The emergence of polychronous groups under varying input patterns, plasticity rules and network connectivities

Polychronous groups are unique temporal patterns of neural activity that exist implicitly within non-linear, recurrently connected networks. Through Hebbian based learning these groups can be strengthened to give rise to larger chains of spatiotemporal activity. Compared to other structures such as Synfire chains, they have demonstrated the potential of a much larger capacity for memory or computation within spiking neural networks. Polychronous groups are believed to relate to the input signals under which they emerge. Here we investigate the quantity of groups that emerge from increasing numbers of repeating input patterns, whilst also comparing the differences between two plasticity rules and two network connectivities. We find - perhaps counter-intuitively - that fewer groups are formed as the number of repeating input patterns increases. Furthermore, we find that a tri-phasic learning rule gives rise to fewer groups than the 'classical' double decaying exponential STDP plasticity window. It is also found that a scale-free network structure produces a similar quantity, but generally smaller groups than a randomly connected Erdös-Rényi structure.