Task demands shift motor learning from adaptation to feedback control in a naturalistic bimanual task
www.biorxiv.org/content/10.1...

"Science is often slow, repetitive, and unpredictable, but engaging with it from a different angle can restore perspective and motivation. For me, stepping outside the lab did not take me away from science. It helped me rediscover why I cared about it" www.science.org/content/arti...

New preprint from my lab! We study how reinforcement learning & selective attention interact. To do so, we built a set of models describing different ways that value & reward prediction error can modulate top-down attention. We compare model outcomes to monkey data from a color value learning task

➡️ Robust control (reject "unmodelled" disturbances),

➡️ Online adaptive control,

➡️ Trial-by-trial adaptation.

These components are separable behaviourally and reveal individual traits characterising how we handle external disturbances...

Very happy to put this work out!

Movement errors are reduced even in unpredictable environments, where anticipation is not possible.

We addressed the complex processes interacting within an ongoing action to achieve this...

Homepage — PL Neuro

PL Neuro is officially live! plneuro.xyz

We exist to break bottlenecks, accelerate progress in neurotech & NeuroAI, and to invest in innovations that benefit humanity.

PL Neuro will focus on 3 core areas:
- Neural augmentation
- Biologically-inspired intelligence
- Whole brain emulation

#philsky #philsci #booksky #evosky #histsci #AcademicSky #BNPreorder

Very late to the show on Bluesky but finally found time to join! And I’ll start with some belated news 🎉 I have officially started my own research group as a CNRS researcher at the Institut de Neurosciences de la Timone in Marseille 🎉

(2/2) This is consistent with our random network results, that random connectivities within same architecture produce similar dynamics, if the task and fb control is preserved. From Fig. 6 it looks like when the architecture itself changes, then the dynamics vary more. Thanks for pointing to this!

(1/2)Very interesting and elaborate results!
If I'm reading Fig. 6 correctly, same-type networks yield similar behavioral corr's, with dynamical distances showing slightly more variance within a given architecture...

Thanks! It's always a concern, but here we have some extremely different ask structures actually, e.g., our human participant moving objects across a table in trials order of ~ 10s, compared to mice lever pulling in trials order of ~ 100ms. Which makes me think there's more to it than that

Very happy to share our review on Reinforcement Learning vs Statistical Learning, with @ambrafer.bsky.social and @predictivebrain.bsky.social:

www.sciencedirect.com/science/arti...

A nice summary:
www.sainsburywellcome.org/blog/two-eng...

To elaborate: multiple random networks under opt. fb. ctrl with a fixed cost (task) produce similar neural dynamics. Changes in the cost led to differences. We thought what may be conserved is the fb ctrl & task structure rather than specific circuit connectivity. Eager to read the paper in detail!

Feedback control of random networks as a model of flexible motor cortical dynamics across tasks Kalidindi and Crevecoeur develop a computational framework linking feedback-controlled networks to limb dynamics. They demonstrate that optimal control of fixed network reproduces key motor cortical d...

Nice work, congratulations! In a recent model we found that similar neural dynamics may be due to similar task structure independent of network connectivity (supp. Figure). Could a simpler explanation be that all animals experienced essentially the same task structure?
www.cell.com/cell-reports...

Thanks Kevin!

Favourite paper I have read this year. Check it out! Great work @harikalidindi.bsky.social and @fredcrevecoeur.bsky.social!

awesome paper bridging the gap between RNN and optimal control models of motor control

Enjoyed a lot doing this work with @fredcrevecoeur.bsky.social throughout! Glad it's finally out 🎉🎉🎉

Here is the accompanying code for implementing:
github.com/neurohari/si...

Not playing around: Why neuroscience needs toy models Amid the rise of billion-parameter models, I argue that toy models, with just a few neurons, remain essential—and may be all neuroscience needs.

Amid the rise of billion-parameter models, I argue that toy models, with just a few neurons, remain essential—and may be all neuroscience needs, writes @marcusghosh.bsky.social.

#neuroskyence

www.thetransmitter.org/theoretical-...

Now published in the Journal of Neurophysiology:
journals.physiology.org/doi/full/10....

Get in touch if you think this tool could help in your science! We will be developing improvements and extensions over the next year.

Image of robots struggling with a social dilemma.

1/ Why does RL struggle with social dilemmas? How can we ensure that AI learns to cooperate rather than compete?

Introducing our new framework: MUPI (Embedded Universal Predictive Intelligence) which provides a theoretical basis for new cooperative solutions in RL.

Preprint🧵👇

(Paper link below.)

Why AlexNet Died in AI but Lingers in Neuroscience — Through the Lens of Popper and Kuhn When I talk to any of my machine learning researcher colleagues in 2025, they tell me that a model from 2023 is prehistoric. In AI, a year…

Good piece by @kohitij.bsky.social on why neuroscientists use an "outdated" vision model. Neuroscience is different than AI and that's ok! medium.com/@kohitij_716...

Very interesting work!

The brain computes by processing information over time through interactions between connectivity and dynamics that are hard to model. Here we infer these interactions from data and find they better predict cognitive performance! www.nature.com/articles/s41... w/ @lindenmp.bsky.social

Join us for Fall 2026. In our group, you can run studies from human behavior and neuroimaging, to large-scale NHP ephys, and join them up with a robust computational foundation. Bonus: you can help build the reading list.

European universities leading the way

Thread of French and Dutch research institutes slowly unsubscribing from web of science (and thence impact factors).

0/10 Thanks for the interest in our preprint. Some takes say it negates or fully supports the “manifold hypothesis”, neither quite right. Our results show that if you only focus on the manifold capturing most of task-related variance, you could miss important dynamics that actually drive behavior.

How I contributed to rejecting one of my favorite papers of all time I believe we should talk about the mistakes we make.

How I contributed to rejecting one of my favorite papers of all times, Yes, I teach it to students daily, and refer to it in lots of papers. Sorry. open.substack.com/pub/kording/...

What neuroscience can tell AI about learning in continuously changing environments Nature Machine Intelligence - Durstewitz et al. explore what artificial intelligence can learn from the brain’s ability to adjust quickly to changing environments. By linking neuroscience...

Unlike current AI systems, animals can quickly and flexibly adapt to changing environments.

This is the topic of our new perspective in Nature MI (rdcu.be/eSeif), where we relate dynamical and plasticity mechanisms in the brain to in-context and continual learning in AI. #NeuroAI