Simon Schug
@smonsays.bsky.social
📤 916
📥 231
📝 21
postdoc @princeton computational cognitive science ∪ machine learning
https://smn.one
pinned post!
Neural networks used to struggle with compositionality but transformers got really good at it. How come? And why does attention work so much better with multiple heads? There might be a common answer to both of these questions.
about 1 year ago
1
11
0
reposted by
Simon Schug
Brenden Lake
7 days ago
Checking out the Princeton trails on our lab retreat
0
27
1
Does scaling lead to compositional generaliztation? Our
#NeurIPS2025
Spotlight paper suggests that it can -- with the right training distribution. 🧵 A short thread:
6 days ago
1
14
1
reposted by
Simon Schug
Brenden Lake
5 months ago
I'm joining Princeton University as an Associate Professor of Computer Science and Psychology this fall! Princeton is ambitiously investing in AI and Natural & Artificial Minds, and I'm excited for my lab to contribute. Recruiting postdocs and Ph.D. students in CS and Psychology — join us!
4
47
2
Are transformers smarter than you? Hypernetworks might explain why. Come checkout our Oral at
#ICLR
tomorrow (Apr 26th, poster at 10:00, Oral session 6C in the afternoon).
openreview.net/forum?id=V4K...
7 months ago
1
10
0
reposted by
Simon Schug
Taylor Webb
8 months ago
LLMs have shown impressive performance in some reasoning tasks, but what internal mechanisms do they use to solve these tasks? In a new preprint, we find evidence that abstract reasoning in LLMs depends on an emergent form of symbol processing
arxiv.org/abs/2502.20332
(1/N)
loading . . .
Emergent Symbolic Mechanisms Support Abstract Reasoning in Large Language Models
Many recent studies have found evidence for emergent reasoning capabilities in large language models, but debate persists concerning the robustness of these capabilities, and the extent to which they ...
https://arxiv.org/abs/2502.20332
4
114
36
reposted by
Simon Schug
Konrad Kording
9 months ago
New blog post: The principle of neuroscience.
medium.com/@kording/the...
loading . . .
The Principle of Neural Science
I first encountered Principles of Neural Science as a young student of neuroscience. The book was filled with delightful narratives…
https://medium.com/@kording/the-principle-of-neural-science-92eb016c95e8
3
46
6
reposted by
Simon Schug
Marine Schimel
9 months ago
For my first Bluesky post, I'm very excited to share a thread on our recent work with Mitra Javadzadeh, investigating how connections between cortical areas shape computations in the neocortex! [1/7]
www.biorxiv.org/content/10.1...
loading . . .
Dynamic consensus-building between neocortical areas via long-range connections
The neocortex is organized into functionally specialized areas. While the functions and underlying neural circuitry of individual neocortical areas are well studied, it is unclear how these regions op...
https://www.biorxiv.org/content/10.1101/2024.11.27.625691v1
1
19
12
reposted by
Simon Schug
Guillaume Bellec
10 months ago
Pre-print 🧠🧪 Is mechanism modeling dead in the AI era? ML models trained to predict neural activity fail to generalize to unseen opto perturbations. But mechanism modeling can solve that. We say "perturbation testing" is the right way to evaluate mechanisms in data-constrained models 1/8
4
116
48
reposted by
Simon Schug
Mark D Humphries
11 months ago
Cutting it a bit fine, but here’s my review of the year in neuroscience for 2024 The eighth of these, would you believe? We’ve got dark neurons, tiny monkeys, the most complete brain wiring diagram ever constructed, and much more… Published on The Spike Enjoy!
medium.com/the-spike/20...
loading . . .
2024: A Review of the Year in Neuroscience
Feeling a bit wired
https://medium.com/the-spike/2024-a-review-of-the-year-in-neuroscience-84d343155146
7
190
91
reposted by
Simon Schug
Kris Jensen
11 months ago
I wrote an introduction to RL for neuroscience last year that was just published in NBDT:
tinyurl.com/5f58zdy3
This review aims to provide some intuition for and derivations of RL methods commonly used in systems neuroscience, ranging from TD learning through the SR to deep and distributional RL!
loading . . .
