Expressive Power of Non-deterministic Evolving Recurrent Neural Networks in Terms of Their Attractor Dynamics

DOI10.1007/978-3-319-21819-9_10zbMath1465.68084OpenAlexW2237534953MaRDI QIDQ2945562

Jérémie Cabessa, Jacques Duparc

Publication date: 14 September 2015

Published in: Unconventional Computation and Natural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-21819-9_10

zbMATH Keywords

attractorsanalog computationTuring machinesrecurrent neural networksexpressive powerneural computationevolving systems

Mathematics Subject Classification ID

Formal languages and automata (68Q45) Neural networks for/in biological studies, artificial life and related topics (92B20) Gradient-like behavior; isolated (locally maximal) invariant sets; attractors, repellers for topological dynamical systems (37B35) Other nonclassical models of computation (68Q09)

Related Items (2)

Computational capabilities of analog and evolving neural networks over infinite input streams ⋮ Expressive power of first-order recurrent neural networks determined by their attractor dynamics

Cites Work

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• The expressive power of analog recurrent neural networks on infinite input streams
• Expressive power of first-order recurrent neural networks determined by their attractor dynamics
• Analog computation via neural networks
• On the computational power of neural nets
• The Computational Power of Interactive Recurrent Neural Networks
• A Hierarchical Classification of First-Order Recurrent Neural Networks
• On ω-regular sets
• A logical calculus of the ideas immanent in nervous activity

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