Memristor|based spiking neural networks

Memristor-based spiking neural networks: cooperative development ...

This review focuses on the potential applications of memristors in this area. It starts from the principles of SNNs, including neuron models and synaptic ...

Spiking Neural Network (SNN) With Memristor Synapses Having ...

In this work, we conducted an SNN simulation by adding a device model with a non-linear weight update to test the impact on SNN performance.

Memristor-based spiking neural network with online reinforcement ...

This paper presents an online reinforcement learning algorithm in which the change of connection weights is carried out after processing each environment state.

Memristor-based Deep Spiking Neural Network with a Computing-In ...

We introduce a scalable deep SNN to address the problem of latency and energy efficiency. We integrate a Computing-In-Memory (CIM) architecture built with a ...

An artificial spiking afferent nerve based on Mott memristors ... - Nature

Recently, several hardware implementations of spiking neural networks based on traditional complementary metal-oxide semiconductor technology or ...

Memristor-based Spiking Neural Networks - ePrints Soton

In addition, investigations of the neural network sen- sitivity to global parameters such as spike train length, the read noise, and the weight updating stop ...

On-Device Learning in Memristor Spiking Neural Networks

A key feature of these circuits is the use of memristors to emulate the membrane potential of spiking neurons, as opposed to the conventional use of a capacitor ...

Memristor-based Deep Spiking Neural Network with a Computing-In ...

Spiking Neural Networks (SNNs) are artificial neural network models that show significant advantages in terms of power and energy when realizing deep learning ...

Enhancing Robustness of Memristor Crossbar‐Based Spiking ...

This study explores challenges faced by memristor crossbar-based spiking neural networks (SNNs) due to nonidealities.

An Efficient and Accurate Memristive Memory for Array-based ... - arXiv

Abstract page for arXiv paper 2306.06551: An Efficient and Accurate Memristive Memory for Array-based Spiking Neural Networks.

Text classification in memristor-based spiking neural networks

Abstract. Memristors, emerging non-volatile memory devices, have shown promising potential in neuromorphic hardware designs, especially in ...

Efficient Spiking Neural Networks with Biologically Similar Lithium ...

A Li-based memristor (Li x AlO y ) was proposed to emulate the biological synapse, leveraging the similarity of Li as a homologous main group element to Na and ...

Spiking neural networks based on two-dimensional materials - Nature

Here we present the use of multilayer hexagonal boron nitride based memristors to implement spiking neural networks for image classification.

Memristor-based Spiking Neural Networks - ePrints Soton

This thesis aims to build a cohesive pipeline for bringing memristor-based SNNs to practical use following these two pathways.

A Hybrid CMOS-Memristor Spiking Neural Network Supporting ...

We propose a neuro-synaptic architecture that co-integrates in a unique platform with a single type of synaptic device to implement two distinct learning rules.

Memristive Spiking Neural Network for Neuromorphic Computing

Author(s): Zhou, Peng | Advisor(s): Kang, Sung-Mo; Eshraghian, Jason | Abstract: This dissertation is dedicated to using Memristive Spiking Neural Networks ...

Toward Reflective Spiking Neural Networks Exploiting Memristive ...

Models of Spiking Neurons. The synergy between neuroscience and novel mathematical approaches can be a solution for building novel systems exhibiting reflective ...

Exploring Neuromorphic Computing Based on Spiking Neural ...

Termed as Spiking Neural Networks (SNNs) [133], these networks lead to possibilities of sparse, event-driven neuronal computations and temporal encoding–a shift ...

Text Classification in Memristor-based Spiking Neural Networks - arXiv

Memristor-based SNNs have been successfully applied in a wide range of various applications, including image classification and pattern ...

Neuromorphic Spiking Neural Networks and Their Memristor-CMOS ...

Hybrid memristor-CMOS approaches have been proposed to implement large-scale neural networks with learning capabilities, offering a scalable and lower-cost ...