Learning Precise Spike Train–to–Spike Train Transformations in Multilayer Feedforward Neuronal Networks

DOI10.1162/NECO_a_00829zbMath1414.92005arXiv1412.4210OpenAlexW2293092608WikidataQ50529635 ScholiaQ50529635MaRDI QIDQ5380422

Arunava Banerjee

Publication date: 4 June 2019

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

Full work available at URL: https://arxiv.org/abs/1412.4210

zbMATH Keywords

learningmultilayer feedforward neuronal networksspike train-to-spike train transformations

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20)

Related Items (2)

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks ⋮ On the Algorithmic Power of Spiking Neural Networks

Cites Work

• A gradient descent rule for spiking neurons emitting multiple spikes
• Connectomic constraints on computation in feedforward networks of spiking neurons
• Error-backpropagation in temporally encoded networks of spiking neurons
• On the sensitive dependence on initial conditions of the dynamics of networks of spiking neurons
• A Novel Spike Distance
• Spiking Neuron Models
• On the Phase-Space Dynamics of Systems of Spiking Neurons. I: Model and Experiments
• Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting
• Supervised Learning in Multilayer Spiking Neural Networks
• Learning representations by back-propagating errors

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