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.

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

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

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

At equilibrium (where 👁 {\displaystyle dn/dt=0}
), the activation 👁 {\displaystyle n}
reaches the value

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