Backpropagation Techniques in SNN and Application in Image Segmentation-Reference-Cited by-同舟云学术

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Backpropagation Techniques in SNN and Application in Image Segmentation

Published:2024-03-15 Issue: Volume:110 Page:1475-1481
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Container-title:SoutheastCon 2024
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Author:

Chakravarty Sumit¹,Tanveer M. Hassan²,Voicu Razvan C.²,Banerjee Madhushri³,Mahdi Ghulam⁴

Affiliation:

1. Kennesaw State University,Department of Electrical Engineering,Marietta,Georgia,USA

2. Kennesaw State University,Department of Robotics & Mechatronics Engineering,Marietta,Georgia,USA

3. Georgia Gwinett College,Department of IT,Atlanta,USA

4. Roswell Innovation Center,Kimberly Clark Coorporation,Atlanta,GA,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10500015/10500024/10500286.pdf?arnumber=10500286

Reference18 articles.

1. Networks of spiking neurons: The third generation of neural network models

2. Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-Based Optimization to Spiking Neural Networks

3. Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks

4. SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks

5. Direct Training for Spiking Neural Networks: Faster, Larger, Better

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