Artículo

La versión final de este artículo es de uso interno de la institución.
Consulte el artículo en la página del editor
Consulte la política de Acceso Abierto del editor

Abstract:

A generalization of the Little-Hopfield neural network model for associative memories is presented that considers the case of a continuum of processing units. The state space corresponds to an infinite dimensional euclidean space. A dynamics is proposed that minimizes an energy functional that is a natural extension of the discrete case. The case in which the synaptic weight operator is defined through the autocorrelation rule (Hebb rule) with orthogonal memories is analyzed. We also consider the case of memories that are not orthogonal. Finally, we discuss the generalization of the non deterministic, finite temperature dynamics.

Registro:

Documento: Artículo
Título:Associative memories in infinite dimensional spaces
Autor:Segura, E.C.; Perazzo, R.P.J.
Filiación:Departamento de Computacion, Universidad de Buenos Aires, Pabellon I Ciudad Universitaria, Buenos Aires, 1428, Argentina
Centro de Estudios Avanzados, Universidad de Buenos Aires, Argentina
Palabras clave:Associative storage; Correlation methods; State space methods; Autocorrelation; Glauber dynamics; Hopfield models; Infinite dimensional euclidean space; Neural networks
Año:2000
Volumen:12
Número:2
Página de inicio:129
Página de fin:144
DOI: http://dx.doi.org/10.1023/A:1009689025427
Título revista:Neural Processing Letters
Título revista abreviado:Neural Process Letters
ISSN:13704621
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_13704621_v12_n2_p129_Segura

Referencias:

  • Little, W.A., The existence of persistent states in the brain (1974) Math. Biosci., 19, pp. 101-120
  • Little, W.A., Analytic study of the memory storage capacity of a neural network (1978) Math. Biosci., 39, pp. 281-290
  • Hebb, D.O., (1949) The Organization of Behavior: A Neuropsychological Theory, , New York, Wiley
  • Amit, D.J., (1989) Modeling Brain Function, , Cambridge, Cambridge University Press
  • Hopfield, J.J., Neural networks and physical systems with emergent collective computational abilities (1982) Proc. Natl. Acad. Sci., 79, pp. 2554-2558
  • Hopfield, J.J., Neurons with graded response have collective computational properties like those of two-state neurons (1984) Proc. Natl. Acad. Sci., 81, pp. 3088-3092
  • Feynman, R.P., Hibbs, A.R., (1965) Quantum Mechanics and Path Integrals, , New York, McGraw-Hill
  • Fodor, J.A., (1983) The Modularity of Mind, , Cambridge, MIT Press
  • Van Kampen, N.G., (1997) Stochastic Processes in Physics and Chemistry, , Amsterdam, Elsevier
  • Hinton, G.E., Sejnowsky, T.J., Optimal perceptual inference (1983) Proc. IEEE Conf. Computer Vision and Pattern Recognition (Washington, 1983), pp. 448-453. , New York, IEEE
  • Peretto, P., Collective properties of neural networks: A statistical physics approach Biological Cybernetics, 50, pp. 51-62
  • Glauber, R.J., Time dependent statistics of the Ising model (1963) J. of Math. Phys., 4, pp. 294-307

Citas:

---------- APA ----------
Segura, E.C. & Perazzo, R.P.J. (2000) . Associative memories in infinite dimensional spaces. Neural Processing Letters, 12(2), 129-144.
http://dx.doi.org/10.1023/A:1009689025427
---------- CHICAGO ----------
Segura, E.C., Perazzo, R.P.J. "Associative memories in infinite dimensional spaces" . Neural Processing Letters 12, no. 2 (2000) : 129-144.
http://dx.doi.org/10.1023/A:1009689025427
---------- MLA ----------
Segura, E.C., Perazzo, R.P.J. "Associative memories in infinite dimensional spaces" . Neural Processing Letters, vol. 12, no. 2, 2000, pp. 129-144.
http://dx.doi.org/10.1023/A:1009689025427
---------- VANCOUVER ----------
Segura, E.C., Perazzo, R.P.J. Associative memories in infinite dimensional spaces. Neural Process Letters. 2000;12(2):129-144.
http://dx.doi.org/10.1023/A:1009689025427