FRACTAL TRANSITION IN CONTINUOUS RECURRENT NEURAL NETWORKS

DOI10.1142/S0218127401002158zbMath1090.37569OpenAlexW1997029830WikidataQ127516636 ScholiaQ127516636MaRDI QIDQ5474165

Shozo Sato, Kazutoshi Gohara

Publication date: 23 June 2006

Published in: International Journal of Bifurcation and Chaos (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s0218127401002158

Mathematics Subject Classification ID

Dynamical systems in biology (37N25) Neural networks for/in biological studies, artificial life and related topics (92B20) Notions of recurrence and recurrent behavior in topological dynamical systems (37B20)

Related Items (3)

AUTOMATA ON FRACTAL SETS OBSERVED IN HYBRID DYNAMICAL SYSTEMS ⋮ APPROXIMATION OF THE FRACTAL TRANSITION USING ATTRACTORS EXCITED BY PERIODIC INPUTS ⋮ Abstract similarity, fractals and chaos

Cites Work

• Unnamed Item
• A learning algorithm to teach spatiotemporal patterns to recurrent neural networks
• DYNAMICAL SYSTEMS EXCITED BY TEMPORAL INPUTS: FRACTAL TRANSITION BETWEEN EXCITED ATTRACTORS
• Neural networks and physical systems with emergent collective computational abilities.
• Neurons with graded response have collective computational properties like those of two-state neurons.
• POINCARÉ MAPPING OF CONTINUOUS RECURRENT NEURAL NETWORKS EXCITED BY TEMPORAL EXTERNAL INPUT

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