Yoo Yongseok, Kim Woori
Department of Electronics Engineering, Incheon National University, Yeonsu-gu, Incheon 22012, Korea
Department of Special Education, Chonnam National University, Yeosu, Jeonnam 59626, Korea
Neural Comput. 2017 Mar;29(3):716-734. doi: 10.1162/NECO_a_00902. Epub 2016 Oct 20.
Neural systems are inherently noisy. One well-studied example of a noise reduction mechanism in the brain is the population code, where representing a variable with multiple neurons allows the encoded variable to be recovered with fewer errors. Studies have assumed ideal observer models for decoding population codes, and the manner in which information in the neural population can be retrieved remains elusive. This letter addresses a mechanism by which realistic neural circuits can recover encoded variables. Specifically, the decoding problem of recovering a spatial location from populations of grid cells is studied using belief propagation. We extend the belief propagation decoding algorithm in two aspects. First, beliefs are approximated rather than being calculated exactly. Second, decoding noises are introduced into the decoding circuits. Numerical simulations demonstrate that beliefs can be effectively approximated by combining polynomial nonlinearities with divisive normalization. This approximate belief propagation algorithm is tolerant to decoding noises. Thus, this letter presents a realistic model for decoding neural population codes and investigates fault-tolerant information retrieval mechanisms in the brain.
神经系统本质上是有噪声的。大脑中一种经过充分研究的降噪机制示例是群体编码，即用多个神经元来表示一个变量，使得编码变量能够以更少的错误被恢复。研究一直假设理想观测器模型用于解码群体编码，而神经群体中的信息能够被检索的方式仍然难以捉摸。本文探讨了一种现实的神经回路能够恢复编码变量的机制。具体而言，使用信念传播研究了从网格细胞群体中恢复空间位置的解码问题。我们在两个方面扩展了信念传播解码算法。第一，信念是近似而非精确计算得到的。第二，将解码噪声引入解码回路。数值模拟表明，通过将多项式非线性与除法归一化相结合，可以有效地近似信念。这种近似信念传播算法对解码噪声具有容忍性。因此，本文提出了一个用于解码神经群体编码的现实模型，并研究了大脑中的容错信息检索机制。
