Mathematical analysis of a neural network with inhibitory coupling

Marie Cottrell

Stochastic Processes and their Applications, 1992, vol. 40, issue 1, 103-126

Abstract: We study the role of inhibition in a nearest-neighbours-connected neural model. The state of the network is a Markov process of which we study the ergodic properties or divergence characteristics using the parameters of the system. We prove that, when inhibition is smaller than a certain threshold, the network is ergodic and works in a stationary way. Conversely, when inhibition increases, the network is divided into two groups: active and inactive neurons. We observe by means of computer simultation that striped or moiré responses appear, whose shape and width depend on considered neighbourhood size. The model resembles the biological reality of the young animal's cerebellar cortex.

Keywords: Markov; chains; neural; networks; inhibitory; coupling; cerebellar; cortex; stimuli (search for similar items in EconPapers)
Date: 1992
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Citations: View citations in EconPapers (4)

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