Empirical Models as a Basis for Synthesis of Large Spiking Neural Networks with Pre-Specified Properties

Mikhail Kiselev

2014

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 Harvard Style

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


in Bibtex Style

@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},
}


in EndNote Style

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