Before revision:  Nevertheless, training spiking neural networks with sparse activity on hardware devices remains a major challenge . Primarily this is due to the lack of suitable training methods that take into account device-specific imperfections and operate at the level of individual spikes instead of firing rates. To tackle this issue, we developed a hardware-in-the-loop strategy to train multi-layer spiking networks using surrogate gradients on the analog BrainScales-2 chip.
After revision:  Nevertheless, training spiking neural networks with sparse activity on hardware devices remains a major challenge . Primarily this is due to the lack of efficient training algorithms. Here, we introduce a general in-the-loop learning framework that resolves these issues. Using the BrainScales-2 chip.