Rui Ponte Costa

Thank you Mark for involving us! Very exciting story!

The contextual machine of the brain? Beautiful data!

First preprint from the lab! Using intracellular recordings & analysis of 2-photon imaging data, we show that spiking & neuromodulatory input during experience drive a reorganization of visuomotor inputs in V1 layer 2/3 neurons, consistent with enhanced visuomotor cancellation - bioRxiv link below.

Vectorized instructive signals in cortical dendrites - Nature Mice learning a neurofeedback brain–computer interface task show neuron-specific teaching signals in cortical dendrites, consistent with a vectorized solution for credit assignment in the brain.

This paper on how the brain may do gradient descent is very cool: www.nature.com/articles/s41...

Computational and Cognitive Neuroscience Summer School

Please repost: Applications for the CSHA Computational and Cognitive Neuroscience Summer School are open! Join us this summer in Suzhou for a fantastic and unique experience. Deadline April 1st: ccnss.net

Sounds a very exciting endeavour!

Quantitative Approaches to Behavior and Virtual Reality - CAJAL […]

You have until March 3rd to apply for the Cajal summer school on Quantitative Approaches to Behavior and VR at Champalimaud! Come surf and track animals with us 🏄🪰🐟🚶
cajal-training.org/on-site/quan...

Maybe we could try something like that will pm

Really sorry that you have to go through this again! Is there someway we @cosynemeeting.bsky.social can help?

Deadline in 3 days (Thursday 5th) to apply for a PhD with me and @ktsetsos.bsky.social at Trinity College Dublin in Cognitive Computational Neuroscience / neuro-AI. Please share!

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 🙏

On research careers in academia and industry The epilogue to a series on Cognitive Science and AI

Should you go to academia or industry for research in AI or cognitive science? It's the most common question I get asked by PhD students, and I've written up some of my thoughts on the answer, as an epilogue to my research-focused series on these fields: infinitefaculty.substack.com/p/on-researc...

At @elife.bsky.social you can now include explainer videos with every figure. Like going to a seminar while you engage with the paper. First example here elifesciences.org/articles/106...

Click the arrows next to each figure to get a video of @mathiassablemeyer.bsky.social explaining it for you!

@kordinglab.bsky.social and I ran a summer school last year to help young profs (<5 yrs) in systems/comp neuro thrive.

compneurosci.com/Neuro4Pros/i...

It was great! Now we want to know if you'd be interested in participating if we did this again this year?

Let us know!

Dual neuromodulatory dynamics underlie birdsong learning - Nature Dopamine release in the basal ganglia of the zebra finch is driven by neurons associated with reinforcement learning and by cholinergic signalling, and tracks performance quality during long-term lear...

Proud to have contributed to @jiaxuanqi.bsky.social's masterpiece out @nature.com! She shows that dopamine transients track the learned quality of song during juvenile learning and that dopamine release is driven not just by VTA firing, but by a local cholinergic mechanism! (1/x)

Wanna compare dynamics across neural data, RNNs, or dynamical systems? We got a fast and furious method🏎️
The 1st preprint of my PhD 🥳 fast dynamical similarity analysis (fastDSA):
📜: arxiv.org/abs/2511.22828
💻: github.com/CMC-lab/fast...
I’ll be @cosynemeeting.bsky.social - happy to chat 😉

Yes, but that 'target' is sensory information right? So in practice this 'target' is new sensory data that the brain will try to predict/match. Of course that in practice we model these tasks things using SL, but this is an abstraction, in reality the brain doesn't have *direct* access to targets.

New paper led by wonder postdocs Francesca Greenstreet and @jessegeerts.bsky.social and @clopathlab.bsky.social trying to understand why –in the "what for" sense– there are multiple motor learning systems –supervised and RL-based– in the brain.

Check out Jesse's 🧵

www.biorxiv.org/content/10.6...

