Topologically continuous associative memory: A theoretical foundation

We introduce a neural network with associative memory and a continuous topology, i.e. its processing units are elements of a continuous metric space and the state space is Euclidean and infinite dimensional. This approach is intended as a generalization of the previous ones due to Little and Hop-fie...

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Detalles Bibliográficos
Autor principal: Segura, E.C.
Formato: CONF
Materias:
Associative memory
Continuous topology
Dynamical systems
Hopfield model
Infinite dimensional state space
Stability
Associative memories
Attraction basin
Continuous approach
Discrete models
Energy functions
Euclidean
Hopfield models
Infinite dimensional
Metric spaces
Neural systems
New results
Plausible model
Processing units
State space
Stationary solutions
Theoretical foundations
Variational approaches
Artificial intelligence
Associative processing
Associative storage
Convergence of numerical methods
Topology
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_97809727_v_n_p112_Segura
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

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