InterJournal Complex Systems, 1802
Status: Accepted
Manuscript Number: [1802]
Submission Date: 2006
A spatiotemporal coupled Lorenz model drives emergent cognitive process
Author(s): Tetsuji Emura

Subject(s): CX.41



It proposes a new Lorenz model with an excitatory-excitatory connection matrix (EEC model) or an excitatory-inhibitory connection matrix (EIC model) which consists of the three temporal coupling coefficients c{1,2,3} and three spatial coupling coefficients d{1,2,3}. This spatiotemporal coupled Lorenz (STCL) model is a network-based model which considered that three nonlinear oscillators are three neurons that is mutually coupled by Lorenz manifold. In the proposed model, there appear self-organized phase transition phenomena. In EIC model in the domain of certain c{1,2,3}, when only the value of d{1,2,3} is changed, phase transition phenomena appear like: chaos -> limit cycle -> intermittent chaos -> fixed point. It introduces an abstract coincidence detector (ACD) model to evaluate the spatial synchronization of neurons, and introduces the Hopfield models to decide the three spatial coupling coefficients d{1,2,3} which govern emergent abilities. It shows that boundary regions of each phase of the self-organized phase transition phenomena which appear in the proposed model have information processing ability, and manifests that a proposed model is useful to an architecture for subsystem on the emergent systems. Proposed emergent system which is the n subsystems (n STCL models plus n ACD models) are connected to the external input of the n neurons mutually connected neural network through the connecting weight with an external stimulus. There are two types in this model. One of these is a digital-digital network model (a DDN model) that is mounted to the digital subsystems, another model is an analog-digital network model (an ADN model) that is mounted to the analog subsystems. Both these types are auto-correlation type associative memory models. Even though having no learning synapse weight systems in these models, the models show several autonomous dynamics of retrieving embedded patterns by exciting an external stimulus from the subsystems. At the last, it introduces the methods of controlling of the proposed models that is called "self-reference model". Ref.: Emura, T., PLA, 349, 306-313 (2006)

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