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Interdisciplinary research broadens the view of particular problems yielding fresh and possibly unexpected insights. This is the case of neuromorphic engineering where technology and neuroscience cross-fertilize each other. For example, consider on one side the recently discovered memristor, postulated in 1974, thanks to research in nanotechnology electronics. On the other side, consider the mechanism known as Spike-Time-Dependent-Plasticity (STDP) which describes a neuronal synaptic learning mechanism that outperforms the traditional Hebbian synaptic plasticity proposed in 1949. STDP was originally postulated as a computer learning algorithm, and is being used by the machine intelligence and computational neuroscience community. At the same time its biological and physiological foundations have been reasonably well established during the past decade. If memristance and STDP can be related, then (a) recent discoveries in nanophysics and nanoelectronic principles may shed new lights into understanding the intricate molecular and physiological mechanisms behind STDP in neuroscience, and (b) new neuromorphic-like computers built out of nanotechnology memristive devices could incorporate the biological STDP mechanisms yielding a new generation of self-adaptive ultra-high-dense intelligent machines. Here we show that by combining memristance models with the electrical wave signals of neural impulses (spikes) converging from pre- and post-synaptic neurons into a synaptic junction, STDP behavior emerges naturally. This result serves to understand how neural and memristance parameters modulate STDP, which might bring new insights to neurophysiologists in searching for the ultimate physiological mechanisms responsible for STDP in biological synapses. At the same time, this result also provides a direct mean to incorporate STDP learning mechanisms into a new generation of nanotechnology computers employing memristors.

Memristance can explain Spike-Time-Dependent-Plasticity in Neural Synapses