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Analysis and simulation of networks of mutually inhibiting neurons

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Abstract

Networks of mutually inhibiting neurons are analyzed and simulated on a digital computer. In the analysis and simulation a continuous-variable model of the neuron is used as the basic element. It consists of a many-input adder, a first-order low-pass filter and a diode-type nonlinearity. A mutually inhibiting network is formed by connecting the output of every element to inputs of the other elements through weight-coefficient setting units. Each element of the network is assumed to receive a certain number of constant inputs from elements of other networks.

An autonomous system of nonlinear differential equations is introduced to describe the network dynamics, and the steady-state solutions of the system are investigated in detail. The network has a unique equilibrium solution, multiple equilibrium solutions or a periodic solution depending on the weight-coefficients and the inputs. It is shown that these three cases correspond to three types of information processing functions: the sharpening of input patterns, the temporary storage of information and the generation of periodic signals.

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Authors and Affiliations

  1. Faculty of Engineering, University of Tokyo, Tokyo, Japan

    Iwao Morishita & Akio Yajima

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  1. Iwao Morishita
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  2. Akio Yajima
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Morishita, I., Yajima, A. Analysis and simulation of networks of mutually inhibiting neurons. Kybernetik 11, 154–165 (1972). https://doi.org/10.1007/BF00270672

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  • DOI: https://doi.org/10.1007/BF00270672

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