A differential memristive synapse circuit for on-line learning in neuromorphic computing systems

Spike-based learningwithmemristive devices in neuromorphic computing architectures typically uses learning circuits that require overlapping pulses frompreand post-synaptic nodes. This imposes severe constraints on the length of the pulses transmitted in the network, and on the network’s throughput. Furthermore,most of these circuits do not decouple the currentsflowing through memristive devices from the one stimulating the target neuron. This can be a problemwhen using devices with high conductance values, because of the resulting large currents. In this paper, we propose a novel circuit that decouples the current produced by thememristive device from the one used to stimulate the post-synaptic neuron, by using a novel differential scheme based on theGilbert normalizer circuit.We showhow this circuit is useful for reducing the effect of variability in the memristive devices, and how it is ideally suited for spike-based learningmechanisms that do not require overlapping preand post-synaptic pulses.We demonstrate the features of the proposed synapse circuit with SPICE simulations, and validate its learning properties with high-level behavioral network simulations which use a stochastic gradient descent learning rule in two benchmark classification tasks.