Spike-based Learning Rules and Stabilization of Persistent Neural Activity

Part of Advances in Neural Information Processing Systems 12 (NIPS 1999)

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Xiaohui Xie, H. Sebastian Seung


We analyze the conditions under which synaptic learning rules based on action potential timing can be approximated by learning rules based on firing rates. In particular, we consider a form of plasticity in which synapses depress when a presynaptic spike is followed by a postsynaptic spike, and potentiate with the opposite temporal ordering. Such differen(cid:173) tial anti-Hebbian plasticity can be approximated under certain conditions by a learning rule that depends on the time derivative of the postsynaptic firing rate. Such a learning rule acts to stabilize persistent neural activity patterns in recurrent neural networks.