Enhancing temporal learning in recurrent spiking networks for neuromorphic applications
Training Recurrent Spiking Neural Networks (RSNNs) with binary spikes for tasks of extended time scales presents a challenge due to the amplified vanishing gradient problem during back propagation through time. This paper introduces three crucial elements that significantly enhance the memory and ca...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
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| Series: | Neuromorphic Computing and Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2634-4386/add293 |
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