Objective
Exploiting the chaotic principles of neural activity, the project proposes to develop a biologically motivated coding scheme of perceptual information based on periodic behaviour, as opposed to the common one based on equilibrium, which exploits chaotic behaviour for representing the intrinsic variability of perceptual stimuli and chaos synchronisation for categorising them into distinct internal representations. Such an aim will be pursued by means of three research threads. I- Aims at investigating the role of the irregular oscillatory behaviour of neurons in representing imprecise knowledge. II- Aims at developing biologically inspired pattern recognition methods based on approximately periodic chaotic behaviour. III- Aims at verifying if the periodic behaviour-based stimuli coding of the neurons manifests itself also at physiological level, in particular in the first relay stations of the somatosensory system and the EEG signals. Exploiting the chaotic principles of neural activity, the project proposes to develop a biologically motivated coding scheme of perceptual information based on periodic behaviour, as opposed to the common one based on equilibria, which exploits chaotic behaviour for representing the intrinsic variability of perceptual stimuli and chaos synchronisation for categorising them into distinct internal representations. Such an aim will be pursued by means of three research threads. I- Aims at investigating the role of the irregular oscillatory behaviour of neurons in representing imprecise knowledge. II- Aims at developing biologically inspired pattern recognition methods based on approximately periodic chaotic behaviour. III- Aims at verifying if the periodic behaviour-based stimuli coding of the neurons manifests itself also at physiological level, in particular in the first relay stations of the somatosensory system and the EEG signals.
OBJECTIVES
There are three objectives.
I) To consider the approximate chaos synchronisation model to establish the role of the approximately periodic natural oscillations of neurons in coding imprecise information, to determine how this irregular behaviour is tuned to represent and recognise external stimuli, and to determine how non-periodic stimuli are periodically coded.
II) To combine the previous results with the approximate chaos synchronisation model in order to develop a biologically inspired chaos-based signal classification method.
III) To determine if the approximately periodic behaviour manifests itself either at microscopic (small neural groups of the somatosensory system) or macroscopic level (EEG signals) which would make it possible to apply the chaos-based temporal patterns recognitions method of the second objective for EEG classification purposes.
DESCRIPTION OF WORK
The APEREST work plan consists of five Work packages, three scientific, one for the management, and one for the dissemination of the results. The first scientific Workpackage is devoted to: the analysis of available experimental data in order to determine the level of applicability of the approximate chaos synchronisation theory on stimuli representation at microscopic level; to the experimental verification of the previous conclusions and to the generation of experimental data for the second Work package and for determining the effects of breaking of the periodicity and/or synchronisation of the neural activity. The second scientific Work package is dedicated to simplify the already available chaos-based signal classification algorithm and to remove its restriction to approximately periodic signals, both these taking (biological) inspiration from the results of the Work package 1. Nonetheless, the developed algorithms will not necessary imitate their biological counterpart but will rather be an implementation of them which are suitable for engineering applications.
The third scientific Work package is dedicated to:
understand if the chaos-based classification method developed by the Work package 2 can be applied to EEG signals;
to evaluate the chaos-based classification as a way to map cortical activity in terms of functionally interacting regions and to determine if synchronicity between cortical neuronal networks with chaotic behaviour depends on cortico-cortical connections;
to compare the results obtained with he chaos-based classification method with those obtained with EEG linear (coherence) analysis.
Fields of science
Call for proposal
Data not availableFunding Scheme
CSC - Cost-sharing contractsCoordinator
28040 MADRID
Spain