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Authors: Alicia Costalago Meruelo 1 ; David M. Simpson 1 ; S. Veres 2 and Philip L. Newland 1

Affiliations: 1 University of Southampton, United Kingdom ; 2 University of Sheffield, United Kingdom

ISBN: 978-989-758-054-3

Keyword(s): Reflex, Artificial Neural Network, ANNs, Time Delay Neural Network, Metaheuristic Algorithm, Evolutionary Programming, Particle Swarm Optimisation, Chordotonal Organ, Locust.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computational Neuroscience ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neuroinformatics and Bioinformatics ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: In many animals intersegmental reflexes are important for postural control and movement making them ideal candidates for the bio-inspired design of medical treatment for neuromuscular injuries in cases such as drop foot and possibly in robot design. In this paper we study an intersegmental reflex of the foot (tarsus) of the locust hind leg, which is a reflex that raises the tarsus when the tibia is flexed and depresses it when the tibia is extended. A novel method is described to quantify the intersegmental responses in which an Artificial Neural Network, the Time Delay Neural Network, is applied. The architecture of the network is optimised through a metaheuristic algorithm to produce accurate predictions with short computational time and complexity and high generalisation to different individual responses. The results show that ANNs provide accurate predictions when trained with an average reflex response to Gaussian White Noise stimulation compared to autoregressive models. Further more, the network model can calculate the individual responses from each of the animals and responses to another input such as a sinusoid. A detailed understanding of such a reflex response could be included in the design of orthoses or functional electrical stimulation treatments to improve walking in patients with neuromuscular disorders. (More)

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Paper citation in several formats:
Costalago Meruelo A., M. Simpson D., Veres S. and L. Newland P. (2014). Artificial Neural Network Models of Intersegmental Reflexes.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014) ISBN 978-989-758-054-3, pages 24-31. DOI: 10.5220/0005029000240031

@conference{ncta14,
author={Alicia Costalago Meruelo and David M. Simpson and S. Veres and Philip L. Newland},
title={Artificial Neural Network Models of Intersegmental Reflexes},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)},
year={2014},
pages={24-31},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005029000240031},
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 - Artificial Neural Network Models of Intersegmental Reflexes
SN - 978-989-758-054-3
AU - Costalago Meruelo A.
AU - M. Simpson D.
AU - Veres S.
AU - L. Newland P.
PY - 2014
SP - 24
EP - 31
DO - 10.5220/0005029000240031

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