Emulating long-term synaptic dynamics with memristive devices

The potential of memristive devices is often seeing in implementing neuromorphic architectures for achieving brain-like computation. However, the designing procedures do not allow for extended manipulation of the material, unlike CMOS technology, the properties of the memristive material should be harnessed in the context of such computation, under the view that biological synapses are memristors. Here we demonstrate that single solid-state TiO2 memristors can exhibit associative plasticity phenomena observed in biological cortical synapses, and are captured by a phenomenological plasticity model called “triplet rule”. This rule comprises of a spike-timing dependent plasticity regime and a “classical” hebbian associative regime, and is compatible with a large amount of electrophysiology data. Via a set of experiments with our artificial, memristive, synapses we show that, contrary to conventional uses of solid-state memory, the co-existence of field- and thermally-driven switching mechanisms that could render bipolar and/or unipolar programming modes is a salient feature for capturing long-term potentiation and depression synaptic dynamics. We further demonstrate that the non-linear accumulating nature of memristors promotes long-term potentiating or depressing memory transitions.