A Hebbian-Based Reinforcement Learning Framework for Spike-Timing-Dependent Synapses

In this study a combination of both the Hebbian-based and reinforcement learning rule is presented. The concept permits the Hebbian rules to update the values of the synaptic parameters using both the value and the sign supplied by a reward value at any time instant. The latter is calculated as the distance between the output of the network and a reference signal. The network is a spiking neural network with spike-timing-dependent synapses. It is tested to learn the XOR computations on a temporally-coded basis. Results show that the network is able to capture the required dynamics and that the proposed framework can reveal indeed an integrated version of both Hebbian and reinforcement learning. This supports adopting the introduced approach for intuitive signal processing and computations.