Wang Zhuo, Stocker Alan A, Lee Daniel D
Department of Mathematics, University of Pennsylvania, Philadelphia, PA 19104, U.S.A.
Departments of Psychology and Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, U.S.A.
Neural Comput. 2016 Dec;28(12):2656-2686. doi: 10.1162/NECO_a_00900. Epub 2016 Oct 20.
The efficient coding hypothesis assumes that biological sensory systems use neural codes that are optimized to best possibly represent the stimuli that occur in their environment. Most common models use information-theoretic measures, whereas alternative formulations propose incorporating downstream decoding performance. Here we provide a systematic evaluation of different optimality criteria using a parametric formulation of the efficient coding problem based on the [Formula: see text] reconstruction error of the maximum likelihood decoder. This parametric family includes both the information maximization criterion and squared decoding error as special cases. We analytically derived the optimal tuning curve of a single neuron encoding a one-dimensional stimulus with an arbitrary input distribution. We show how the result can be generalized to a class of neural populations by introducing the concept of a meta-tuning curve. The predictions of our framework are tested against previously measured characteristics of some early visual systems found in biology. We find solutions that correspond to low values of [Formula: see text], suggesting that across different animal models, neural representations in the early visual pathways optimize similar criteria about natural stimuli that are relatively close to the information maximization criterion.
高效编码假说假定生物感觉系统使用经过优化的神经编码，以便尽可能最佳地呈现其环境中出现的刺激。大多数常见模型使用信息论度量，而其他公式则建议纳入下游解码性能。在此，我们基于最大似然解码器的[公式：见正文]重构误差，使用高效编码问题的参数化公式，对不同的最优性标准进行了系统评估。这个参数族包括信息最大化标准和平方解码误差这两种特殊情况。我们通过解析得出了单个神经元对具有任意输入分布的一维刺激进行编码的最优调谐曲线。我们展示了如何通过引入元调谐曲线的概念将结果推广到一类神经群体。我们根据先前测量的生物学中一些早期视觉系统的特征，对我们框架的预测进行了测试。我们找到的解决方案对应于[公式：见正文]的低值，这表明在不同的动物模型中，早期视觉通路中的神经表征针对相对接近信息最大化标准的自然刺激优化了相似的标准。
