… This work is aimed to study experimental and theoretical … based Spiking Neural Networks
(SNNs). First, the possibility of weight change using Spike-Timing-Dependent Plasticity (STDP) …

… models for the simulation of spiking neural networks, including … by using CMOS technology
[7], [8] and memristive devices … To extend the compact model to the statistical study, we …

… The programming of memristive device with CMOS neuron circuit, in STDP-based schemes,
has been investigated in a number of works, which highlighted the need to include compact …

… memristive devices, given their ability to emulate synaptic … two different learning rules,
namely, STDP and BCM, in a … primitives such as statistical learning and neural selectivity [5], […

… In Section 6, we apply STDP to spiking neural network (SNN). In … the processed signal to
the memristive synapse, and finally … , which is a brain-inspired, event-driven machine learning …

… The isolation of memory and processing compounds data … that face NN acceleration using
memristive hardware and how … Machine Learning, are continuously being developed to help …

… method for network training in SNN is STDP, we analyze and … , using the Spiking Neural
Network (SNN) structure, modeling … research area in machine learning and cognitive computing …

… neuromorphic hardware where spiking neural networks (SNNs) … It is so simple that precise
control of the memristive resistance is … higher accuracy than other machine learning methods. …

… in a single-layer spiking neural network consisting of simple … tasks of machine learning and
artificial neural networks [1], [2]. In … Although memristive STDP may differ significantly from the …

… (4M2R) synapse composed of 4M2R for spiking neural network (… In this way, the composite
signal sent from Post to Pre is … Chen, “SNIB: Improving spike-based machine learning using …