Understanding how the brain constructs and simulates world models is a fundamental challenge in neuroscience. We are looking for someone to join our crew to explore the neuronal circuit mechanisms for learning and simulating worldContinue reading
Tag: plasticity
The lab at NeurIPS 2023
We’re at NeurIPS with two papers this year. If you are in New Orleans, come to see us! Dis-inhibitory neuronal circuits can control the sign of synaptic plasticity. Rossbroich, J. and Zenke, F. (2023) doi:Continue reading
Paper: Disinhibitory neuronal circuits are ideally poised to control the sign of synaptic plasticity
In Julian’s new paper “Dis-inhibitory neuronal circuits can control the sign of synaptic plasticity“ accepted at NeurIPS we look at how to reconcile normative theories of gradient-based learning in the brain with phenomenological models ofContinue reading
Paper: Latent Predictive Learning
Our paper “The combination of Hebbian and predictive plasticity learns invariant object representations in deep sensory networks” is now published in Nature Neuroscience. https://www.nature.com/articles/s41593-023-01460-y Sensory networks in our brain represent environmental objects as points onContinue reading
New preprint: Improving equilibrium propagation without weight symmetry
I am happy to announce our new preprint on “Improving equilibrium propagation (EP) without weight symmetry through Jacobian homeostasis,” led by Axel. https://arxiv.org/abs/2309.02214 EP prescribes a local learning rule and uses recurrent dynamics for creditContinue 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: Brain-Inspired Learning on Neuromorphic Substrates
I’m happy to share our new overview paper (https://ieeexplore.ieee.org/document/9317744, preprint: arxiv.org/abs/2010.11931) on brain-inspired learning on neuromorphic substrates in (spiking) recurrent neural networks. We systematically analyze how the combination of Real-Time-Recurrent Learning (RTRL; Williams and Zipser,Continue 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
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
Hiring: Cortical models of predictive processing and learning
We are looking for an intrepid Ph.D. candidate to work on theoretical models of predictive processing and learning. This collaborative project with the Keller Lab is based on the hypothesis that cortical circuits build modelsContinue reading