This fun project started as a summer student project at Telluride 2021 led and advised by Chiara Bartolozzi and myself. An incredibly motivated group of summer students taught a robotic fingertip with tactile sensors andContinue reading
Author: fzenke
Holomorphic Equilibrium Propagation
Thrilled to introduce Holomorphic Equilibrium Propagation (hEP) in this new preprint led by Axel Laborieux. We extend classic equilibrium propagation to the complex domain and show that it computes exact gradients with finite size oscillations,Continue reading
The lab on tour
September was meeting month with the lab attending several meetings and conferences, including the Bernstein conference in Berlin and the FMI annual meeting in Grindelwald. After a long hiatus it was great to finally engageContinue reading
tinyML Neuromorphic Engineering Forum
Happy to announce the tinyML Neuromorphic Engineering Forum, a virtual one-day meeting on September 27, 2022 to bring the tinyML and neurmorphic community closer. The forum is spearheaded by Charlotte Frenkel and co-organized by colleaguesContinue reading
Announcing SNUFA 2022
We are happy to announce SNUFA 2022, an online workshop focused on research advances in the field of “Spiking Networks as Universal Function Approximators.” SNUFA 2022 will take place online 9-10 November 2022, European afternoons.Continue reading
Axel awarded an SNSF Swiss Postdoctoral Fellowship
Congratulations, Axel (woot woot), for being awarded an SNSF Swiss Postdoctoral Fellowship, the interim Swiss equivalent of a Marie Curie European Fellowship, to work on fundamental questions pertaining to neuronal circuit architectures and learning. WeContinue reading
Fluctuation-driven initialization for spiking neural network training
Surrogate gradients are a great tool for training spiking neural networks in computational neuroscience and neuromorphic engineering, but what is a good initialization? In our new preprint co-led by Julian and Julia, we lay outContinue reading
Spektrum der Wissenschaft story on self-calibration in neuromorphic systems
We are very happy that in their current issue Spektrum der Wissenschaft picked up our collaboration story with Uni Heidelberg on self-calibration through surrogate gradient learning in analog neuromorphic systems.
High school students visiting the FMI
Lab members organized a station for a recent outreach event during which more than 200 high school students from Basel and surroundings visited the FMI to learn more about genetics and neuroscience research. At theContinue reading
Hebbian plasticity could be the brain’s trick to make self-supervised learning work
Please take a look at our new preprint “The combination of Hebbian and predictive plasticity learns invariant object representations in deep sensory networks.” https://biorxiv.org/cgi/content/short/2022.03.17.484712v2 In this work led by Manu Srinath Halvagal we argue thatContinue reading