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.
