Synfire chains and catastrophic interference

The brain must be capable of achieving extraordinarily precise sub-millisecond timing with imprecise neural hardware. We discuss how this might be possible using synfire chains (Abeles, 1991). We present a learning algorithm for a sparsely-distributed network of spiking neurons (Sougné, 1998, 1999) allowing it to learn these synfire chains. 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 classic forgetting pattern described by Barnes & Underwood (1959)