In this paper, we perform weight convergence analysis to understand the proper step size during SpikeProp learning and hence avoid surges during the training ...
The results show that using adaptive learning rate significantly improves the weight convergence and speeds up learning as well.
In this paper, we perform weight convergence analysis to understand the proper step size during SpikeProp learning and hence avoid surges during the training ...
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Using the results of weight convergence analysis, an optimum adaptive learning rate is proposed in each iteration which will yield suitable step size within ...
Using the results of weight convergence analysis, we propose an optimum adaptive learning rate in each iteration which will yield suitable step size within the ...
The results show that using adaptive learning rate significantly improves the weight convergence and speeds up learning as well.
A Spiking Neural Network (SNN) training using SpikeProp and its variants is usually affected by sudden rise in learning cost called surges.
Entity Metadata (schema) ; release_year, 2015 ; subtitle ; title, Adaptive learning rate of SpikeProp based on weight convergence analysis ; version ; volume, 63.
Jan 23, 2024 · In this paper, we proposed a new supervised learning algorithm for multiple-layer spiking neural networks based on the typical SpikeProp method.
Wright Shrestha, S.B. and song, Q. (2015) Adaptive Learning Rate of SpikeProp Based on Weight Convergence Analysis. Neural Networks, 63, 185-198.