Bio-inspired systems and tools are able to bring together results from different areas of knowledge, including biology, engineering and other physical sciences, interested in studying and using models and techniques inspired from or applied to biological systems.
Published in Chapter:
Bio-Inspired Dynamical Tools for Analyzing Cognition
Manuel G. Bedia (University of Zaragoza, Spain), Juan M. Corchado (University of Salamanca, Spain), and Luis F. Castillo (National University, Colombia)
Copyright: © 2009
|Pages: 6
DOI: 10.4018/978-1-59904-849-9.ch040
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.