Content Comparison

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In this blog, I introduced the potential of Deep Learning with SNNs and explained why they are important. This is a highly active area of research, and my colleagues and I in Intel’s Neuromorphic Computing Lab will continue to share more details on different aspects of training deep SNNs, the challenges, best practices, and more. In the meantime, feel free to tinker with lava-dland its tutorials to train deep SNNs. The subsequent blogs in this series will be populated below as they become available.

• The curious case of Spike Function - the challenges to backpropagating through spike function discontinuity (coming soon).

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1. Maass, W. Noisy spiking neurons with temporal coding have more computational power than sigmoidal neurons. Advances in Neural Information Processing Systems 9, 211–217, 1996 ↩︎

2. Maass, W. Networks of spiking neurons: The third generation of neural network models. Neural Networks, 1659 – 1671, 1997. ↩︎

3. Orchard, G. et al. Efficient neuromorphic signal processing with loihi 2. 2021 IEEE Workshop on Signal Processing Systems (SiPS), 2021. ↩︎

4. Yin, B et al. Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks. Nature Machine Intelligence, 905 – 913, 2021. ↩︎

5. Davies, M. et al. Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook. Proceedings of the IEEE, 2021. ↩︎

6. Tasbolat, T. et al. Event-driven visual-tactile sensing and learning for robots. Robotics: Science and Systems 2020. ↩︎

7. https://www.techinsights.com/blog/image-sensor/dynamic-vision-sensors-brief-overview-image-sensor-techstream-blog ↩︎

8. https://inilabs.com/products/dynamic-audio-sensor/ ↩︎

9. Amir, A. et al. A low power, fully event-based gesture recognition system. Proceedings of the IEEE conference on computer vision and pattern recognition, 2017. ↩︎

10. Guangzhi, T. et al. Deep reinforcement learning with population-coded spiking neural network for continuous control. Conference on Robot Learning, PMLR, 2021. ↩︎

11. Rao, A. et al. A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware. Nature Machine Intelligence 4.5 (2022): 467-479. ↩︎

12. DeWolf, T et al. Neuromorphic control of a simulated 7-DOF arm using Loihi. Neuromorphic Computing and Engineering 3 014007, 2023.

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