Modeling brain function—the world of attractor neural networks
Modeling brain function—the world of attractor neural networks
Continuous attractors and oculomotor control
Neural Networks - Special issue on neural control and robotics: biology and technology
Self-organizing continuous attractor networks and motor function
Neural Networks
Memory Capacity of Balanced Networks
Neural Computation
Computing with Continuous Attractors: Stability and Online Aspects
Neural Computation
Dynamics and computation of continuous attractors
Neural Computation
Analysis of continuous attractors for 2-D linear threshold neural networks
IEEE Transactions on Neural Networks
Representations of continuous attractors of recurrent neural networks
IEEE Transactions on Neural Networks
Permitted and forbidden sets in discrete-time linear threshold recurrent neural networks
IEEE Transactions on Neural Networks
Continuous attractors of Lotka-Volterra recurrent neural networks with infinite neurons
IEEE Transactions on Neural Networks
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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.