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

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