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A Leaky‐Integrate‐and‐Fire Neuron Analog Realized with a Mott Insulator

Pablo Stoliar, Julien Tranchant, B. Corraze, Étienne Janod, Marie‐Paule Besland, Federico Tesler, Marcelo J. Rozenberg, Laurent Cario

2017Advanced Functional Materials286 citationsDOIOpen Access PDF

Abstract

During the last half century, the tremendous development of computers based on von Neumann architecture has led to the revolution of the information technology. However, von Neumann computers are outperformed by the mammal brain in numerous data‐processing applications such as pattern recognition and data mining. Neuromorphic engineering aims to mimic brain‐like behavior through the implementation of artificial neural networks based on the combination of a large number of artificial neurons massively interconnected by an even larger number of artificial synapses. In order to effectively implement artificial neural networks directly in hardware, it is mandatory to develop artificial neurons and synapses. A promising advance has been made in recent years with the introduction of the components called memristors that might implement synaptic functions. In contrast, the advances in artificial neurons have consisted in the implementation of silicon‐based circuits. However, so far, a single‐component artificial neuron that will bring an improvement comparable to what memristors have brought to synapses is still missing. Here, a simple two‐terminal device is introduced, which can implement the basic functions leaky integrate and fire of spiking neurons. Remarkably, it has been found that it is realized by the behavior of strongly correlated narrow‐gap Mott insulators subject to electric pulsing.

Topics & Concepts

Neuromorphic engineeringMemristorVon Neumann architectureArtificial neuronComputer scienceArtificial neural networkMassively parallelSpiking neural networkMott insulatorPhysical neural networkArtificial intelligenceBiological neural networkComputer architectureElectrical engineeringTypes of artificial neural networksRecurrent neural networkPhysicsMachine learningParallel computingEngineeringOperating systemQuantum mechanicsAdvanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesTransition Metal Oxide Nanomaterials