Journal
NEURAL COMPUTATION
Volume 28, Issue 11, Pages 2461-2473Publisher
MIT PRESS
DOI: 10.1162/NECO_a_00884
Keywords
-
Funding
- Bill and Melinda Gates Foundation [OPP1110049]
- Bill and Melinda Gates Foundation [OPP1110049] Funding Source: Bill and Melinda Gates Foundation
Ask authors/readers for more resources
Error backpropagation in networks of spiking neurons (SpikeProp) shows promise for the supervised learning of temporal patterns. However, its widespread use is hindered by its computational load and occasional convergence failures. In this letter, we show that the neuronal firing time equation at the core of SpikeProp can be solved analytically using the Lambert W function, offering a marked reduction in execution time over the step-based method used in the literature. Applying this analytical method to SpikeProp, we find that training time per epoch can be reduced by 12% to 56% under different experimental conditions. Finally, this work opens the way for further investigations of SpikeProp's convergence behavior.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available