Institut de la Vision, Université Pierre-and-Marie-Curie 06, Sorbonne Universités, INSERM, CNRS, 75012Paris,
Behav Brain Sci. 2018 Jul 16;42:e215. doi: 10.1017/S0140525X19000049.
"Neural coding" is a popular metaphor in neuroscience, where objective properties of the world are communicated to the brain in the form of spikes. Here I argue that this metaphor is often inappropriate and misleading. First, when neurons are said to encode experimental parameters, the neural code depends on experimental details that are not carried by the coding variable (e.g., the spike count). Thus, the representational power of neural codes is much more limited than generally implied. Second, neural codes carry information only by reference to things with known meaning. In contrast, perceptual systems must build information from relations between sensory signals and actions, forming an internal model. Neural codes are inadequate for this purpose because they are unstructured and therefore unable to represent relations. Third, coding variables are observables tied to the temporality of experiments, whereas spikes are timed actions that mediate coupling in a distributed dynamical system. The coding metaphor tries to fit the dynamic, circular, and distributed causal structure of the brain into a linear chain of transformations between observables, but the two causal structures are incongruent. I conclude that the neural coding metaphor cannot provide a valid basis for theories of brain function, because it is incompatible with both the causal structure of the brain and the representational requirements of cognition.
“神经编码”是神经科学中一个常用的隐喻，客观世界的属性以尖峰的形式传递给大脑。在这里，我认为这个隐喻常常是不恰当和具有误导性的。首先，当神经元被说成是在编码实验参数时，神经编码依赖于实验细节，而这些细节并不由编码变量（例如，尖峰计数）携带。因此，神经编码的表示能力比通常所暗示的要有限得多。其次，神经编码只有通过参考具有已知意义的事物才能携带信息。相比之下，感知系统必须从感觉信号和动作之间的关系中构建信息，形成内部模型。神经编码在这方面是不够的，因为它们没有结构，因此无法表示关系。第三，编码变量是与实验的时间性相关的可观察变量，而尖峰是在分布式动力系统中介导耦合的定时动作。编码隐喻试图将大脑的动态、循环和分布式因果结构拟合到可观察变量之间的线性转换链中，但这两种因果结构是不一致的。我得出结论，神经编码隐喻不能为大脑功能理论提供有效的基础，因为它与大脑的因果结构和认知的表示要求都不兼容。
