Recurrent network model of the neural mechanism of short-term active memory
Neural Computation
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Simple model of spiking neurons
IEEE Transactions on Neural Networks
Structure and Dynamics of Random Recurrent Neural Networks
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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We present in this paper a general model of recurrent networks ofspiking neurons, composed of several populations, and whose interactionpattern is set with a random draw. We use for simplicity discrete timeneuron updating, and the emitted spikes are transmitted through randomlydelayed lines. In excitatory-inhibitory networks, we show thatinhomogeneous delays may favour synchronization provided that theinhibitory delays distribution is significantly stronger than theexcitatory one. In that case, slow waves of synchronous activity appear(this synchronous activity is stronger in inhibitory population). Thissynchrony allows for a fast adaptivity of the network to various inputstimuli. In networks observing the constraint of short range excitationand long range inhibition, we show that under some parameter settings,this model displays properties of –1– dynamic retention –2– input normalization –3– target tracking. Those properties are of interest for modelling biological topologically organized structures, and for roboticapplications taking place in noisy environments where targets vary insize, speed and duration.