Department of Basic Science, Graduate School of Arts and Sciences, University of Tokyo, Meguro-ku, Tokyo, Japan.
Research Center for Complex Systems Biology, University of Tokyo, Bunkyo-ku, Tokyo, Japan.
PLoS Comput Biol. 2024 Mar 13;20(3):e1011897. doi: 10.1371/journal.pcbi.1011897. eCollection 2024 Mar.
Various animals, including humans, have been suggested to perform Bayesian inferences to handle noisy, time-varying external information. In performing Bayesian inference by the brain, the prior distribution must be acquired and represented by sampling noisy external inputs. However, the mechanism by which neural activities represent such distributions has not yet been elucidated. Our findings reveal that networks with modular structures, composed of fast and slow modules, are adept at representing this prior distribution, enabling more accurate Bayesian inferences. Specifically, the modular network that consists of a main module connected with input and output layers and a sub-module with slower neural activity connected only with the main module outperformed networks with uniform time scales. Prior information was represented specifically by the slow sub-module, which could integrate observed signals over an appropriate period and represent input means and variances. Accordingly, the neural network could effectively predict the time-varying inputs. Furthermore, by training the time scales of neurons starting from networks with uniform time scales and without modular structure, the above slow-fast modular network structure and the division of roles in which prior knowledge is selectively represented in the slow sub-modules spontaneously emerged. These results explain how the prior distribution for Bayesian inference is represented in the brain, provide insight into the relevance of modular structure with time scale hierarchy to information processing, and elucidate the significance of brain areas with slower time scales.
各种动物，包括人类，被认为可以进行贝叶斯推理，以处理嘈杂、时变的外部信息。在大脑中进行贝叶斯推理时，必须通过对嘈杂的外部输入进行采样来获取和表示先验分布。然而，神经活动表示这种分布的机制尚未阐明。我们的研究结果表明，具有模块化结构的网络，由快速和慢速模块组成，擅长表示这种先验分布，从而能够进行更准确的贝叶斯推理。具体来说，由一个与输入和输出层相连的主模块和一个只有与主模块相连的慢速神经活动的子模块组成的模块化网络，优于具有均匀时间尺度的网络。先验信息由慢速子模块具体表示，它可以在适当的时间内整合观测信号，并表示输入均值和方差。因此，神经网络可以有效地预测时变输入。此外，通过从具有均匀时间尺度和无模块化结构的网络开始训练神经元的时间尺度，上述慢-快模块化网络结构以及先验知识在慢速子模块中选择性表示的角色分工会自发出现。这些结果解释了大脑如何表示贝叶斯推理的先验分布，深入了解了具有时间尺度层次结构的模块化结构与信息处理的相关性，并阐明了具有较慢时间尺度的大脑区域的重要性。
