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 canimpact 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.
Original language | English |
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Title of host publication | Unknown Host Publication |
Publisher | i6doc.com publ. |
Pages | 11-16 |
Number of pages | 6 |
ISBN (Print) | ISBN 978-287419095-7 |
Publication status | Published (in print/issue) - 17 Mar 2014 |
Event | 22st European Symposium on Artificial Neural Networks, Computational Intelligence And Machine Learning - Bruges, Belgium Duration: 17 Mar 2014 → … |
Conference
Conference | 22st European Symposium on Artificial Neural Networks, Computational Intelligence And Machine Learning |
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Period | 17/03/14 → … |