Efficient Sensory Encoding and Bayesian Inference with Heterogeneous Neural Populations
Efficient Sensory Encoding and Bayesian Inference with Heterogeneous Neural Populations
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DOI：
10.1162/neco_a_00638
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发表时间：
2014-10-01
影响因子：
2.9
通讯作者：
Simoncelli, Eero P.
中科院分区：
文献类型：
--
作者：
Ganguli, Deep;Simoncelli, Eero P.
有效编码假说认为，感觉系统最大限度地提高了传递给大脑的有关环境的信息。我们开发了一个精确的和可测试的形式，这一假设的背景下，编码的感觉变量与人口的嘈杂的神经元，每个特点是调谐曲线。我们参数化的人口与两个连续的功能，控制的密度和振幅的调谐曲线，假设调谐宽度与细胞密度成反比。这种参数化使我们能够解决，在封闭的形式，信息最大化分配的调谐曲线作为感官变量的先验概率分布的函数。对于最优群体，细胞密度与先验成比例，使得具有较窄调谐的更多细胞被分配来编码较高概率刺激，并且每个细胞传输刺激概率质量的相等部分。我们还计算了依赖于这种神经表征的感知系统的刺激辨别能力，并发现最佳可实现的辨别阈值与感官先验成反比。我们研究如何在调谐曲线的最佳人口隐含编码的先验信息可用于感知推理，并推导出一种新的解码器，贝叶斯人口矢量，它非常接近贝叶斯最小二乘估计，有明确的访问之前。最后，我们将这些结果推广到S形调谐曲线，相关的神经变异性，以及更广泛的一类目标函数。这些结果提供了一个原则性的嵌入感官先验信息的神经种群和产量预测，很容易测试与环境，生理和感知数据。
The efficient coding hypothesis posits that sensory systems maximize information transmitted to the brain about the environment. We develop a precise and testable form of this hypothesis in the context of encoding a sensory variable with a population of noisy neurons, each characterized by a tuning curve. We parameterize the population with two continuous functions that control the density and amplitude of the tuning curves, assuming that the tuning widths vary inversely with the cell density. This parameterization allows us to solve, in closed form, for the information-maximizing allocation of tuning curves as a function of the prior probability distribution of sensory variables. For the optimal population, the cell density is proportional to the prior, such that more cells with narrower tuning are allocated to encode higher-probability stimuli and that each cell transmits an equal portion of the stimulus probability mass. We also compute the stimulus discrimination capabilities of a perceptual system that relies on this neural representation and find that the best achievable discrimination thresholds are inversely proportional to the sensory prior. We examine how the prior information that is implicitly encoded in the tuning curves of the optimal population may be used for perceptual inference and derive a novel decoder, the Bayesian population vector, that closely approximates a Bayesian least-squares estimator that has explicit access to the prior. Finally, we generalize these results to sigmoidal tuning curves, correlated neural variability, and a broader class of objective functions. These results provide a principled embedding of sensory prior information in neural populations and yield predictions that are readily testable with environmental, physiological, and perceptual data.