Spikemax: Spike-based Loss Methods for Classification
Author:
Affiliation:
1. Institute for Infocomm Research, Agency for Science Technology and Research (A*STAR),Singapore
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9891857/9889787/09892379.pdf?arnumber=9892379
Reference43 articles.
1. Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks
2. Training Deep Spiking Neural Networks Using Backpropagation
3. A supervised multi-spike learning algorithm based on gradient descent for spiking neural networks
4. Event Based Weight Update for Learning Infinite Spike Train
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2. Learnable axonal delay in spiking neural networks improves spoken word recognition;Frontiers in Neuroscience;2023-11-09
3. First-spike coding promotes accurate and efficient spiking neural networks for discrete events with rich temporal structures;Frontiers in Neuroscience;2023-10-02
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5. Spikemoid: Updated Spike-based Loss Methods for Classification;2023 International Joint Conference on Neural Networks (IJCNN);2023-06-18
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