Abstract
Neuromorphic networks of artificial neurons and synapses can solve computationally hard problems with energy efficiencies unattainable for von Neumann architectures. For image processing, silicon neuromorphic processors outperform graphic processing units in energy efficiency by a large margin, but deliver much lower chip-scale throughput. The performance-efficiency dilemma for silicon processors may not be overcome by Moore’s law scaling of silicon transistors. Scalable and biomimetic active memristor neurons and passive memristor synapses form a self-sufficient basis for a transistorless neural network. However, previous demonstrations of memristor neurons only showed simple integrate-and-fire behaviors and did not reveal the rich dynamics and computational complexity of biological neurons. Here we report that neurons built with nanoscale vanadium dioxide active memristors possess all three classes of excitability and most of the known biological neuronal dynamics, and are intrinsically stochastic. With the favorable size and power scaling, there is a path toward an all-memristor neuromorphic cortical computer.
Highlights
Neuromorphic networks of artificial neurons and synapses can solve computationally hard problems with energy efficiencies unattainable for von Neumann architectures
Even if the brain’s connectivity were reproduced, artificial neurons and synapses built with non-biomimetic complementary metal-oxide-semiconductor (CMOS) circuits are not capable of emulating the rich dynamics of biological counterparts without sacrificing the energy consumption and size
In this article, using scalable vanadium dioxide (VO2) active memristors, we show that memristor neurons possess most of the known biological neuronal dynamics
Summary
Neuromorphic networks of artificial neurons and synapses can solve computationally hard problems with energy efficiencies unattainable for von Neumann architectures. Active memristor based spiking neurons were demonstrated[5] with biomimetic properties such as all-or-nothing spiking, refractory period, and tonic spiking and bursting. These demonstrations were interpreted by leaky integrate-and-fire (LIF) models[6]. Network-wise, most of the prior art pursued hybrid approaches that combine passive memristors with software neurons or CMOS neurons[9,10,11,12] Such hybrid approaches promise bio-competitive synaptic scalability, but still suffer the poor size and power scalability of Si neurons (See Supplementary Fig. 2). The central plots are experimental and simulated action potentials (top), the Na+ channel membrane potential VNa (middle), and simulated Na+ and K+ channel currents (bottom)
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