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Author: Dalius Krunglevicius

Affiliation: Vilnius University, Lithuania

ISBN: 978-989-758-054-3

Keyword(s): Artificial Neural Networks, Spike-Timing-Dependent Plasticity, STDP, Hebbian Learning, Unsupervised Learning, Temporal Coding, Neuroscience.

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 Based Data Mining and Complex Information Processing ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Supervised and Unsupervised Learning ; Theory and Methods

Abstract: Spike-timing-dependent plasticity (STDP) is a set of Hebbian learning rules which are firmly based on biological evidence. It has been demonstrated that one of the STDP learning rules is suited for learning spatiotemporal patterns in a very noisy environment. Parameters of the neuron are only optimal, however, for a certain range of quantity of injected noise. This means the level of noise must be known beforehand so that the parameters can be set accordingly. That could be a real problem when noise levels vary over time. We found that the model of a leaky-integrate-and-fire inhibitory neuron with an inverted STDP learning rule is capable of adjusting its response rate to a particular level of noise. In this paper we suggest a method that uses an inverted SDTP learning rule to modulate spiking rate of the trained neuron. This method is adaptive to noise levels; subsequently spiking neuron can be trained to learn the same spatiotemporal pattern with a wide range of background noise inj ected during the learning process. (More)

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Paper citation in several formats:
Krunglevicius, D. (2014). STDP Learning Under Variable Noise Levels.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014) ISBN 978-989-758-054-3, pages 165-171. DOI: 10.5220/0005072401650171

@conference{ncta14,
author={Dalius Krunglevicius.},
title={STDP Learning Under Variable Noise Levels},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)},
year={2014},
pages={165-171},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005072401650171},
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 - STDP Learning Under Variable Noise Levels
SN - 978-989-758-054-3
AU - Krunglevicius, D.
PY - 2014
SP - 165
EP - 171
DO - 10.5220/0005072401650171

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