Research map / Architectures and efficiency / Spiking networks
Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use ST-RSBP with a 400-R400-400 recurrent architecture instead of HM2-BP with 800-800 feedforward layers | SNN · TI46-Alpha | accuracy (best): 90.98% -> 93.35% | Helped |
| Use ST-RSBP with 200-R200-200 instead of HM2-BP with 200-200 | SNN · TI46-Digits | accuracy (best): 98.50% -> 99.39% | Helped |
| Use ST-RSBP with a 400-R400-400 recurrent architecture instead of Phased-LSTM | speech classifier · N-Tidigits | accuracy (best): 91.25% -> 93.90% | Helped |
| Use ST-RSBP with a 400-R400 architecture instead of DL BP with 3x512 layers | image classifier · Fashion-MNIST | accuracy (best): 89.06% -> 90.13% | Helped |
| Use ST-RSBP instead of HM2-BP with the same 15C5-P2-40C5-P2-300 spiking CNN architecture | spiking CNN · MNIST | accuracy (best): 99.49% -> 99.62% | Helped |
Closest papers by meaning
- Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networks (NeurIPS 2018 · similarity 0.92)
- Training Spiking Neural Networks with Event-driven Backpropagation (NeurIPS 2022 · similarity 0.92)
- Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagation (NeurIPS 2024 · similarity 0.91)
- Sparse Spiking Neural Network: Exploiting Heterogeneity in Timescales for Pruning Recurrent SNN (ICLR 2024 · similarity 0.90)
- Take A Shortcut Back: Mitigating the Gradient Vanishing for Training Spiking Neural Networks (NeurIPS 2024 · similarity 0.89)
- Enhancing Training of Spiking Neural Network with Stochastic Latency (AAAI 2024 · similarity 0.89)
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