We wrote a little #NeuroAI piece about in-context learning & neural dynamics vs. continual learning & plasticity, both mechanisms to flexibly adapt to changing environments: arxiv.org/abs/2507.02103 We relate this to non-stationary rule learning tasks with rapid performance jumps. Feedback welcome!

(1/7) New preprint from Rajan lab! 🧠🤖 @ryanpaulbadman1.bsky.social & Riley Simmons-Edler show–through cog sci, neuro & ethology–how an AI agent with fewer ‘neurons’ than an insect can forage, find safety & dodge predators in a virtual world. Here's what we built Preprint: arxiv.org/pdf/2506.06981

Humans and animals can rapidly learn in new environments. What computations support this? We study the mechanisms of in-context reinforcement learning in transformers, and propose how episodic memory can support rapid learning. Work w/ @kanakarajanphd.bsky.social : arxiv.org/abs/2506.19686

Pleased to share our ICML Spotlight with @eberleoliver.bsky.social, Thomas McGee, Hamza Giaffar, @taylorwwebb.bsky.social. Position: We Need An Algorithmic Understanding of Generative AI What algorithms do LLMs actually learn and use to solve problems?🧵1/n openreview.net/forum?id=eax...

Very proud of @rtpramod.bsky.social and the rest of our team for this lovely work showing that the brain's Physics Network represents object-to-object contact and predicted future events:

Our work, out at Cell, shows that the brain’s dopamine signals teach each individual a unique learning trajectory. Collaborative experiment-theory effort, led by Sam Liebana in the lab. The first experiment my lab started just shy of 6y ago & v excited to see it out: www.cell.com/cell/fulltex...

For almost a decade, there's been a lot of (justified) hand-wringing and paper-writing about fairness issues in AI. This case gets to the heart of a very important question - how much of that work has materially improved the lives of real people? Grateful for this careful & honest investigation.

Our new preprint from Rajan lab (Harvard): "Deep RL Needs Deep Behavior Analysis: Exploring Implicit Planning by Model-Free Agents in Open-Ended Environments" Sophisticated & sometimes insect-like planning, exploration, predator evasion, and foraging strategies by DRL. arxiv.org/abs/2506.06981

A big challenge for comp social neuro is to go to more naturalistic small groups while still doing controlled, goal-oriented experiment & analysis. We present a strong effort in that direction (from RIKEN) showing how humans balance memory, reciprocity, value. w/ fMRI www.biorxiv.org/content/10.1...

"We find that all five studied off-the-shelf [military-related] LLMs show forms of escalation and difficult-to-predict escalation patterns.. models tend to develop arms-race dynamics, leading to greater conflict, and in rare cases, even to the deployment of nuclear weapons." arxiv.org/abs/2401.03408

"In contrast to past systematic replication efforts.. replication attempts here produced the expected effects with significance testing (P < 0.05) in 86% of attempts... justifies confidence in rigour-enhancing methods to increase the replicability of new discoveries" www.nature.com/articles/s41...

"We show that even in a simple, idealised network model, many mechanistically different plasticity rules are equally compatible with empirical data... Our results suggest the need for a shift in the study of plasticity rules" www.biorxiv.org/content/10.1...

Next we created a nonlinear latent variable model of OFC activity in our task using CEBRA, the awesome new method from @trackingactions.bsky.social' lab, to understand how the task is encoded in the neural circuit at the level of aggregate neural dynamics

Absolutely thrilled to share my postdoc work in the Axel lab. We found odor-evoked representations of the intrinsic value of information in mouse orbitofrontal cortex and showed that mice desire knowledge as its own reward. Now on bioRxiv! www.biorxiv.org/content/10.1...