How Do Efficient Coding Strategies Depend on Origins of Noise in Neural Circuits?

Neural circuits reliably encode and transmit signals despite the presence of noise at multiple stages of processing. The efficient coding hypothesis, a guiding principle in computational neuroscience, suggests that a neuron or population of neurons allocates its limited range of responses as efficiently as possible to best encode inputs while mitigating the effects of noise. Previous work on this question relies on specific assumptions about where noise enters a circuit, limiting the generality of the resulting conclusions. Here we systematically investigate how noise introduced at different stages of neural processing impacts optimal coding strategies. Using simulations and a flexible analytical approach, we show how these strategies depend on the strength of each noise source, revealing under what conditions the different noise sources have competing or complementary effects. We draw two primary conclusions: (1) differences in encoding strategies between sensory systems—or even adaptational changes in encoding properties within a given system—may be produced by changes in the structure or location of neural noise, and (2) characterization of both circuit nonlinearities as well as noise are necessary to evaluate whether a circuit is performing efficiently.

尽管神经环路（neural circuits）在多个处理阶段存在噪声，仍能可靠地编码并传递信号。作为计算神经科学（computational neuroscience）领域的指导性原则，有效编码假说（efficient coding hypothesis）提出：单个神经元或神经元群体可尽可能高效地分配其有限的响应范围，在减轻噪声影响的同时最优地编码输入信息。此前针对该问题的研究均依赖于噪声进入环路的具体假设，这限制了所得结论的普适性。本文系统探究了神经处理不同阶段引入的噪声对最优编码策略的影响。通过仿真实验与灵活的分析方法，我们揭示了这些策略如何随各噪声源的强度变化而改变，并阐明了不同噪声源产生拮抗或互补效应的条件。我们得到两项核心结论：（1）感觉系统间编码策略的差异——乃至单一系统内编码特性的适应性变化——或可由神经噪声的结构或位置改变所引发；（2）若要评估某环路是否实现高效编码，需同时对环路的非线性特性与噪声进行表征。