Analysis of Cyclic Dynamics for Networks of Linear Threshold Neurons
Neural Computation
A Competitive-Layer Model for Feature Binding and Sensory Segmentation
Neural Computation
On neurodynamics with limiter function and linsker's developmental model
Neural Computation
IEEE Transactions on Neural Networks
A multichip aVLSI system emulating orientation selectivity of primary visual cortical cells
IEEE Transactions on Neural Networks
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Multistable neural networks have attracted much interests in recent years, since the monostable networks are computationally restricted. This paper studies a N linear threshold neurons recurrent networks without Self-Excitatory connections. Our studies show that this network performs a Winner-Take-All (WTA) behavior, which has been recognized as a basic computational model done in brain. The contributions of this paper are: (1) It proves by mathematics that the proposed model is Non-Divergent. (2) An important implication (Winner-Take-All) of the proposed network model is studied. (3) Digital computer simulations are carried out to validate the performance of the theory findings.