Elman Backpropagation as Reinforcement for Simple Recurrent Networks

DOI10.1162/neco.2007.19.11.3108zbMath1143.68539OpenAlexW2061043032WikidataQ51905507 ScholiaQ51905507MaRDI QIDQ5441309

André Grüning

Publication date: 11 February 2008

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

Full work available at URL: http://epubs.surrey.ac.uk/22857/23/neco.2007.19.11.3108.pdf

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

Supervised Learning in Multilayer Spiking Neural Networks ⋮ Learning Spatiotemporally Encoded Pattern Transformations in Structured Spiking Neural Networks

Cites Work

• Dynamical recognizers: real-time language recognition by analog computers
• The calculi of emergence: Computation, dynamics and induction
• Simple Recurrent Networks Learn Context-Free and Context-Sensitive Languages by Counting
• Stack-like and queue-like dynamics in recurrent neural networks
• Synaptic noise as a means of implementing weight-perturbation learning
• Recurrent Neural Networks with Small Weights Implement Definite Memory Machines
• The Crystallizing Substochastic Sequential Machine Extractor: CrySSMEx
• Attention-Gated Reinforcement Learning of Internal Representations for Classification
• Predicting the future of discrete sequences from fractal representations of the past

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