Randomized Forward Mode Gradient for Spiking Neural Networks in Scientific Machine Learning

Understand

Spiking neural networks (SNNs) represent a promising approach in machine learning, combining the hierarchical learning capabilities of deep neural networks with the energy efficiency of spike-based computations.

• Traditional end-to-end training of SNNs is often based on back-propagation, where weight updates are derived from gradients computed through the chain rule.
• However, this method encounters challenges due to its limited biological plausibility and inefficiencies on neuromorphic hardware.
• In this study, we introduce an alternative training approach for SNNs.