Dynamics and architecture for neural computation
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Publication:1106025
DOI10.1016/0885-064X(88)90021-0zbMath0649.68082MaRDI QIDQ1106025
Publication date: 1988
Published in: Journal of Complexity (Search for Journal in Brave)
attractorspattern recognitionneural computationnonlinear dynamical systemBackpropagationmodular learning
Related Items (12)
A neural-network system for control of eye movements: Basic mechanisms ⋮ Identification of nonlinear dynamics using a general-spatio-temporal network ⋮ Dual non-autonomous deep convolutional neural network for image denoising ⋮ Application of adjoint operators to neural learning ⋮ Generalization in Interactive Networks: The Benefits of Inhibitory Competition and Hebbian Learning ⋮ Training spatially homogeneous fully recurrent neural networks in eigenvalue space. ⋮ Forward sensitivity analysis for contracting stochastic systems ⋮ A study of network dynamics ⋮ A persistent adjoint method with dynamic time-scaling and an application to mass action kinetics ⋮ Notions of associative memory and sparse coding ⋮ Adjoint-operators and non-adiabatic learning algorithms in neural networks ⋮ Qualitative analysis and decentralized controller synthesis for a class of large-scale systems with symmetrically interconnected subsystems
Cites Work
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- Self-organization and associative memory
- Identification of nonlinear dynamics using a general-spatio-temporal network
- Conformal transformation of kernel functions: A data-dependent way to improve support vector machine classifiers
- Absolute stability of global pattern formation and parallel memory storage by competitive neural networks
- Basic Concepts
- Neural networks and physical systems with emergent collective computational abilities.
- Neurons with graded response have collective computational properties like those of two-state neurons.
- Characteristics of Random Nets of Analog Neuron-Like Elements
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