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
Category: preprint
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
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
Paper: Nonlinear transient amplification in recurrent neural networks with short-term plasticity
Update (21.12.2021): Now published https://elifesciences.org/articles/71263 I am pleased to share our new preprint: “Nonlinear transient amplification in recurrent neural networks with short-term plasticity” led by Yue Kris Wu in which we study possible mechanisms forContinue reading
Preprint: The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks
We just put up a new preprint https://www.biorxiv.org/content/10.1101/2020.06.29.176925v1 in which we take a careful look at what makes surrogate gradients work. Spiking neural networks are notoriously hard to train using gradient-based methods due to theirContinue reading