ACCURATE, ENERGY-EFFICIENT CLASSIFICATION WITH SPIKING RANDOM NEURAL NETWORK

DOI10.1017/S0269964819000147zbMath1484.62076arXiv1906.08864OpenAlexW2945144713WikidataQ127816698 ScholiaQ127816698MaRDI QIDQ5070878

Khaled F. Hussain, Erol Gelenbe, Mohamed Yousef Bassyouni

Publication date: 14 April 2022

Published in: Probability in the Engineering and Informational Sciences (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1906.08864

zbMATH Keywords

artificial neural networkneuromorphic computingspiking neural networksrandom neural network

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Artificial neural networks and deep learning (68T07) Neural nets and related approaches to inference from stochastic processes (62M45)

Uses Software

• UCI-ml
• ImageNet
• FaceNet
• AlexNet
• SpiNNaker

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Interconnected wireless sensors with energy harvesting
• Multilayer feedforward networks are universal approximators
• Stability of Product Form G-Networks
• Queues with negative arrivals
• Product-form queueing networks with negative and positive customers
• G-networks by triggered customer movement
• First draft of a report on the EDVAC
• 10.1162/153244303322533223
• G-networks with resets

This page was built for publication: ACCURATE, ENERGY-EFFICIENT CLASSIFICATION WITH SPIKING RANDOM NEURAL NETWORK