The set of equations describing the change of the state of activity of the neurons is

in which *a* is a time constant, is the strength of the
synaptic connection from neuron *j* to neuron *i*, and is the
additional feedforward input to the neuron besides those described by
the feedback connection matrix . A second set of equations
describes the way the synapses change with time due to neuronal
activity. The learning rule proposed here is

in which *B* is a time constant and is the feedback learning
signal as described in the following.

The feedback learning signal is generated by a Hopfield type
associative memory network:
,
in which is the strength of the associative connection from
neuron *j* to neuron *i*, which is the recent correlation between the
neuronal activities and determined by Hebbian learning with
a decay term [5,6,10]

in which is a time constant. The and are only involved in learning and do not directly affect the network outputs.