs. a decrease of the sensory variable. This happens in brains from worm to man, and in the sensing of temperature, odor, light, and sound. Here we analyze the possible benefits of "pathway splitting" using information theory. We derive the most efficient split of a pathway into ON and OFF neurons and predict the response range of each neuron type as a function of noise and stimulus statistics. Our theory offers insight into this ubiquitous phenomenon of neural organization and suggests new experiments in diverse sensory systems.In many sensory systems the neural signal is coded by the coordinated response of heterogeneous populations of neurons. What computational benefit does this diversity confer on information processing? We derive an efficient coding framework assuming that neurons have evolved to communicate signals optimally given natural stimulus statistics and metabolic constraints. Incorporating nonlinearities and realistic noise, we study optimal population coding of the same sensory variable using two measures: maximizing the mutual information between stimuli and responses, and minimizing the error incurred by the optimal linear decoder of responses. Our theory is applied to a commonly observed splitting of sensory neurons into ON and OFF that signal stimulus increases or decreases, and to populations of monotonically increasing responses of the same type, ON. Depending on the optimality measure, we make different predictions about how to optimally split a population into ON and OFF, and how to allocate the firing thresholds of individual neurons given realistic stimulus distributions and noise, which accord with certain biases observed experimentally.