We first analyzed the thermally-induced first order phase transition from a Mott insulator to a highly conducting state. The current-voltage characteristic of a cross-point device that has a thin film of such a material sandwiched between two metal electrodes displays a current-controlled or 'S'-type negative differential. We derived analytical equations for the behavior these devices, and found that the resulting dynamical model was mathematically equivalent to the "memristive system" formulation of Leon Chua; we thus call these devices "Mott Memristors. We built Pearson-Anson oscillators based on a parallel circuit of one Mott memristor and one capacitor, and demonstrated subnanosecond and subpicoJoule switching time and energy. We then built a neuristor using two Mott memristors and two capacitors, which emulates the Hodgkin-Huxley model of the axon action potential of a neuron. Finally, through SPICE, we demonstrate that spiking neuristors are capable of Boolean logic and Turing complete computation by designing and simulating the one dimensional cellular nonlinear network based on 'Rule 137'.
They have written a paper which will appear in Nature Materials - "A scalable neuristor built with Mott memristors".
The Hodgkin–Huxley model for action potential generation in biological axons is central for understanding the computational capability of the nervous system and emulating its functionality. Owing to the historical success of silicon complementary metal-oxide-semiconductors, spike-based computing is primarily confined to software simulations and specialized analogue metal–oxide–semiconductor field-effect transistor circuits. However, there is interest in constructing physical systems that emulate biological functionality more directly, with the goal of improving efficiency and scale. The neuristor was proposed as an electronic device with properties similar to the Hodgkin–Huxley axon, but previous implementations were not scalable. Here we demonstrate a neuristor built using two nanoscale Mott memristors, dynamical devices that exhibit transient memory and negative differential resistance arising from an insulating-to-conducting phase transition driven by Joule heating. This neuristor exhibits the important neural functions of all-or-nothing spiking with signal gain and diverse periodic spiking, using materials and structures that are amenable to extremely high-density integration with or without silicon transistors.
Background on Computing With Spiking Neuron Networks
47 pages on Computing with Spiking Neuron Networks.
Abstract Spiking Neuron Networks (SNNs) are often referred to as the 3rd generation of neural networks. Highly inspired from natural computing in the brain and recent advances in neurosciences, they derive their strength and interest from an accurate modeling of synaptic interactions between neurons, taking into account the time of spike firing. SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. Based on dynamic event-driven processing, they open up new horizons for developing models with an exponential capacity of memorizing and a strong ability to fast adaptation. Today, the main challenge is to discover efficient learning rules that might take advantage of the specific features of SNNs while keeping the nice properties (general-purpose, easy-to-use, available simulators, etc.) of traditional connectionist models.
SOURCES - Nature Materials, University Texas, Universit de Lyon Laboratoire de Recherche en Informatique
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