Experiment-based Models to Study Local Learning Rules for Spiking Neural Networks
Giulia Amos, Maria Saramago, Alexandre Suter, Tim Schmid, Jens Duru, Sean Weaver, Benedikt Maurer, Stephan Ihle, Janos Vörös, Katarina Vulić · Bernstein Conference 2024
Spiking neural networks (SNNs) are characterized by low power consumption, highly parallel computation and event-driven information processing [1]. Many of the concepts that promise to improve the efficiency of SNNs compared to conventional deep neural networks, such as spikes, sparsity, and static suppression, are inspired by our understanding of how the brain processes information. Hence, we can further advance the development of SNNs by improving our current knowledge of how biological neural networks learn through spike-based local plasticity mechanisms. In recent years, many spike-based