کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10332139 | 687146 | 2005 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A gradient descent rule for spiking neurons emitting multiple spikes
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نظریه محاسباتی و ریاضیات
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چکیده انگلیسی
A supervised learning rule for Spiking Neural Networks (SNNs) is presented that can cope with neurons that spike multiple times. The rule is developed by extending the existing SpikeProp algorithm which could only be used for one spike per neuron. The problem caused by the discontinuity in the spike process is counteracted with a simple but effective rule, which makes the learning process more efficient. Our learning rule is successfully tested on a classification task of Poisson spike trains. We also applied the algorithm on a temporal version of the XOR problem and show that it is possible to learn this classical problem using only one spiking neuron making use of a hair-trigger situation.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Processing Letters - Volume 95, Issue 6, 30 September 2005, Pages 552-558
Journal: Information Processing Letters - Volume 95, Issue 6, 30 September 2005, Pages 552-558
نویسندگان
Olaf Booij, Hieu tat Nguyen,