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 complex
networks of spiking neurons. However the potential and limitations of this
learning rule could so far only be tested through computer simulations. This article
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
finding on biofeedback in monkeys.
