The mechanisms of the complex functions attributed mostly to the cerebral cortex are hidden in the collective behaviour of a vast neural network that cannot practically be described in detail or in general. Cyclic modes of activity which emerge spontaneously in the dynamics of neural networks may underly possible mechanisms of short-term memory and associative thinking. The transitions from seemingly random activity patterns to cyclic activity have been examined in isolated networks with pseudorandomly chosen synapses and in networks with very simple architectures.
The basic computer model (Clark, Rafelski & Winston, 1985) envisions a collection of neurons, linked by a network of axons and dendrites that synapse onto one another. The synaptic interactions are modeled by a connection matrix V. The net algebraic strength of the connections from neuron j to neuron i, represented by the matrix element Vij can be positive (excitatory), negative (inhibitory) or zero (no connection). In the present study, the Vij were chosen randomly, but in accord with certain specified gross network parameters, viz.
N = net size = number of neurons in net,
m = connection density = probability that a given j → i link exists,
h = fraction of inhibitory neurons.
No more than one connection (‘synapse’) was allowed from any source neuron j to a given target neuron i.
The neurons update their states synchronously, corresponding to the assumption of a universal time delay δ for direct signal transmission.