Yusuke Sakemi

Scalable Training of Continuous-Time Spiking Neural Networks with Differentiable Spike-Time Discretization

Yusuke Sakemi, Tomoya Takeuchi, Takeo Hosomi, Kazuyuki Aihara

Continuous-time spiking neural networks (SNNs) provide an event-driven framework for temporal computation, computational neuroscience, and neuromorphic hardware. However, training deep continuous-time SNNs is severely constrained by the memory required for exact spike-time comput…Read more