Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity

Part of Advances in Neural Information Processing Systems 20 (NIPS 2007)

Bibtex Metadata Paper


Dejan Pecevski, Wolfgang Maass, Robert Legenstein


Reward-modulated spike-timing-dependent plasticity (STDP) has recently emerged as a candidate for a learning rule that could explain how local learning rules at single synapses support behaviorally relevant adaptive changes in com- plex networks of spiking neurons. However the potential and limitations of this learning rule could so far only be tested through computer simulations. This ar- ticle provides tools for an analytic treatment of reward-modulated STDP, which allow us to predict under which conditions reward-modulated STDP will be able to achieve a desired learning effect. In particular, we can produce in this way a theoretical explanation and a computer model for a fundamental experimental ļ¬nding on biofeedback in monkeys (reported in [1]).