Authors:
Emanuela Merelli
1
and
Marco Piangerelli
2
Affiliations:
1
University of Camerino, Italy
;
2
Università di Camerino, Italy
Keyword(s):
LSTM-RNNs, Brain functional activities, epilepsy, complex systems, S[B] Paradigm.
Related
Ontology
Subjects/Areas/Topics:
Adaptive Architectures and Mechanisms
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Complex Artificial Neural Network Based Systems and Dynamics
;
Computational Intelligence
;
Computational Neuroscience
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
The human brain is the self-adaptive system par excellence. We claim that a hierarchical model for self-adaptive
system can be built on two levels, the upper structural level S and the lower behavioral level B.
The higher order structure naturally emerges from interactions of the system with its environment and it acts
as coordinator of local interactions among simple reactive elements. The lower level regards the topology
of the network whose elements self-organize to perform the behavior of the system. The adaptivity feature
follows the self-organizing principle that supports the entanglement of lower level elements and the higher
order structure. The challenging idea in this position paper is to represent the two-level model as a second
order Long Short-Term Memory Recurrent Neural Network, a bio-inspired class of artificial neural networks,
very powerful for dealing with the dynamics of complex systems and for studying the emergence of brain
activities. It is our aim to
experiment the model over real Electrocorticographical data (EcoG) for detecting
the emergence of long-term neurological disorders such as epileptic seizures.
(More)