Robust spike-train learning in spike-event based weight update

DOI10.1016/j.neunet.2017.08.010zbMath1434.68454OpenAlexW2754486806WikidataQ47185243 ScholiaQ47185243MaRDI QIDQ2179081

Sumit Bam Shrestha, Qing Song

Publication date: 12 May 2020

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2017.08.010

zbMATH Keywords

robust stabilitysupervised learningspiking neural networkweight convergenceadaptive learning ratemultilayer spike-train learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20) Robust stability (93D09)

Related Items (1)

Analyzing and Accelerating the Bottlenecks of Training Deep SNNs With Backpropagation

Uses Software

• UCI-ml

Cites Work

• Adaptive learning rate of SpikeProp based on weight convergence analysis
• A gradient descent rule for spiking neurons emitting multiple spikes
• Error-backpropagation in temporally encoded networks of spiking neurons
• Robust learning in SpikeProp
• A supervised multi-spike learning algorithm based on gradient descent for spiking neural networks
• Rate Coding Versus Temporal Order Coding: What the Retinal Ganglion Cells Tell the Visual Cortex
• Reinforcement Learning Through Modulation of Spike-Timing-Dependent Synaptic Plasticity
• Robustness of discrete-time direct adaptive controllers
• Robustness analysis of discrete-time adaptive control systems using input-output stability theory: a tutorial
• Lower Bounds for the Computational Power of Networks of Spiking Neurons
• Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting
• Supervised Learning in Multilayer Spiking Neural Networks

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