Alex van Meegen

Excited to be giving the van Vreeswijk Theoretical Neuroscience Seminar this Wednesday, Jan 14, where I'll talk about "Computation Through Neuronal-Synaptic Dynamics"!
www.wwtns.online

Our paper on data constrained RNN that generalize to optogenetic perturbations now citable on eLife:
doi.org/10.7554/eLif...

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...

🧵Excited to present our latest work at #Neurips25! Together with @avm.bsky.social, we discover 𝐜𝐡𝐚𝐧𝐧𝐞𝐥𝐬 𝐭𝐨 𝐢𝐧𝐟𝐢𝐧𝐢𝐭𝐲: regions in neural networks loss landscapes where parameters diverge to infinity (in regression settings!)

We find that MLPs in these channels can take derivatives and compute GLUs 🤯

Finally got the job ad—looking for 2 PhD students to start spring next year:

www.gao-unit.com/join-us/

If comp neuro, ML, and AI4Neuro is your thing, or you just nerd out over brain recordings, apply!

I'm at neurips. DM me here / on the conference app or email if you want to meet 🏖️🌮

Different learning algorithms achieve shared optimal outcomes in humans, rats, and mice Animals must exploit environmental regularities to make adaptive decisions, yet the learning algorithms that enabels this flexibility remain unclear. A central question across neuroscience, cognitive science, and machine learning, is whether learning relies on generative or discriminative strategies. Generative learners build internal models the sensory world itself, capturing its statistical structure; discriminative learners map stimuli directly onto choices, ignoring input statistics. These strategies rely on fundamentally different internal representations and entail distinct computational trade-offs: generative learning supports flexible generalisation and transfer, whereas discriminative learning is efficient but task-specific. We compared humans, rats, and mice performing the same auditory categorisation task, where category boundaries and rewards were fixed but sensory statistics varied. All species adapted their behaviour near-optimally, consistent with a normative observer constrained by sensory and decision noise. Yet their underlying algorithms diverged: humans predominantly relied on generative representations, mice on discriminative boundary-tracking, and rats spanned both regimes. Crucially, end-point performance concealed these differences, only learning trajectories and trial-to-trial updates revealed the divergence. These results show that similar near-optimal behaviour can mask fundamentally different internal representations, establishing a comparative framework for uncovering the hidden strategies that support statistical learning. ### Competing Interest Statement The authors have declared no competing interest. Wellcome Trust, https://ror.org/029chgv08, 219880/Z/19/Z, 225438/Z/22/Z, 219627/Z/19/Z Gatsby Charitable Foundation, GAT3755 UK Research and Innovation, https://ror.org/001aqnf71, EP/Z000599/1

paper🚨
When we learn a category, do we learn the structure of the world, or just where to draw the line? In a cross-species study, we show that humans, rats & mice adapt optimally to changing sensory statistics, yet rely on fundamentally different learning algorithms.
www.biorxiv.org/content/10.1...

Prediction of neural activity in connectome-constrained recurrent networks - Nature Neuroscience The authors show that connectome datasets alone are generally not sufficient to predict neural activity. However, pairing connectivity information with neural recordings can produce accurate predictio...

Connectome datasets alone are generally not sufficient to predict neural activity. However, pairing connectivity information with neural recordings can produce accurate predictions of activity in unrecorded neurons

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

This was a lot of fun! From my side, it started with a technical Q: what's the relation between two-side cavity and path integrals? Turns out it's a fluctuation correction - and amazingly, this also enable the "O(N) rank" theory by @david-g-clark.bsky.social and @omarschall.bsky.social. 🤯

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

Applying to do a postdoc or PhD in theoretical ML or neuroscience this year? Consider joining my group (starting next Fall) at UT Austin!
POD Postdoc: oden.utexas.edu/programs-and... CSEM PhD: oden.utexas.edu/academics/pr...

A study led by Cina Aghamohammadi is now out in ‪@natcomms.nature.com‬! We developed a mathematical framework for partitioning spiking variability, which revealed that spiking irregularity is nearly invariant for each neuron and decreases along the cortical hierarchy.
www.nature.com/articles/s41...

🎉 "High-dimensional neuronal activity from low-dimensional latent dynamics: a solvable model" will be presented as an oral at #NeurIPS2025 🎉

Feeling very grateful that reviewers and chairs appreciated concise mathematical explanations, in this age of big models.

www.biorxiv.org/content/10.1...
1/2

Lab members are at the Bernstein conference @bernsteinneuro.bsky.social with 9 posters! Here’s the list:

TUESDAY 16:30 – 18:00

P1 62 “Measuring and controlling solution degeneracy across task-trained recurrent neural networks” by @flavioh.bsky.social

Awesome. Congratulations!!

