Analysis of spatio-temporal patterns in associative networks of spiking neurons

A neural network is presented that stores spatio-temporal patterns (synfire chains) in associative networks of spiking neurons and replays them at a controllable speed. An implicit equation is derived and solved numerically which relates the average speed to the network parameters. The replay speed can be controlled by unspecific background signals and also depends on the number of co-activated synfire-chains. Balanced inhibition can prevent the latter dependency. Simulation results confirm the theory, but reveal instabilities for low and high control signals. These boundaries are traced back to four different destabilizing mechanisms