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Authors: Emanuela Merelli 1 and Marco Piangerelli 2

Affiliations: 1 University of Camerino, Italy ; 2 Università di Camerino, Italy

ISBN: 978-989-758-054-3

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 e xperiment the model over real Electrocorticographical data (EcoG) for detecting the emergence of long-term neurological disorders such as epileptic seizures. (More)

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Paper citation in several formats:
Merelli, E. and Piangerelli, M. (2014). RNN-based Model for Self-adaptive Systems - The Emergence of Epilepsy in the Human Brain.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014) ISBN 978-989-758-054-3, pages 356-361. DOI: 10.5220/0005165003560361

@conference{ncta14,
author={Emanuela Merelli. and Marco Piangerelli.},
title={RNN-based Model for Self-adaptive Systems - The Emergence of Epilepsy in the Human Brain},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)},
year={2014},
pages={356-361},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005165003560361},
isbn={978-989-758-054-3},
}

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)
TI - RNN-based Model for Self-adaptive Systems - The Emergence of Epilepsy in the Human Brain
SN - 978-989-758-054-3
AU - Merelli, E.
AU - Piangerelli, M.
PY - 2014
SP - 356
EP - 361
DO - 10.5220/0005165003560361

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