Continuous attractors and oculomotor control
Neural Networks - Special issue on neural control and robotics: biology and technology
Learning continuous attractors in recurrent networks
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Videoconferencing 2000: H.323's year?
Network Computing
Computing with Continuous Attractors: Stability and Online Aspects
Neural Computation
Continuous attractors of a class of neural networks with a large number of neurons
Computers & Mathematics with Applications
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Recurrent neural networks (RNNs) may possess continuous attractors, a property that many brain theories have implicated in learning and memory. There is good evidence for continuous stimuli, such as orientation, moving direction, and the spatial location of objects could be encoded as continuous attractors in neural networks. The dynamical behaviors of continuous attractors are interesting properties of RNNs. This paper proposes studying the continuous attractors for a class of RNNs. In this network, the inhibition among neurons is realized through a kind of subtractive mechanism. It shows that if the synaptic connections are in Gaussian shape and other parameters are appropriately selected, the network can exactly realize continuous attractor dynamics. Conditions are derived to guarantee the validity of the selected parameters. Simulations are employed for illustration.