Computing the Maximum Using Presynaptic Inhibition with Glutamate Receptors
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.
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