Dynamical properties of continuous attractor neural network with background tuning

作者：Yu Jiali^*; Tang Huajin; Li Haizhou; Shi Luping

来源：Neurocomputing, 2013, 99: 439-447.

DOI：10.1016/j.neucom.2012.06.029

• Persistent activity
• Continuous attractors
• Linear recurrent neural networks
• Background input

Persistent activity holds the transient stimulus for up to many seconds even after the stimulus is gone. It has been implemented in a class of models known as continuous attractor neural networks, which have infinite stable states corresponding to persistent activity patterns. Continuous attractor neural network remains stable so does not change systematically in the absence of stimulus input. Continuous attractor is a set of connected stable equilibrium points and has been used to describe the storing of continuous stimuli in neural networks. The background input of the networks plays an important role in continuous attractor neural network. In this paper, dynamical properties of continuous attractor neural network with two background input tuning schemes are investigated: constant input shifting and oscillation background activity. Simulations are employed to illustrate the theory.

• 出版日期2013-1-1