16 citations · 23 across the 3 of their papers we have counts for
3 papers
DelGrad: Exact event-based gradients for training delays and weights on spiking neuromorphic hardware
Julian Göltz, Jimmy Weber, Laura Kriener +5
Spiking neural networks (SNNs) inherently rely on the timing of signals for representing and processing information. Incorporating trainable transmission delays, alongside synaptic…
NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems
Jason Yik, Korneel Van den Berghe, Douwe den Blanken +97
Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research fiel…
Learning efficient backprojections across cortical hierarchies in real time
Kevin Max, Laura Kriener, Garibaldi Pineda García +4
Models of sensory processing and learning in the cortex need to efficiently assign credit to synapses in all areas. In deep learning, a known solution is error backpropagation, whi…