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

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.

Highly Compact Artificial Memristive Neuron with Low Energy Consumption

“…An artificial neuron requires to exhibit functions of leaky integration, automatic fire, and fast recovery. Lately, two works built with Mott memristors have been reported to emulate the functions of neuron, exhibiting threshold‐driven firing response and leaky integration . A phase change neuron was proposed and demonstrated the integration function .…”

“…Lately, two works built with Mott memristors have been reported to emulate the functions of neuron, exhibiting threshold-driven firing response and leaky integration. [18,19] A phase change neuron was proposed and demonstrated the integration function. [5] However, the energy consumed during operations in the reports was several orders of magnitude higher than that of the biological Neuromorphic systems aim to implement large-scale artificial neural network on hardware to ultimately realize human-level intelligence.…”

Memristive Synapses and Neurons for Bioinspired Computing

“…Bioinspired computing systems composed of artificial synapses and neurons have been in the spotlight of multidisciplinary research in the last decade because it enables efficient parallel information processing at ultralow power consumption, which [218] Copyright 2017, Wiley-VCH. c) I-V characteristics of the Pt/FeO x /Ag threshold memristive device.…”

“…Bioplausible neurons are simple but prevalent in neuromorphic computing, while biophysical neurons describe some salient features of biological neurons that bioplausible ones fail to capture, such as hyperpolarization. [217][218][219] Hybrid circuits can be subdivided into one comprising a parallel capacitor with a threshold memristive device and another one comprising comparators and memristive devices without threshold. [215] The cores in the implementation of bioplausible neurons refer to realizing LGP and threshold spike process, and two approaches have been developed in regards to switching characteristics of the adopted memristive devices.…”

“…In 2017, Pablo and his coworkers [218] reported an LIF neuron based on a single MIT memristive device, as shown in Figure 19. Thus, it is not surprising to find that some memristive devices work as capacitors in a given time, in particular at HRS state, and a capacitor is included in the equivalent circuits of some memristive devices.…”