Paul Masset

@paulmasset.bsky.social

Assistant Professor, McGill University | Associate Academic Member, Mila - Quebec AI Institute | Neuroscience and AI, learning and inference, dopamine and cognition https://massetlab.org/

Tremendously excited to announce that I will be joining @rockefeller.edu as an Assistant Professor and Head of Lab starting in January 2027! My group will be broadly focused on theoretical neuroscience, and mathematical problems in neural computation in the large.

Rockefeller campus image from https://commons.wikimedia.org/wiki/File:Rockefeller_University_Campus_aerial_2.jpg, licensed under the Creative Commons Attribution-Share Alike 2.5 Generic license.

Job klaxon 📣 UCL is hiring two NeuroAI / computational neuroscience specialists (Lecturer or Associate Prof) — a big part of the new NeuroAI centre we're growing in Biosciences. Do consider applying if it's relevant to you. JD & details: https://www.jobs.ac.uk/job/DRX021 Happy to take questions.

When it comes to sensory processing, cortex should not get all the credit... In olfaction, a key challenge is identifying odors regardless of concentration. Our new paper in @natneuro.nature.com shows how the olfactory bulb performs this crucial computation before signals even reach the cortex.

New paper alert! 🚨 We found that the brain's compass is remarkably stable at two scales 1️⃣ the system maintains its internal organization for weeks 2️⃣ It "remembers" its orientation for weeks, even after a single visit This may be key to how the brain aligns its other maps. Paper: rdcu.be/e3waP

Postdoc position in Paris: come help develop new generation human brain computer interfaces ⚡🧠💻 Interested? Contact me if you have experience with machine learning (e.g. simulation-based inference, RL, generative/diffusion models) or dynamical systems. See below for + details and retweet 🙏

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When we learn complex tasks, we chunk them into sub-tasks that our brains orchestrate into action sequences. How we do this is not entirely understood. This work explores how to learn and internally control temporally abstracted sub-tasks in RL/AI with sequence models. arxiv.org/abs/2512.20605

Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning

Large-scale autoregressive models pretrained on next-token prediction and finetuned with reinforcement learning (RL) have achieved unprecedented success on many problem domains. During RL, these model...

arxiv.org