Congratulations, Dr Srinath Halvagal. Another doctor from the lab. We had fun and he left his mark. We are sad to see him go, but happy to see him head off for a post-doc withContinue reading
Tag: learning
Hiring: Exploring the circuit mechanisms for learning and simulating a world model
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
Improving equilibrium propagation without weight symmetry through Jacobian homeostasis
We are happy our new paper “Improving equilibrium propagation (EP) without weight symmetry through Jacobian homeostasis,” led by Axel accepted at ICLR 2024. Preprint: https://arxiv.org/abs/2309.02214Code: https://github.com/Laborieux-Axel/generalized-holo-ep EP prescribes a local learning rule and uses recurrentContinue 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
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
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
Hiring: Context-dependent information processing in biological neural networks
We are looking for a Ph.D. student to work on context-dependent information processing in biologically inspired neural networks. We will investigate the effect of stereotypical circuit motifs and neuromodulation on neural information processing and learningContinue 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
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
Talk at TU Berlin on Network and Plasticity Dynamics
I am delighted to get the chance to present my work on learning in spiking neural networks on Tuesday, 15th of May 2018 at 10:15am at TU Berlin. Title: What can we learn about synapticContinue reading