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A new biologically plausible supervised learning method for spiking neurons

Taherkhani, A, Belatreche, A, Li, Y and Maguire, L 2014, A new biologically plausible supervised learning method for spiking neurons , in: 22st European Symposium on Artificial Neural Networks (ESANN) Computational Intelligence And Machine Learning, 23-25 April 2014, Bruges, Belgium.

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Abstract

STDP is believed to play an important role in learning and memory. Additionally, experimental evidence shows that a few strong neural inputs can drive a neuron response and subsequently affect the learning of other inputs. Furthermore, recent studies have shown that local dendritic depolarization can impact STDP induction. This paper integrates these three biological concepts to devise a new biologically plausible supervised learning method for spiking neurons. Experimental results show that the proposed method can effectively map a random spatiotemporal input pattern to a random target output spike train with a much faster learning speed than ReSuMe.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: European Symposium on Artificial Neural Networks (ESANN) 2014 Proceedings : Computational Intelligence and Machine Learning
Publisher: i6doc
Refereed: Yes
Related URLs:
Funders: Non funded research
Depositing User: Yuhua Li
Date Deposited: 19 Jun 2015 18:26
Last Modified: 05 Apr 2016 18:18
URI: http://usir.salford.ac.uk/id/eprint/33104

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