The lab will be presenting two posters at #cosyne2026 #cosyne26 on dopamine-based distributed RL and hippocampal coding from a population-geometry perspective! @zijingwu.bsky.social @chenjiang01.bsky.social #compneurosky #neuroskyence
Chen Jiang
@chenjiang01.bsky.social
PhD student at McGill University working on the intersection of Neuroscience and AI
📍Excited to share that our paper was selected as a Spotlight at #NeurIPS2025! arxiv.org/pdf/2410.03972 It started from a question I kept running into: When do RNNs trained on the same task converge/diverge in their solutions? 🧵⬇️
RL Debates 4: Adam "I literally measured value in the brain" Lowet Adam's talk covered a lot of ground — from his recent work on distributional RL (nature.com/articles/s41...) to a broader discussion of RL & the brain. 📽️ Watch the full meeting here: www.youtube.com/watch?v=Xe7B... 🧠🤖🧠📈
RL Debates 4: Adam "I literally measured value in the brain" Lowet
YouTube video by Sensorimotor AI
youtube.com
Our next paper on comparing dynamical systems (with special interest to artificial and biological neural networks) is out!! Joint work with @annhuang42.bsky.social , as well as @satpreetsingh.bsky.social , @leokoz8.bsky.social , Ila Fiete, and @kanakarajanphd.bsky.social : arxiv.org/pdf/2510.25943
If you're interested in dynamical systems analysis for neuroscience, definitely check out @oliviercodol.bsky.social 's revised version of our RL paper! Very cool results in the new Fig 6, worth it regardless of if you saw our previous version or if it's all new. www.biorxiv.org/content/10.1...
Brain-like neural dynamics for behavioral control develop through reinforcement learning
During development, neural circuits are shaped continuously as we learn to control our bodies. The ultimate goal of this process is to produce neural dynamics that enable the rich repertoire of behavi...
biorxiv.org
A tad late (announcements coming) but very happy to share the latest developments in my previous preprint! Previously, we show that neural representations for control of movement are largely distinct following supervised or reinforcement learning. The latter most closely matches NHP recordings.
Excited to share that our work ‘Simultaneous detection and estimation in olfactory sensing’ with @mattyizhenghe.bsky.social, @neurovenki.bsky.social , @cpehlevan.bsky.social, @jzv.bsky.social and @paulmasset.bsky.social has been launched! 1/7
Simultaneous detection and estimation in olfactory sensing https://www.biorxiv.org/content/10.1101/2025.11.01.686013v1
First paper from the lab! We propose a model that separates estimation of odor concentration and presence and map it on olfactory bulb circuits Led by @chenjiang01.bsky.social and @mattyizhenghe.bsky.social joint work with @jzv.bsky.social and with @neurovenki.bsky.social @cpehlevan.bsky.social
Simultaneous detection and estimation in olfactory sensing https://www.biorxiv.org/content/10.1101/2025.11.01.686013v1
Excited to share that our work ‘Simultaneous detection and estimation in olfactory sensing’ with @mattyizhenghe.bsky.social, @neurovenki.bsky.social , @cpehlevan.bsky.social, @jzv.bsky.social and @paulmasset.bsky.social has been launched! 1/7
Simultaneous detection and estimation in olfactory sensing https://www.biorxiv.org/content/10.1101/2025.11.01.686013v1
Simultaneous detection and estimation in olfactory sensing https://www.biorxiv.org/content/10.1101/2025.11.01.686013v1
Our work with Pablo Tano, @hyunggoo-kim.bsky.social Athar Malik, Alexandre Pouget and @naoshigeuchida.bsky.social exploring how dopamine neurons could enable multi-timescale reinforcement learning in the brain is out in @nature.com www.nature.com/articles/s41...
Multi-timescale reinforcement learning in the brain - Nature
Individual dopaminergic neurons encode future rewards over distinct temporal horizons.
nature.com