An introduction to reinforcement learning for neuroscience | Published in Neurons, Behavior, Data analysis, and Theory
By Kristopher T. Jensen. Reinforcement learning for neuroscientists
https://tinyurl.com/5f58zdy3
6
129
31
reposted by
Simon Schug
Ben Recht
11 months ago
Stitching component models into system models has proven difficult in biology. But how much easier has it been in engineering?
www.argmin.net/p/monster-mo...
loading . . .
Monster Models
Systems-level biology is hard because systems-level engineering is hard.
https://www.argmin.net/p/monster-models
3
12
3
reposted by
Simon Schug
Badr AlKhamissi
11 months ago
🚨 New Paper! Can neuroscience localizers uncover brain-like functional specializations in LLMs? 🧠🤖 Yes! We analyzed 18 LLMs and found units mirroring the brain's language, theory of mind, and multiple demand networks! w/
@gretatuckute.bsky.social
,
@abosselut.bsky.social
,
@mschrimpf.bsky.social
🧵👇
2
105
32
reposted by
Simon Schug
Blake Richards
11 months ago
1/ Okay, one thing that has been revealed to me from the replies to this is that many people don't know (or refuse to recognize) the following fact: The unts in ANN are actually not a terrible approximation of how real neurons work! A tiny 🧵. 🧠📈
#NeuroAI
#MLSky
add a skeleton here at some point
21
151
55
reposted by
Simon Schug
Razvan Pascanu
11 months ago
For my first post on Bluesky .. I'll start by announcing our 2025 edition of EEML which will be in Sarajevo :) ! I'm really excited about it and hope to see many of you there. Please follow the website (and Bluesky account) for more details which are coming soon ..
add a skeleton here at some point
1
32
7
reposted by
Simon Schug
Markus Meister
12 months ago
Have you had private doubts whether we'll ever understand the brain? Whether we'll be able explain psychological phenomena in an exhaustive way that ranges from molecules to membranes to synapses to cells to cell types to circuits to computation to perception and behavior?
1
39
13
reposted by
Simon Schug
Andrew Lampinen
11 months ago
What counts as in-context learning (ICL)? Typically, you might think of it as learning a task from a few examples. However, we’ve just written a perspective (
arxiv.org/abs/2412.03782
) suggesting interpreting a much broader spectrum of behaviors as ICL! Quick summary thread: 1/7
loading . . .
The broader spectrum of in-context learning
The ability of language models to learn a task from a few examples in context has generated substantial interest. Here, we provide a perspective that situates this type of supervised few-shot learning...
https://arxiv.org/abs/2412.03782
2
123
32
reposted by
Simon Schug
Kai Sandbrink
11 months ago
Thrilled to share our NeurIPS Spotlight paper with Jan Bauer*,
@aproca.bsky.social
*,
@saxelab.bsky.social
,
@summerfieldlab.bsky.social
, Ali Hummos*!
openreview.net/pdf?id=AbTpJ...
We study how task abstractions emerge in gated linear networks and how they support cognitive flexibility.
2
65
16
reposted by
Simon Schug
Blake Richards
12 months ago
Great thread from
@michaelhendricks.bsky.social
! Reminds me of something Larry Abbott once said to me at a summer school: Many physicists come into neuroscience assuming that the failure to find laws of the brain was just because biologists aren't clever enough. In fact, there are no laws. 🧠📈 🧪
add a skeleton here at some point
4
68
10
reposted by
Simon Schug
Griffiths Computational Cognitive Science Lab
12 months ago
(1/5) Very excited to announce the publication of Bayesian Models of Cognition: Reverse Engineering the Mind. More than a decade in the making, it's a big (600+ pages) beautiful book covering both the basics and recent work:
mitpress.mit.edu/978026204941...
15
522
134
To help find people at the intersection of neuroscience and AI. Of course let me know if I missed someone or you’d like to be added 🧪 🧠
#neuroskyence
go.bsky.app/CAfmKQs
add a skeleton here at some point
12 months ago
33
50
18
Neural networks used to struggle with compositionality but transformers got really good at it. How come? And why does attention work so much better with multiple heads? There might be a common answer to both of these questions.
about 1 year ago
1
11
0
you reached the end!!
feeds!
log in