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Author: Simon Odense

Affiliation: University of Victoria, Canada

Keyword(s): Computer Vision, Deep Neural Network, RTRBM.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Image Processing and Artificial Vision Applications ; Intelligent Artificial Perception and Neural Sensors ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: Here the potential use of artificial neural networks for the purpose of understanding the biological processes behind perception is investigated. Current work in computer vision is surveyed focusing on methods to determine how a neural network utilizes it's resources. Analogies between feature detectors in deep neural networks and signaling pathways in the human brain are made. With these analogies in mind, procedures are outlined for experiments on perception using the recurrent temporal restricted Boltzmann machine as an example. The potential use of these experiments to help explain disorders of human perception is then described.

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Paper citation in several formats:
Odense, S. (2015). Artificial Neural Networks for In-silico Experiments on Perception. In Proceedings of the 7th International Joint Conference on Computational Intelligence - NCTA, (ECTA 2015) ISBN 978-989-758-157-1, pages 163-167. DOI: 10.5220/0005633701630167

@conference{ncta15,
author={Simon Odense.},
title={Artificial Neural Networks for In-silico Experiments on Perception},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - NCTA, (ECTA 2015)},
year={2015},
pages={163-167},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005633701630167},
isbn={978-989-758-157-1},
}

TY - CONF

JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - NCTA, (ECTA 2015)
TI - Artificial Neural Networks for In-silico Experiments on Perception
SN - 978-989-758-157-1
AU - Odense, S.
PY - 2015
SP - 163
EP - 167
DO - 10.5220/0005633701630167