NEURAL PROCESSING OF LONG LASTING SEQUENCES OF TEMPORAL CODES - Model of Artificial Neural Network based on a Spike Timing-dependant Learning Rule

Dalius Krunglevicius

Abstract

It has been demonstrated, that spike-timing-dependent plasticity (STDP) learning rule can be applied to train neuron to become selective to a spatiotemporal spike pattern. In this paper, we propose a model of neural network that is capable of memorizing prolonged sequences of different spike patterns and learn aggregated data in a larger temporal window.

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Paper Citation


in Harvard Style

Krunglevicius D. (2011). NEURAL PROCESSING OF LONG LASTING SEQUENCES OF TEMPORAL CODES - Model of Artificial Neural Network based on a Spike Timing-dependant Learning Rule . In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 196-204. DOI: 10.5220/0003681401960204


in Bibtex Style

@conference{ncta11,
author={Dalius Krunglevicius},
title={NEURAL PROCESSING OF LONG LASTING SEQUENCES OF TEMPORAL CODES - Model of Artificial Neural Network based on a Spike Timing-dependant Learning Rule},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},
year={2011},
pages={196-204},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003681401960204},
isbn={978-989-8425-84-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)
TI - NEURAL PROCESSING OF LONG LASTING SEQUENCES OF TEMPORAL CODES - Model of Artificial Neural Network based on a Spike Timing-dependant Learning Rule
SN - 978-989-8425-84-3
AU - Krunglevicius D.
PY - 2011
SP - 196
EP - 204
DO - 10.5220/0003681401960204