A gradient descent rule for spiking neurons emitting multiple spikes

DOI10.1016/j.ipl.2005.05.023zbMath1184.68387OpenAlexW1970109917MaRDI QIDQ1044691

Olaf Booij, Hieu Tat Nguyen

Publication date: 18 December 2009

Published in: Information Processing Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ipl.2005.05.023

zbMATH Keywords

parallel processingerror-backpropagationspiking neural networkstemporal pattern recognition

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (15)

Supervised Learning in Multilayer Spiking Neural Networks ⋮ Robust spike-train learning in spike-event based weight update ⋮ New approximation method for smooth error backpropagation in a quantron network ⋮ Adaptive learning rate of SpikeProp based on weight convergence analysis ⋮ Improved gradient-based neural networks for online solution of Lyapunov matrix equation ⋮ Learning Precise Spike Train–to–Spike Train Transformations in Multilayer Feedforward Neuronal Networks ⋮ On the Analytical Solution of Firing Time for SpikeProp ⋮ Spiking Neural Networks for Cortical Neuronal Spike Train Decoding ⋮ SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks ⋮ Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting ⋮ Robust learning in SpikeProp ⋮ A supervised multi-spike learning algorithm based on gradient descent for spiking neural networks ⋮ Spike-Timing Error Backpropagation in Theta Neuron Networks ⋮ An online supervised learning method based on gradient descent for spiking neurons ⋮ On the Algorithmic Power of Spiking Neural Networks

Cites Work

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• Biological and artificial computation: from neuroscience to technology. International work-conference on Artificial and natural neural networks, IWANN '97. Lanzarote, Canary Islands, Spain. June 4--6, 1997. Proceedings
• Error-backpropagation in temporally encoded networks of spiking neurons
• Spatial and temporal pattern analysis via spiking neurons
• Spiking Neuron Models

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