Computer Science > Computer Vision and Pattern Recognition
This paper has been withdrawn by Saeed Reza Kheradpisheh
[Submitted on 9 Nov 2019 (v1), last revised 11 Jan 2020 (this version, v2)]
Title:Action Recognition Using Supervised Spiking Neural Networks
No PDF available, click to view other formatsAbstract:Biological neurons use spikes to process and learn temporally dynamic inputs in an energy and computationally efficient way. However, applying the state-of-the-art gradient-based supervised algorithms to spiking neural networks (SNN) is a challenge due to the non-differentiability of the activation function of spiking neurons. Employing surrogate gradients is one of the main solutions to overcome this challenge. Although SNNs naturally work in the temporal domain, recent studies have focused on developing SNNs to solve static image categorization tasks. In this paper, we employ a surrogate gradient descent learning algorithm to recognize twelve human hand gestures recorded by dynamic vision sensor (DVS) cameras. The proposed SNN could reach 97.2% recognition accuracy on test data.
Submission history
From: Saeed Reza Kheradpisheh [view email][v1] Sat, 9 Nov 2019 07:16:10 UTC (513 KB)
[v2] Sat, 11 Jan 2020 11:07:11 UTC (1 KB) (withdrawn)
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