A learning algorithm to teach spatiotemporal patterns to recurrent neural networks
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Publication:1825160
DOI10.1007/BF00198101zbMath0683.92006WikidataQ114961204 ScholiaQ114961204MaRDI QIDQ1825160
Publication date: 1990
Published in: Biological Cybernetics (Search for Journal in Brave)
neural networksupervised learning algorithmspatiotemporal patternsback propagation methodarbitrary feedback connections
Circuits, networks (94C99) Other natural sciences (mathematical treatment) (92F05) Physiological, cellular and medical topics (92Cxx)
Related Items (5)
Classification of temporal trajectories by continuous-time recurrent nets ⋮ An adaptive fuzzy system for modeling chaos ⋮ Stochastic sensitivity analysis method for neural network learning ⋮ POINCARÉ MAPPING OF CONTINUOUS RECURRENT NEURAL NETWORKS EXCITED BY TEMPORAL EXTERNAL INPUT ⋮ FRACTAL TRANSITION IN CONTINUOUS RECURRENT NEURAL NETWORKS
Cites Work
- Absolute stability of global pattern formation and parallel memory storage by competitive neural networks
- Neural networks and physical systems with emergent collective computational abilities.
- Neurons with graded response have collective computational properties like those of two-state neurons.
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