  Reverberating dynamics of neural network is modelled on PC in order to
illustrate possible role of inhibition as binding controller in the network.
The network is composed of binding neurons. In the binding neuron model the
degree of temporal coherence between synaptic inputs is decisive for
triggering, and slow inhibition is expressed in terms of the degree, which is
necessary for triggering. Two learning mechanisms are implemented in the
network, namely, adjusting synaptic strength and/or propagation delays. By
means of forced playing of external pattern the network is taught to support
dynamics with disconnected and bound patterns of activity. By choosing either
high, or low inhibition one can switch between the disconnected and bound
patterns, respectively. This is interpreted as inhibition-controlled binding in
the network.
