A Model for Hierarchical Associative Memories via Dynamically Coupled GBSB Neural Networks
Many approaches have emerged in the attempt to explain the memory process. One of which is the Theory of Neuronal Group Selection (TNGS), proposed by Edelman . In the present work, inspired by Edelman ideas, we design and implement a new hierarchically coupled dynamical system consisting of GBSB neural networks. Our results show that, for a wide range of the system parameters, even when the networks are weakly coupled, the system evolve towards an emergent global associative memory resulting from the correlation of the lowest level memories.
KeywordsHierarchical memories Coupled neural networks Dynamical systems Artificial neural networks TNGS
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- 1.Edelman, G.M.: Neural darwinism: The theory of neuronal group selection. Basic Books, New York (1987)Google Scholar
- 2.Clancey, W.J.: Situated cognition: on human knowledge and computer representations. In: Learning in doing. Cambridge University Press, Cambridge (1997)Google Scholar
- 5.Anderson, J.A., Silverstein, J.W., Ritz, S.A., Jones, R.S.: 22. In: Distinctive features, categorical perception, probability learning: some applications of a neural model, pp. 283–325. MIT Press, Cambridge (1985)Google Scholar
- 7.Golden, R.M.: The brain-state-in-a-box neural model is a gradient descent algorithm. Journal of Mathematical Psychology 30 (1986)Google Scholar
- 9.Gomes, R.M., Braga, A.P., Borges, H.E.: Energy analysis of hierarchically coupled generalized-brain-state-in-box GBSB neural network. In: Proceeding of V Encontro Nacional de Inteligência Artificial - ENIA 2005, São Leopoldo, Brazil (2005) (to be published)Google Scholar