In this paper, basic theory of novel devices as memristors, their models, and I–V
characteristic have been discussed. They can be used in various potential applications …

Learning in multilayer neural networks (MNNs) relies on continuous updating of large
matrices of synaptic weights by local rules. Such locality can be exploited for massive …

Simultaneous Localization and Map** (SLAM) is a crucial function for most autonomous
systems, allowing them to both navigate through and create maps of unfamiliar …

Back propagation (BP) based on stochastic gradient descent is the prevailing method to
train multilayer neural networks (MNNs) with hidden layers. However, the existence of the …

In this paper, we review different memristive threshold logic (MTL) circuits that are inspired
from the synaptic action of the flow of neurotransmitters in the biological brain. The brainlike …

Advanced computing systems have long been enablers for breakthroughs in artificial
intelligence (AI) and machine learning (ML) algorithms, either through sheer computational …

We introduce the notion of universal memcomputing machines (UMMs): a class of brain-
inspired general-purpose computing machines based on systems with memory, whereby …

Diverse models have been proposed over the past years to explain the exhibiting behavior
of memristors, the fourth fundamental circuit element. The models varied in complexity …

In this paper, we introduce for the first time, a closed-form solution for the memristor-based
memory sneak paths without using any gating elements. The introduced technique fully …

A neuromorphic chip that combines CMOS analog spiking neurons and memristive
synapses offers a promising solution to brain-inspired computing, as it can provide massive …