  A minimal model of polychronous groups in neural networks is presented. The
model is computationally efficient and allows the study of polychronous groups
independent of specific neuron models. Computational experiments were performed
with the model in one- and two-dimensional neural architectures to determine
the dependence of the number of polychronous groups on various connectivity
options. The possibility of using polychronous groups as computational elements
is also discussed.
