The lab is at Cosyne 2026 with three posters! Go and check them out if you are there in Lisbon! 2-069: Learning representations of moving objects in a reconstruction-free cortical circuit modelFriday 13th, 13:15-16:15 (AtenaContinue reading
Tag: learning
New paper: Dreamer-CDP — Reconstruction-Free World Models for Reinforcement Learning
In our new tiny paper accepted at the ICLR workshop on world models we introduce Dreamer-CDP, a Dreamer variant that learns a world model without reconstructing raw pixel observations. Preprint: https://arxiv.org/abs/2603.07083 Standard model-based reinforcement learningContinue reading
Understanding neural circuit principles for representation learning through joint-embedding predictive architectures
We’re happy to share our new preprint “Understanding neural circuit principles for representation learning through joint-embedding predictive architectures” led by Atena and Manu 🚀 We looked into the question how the cortex learns to representContinue reading
Breaking Balance: Encoding local error signals in perturbations of excitation-inhibition balance
Why does the brain maintain such precise excitatory-inhibitory balance? Our new preprint led by Julian Rossbroich explores a provocative idea: Small, targeted deviations from this balance may serve a purpose: to encode local error signalsContinue reading
Congratulations Manu
We are thrilled to celebrate the successful PhD defense and graduation of Dr Manu Srinath Halvagal, whose groundbreaking thesis bridges the fields of neuroscience and machine learning. His dissertation, titled “Predictive Self-Supervised Learning in BrainsContinue reading
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