Synfire chains and catastrophic interference

The brain must be capable of achieving extraordinarily precise sub-milisecond timing with imprecise neural hardware. We discuss how this might be possible using synfire chains (Abeles, 1991) and present a synfire chain learning algorithm for a sparsely-distributed network of spiking neurons (Sougné, 1999). Surprisingly, we show that this learning is not subject to catastrophic interference, a problem that plagues many standard connectionist networks. We show that the forgetting of synfire chains in this type of network closely resembles the classing forgetting pattern described by Barnes