  In this paper, we derive a new model of synaptic plasticity, based on recent
algorithms for reinforcement learning (in which an agent attempts to learn
appropriate actions to maximize its long-term average reward). We show that
these direct reinforcement learning algorithms also give locally optimal
performance for the problem of reinforcement learning with multiple agents,
without any explicit communication between agents. By considering a network of
spiking neurons as a collection of agents attempting to maximize the long-term
average of a reward signal, we derive a synaptic update rule that is
qualitatively similar to Hebb's postulate. This rule requires only simple
computations, such as addition and leaky integration, and involves only
quantities that are available in the vicinity of the synapse. Furthermore, it
leads to synaptic connection strengths that give locally optimal values of the
long term average reward. The reinforcement learning paradigm is sufficiently
broad to encompass many learning problems that are solved by the brain. We
illustrate, with simulations, that the approach is effective for simple pattern
classification and motor learning tasks.