Distributed learning across fast and slow neural systems supports efficient motor adaptation Adaptation is a fundamental aspect of motor learning. Intelligent systems must adapt to perturbations in the environment while simultaneously maintaining stable memories. Classic work has argued that ...

Btw there is also related work by the group of Alex Cayco-Gajic:
www.biorxiv.org/content/10.1...

Cerebellar-driven cortical dynamics can enable task acquisition, switching and consolidation - Nature Communications How the brain maintains a stable world model while swiftly adapting to environmental changes remains unclear. Here, the authors propose that the cerebellum drives cortical dynamics, enabling rapid tas...

The key difference seems to be more on the speed of learning. We show that its only when the cerebellum learns fast and the cortex slow (fixed) we can explain data. Thus cortex=stable world 'model' and the cerebellum=fast adapter. (2/2)

www.nature.com/articles/s41...

Thanks Juan! I do think that virtually all forms of learning in the brain are self-supervised (so unsupervised), as there never is never an explicit target in the brain. (1/2)

Some exciting opportunity for NeuroAI research at Johns Hopkins!

Dorsal striatal dopamine integrates sensory and reward prediction errors to guide perceptual decisions Perceptual decisions are shaped by expectations about sensory stimuli and rewards, learned through sensory and reward prediction errors. Dopamine is known to convey reward prediction errors that shape...

New preprint. We show that in addition to reward prediction errors (RPEs), dorsal striatal dopamine signals encode sensory prediction errors (SPEs), the difference between sensory prior & observed stimulus. www.biorxiv.org/content/10.6...

Leveraging insights from neuroscience to build adaptive artificial intelligence Nature Neuroscience - Adaptive intelligence envisions AI that, like animals, learns online, generalizes and adapts quickly. This Perspective reviews biological foundations, progress in AI and...

Interested in the latest advances in neuroscience (neural dynamics and internal models) and how they can be leveraged to build smarter, adaptive AI?

➡️ My first real solo piece 🖤🫶 @natneuro.nature.com

rdcu.be/eWVmA

2025: A Review of the Year in Neuroscience Enlightening the brain

Just published my review of neuroscience in 2025, on The Spike.

The 10th of these, would you believe?

This year we have foundation models, breakthroughs in using light to understand the brain, a gene therapy, and more

Enjoy!

medium.com/the-spike/20...

"why are there neuroscientists?" Poster

Curious to hear from anyone who attended this workshop nenckiopenlab.org/why/
It explored questions like:

What motivates – and demotivates us – in our research?

How our overt motivations – ones that we speak about in public, in grant writing or science communication – relate to the real ones?

High impact, small size.

We welcome papers that address findings from a single set of experiments, or that are substantial enough to stand alone in 1,500 words or fewer.

Find out more: buff.ly/mVtikMw

A theory of multi-task computation and task selection Neural activity during the performance of a stereotyped behavioral task is often described as low-dimensional, occupying only a limited region in the space of all firing-rate patterns. This region has...

1/X Excited to present this preprint on multi-tasking, with
@david-g-clark.bsky.social and Ashok Litwin-Kumar! Timely too, as “low-D manifold” has been trending again. (If you read thru the end, we escape Flatland and return to the glorious high-D world we deserve.) www.biorxiv.org/content/10.6...

🔬 We're hiring a Junior Group Leader for Data-Driven Digital Twins/System Models in medicine/life sciences at @uni_goettingen! Perfect opportunity for early-career researchers who want to:
- Lead their own research group
- Work at the intersection of AI, simulation, and biomedicine

Reduced rank regression for neural communication: a tutorial for neuroscientists Reduced rank regression (RRR) is a statistical method for finding a low-dimensional linear mapping between a set of high-dimensional inputs and outputs. In recent years, RRR has found numerous applica...

Bichan Wu (@bichanw.bsky.social) & I wrote a tutorial paper on Reduced Rank Regression (RRR) — the statistical method underlying "communication subspaces" from Semedo et al 2019 — aimed at neuroscientists.

arxiv.org/abs/2512.12467