Check out our new preprint where we analyzed the dynamics of over ten thousand neurons across 223 brain areas and found a surprising universal principle that describes the organization of intrinsic timescales across the entire mouse brain, including subcortical structures!
#neuroskyence

Frontiers | Summary statistics of learning link changing neural representations to behavior How can we make sense of large-scale recordings of neural activity across learning? Theories of neural network learning with their origins in statistical phy...

Since I'm back on BlueSky - with @frostedblakess.bsky.social and @cpehlevan.bsky.social we wrote a brief perspective on how ideas about summary statistics from the statistical physics of learning could potentially help inform neural data analysis... (1/2)

Convergent motifs of early olfactory processing are recapitulated by layer-wise efficient coding The architecture of early olfactory processing is a striking example of convergent evolution. Typically, a panel of broadly tuned receptors is selectively expressed in sensory neurons (each neuron exp...

Excited to share new computational work, led by @jzv.bsky.social, driven by Juan Carlos Fernandez del Castillo + contribution from Farhad Pashakanloo. We recover 3 core motifs in the olfactory system of evolutionarily distant animals using a biophysically-grounded model + efficient coding ideas!

Associative synaptic plasticity creates dynamic persistent activity In biological neural circuits, the dynamics of neurons and synapses are tightly coupled. We study the consequences of this coupling and show that it enables a novel form of working memory. In recurren...

(1/26) Excited to share a new preprint led by grad student Albert Wakhloo, with me and Larry Abbott: "Associative synaptic plasticity creates dynamic persistent activity."
www.biorxiv.org/content/10.1...

I wrote a Comment on neurotheory, and now you can read it!

Some thoughts on where neurotheory has and has not taken root within the neuroscience community, how it has shaped those subfields, and where we theorists might look next for fresh adventures.

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

Connectivity structure and dynamics of nonlinear recurrent neural networks Studies of the dynamics of nonlinear recurrent neural networks often assume independent and identically distributed couplings, but large-scale connectomics data indicate that biological neural circuit...

Wanted to share a new version (much cleaner!) of a preprint on how connectivity structure shapes collective dynamics in nonlinear RNNs. Neural circuits have highly non-iid connectivity (e.g., rapidly decaying singular values, structured singular-vector overlaps), unlike classical random RNN models.

Differential kinematic coding in sensorimotor striatum across behavioral domains reflects different contributions to movement - Nature Neuroscience Hardcastle and Marshall et al. show that striatal function is domain specific, required for task-related but not spontaneously expressed movements. This functional distinction is reflected in starkly ...

Excited to announce that my first postdoc paper is now online!

Links:
www.nature.com/articles/s41...
rdcu.be/eAcN7

In it, we examine the perennial question: what changes in the brain when learning a new motor skill?

Read more below to find out 👇

Coming March 17, 2026!
Just got my advance copy of Emergence — a memoir about growing up in group homes and somehow ending up in neuroscience and AI. It’s personal, it’s scientific, and it’s been a wild thing to write. Grateful and excited to share it soon.

Congratulations!!

Physik studieren? Oder ein anderes Fach?

In Göttingen geht jetzt beides, mit dem "Bachelor Interdisziplinär", der Physik mit spannenden Disziplinen Eurer Wahl, wie z.B. Künstlicher Intelligenz, Philosophie, Neurowissenschaften, ... kombiniert.

www.uni-goettingen.de/de/studium/6...

Out today in @nature.com: we show that individual neurons have diverse tuning to a decision variable computed by the entire population, revealing a unifying geometric principle for the encoding of sensory and dynamic cognitive variables.
www.nature.com/articles/s41...

Structure of activity in multiregion recurrent neural networks | PNAS Neural circuits comprise multiple interconnected regions, each with complex dynamics. The interplay between local and global activity is thought to...

(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...

Interested in high-dim chaotic networks? Ever wondered about the structure of their state space? @jakobstubenrauch.bsky.social has answers - from a separation of fixed points and dynamics onto distinct shells to a shared lower-dim manifold and linear prediction of dynamics.

Our new preprint 👀

Emergence of Robust Memory Manifolds Frozen stabilization enables small unstructured neural networks to form robust continuous memories and exhibit separation of timescales without fine-tuning or symmetries.

Our work on how continuous attractors emerge in unstructured RNNs is now out in PRX Life #PRXLife :
journals.aps.org/prxlife/abst...

Work done with @tankutcan.bsky.social

@sesamedusa.bsky.social

I'll be presenting this at #cosyne2025 (poster 3-50)!

I'll also be giving a talk at the "Collectively Emerged Timescales" workshop on this work, plus other projects on emergent dynamics in neural circuits.

Looking forward to seeing everyone in 🇨🇦!