Advances in memristor based artificial neuron fabrication-materials, models, and applications

Abstract

Spiking Neural Network (SNN), widely known as the third-generation neural network, has been frequently investigated due to its excellent spatiotemporal information processing capability, high biological plausibility and low energy consumption characteristics. Analogous to the working mechanism of human brain, SNN system transmits information through spiking action of neurons. Therefore, artificial neurons are critical building blocks for constructing SNN in hardware. Memristors are drawing growing attentions due to low consumption, high speed, and nonlinearity characteristics, which are recently introduced to mimic the functions of biological neurons. Researchers have proposed multifarious memristive materials including organic materials, inorganic materials, or even two- dimensional materials. Taking advantage of the unique electrical behavior of these materials, several neuron models are successfully implemented, such as Hodgkin-Huxley (HH) model, leaky integrate-and-fire (LIF) model and integrate-and-fire (IF) model. In this review, the recent reports of artificial neuron based on memristive devices are discussed. In addition, we highlight the functions and applications through combining artificial neuronal device with sensors or the other electronic devices. Finally, the future challenges and outlooks of memristor-based artificial neurons are discussed, and the development of hardware implementation of brain-like intelligence system based on SNN is also prospected.

Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network

“…Artificial neurons [32] generate an output value representing their activity by applying a nonlinear activation function. In this process, this study assumes that the neuron receives n input signals…”

Adapted MLP-Mixer network based on crossbar arrays of fast and multilevel switching (Co–Fe–B)x(LiNbO3)100−x nanocomposite memristors

“…Memristors, devices capable of reversible dynamical resistive switching, 9,10 may be based on various materials ( e.g. , inorganic, organic, nanocomposite, ferroelectric, two-dimensional) 11–14 and may emulate synapses 15 or neurons 16,17 in NCSs. Memristors have been used for NCS realizations, and schemes such as multilayer perceptrons (MLPs), 18–20 convolutional, 21 long short-term memory 22 and others, 23,24 including macro 25 and neuromorphic vision 26 networks, have been successfully demonstrated.…”