title: Memory dynamics in attractor networks.
abstract: As can be represented by neurons and their synaptic connections, attractor networks are widely believed to underlie biological memory systems and have been used extensively in recent years to model the storage and retrieval process of memory. In this paper, we propose a new energy function, which is nonnegative and attains zero values only at the desired memory patterns. An attractor network is designed based on the proposed energy function. It is shown that the desired memory patterns are stored as the stable equilibrium points of the attractor network. To retrieve a memory pattern, an initial stimulus input is presented to the network, and its states converge to one of stable equilibrium points. Consequently, the existence of the spurious points, that is, local maxima, saddle points, or other local minima which are undesired memory patterns, can be avoided. The simulation results show the effectiveness of the proposed method.
authors.affiliation: Centre for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
authors.forename: Guoqi
authors.initials: G
authors.lastname: Li
authors.affiliation: Department of Advanced Concepts and Nanotechnology (ACN), Data Storage Institute, A∗STAR, 5 Engineer Drive 1, Singapore 117608.
authors.forename: Kiruthika
authors.initials: K
authors.lastname: Ramanathan
authors.affiliation: Department of Advanced Concepts and Nanotechnology (ACN), Data Storage Institute, A∗STAR, 5 Engineer Drive 1, Singapore 117608.
authors.forename: Ning
authors.initials: N
authors.lastname: Ning
authors.affiliation: Centre for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
authors.forename: Luping
authors.initials: L
authors.lastname: Shi
authors.affiliation: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798.
authors.forename: Changyun
authors.initials: C
authors.lastname: Wen
mesh_terms: D000818:Animals; D003198:Computer Simulation; D006801:Humans; D008568:Memory / Q000502:physiology*; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics