1994 Special Issue: Winner-take-all networks for physiological models of competitive learning
Neural Networks - Special issue: models of neurodynamics and behavior
Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
Object selection based on oscillatory correlation
Neural Networks
Biophysiologically plausible implementations of the maximum operation
Neural Computation
A winner-take-all mechanism based on presynaptic inhibition feedback
Neural Computation
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Neurophysiological investigations suggest that presynaptic ionotropic receptors are important mechanism for controlling synaptic transmission. In this paper, presynaptic kainate receptors are incorporated in a feedforward inhibitory neural network in order to investigate their role in the cortical information processing. Computer simulations showed that the proposed mechanism is able to compute the function maximum by disinhibiting the cell with the maximal amplitude. The maximum is computed with high precision even in the case where inhibitory synaptic weights are weak and (or) asymmetric. Moreover, the network is able to track time-varying input and to select multiple winners. These capabilities do not depend on the dimensionality of the network. Also, the model is able to implement the winner-take-all behaviour.