Learning Beyond Finite Memory in Recurrent Networks of Spiking Neurons

DOI10.1162/neco.2006.18.3.591zbMath1087.92012OpenAlexW4232099935WikidataQ51950476 ScholiaQ51950476MaRDI QIDQ5469501

Peter Tiňo, Ashely J. S. Mills

Publication date: 19 May 2006

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

Full work available at URL: https://doi.org/10.1162/neco.2006.18.3.591

Mathematics Subject Classification ID

Neural biology (92C20)

Related Items (3)

Second-order information bottleneck based spiking neural networks for sEMG recognition ⋮ Improved spikeprop for using particle swarm optimization ⋮ A supervised multi-spike learning algorithm based on gradient descent for spiking neural networks

Cites Work

• Error-backpropagation in temporally encoded networks of spiking neurons
• Spiking neurons and the induction of finite state machines.
• On the computational power of neural nets
• Decoding a Temporal Population Code
• Spatial and temporal pattern analysis via spiking neurons
• Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations
• Rule Extraction from Recurrent Neural Networks: ATaxonomy and Review
• Lower Bounds for the Computational Power of Networks of Spiking Neurons

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