Seminal observations performed by Skarda and Freeman on the olfactory bulb of rabbits during cognitive tasks have suggested to locate the basal state of behavior in the network's spatio-temporal dynamics. Following these neurophysiological observations, the authors have investigated in previous papers the possibility to store external stimuli in spatio-temporal dynamical attractors of recurrent neural networks. To this aim, an efficient learning algorithm, based on a time asymmetric Hebbian mechanism, has been proposed. The underlying idea is to obtain -as much as possible- a natural i.e. unconstrained mapping between the external stimuli and the spontaneous internal dynamics of the network. The dynamical regime called "frustrated chaos" by the authors appears to play a substantial role in the establishment of this mapping. In this paper, adopting a symbolic coding of the output, new investigations are performed on the presence and the importance of spurious data. It is shown how the presence of chaos contributes to stop their proliferation.
Recurrent Neural Network, Hebbian Learning, Complex Dynamics, Chaos
Colin Molter, Utku Salihoglu, Hugues Bersini. (2006)
How to prevent spurious data in a chaotic brain.
Proceeding of the IEEE World Congress on Computational Intelligence. IEEE. In press.