The Effect of Critical State on the Performance of Learning, Memory and Pattern Recognition in Neural Networks with Biological Characteristics
Abstract：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.
Keywords: complex networks; critical state; biological neural networks; neural plasticity; mean-field analysis
Contact:
HUANG Xuhui
E-mail: xuhui.huang@nlpr.ia.ac.cn