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Authors: Christian W. Rempis ; Hazem Toutounji and Frank Pasemann

Affiliation: Osnabrueck University, Germany

ISBN: 978-989-8565-77-8

Keyword(s): Neuromodulation, Benchmark, Learning, Sensori-motor Loop, Neurorobotics.

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

Abstract: Learning recurrent neural networks as behavior controllers for robots requires measures to guide the learning towards a desired behavior. Organisms in nature solve this problem with feedback signals to assess their behavior and to refine their actions. In line with this, a neural framework is developed where the synaptic learning is controlled by artificial neuromodulators that are produced in response to (undesired) sensory signals. To test this framework and to get a base line to evaluate further approaches, we perform five classical benchmark experiments with a simple random plasticity method. We show that even with this simple plasticity method, behaviors can already be found for all experiments, even for comparably large networks with over 90 plastic synapses. The performance depends strongly on the complexity of the task and less on the chosen network topology. This suggests that controlling learning with neuromodulators is a viable approach that is promising to work also with m ore sophisticated plasticity methods in the future. (More)

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Paper citation in several formats:
W. Rempis C., Toutounji H. and Pasemann F. (2013). Evaluating Neuromodulator-controlled Stochastic Plasticity for Learning Recurrent Neural Control Networks.In Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013) ISBN 978-989-8565-77-8, pages 489-496. DOI: 10.5220/0004554504890496

@conference{ncta13,
author={Christian W. Rempis and Hazem Toutounji and Frank Pasemann},
title={Evaluating Neuromodulator-controlled Stochastic Plasticity for Learning Recurrent Neural Control Networks},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013)},
year={2013},
pages={489-496},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004554504890496},
isbn={978-989-8565-77-8},
}

TY - CONF

JO - Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013)
TI - Evaluating Neuromodulator-controlled Stochastic Plasticity for Learning Recurrent Neural Control Networks
SN - 978-989-8565-77-8
AU - W. Rempis C.
AU - Toutounji H.
AU - Pasemann F.
PY - 2013
SP - 489
EP - 496
DO - 10.5220/0004554504890496

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