Model of a neuron trained to extract periodicity

Abstract

In the auditory system, there should be elements that convert temporal parameters into spatial ones. To simulate such conversion, various neural networks are used. In this study, we modeled this conversion, carried out by one complex neuron on the basis of learning without a teacher. We postulate that conversion of the time code into a spatial code is observed at the input of the model. We admit that every aciculum of a complex neuron responds as a coincidence detector, and after each coincidence at any synapse, the neuron generates a spike. Every spike at the output of a neuron changes the weight of all acicula according to the Hebb principle. Training of the model is done without a teacher simply owing to model’s multiple perception of a certain type of signals. In the given case, such signals are the actual activity of the cochlear nucleus of frog, which arises as a response to an amplitude-modulated tone. After the action of such signals, the model behaved as a detector of the modulation frequency used during training. Such a situation existed up to modulation frequencies near 40 Hz. At higher modulation frequencies, the model even extracted signals with a doubled modulation period.