Grossberg network

Grossberg network is an artificial neural network introduced by Stephen Grossberg. It is a self organizing, competitive network based on continuous time.[1] Grossberg, a neuroscientist and a biomedical engineer, designed this network based on the human visual system.

Shunting model

The shunting model is one of Grossberg's neural network models, based on a Leaky integrator, described by the differential equation

{dn \over dt}\;=\;-An\;+(B-n)E\;-(C+n)I

,

where $n=n(t)$ represents the activation level of a neuron, $E=E(t)$ and $I=I(t)$ represent the excitatory and inhibitory inputs to the neuron, and $A$ , $B$ , and $C$ are constants representing the leaky decay rate and the maximum and minimum activation levels.

At equilibrium (where $dn/dt=0$ ), the activation $n$ reaches the value

n\;=\;{BE-CI \over A+E+I}

.

gollark: Which is very stupid in my opinion.

gollark: I've managed to avoid viewbombing most of the time, but it's annoying still.

gollark: <@!175822114916532224> TIMEZONES

gollark: Er, one christmas dragon.

gollark: I have one holiday dragon, no more.

References

Martin T. Hagan; Howard B. Demuth; Mark H. Beale (January 2002) [1996]. "Chapter 15: Grossberg Network". Neural Network Design (1st ed.). PWS Publishing Co. pp. 15–1. ISBN 978-0971732100.

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[Hagan-1] Martin T. Hagan; Howard B. Demuth; Mark H. Beale (January 2002) [1996]. "Chapter 15: Grossberg Network". Neural Network Design (1st ed.). PWS Publishing Co. pp. 15–1. ISBN 978-0971732100.