Information processing with artificial spiking neural networks

Friedemann Zenke

Friedrich Miescher Institute

Brains rely on spiking neural networks for ultra-low-power information processing. Building artificial intelligence with similar efficiency requires learning algorithms to instantiate complex spiking neural networks and brain-inspired neuromorphic hardware to emulate them efficiently. Toward this end, I will briefly introduce surrogate gradients as a general framework for training spiking neural networks and showcase their robustness and self-calibration capabilities on analog neuromorphic hardware. Drawing further inspiration from biology, I will discuss the impact of homeostatic plasticity and network initialization in the excitatory-inhibitory balanced regime on deep spiking neural network training. Finally, I will show how approximations relate surrogate gradients to biologically plausible online learning rules with a minor impact on their effectiveness.