Supervised Learning in SNN via Reward-Modulated Spike-Timing-Dependent Plasticity for a Target Reaching Vehicle

Supervised Learning in SNN via Reward-Modulated Spike-Timing-Dependent Plasticity for a Target Reaching Vehicle

Spiking neural networks (SNNs) offer many advantages over traditional artificial neural networks (ANNs) such as biological plausibility, fast information processing, and energy efficiency. Although SNNs have been used to solve a variety of control tasks using the Spike-Timing-Dependent Plasticity (S...

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Bibliographic Details
Main Authors: Zhenshan Bing, Ivan Baumann, Zhuangyi Jiang, Kai Huang, Caixia Cai, Alois Knoll
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-05-01
Series:Frontiers in Neurorobotics
Subjects:
spiking neural network
R-STDP
supervised learning
end-to-end control
autonomous locomotion
Online Access:https://www.frontiersin.org/article/10.3389/fnbot.2019.00018/full

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