Paper Info
Title
The winnerless competition paradigm in cellular nonlinear networks: Models and applications
Abstract
Starting from the biological background on the olfactory architecture of both insects and mammalians, different nonlinear systems able to respond to spatial-distributed external stimuli with spatial–temporal dynamics have been investigated in the last decade. Among these, there is a class of neural networks that produces quasi-periodic trajectories that pass near heteroclinic contours and prove to be global attractors for the system. For this reason, these networks are called winnerless competition (WLC) networks. The sequence of saddle points crossed by each trajectory depends on the spatial input presented to the network and can be used as a ‘code’ representing a specific class of stimuli. Thanks to the intrinsic discrimination, WLC networks are often used for classification. In this paper, this capability is exploited within a framework for action-oriented perception. WLC networks are here used as bio-inspired architectures for the association between stimuli and ‘percepts’. After presenting the theoretical basis of the WLC network in the classic Lotka–Volterra system, we investigate how WLC networks can be formalized in terms of cellular nonlinear networks (CNNs) hosting different kinds of cells: the FitzHugh–Nagumo neuron, the Izhikevich neuron and the single layer CNN standard cell. In order to find efficient ways to code environmental stimuli for action generation, we analyze and compare these WLC-based CNNs in terms of number of generated classes and robustness against the initial conditions. Based on the simulation results, we apply the best-performing system to solve a perceptual task involving navigation and obstacle avoidance. We demonstrate how the large memory capacity shown by the WLC–CNN is able to contribute to the new perceptual framework for autonomous artificial agents, where the association between stimuli and sequences is learned through the experience. Copyright © 2008 John Wiley & Sons, Ltd.
Year
DOI
Venue
2009
10.1002/cta.v37:4
I. J. Circuit Theory and Applications
Keywords
Field
DocType
different nonlinear system,izhikevich neuron,cnn standard cell,winnerless competition paradigm,cellular nonlinear network,best-performing system,different kind,wlc network,nagumo neuron,volterra system,wlc-based cnns,perception,network model,classification
Obstacle avoidance,Attractor,Nonlinear system,Control theory,Computer science,Robustness (computer science),Artificial intelligence,Standard cell,Artificial neural network,Perception,Trajectory
Journal
Volume
Issue
ISSN
37
4
0098-9886
Citations 
PageRank 
References 
5
0.58
11
Authors
5
Name
Order
Citations
PageRank
Paolo Arena126147.43
Luigi Fortuna2761128.37
Davide Lombardo3172.16
Luca Patané410417.31
Manuel G. Velarde57214.65