Bio-Inspired Dynamical Tools for Analyzing Cognition

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
OnDemand PDF Download:


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.
Chapter Preview

Spatio-Temporal Neural Coding Generator

The ability to process sequential information has long been seen as one of the most important functions of “intelligent” systems (Huerta et al., 2004). As it will be shown afterwards, winnerless competition principle appears as a major type of mechanism of sequential memory processing. The underlying concept is that sequential memory can be encoded in a (multidimensional) dynamical system by means of heteroclinic trajectories connecting several saddle points. Each of the saddle points is assumed to be remembered for further action (Afraimovich et al., 2004).

Key Terms in this Chapter

Computational System: Computation is a general term for any type of information processing that can be represented mathematically. This includes phenomena ranging from simple calculations to human thinking. A device able to make computations is called computational system.

Heteroclinic Orbits: In the phase portrait of a dynamical system, a heteroclinic orbit (sometimes called a heteroclinic connection) is a path in phase space which joins two different equilibrium points. If the equilibrium points at the start and end of the orbit are the same, the orbit is a homoclinic orbit.

Bio-Inspired Techniques: 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.

Adaptive Behaviour: Type of behavior that allows an individual to substitute a disruptive behavior to something more constructive and able to adapt to a given situation.

Stability-Plasticity Dilemma: It explores how a learning system remains adaptive (plastic) in response to significant input, yet remains stable in response to irrelevant input.

Winnerless Competition Process: Dynamical process whose main point is the transformation of the incoming identity or spatial inputs into identity-temporal output based on the intrinsic switching dynamics of the neural system.

Dynamical Recurrent Networks: Complex nonlinear dynamic system described by a set of nonlinear differential or difference equations with extensive connection weights.

Complete Chapter List

Search this Book: