5 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.RO2023★ 1 cited
Synaptic motor adaptation: A three-factor learning rule for adaptive robotic control in spiking neural networks
Samuel Schmidgall, Joe Hays
Legged robots operating in real-world environments must possess the ability to rapidly adapt to unexpected conditions, such as changing terrains and varying payloads. This paper in…
cs.NE2023★ 5 cited
Brain-inspired learning in artificial neural networks: a review
Samuel Schmidgall, Jascha Achterberg, Thomas Miconi +4
Artificial neural networks (ANNs) have emerged as an essential tool in machine learning, achieving remarkable success across diverse domains, including image and speech generation,…
cs.NE2022
Learning to learn online with neuromodulated synaptic plasticity in spiking neural networks
Samuel Schmidgall, Joe Hays
We propose that in order to harness our understanding of neuroscience toward machine learning, we must first have powerful tools for training brain-like models of learning. Althoug…