title: On building a memory evolutive system for application to learning and cognition modeling.
abstract: We address here aspects of the implementation of a memory evolutive system (MES), based on the model proposed by A. Ehresmann and J. Vanbremeersch (2007), by means of a simulated network of spiking neurons with time dependent plasticity. We point out the advantages and challenges of applying category theory for the representation of cognition, by using the MES architecture. Then we discuss the issues concerning the minimum requirements that an artificial neural network (ANN) should fulfill in order that it would be capable of expressing the categories and mappings between them, underlying the MES. We conclude that a pulsed ANN based on Izhikevich's formal neuron with STDP (spike time-dependent plasticity) has sufficient dynamical properties to achieve these requirements, provided it can cope with the topological requirements. Finally, we present some perspectives of future research concerning the proposed ANN topology.
authors.affiliation: University of Sao Paulo, Escola Politecnica, Sao Paulo, SP, 05586-090, Brazil.
authors.forename: Julio
authors.initials: J
authors.lastname: de Lima do Rego Monteiro
authors.forename: Joao Eduardo
authors.initials: JE
authors.lastname: Kogler
authors.forename: Joao Henrique Ranhel
authors.initials: JH
authors.lastname: Ribeiro
authors.forename: Marcio Lobo
authors.initials: ML
authors.lastname: Netto
mesh_terms: D000818:Animals; D005075:Biological Evolution; D003071:Cognition / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D007858:Learning / Q000502:physiology*; D008568:Memory / Q000502:physiology*; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000502:physiology; D009473:Neuronal Plasticity / Q000502:physiology; D009474:Neurons / Q000502:physiology; D017711:Nonlinear Dynamics; D013569:Synapses / Q000502:physiology