New paper with @deanpospisil.bsky.social , in which we introduce a new estimator for the "signal eigenspectrum" (i.e., the eigenvalues of the noiseless population responses). We re-analyze data from Stringer et al 2019 and show eigenvalues of mouse V1 are well explained by a broken power.
Thrilled to see the first preprint of the lab out 🤩 Check it out if you need to compare dynamics in your data and RNN (or any other combinations of dynamical systems)!
When we try to formalize a neuron computationally, we don't translate biology into code—we perform a violent collapse.
www.ocrampal.com/what-a-neuro...
#philosophy #science #psychology #AI #intelligence #physics #biology #philmind #philsci #philsky #philpsy #neurosky #neuroskyence
Episode #36 in #TheoreticalNeurosciencePodcast: On low-dimensional manifolds in motor cortex – with Sara Solla @sasolla.bsky.social
theoreticalneuroscience.no/thn36
Manifold analysis has changed our thinking on how cortex works. One of the pioneers of this modelling approach explains.
Goal selection through the lens of subjective functions:
arxiv.org/abs/2512.15948
I welcome any feedback on these preliminary ideas.
We took a stab at how to infer both the dynamics and control parameters of partially-observable systems.
It’s a nasty problem, but @vgeadah.bsky.social made tremendous progress, ending up with some really elegant formalisms.
Do we need to study animals in the wild to fully understand the brain? Maybe. OTOH, I sit in front a computer all day.
bigthink.com/neuropsych/n...
#neuroscience
New paper for #neurips2025!
AI models adjust millions of internal settings to get better at a task. But how are these adjustments determined? For decades, we've mostly figured this out through trial & error.
We took a different approach...🧵 (1/6)
🔗 openreview.net/forum?id=oMi...
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...
1/6 New preprint 🚀 How does the cortex learn to represent things and how they move without reconstructing sensory stimuli? We developed a circuit-centric recurrent predictive learning (RPL) model based on JEPAs.
🔗 doi.org/10.1101/2025...
Led by @atenagm.bsky.social @mshalvagal.bsky.social
📍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?
🧵⬇️
Now in PRX: Theory linking connectivity structure to collective activity in nonlinear RNNs!
For neuro fans: conn. structure can be invisible in single neurons but shape pop. activity
For low-rank RNN fans: a theory of rank=O(N)
For physics fans: fluctuations around DMFT saddle⇒dimension of activity
Three panel thing. In the left panel we use error bars. In the second, we take statistical significance as the biggest number but still have error bars. In LLM science, we just have the biggest number
What if we did a single run and declared victory
Very excited to release a new blog post that formalizes what it means for data to be compositional, and shows how compositionality can exist at multiple scales. Early days, but I think there may be significant implications for AI. Check it out! ericelmoznino.github.io/blog/2025/08...
📰 I really enjoyed writing this article with @thetransmitter.bsky.social! In it, I summarize parts of our recent perspective article on neural manifolds (www.nature.com/articles/s41...), with a focus on highlighting just a few cool insights into the brain we've already seen at the population level.
Is it possible to go from spikes to rates without averaging?
We show how to exactly map recurrent spiking networks into recurrent rate networks, with the same number of neurons. No temporal or spatial averaging needed!
Presented at Gatsby Neural Dynamics Workshop, London.
I wonder, where would be a good place to do modeling and chat with many people that study different species or do comparative studies? (asking for a friend)
A summary figure for a NeurIPS competition where AI agents compete with mice in a visual foraging task.
Mice learn these tasks and are robust to perturbations like fog. Now, we invite you all to make AI agents to beat mice.
We present our #NeurIPS competition. You can learn about it here: robustforaging.github.io (7/n)
This paper carefully examines how well simple units capture neural data.
To quote someone from my lab (they can take credit if they want):
Def not news to those of us who use [ANN] models, but a good counter argument to the "but neurons are more complicated" crowd.
arxiv.org/abs/2504.08637
🧠📈 🧪
"These findings validate core predictions of Spatial Computing by showing that oscillatory dynamics not only gate information in time but also shape where in the cortex cognitive content is represented."
More on Spatial Computing:
doi.org/10.1038/s414...
(1/23) In addition to the new Lady Gaga album "Mayhem," my paper with Manuel Beiran, "Structure of activity in multiregion recurrent neural networks," has been published today.
PNAS link: www.pnas.org/doi/10.1073/...
(see dclark.io for PDF)
An explainer thread...
Music is universal. It varies more within than between societies and can be described by a few key dimensions. That’s because brains operate by using the raw materials of music: oscillations (brainwaves).
www.science.org/doi/10.1126/...
#neuroscience
1/N
How do neural dynamics in motor cortex interact with those in subcortical networks to flexibly control movement? I’m beyond thrilled to share our work on this problem, led by Eric Kirk @eric-kirk.bsky.social with help from Kangjia Cai!
www.biorxiv.org/content/10.1...
Thrilled to announce I'll be starting my own neuro-theory lab, as an Assistant Professor at @yaleneuro.bsky.social @wutsaiyale.bsky.social this Fall!
My group will study offline learning in the sleeping brain: how neural activity self-organizes during sleep and the computations it performs. 🧵
screenshot of biorxiv paper titled "Neuromorphic hierarchical modular reservoirs", authors Filip Milisav, Andrea I Luppi, Laura E Suárez, Guillaume Lajoie, Bratislav Misic
aside from this being a v cool paper I also want to congratulate the authors on the incredible SNR achieved in the title via a complete absence of filler words
Neuromorphic hierarchical modular reservoirs
www.biorxiv.org/content/10.1...
New preprint! 🧠🤖
How do we build neural decoders that are:
⚡️ fast enough for real-time use
🎯 accurate across diverse tasks
🌍 generalizable to new sessions, subjects, and even species?
We present POSSM, a hybrid SSM architecture that optimizes for all three of these axes!
🧵1/7
Curious about the history of the manifold/trajectory view of neural activity.
My own first exposure was Gilles Laurent's chapter in "21 Problems in Systems Neuroscience", where he cites odor trajectories in locust AL (2005). This was v inspiring as a biophysics student studying dynamical systems...
I think the biological evidence points to this not being the case. We can see instances where synapses literally undergo a form of reverse plasticity, e.g. see here: www.cell.com/trends/cogni...
I think it cannot be assumed that we never wipe memories from our brains completely!
How a neuroscientist solved the mystery of his own long COVID
How a neuroscientist solved the mystery of his own
#LongCovid and lead to a new scientific discovery. Inspiring story.
Thank you for sharing your journey @jeffmyau.bsky.social
www.youcanknowthings.com/how-one-neur...
I love how this paper uses cortico-thalamic interactions(context switching) for continual learning.
arxiv.org/abs/2205.11713
