We’ve got an exciting new thing to share! We have causal evidence (using TMR) that memory reactivation during sleep promotes abstract understanding of underlying structure, allowing transfer learning in a new domain with zero superficial feature overlap with the learned one.
New preprint! 🧠
How do RNNs learn abstract rules from sequences, independent of specific stimuli?
By Vezha Boboeva, with Alberto Pezzotta & George Dimitriadis
"From sequences to schemas: low-rank recurrent dynamics underlie abstract relational representations"
www.biorxiv.org/content/10.6...
Two Analytical Connectionism-related updates:
1. ⏰ 1 week left to apply! Interested in language + AI & cognition? Don’t miss it: www.analytical-connectionism.net/school/2026/
2. 📜 Lecture notes from the first two editions are finally out: proceedings.mlr.press/v320/
We're hiring! This is a unique opportunity to translate our understanding of neural computation - from circuit-level mechanisms to computational principles - into the human brain, through the establishment of cutting-edge human neural recording capabilities with collaborators in London and abroad.
We’re hiring a Group Leader!
Join us to lead a transformative initiative in human systems neuroscience.
Find out more and apply ⤵️
www.sainsburywellcome.org/content/curr...
Postdoc opening!
Come work with us on deep learning theory relevant to AI safety
Deadline: 7 Apr 2026
Details and application: www.ucl.ac.uk/work-at-ucl/...
Very excited by this year's Analytical Connectionism Summer School!
A dream lineup of speakers on the topic of language acquisition in minds and machines
Bursaries available to cover costs
Aug 17 – Aug 28, 2026 Gothenburg
Details: www.analytical-connectionism.net//school/2026/
A great entry into the proposals available for physiologically plausible gradient descent!
I think the way they use dendrite targeting inhibition in this model is particularly elegant.
Time to start testing these ideas folks!!!
#neuroscience 🧪 #NeuroAI
The First 1,000 Days (1kD) Project - Collecting and Analyzing an Ultra-Dense Naturalistic Dataset of Human Baby Development www.biorxiv.org/content/10.64898/2026.03...
Looking for alternatives to quadratic functions for closed-form analysis in optimization? This post explores matrix Riccati dynamics and their applications to neural networks. francisbach.com/closed-form-...
Here's a lovely #blueprint on a new study from our lab led by @royeyono.bsky.social.
tl;dr: it implies that there may be interneurons whose role is to normalize credit assignment signals during learning.
#neuroscience 🧪
A new Department of Cognitive Science is being created at Bocconi University in Milan, Italy.
Here is the call for a cluster hire (for around 10 faculty) in all areas of cognitive science, at both junior and senior levels:
www.unibocconi.it/en/faculty-a...
Deadline: May 4th, 2026
Poster tonight at #cosyne26 (1-079)!
@wanqingjiang.bsky.social & @noehamou.bsky.social show that mice learn hidden community structure in a 15-odour graph even when transition statistics are flat.
Fun collaboration with @saxelab.bsky.social that started with East London coffees ☕!
Really neat work by Fountas and colleagues at UCL:
arxiv.org/abs/2603.04688
They propose that consolidation reflects a form of "predictive forgetting" that aids generalization.
Thanks @natmesanash.bsky.social for covering our new work, in @thetransmitter.bsky.social!
📢📢 Announcing this year's conference on the Mathematics of Neuroscience & AI (Rome, 9-12th June). We’ve got a stellar line-up and venue, and invite everyone to join:
www.neuromonster.org
📢 Job alert - Deep Learning Theory & AI Safety
Applications open for a postdoc fellow (@saxelab.bsky.social lab) to study artificial deep networks using techniques from applied maths & stat physics.
⏰ Deadline: 26 Mar 2026
🤝 In collaboration with @stefsm.bsky.social
ℹ️ www.ucl.ac.uk/life-science...
Excited to be co-organising a #cosyne2026 workshop with Alison Comrie on 'algorithms for learning from scratch'! With a great line-up of speakers, we'll be tackling the question of what processes enable naive biological & artificial agents to adapt to new situations. Info here: tinyurl.com/4u8enf7k
📢 We’re now accepting applications for the 2026 School on Analytical Connectionism dedicated this year to Language Acquisition.
📍 Gothenburg, Sweden
🗓️ August 17–28, 2026
☠️ Apply by April 17!
🔗 analytical-connectionism.net/school/2026/
👇 Meet the experts joining us this summer!
Thrilled to finally share this work! 🧠🔊
Using a new reinforcement-free task we show mice (like humans) extract abstract structure from sound (unsupervised) & dCA1 is causally required by building factorised, orthogonal subspaces of abstract rules.
Led by Dammy Onih!
www.biorxiv.org/content/10.6...
How to apply:
Salary: USD 80,000–100,000 (50-74k GBP) annualised
Initial contract: 6 months, w/ extension based on funding
Details: docs.google.com/document/d/1...
Application: forms.gle/xKukH74iX16p...
4
We’re hiring postdocs/research scientists! Your interests can be anywhere on the spectrum from pure theory to empirically testing predictions relevant to AI safety.
Our theoretical work relies on dynamical systems and tools from statistical physics.
3
We avoid many unwanted outcomes in the physical world using our knowledge of physics, and basic deep learning theory should eventually enable the same for AI.
We focus on simple, analytically tractable “model organisms” that capture essential learning dynamics and behaviours.
2
Excited to launch Principia, a nonprofit research organisation at the intersection of deep learning theory and AI safety.
Our goal is to develop theory for modern machine learning systems that can help us understand complex network behaviors, including those critical for AI safety and alignment.
1
Our paper is out in @natneuro.nature.com!
www.nature.com/articles/s41...
We develop a geometric theory of how neural populations support generalization across many tasks.
@zuckermanbrain.bsky.social
@flatironinstitute.org
@kempnerinstitute.bsky.social
1/14
A great question, I'm not sure. It's important to understand if muon shares similar inductive biases.
I agree, there seem to be connections but it's not fully clear to me why. SLT is a static theory, and yet Daniel Murfet and others have shown that the stages we see also correspond to SLT posteriors of increasing complexity.
Upcoming online talk next Monday 9th February, at the ELLIS Reading Group on Mathematics & Efficiency of Deep Learning!
Open to all. Info at
sites.google.com/view/efficie...
Equipped with this theory, we make new predictions about how network width, data distribution, and initialization affect learning dynamics. For example, increasing the number of attention heads in linear attention shortens the plateaus in learning.
So when progressing simple -> complex, linear networks learn solutions of increasing rank, ReLU networks learn solutions with increasing kinks, convolutional networks learn solutions with increasing convolutional kernels, and attention models learn solutions with increasing heads.
Here the notion of simplicity is the number of effective units in the architecture: hidden neurons, convolutional kernels, or attention heads.
