title: Concatenated retrieval of correlated stored information in neural networks.
abstract: We consider a coupled map lattice defined on a hypercube in M dimensions, taken here as the information space, to model memory retrieval and information association by a neural network. We assume that both neuronal activity and spike timing may carry information. In this model the state of the network at a given time t is completely determined by the intensity y(sigma,t) with which the information pattern represented by the integer sigma is being expressed by the network. Logistic maps, coupled in the information space, are used to describe the evolution of the intensity function y(sigma(upper arrow),t) with the intent to model memory retrieval in neural systems. We calculate the phase diagram of the system regarding the model ability to work as an associative memory. We show that this model is capable of retrieving simultaneously a correlated set of memories, after a relatively long transient that may be associated to the retrieving of concatenated memorized patterns that lead to a final attractor.
authors.affiliation: Instituto de Física, Universidade Federal do Rio Grande do Sul, Caixa Postal 15051, 91501-970 Porto Alegre, RS, Brazil. rita@if.ufrgs.br
authors.forename: R M C
authors.initials: RM
authors.lastname: de Almeida
authors.forename: A
authors.initials: A
authors.lastname: Espinosa
authors.forename: M A P
authors.initials: MA
authors.lastname: Idiart
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D003198:Computer Simulation; D016247:Information Storage and Retrieval / Q000379:methods*; D016015:Logistic Models; D017774:Long-Term Potentiation / Q000502:physiology*; D008568:Memory / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D013223:Statistics as Topic