MAP-SNN training method
Multiplicity, adaptability and plasticity in bio-plausible SNN training
A training method addressing the time-discretization problem in spiking neural networks while improving bio-plausibility.
- An algorithm investigating the discretization problem in time-iteration, demonstrating robustness under varying iterative step lengths.
- A spike-frequency-adaptation mode as a multiple-spike-pattern implementation, for efficient spike inference.
- A State-Free Synaptic Response Model (SFSRM) enhancing temporal expressiveness, compatible with deep learning frameworks.
Published in Frontiers in Neuroscience, 2022.