This article presents a review of the current development and challenges in memristor
modeling. We review the mechanisms of memristive devices based on various …

The volume, veracity, variability, and velocity of data produced from the ever increasing
network of sensors connected to Internet pose challenges for power management …

Neuromorphic computers comprised of artificial neurons and synapses could provide a
more efficient approach to implementing neural network algorithms than traditional …

Memristor synapse with activated synaptic plasticity can be taken as an adaptive connection
synaptic weight. To demonstrate its kinetic effects, in this article, we present an improved …

Neuromorphic networks of artificial neurons and synapses can solve computationally hard
problems with energy efficiencies unattainable for von Neumann architectures. For image …

A multitude of chemical, biological, and material systems present an inductive behavior that
is not electromagnetic in origin. Here, it is termed a chemical inductor. We show that the …

Analog circuit implementation of neuron model is an essential category of neuromorphic
circuit since it can reproduce neuron firing patterns and assist in exploring neuron-based …

Dynamical modeling of nervous systems is of fundamental importance in many scientific
fields containing the topics relative to computational neuroscience and biophysics. Many …

This paper classifies all memristors into three classes called Ideal, Generic, or Extended
memristors. A subclass of Generic memristors is related to Ideal memristors via a one-to-one …

Signal transmission in the brain relies on voltage-gated ion channels, which exhibit the
electrical behaviour of memristors, resistors with memory. State-of-the-art technologies …