临界态对生物神经网络学习、记忆以及模式识别能力的影响

项目名称： 临界态对生物神经网络学习、记忆以及模式识别能力的影响

项目编号： No.11505283

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 数理科学和化学

项目作者： 黄旭辉

作者单位： 中国科学院自动化研究所

项目金额： 18万元

中文摘要： 大脑功能的实现依赖于其保持在一个优化的状态。但目前对这一状态的特征及其与脑功能的关系还远未明了。已有大量实验提出了大脑工作在临界状态附近的猜想，实验也揭示处于临界态的神经系统具有功能优势，如实现最大的刺激响应范围、最优的信息传播以及最大的信息存储容量。然而迄今为止，临界态是否有利于生物神经网络高效实现学习与记忆等实际功能还不清楚。本项目将基于以往探讨可塑性神经网络学习、记忆以及模式识别等具体功能的研究范式，建立具有生物学特征的神经网络模型，从而研究临界态是否有利于网络的功能实现，即考虑网络处于临界态、亚临界态以及超临界态（简称“三态”）下实现上述功能的性能差异。具体研究三态下网络分类识别能力、记忆空间斑图能力的差异，探讨重要参数对三态下网络功能实现的影响，以及分析三态影响网络功能的内在机理。这些研究将既有助于深入认识脑状态的本质及其与脑功能的关系，也将对设计类脑智能系统有重要的启发意义。

中文关键词： 复杂网络；临界态；生物神经网络；神经可塑性；；平均场分析

英文摘要： The realization of various functions of the brain relies on its being organized at an optimized state. However, the nature of such a state, as well as its relation to brain functions, remains largely elusive. A number of previous studies have hypothesized that the brain works at or close to the critical state. It has also been reported that nervous systems that are critical have various functional advantages, such as maximized dynamic range, optimized information transmission and maximized information storage capacity. Nevertheless, so far it is unclear whether the critical state can benefit more concrete functions of biological neural networks, such as learning and memory. In this project, based on the established paradigms on learning, memory and pattern recognition of plastic neural networks, we will explore the functional benefits of being organized at a critical state for neural networks with real biological characteristics. That is, we will examine the difference in various performances when the networks are critical, subcritical and supercritical (hereinafter referred to as the three-states). Specifically, we will explore the following aspects:(1) difference in learning and classification capability among the three-states; (2) the difference in memory capability of encoding spatial patterns among the three-states; (3) the effects of important model parameters on functional performance among the three-states and (4) analyzing the mechanism by which learning, memory and pattern recognition are affected by the three-states. These studies will not only help us understand the nature of the optimized brain state and how it gives rise to various brain functions, but also inspire the design of brain-like intelligent systems.

英文关键词： complex networks;critical state;biological neural networks ;neural plasticity;mean-field analysis