Abstract
We put forward a model for neural representation of intervals of time. The model is comprised of ordinary recurrent neural networks. Assumptions specific to our model are the following two: membrane potential of each neuron is bistable; each neuron receives random noise input in addition to the recurrent input. Results of computer simulation show that the network activity triggered at an initial time continues for prolonged duration followed by an abrupt self-termination. This time course seems quite suitable for representation of intervals of time. Weber's law, a hallmark of human and animal interval timing, is also reproduced.
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