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Bio-Inspired Dynamical Tools for Analyzing Cognition

Bio-Inspired Dynamical Tools for Analyzing Cognition

Manuel G. Bedia, Juan M. Corchado, Luis F. Castillo
Copyright: © 2009 |Pages: 6
ISBN13: 9781599048499|ISBN10: 1599048493|EISBN13: 9781599048505
DOI: 10.4018/978-1-59904-849-9.ch040
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MLA

Bedia, Manuel G., et al. "Bio-Inspired Dynamical Tools for Analyzing Cognition." Encyclopedia of Artificial Intelligence, edited by Juan Ramón Rabuñal Dopico, et al., IGI Global, 2009, pp. 256-261. https://doi.org/10.4018/978-1-59904-849-9.ch040

APA

Bedia, M. G., Corchado, J. M., & Castillo, L. F. (2009). Bio-Inspired Dynamical Tools for Analyzing Cognition. In J. Rabuñal Dopico, J. Dorado, & A. Pazos (Eds.), Encyclopedia of Artificial Intelligence (pp. 256-261). IGI Global. https://doi.org/10.4018/978-1-59904-849-9.ch040

Chicago

Bedia, Manuel G., Juan M. Corchado, and Luis F. Castillo. "Bio-Inspired Dynamical Tools for Analyzing Cognition." In Encyclopedia of Artificial Intelligence, edited by Juan Ramón Rabuñal Dopico, Julian Dorado, and Alejandro Pazos, 256-261. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-59904-849-9.ch040

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Abstract

The knowledge about higher brain centres in insects and how they affect the insect’s behaviour has increased significantly in recent years by theoretical and experimental investigations. Nowadays, a large body of evidence suggests that higher brain centres of insects are important for learning, short-term, longterm memory and play an important role for context generalisation (Bazhenof et al., 2001). Related to these subjects, one of the most interesting goals to achieve would be to understand the relationship between sequential memory encoding processes and the higher brain centres in insects in order to develop a general “insect-brain” control architecture to be implemented on simple robots. In this contribution, it is showed a review of the most important and recent results related to spatio-temporal coding and it is suggested the possibility to use continuous recurrent neural networks (CRNNs) (that can be used to model non-linear systems, in particular Lotka-Volterra systems) in order to find out a way to model simple cognitive systems from an abstract viewpoint. After showing the typical and interesting behaviors that emerge in appropriate Lotka- Volterra systems (in particular, winnerless competition processes) next sections deal with a brief discussion about the intelligent systems inspired in studies coming from the biology.

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