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Author: Mikhail Kiselev

Affiliation: Chuvash State University, Russian Federation

ISBN: 978-989-758-054-3

Keyword(s): Spiking Neural Network, Empirical Model, Dynamic Threshold, Short Term Synaptic Depression, Decision Tree, Multiple Adaptive Regression Splines.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computational Neuroscience ; 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: Analysis of behaviour of large neuronal ensembles using mean-field equations and similar approaches was an important instrument in theory of spiking neural networks during almost all its history. However, it often implies dealing with complex systems of integro-differential equations which are very hard not only for obtaining explicit analytical solution but also for simpler tasks like stability analysis. Building empirical models on the basis of experimental data gathered in process of simulation of small size networks is considered in the paper as a practical alternative to these traditional methods. A methodology for creation and verification of such models using decision trees, multiple adaptive regression splines and other data mining algorithms is discussed. This idea is illustrated by the two examples – prediction of probability of avalanche-like excitation growth in the network and analysis of conditions necessary for development of strong firing frequency oscillations.

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Paper citation in several formats:
Kiselev, M. (2014). Empirical Models as a Basis for Synthesis of Large Spiking Neural Networks with Pre-Specified Properties.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014) ISBN 978-989-758-054-3, pages 264-269. DOI: 10.5220/0005134102640269

@conference{ncta14,
author={Mikhail Kiselev.},
title={Empirical Models as a Basis for Synthesis of Large Spiking Neural Networks with Pre-Specified Properties},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)},
year={2014},
pages={264-269},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005134102640269},
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 - Empirical Models as a Basis for Synthesis of Large Spiking Neural Networks with Pre-Specified Properties
SN - 978-989-758-054-3
AU - Kiselev, M.
PY - 2014
SP - 264
EP - 269
DO - 10.5220/0005134102640269

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