Reinforcement Learning Through Modulation of Spike-Timing-Dependent Synaptic Plasticity

The persistent modification of synaptic efficacy as a function of the relative timing of pre- and postsynaptic spikes is a phenomenon known as spiketiming-dependent plasticity (STDP). Here we show that the modulation of STDP by a global reward signal leads to reinforcement learning. We first derive analytically learning rules involving reward-modulated spike-timing-dependent synaptic and intrinsic plasticity, by applying a reinforcement learning algorithm to the stochastic Spike Response Model of spiking neurons. These rules have several features common to plasticity mechanisms experimentally found in the brain. We then demonstrate in simulations of networks of integrateand-fire neurons the efficacy of two simple learning rules involving modulated STDP. One rule is a direct extension of the standard STDP model (modulated STDP), while the other one involves an eligibility trace stored at each synapse that keeps a decaying memory of the relationships between the recent pairs of pre...

• Type: Journal
• Year: 2007
• Where: NECO
• Authors: Razvan V. Florian
• Keywords: NECO 2007, Postsynaptic Spike, Reward Signal, STDP