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Authors: Frédéric Alexandre ; Maxime Carrere and Randa Kassab

Affiliation: Inria Bordeaux Sud-Ouest, LaBRI, Université de Bordeaux and CNRS, France

ISBN: 978-989-758-054-3

Keyword(s): Information Representation, Computational Neuroscience, Pavlovian Conditioning.

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

Abstract: Artificial Neural Networks are very efficient adaptive models but one of their recognized weaknesses is about information representation, often carried out in an input vector without a structure. Beyond the classical elaboration of a hierarchical representation in a series of layers, we report here inspiration from neuroscience and argue for the design of heterogenous neural networks, processing information at feature, configuration and history levels of granularity, and interacting very efficiently for high-level and complex decision making. This framework is built from known characteristics of the sensory cortex, the hippocampus and the prefrontal cortex and is exemplified here in the case of pavlovian conditioning, but we propose that it can be advantageously applied in a wider extent, to design flexible and versatile information processing with neuronal computation.

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Paper citation in several formats:
Alexandre, F.; Carrere, M. and Kassab, R. (2014). Feature, Configuration, History - A Bio-inspired Framework for Information Representation in Neural Networks.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014) ISBN 978-989-758-054-3, pages 316-321. DOI: 10.5220/0005156003160321

@conference{ncta14,
author={Frédéric Alexandre. and Maxime Carrere. and Randa Kassab.},
title={Feature, Configuration, History - A Bio-inspired Framework for Information Representation in Neural Networks},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)},
year={2014},
pages={316-321},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005156003160321},
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 - Feature, Configuration, History - A Bio-inspired Framework for Information Representation in Neural Networks
SN - 978-989-758-054-3
AU - Alexandre, F.
AU - Carrere, M.
AU - Kassab, R.
PY - 2014
SP - 316
EP - 321
DO - 10.5220/0005156003160321

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