What are the systems in neuroscience that we really have something that we can call “explanation” at all relevant levels, other than reflexive feed-forward like circuits.
Here are a few that I would argue are getting there. Obviously not complete explanations but genuinely satisfying.
That’s why i bug you and @audiodrome.bsky.social with long form slack messages 😉
How do neural circuits in the brain implement normalization? 🧠
In our new paper, we show that just normalizing sensory input isn't enough. Crucially, we must also normalize the error signals! 🧵👇
Paper: arxiv.org/abs/2603.17676
2-164 Population structure of reward-induced remapping in the hippocampal CA1
@chenjiang01.bsky.social with @ahalawa.bsky.social @mattyizhenghe.bsky.social @marisosa.bsky.social
With this one in print, I think I finally earned that PhD... 😅
Presented for the first time at the cosyne when the world ended (March 2020). I'll bring over a summary thread from twitter when it was still twitter...
www.sciencedirect.com/science/arti...
We introduce epiplexity, a new measure of information that provides a foundation for how to select, generate, or transform data for learning systems. We have been working on this for almost 2 years, and I cannot contain my excitement! arxiv.org/abs/2601.03220 1/7
Hierarchical unsupervised ?
Another popular answer might be key learning
Generative AI systems are being built primarily for entertainment, design and communication, but their potential for neuroscience is vast. @shahabbakht.bsky.social explores how this technology could help capture an animal’s ecological experience.
#neuroskyence
www.thetransmitter.org/artificial-i...
😉
LiNC Lab Family Photo, everyone dressed in transit themed clothes.
This year's Lab X-mas Family Photos are very zeitgeisty for those living in #Montreal:
#Xmas #Transit
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...
“I don’t tell lies, though I may invent the truth” was his response at one point
I’m pleased to share our new paper, “Hippocampal ripple diversity organizes neuronal reactivation dynamics in the offline brain”, out in @cp-neuron.bsky.social !
With @vitorlds.bsky.social and David Dupret, we show that diversity in ripple current profiles shapes reactivation dynamics
For those interested in open neuroscience learning tools, check out the preprint for “RetINaBox: A hands-on tool for experimental neuroscience" that a couple students in my lab worked on in collaboration with the Trenholm lab:
www.biorxiv.org/content/10.1...
🧠📈 🧪
Stoked to see this paper finally out!
It answers two big questions: where visual objects are encoded in the brain, and how head-direction cells get oriented using visual landmarks.
Super fun collaboration with @mace-lab.bsky.social and Stuart Trenholm.
www.science.org/doi/10.1126/...
Thrilled to share this work, long time in the making! Carried through creatively by @siddjakes.bsky.social after initial design & piloting by @trackingskills.bsky.social, with help from @trackingactions.bsky.social. Modeling in collaboration with Massimo Vergassola & Nicola Rigolli.
NWB just announced that they’re heading for a fiscal cliff next year. 😔
It feels like NWB was really just taking off in terms of data reuse — efforts like these take time and investment.
If you want to help push back their cliff, reach out to @bendichter.com
![What do representations tell us about a system? Image of a mouse with a scope showing a vector of activity patterns, and a neural network with a vector of unit activity patterns
Common analyses of neural representations: Encoding models (relating activity to task features) drawing of an arrow from a trace saying [on_____on____] to a neuron and spike train. Comparing models via neural predictivity: comparing two neural networks by their R^2 to mouse brain activity. RSA: assessing brain-brain or model-brain correspondence using representational dissimilarity matrices](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:e6ewzleebkdi2y2bxhjxoknt/bafkreiav2io2ska33o4kizf57co5bboqyyfdpnozo2gxsicrfr5l7qzjcq)
What do representations tell us about a system? Image of a mouse with a scope showing a vector of activity patterns, and a neural network with a vector of unit activity patterns Common analyses of neural representations: Encoding models (relating activity to task features) drawing of an arrow from a trace saying [on_____on____] to a neuron and spike train. Comparing models via neural predictivity: comparing two neural networks by their R^2 to mouse brain activity. RSA: assessing brain-brain or model-brain correspondence using representational dissimilarity matrices
In neuroscience, we often try to understand systems by analyzing their representations — using tools like regression or RSA. But are these analyses biased towards discovering a subset of what a system represents? If you're interested in this question, check out our new commentary! Thread:
Check out our new review/perspective (w/ @juangallego.bsky.social & Devika Narain) on neural manifolds in the brain! It was a lot of fun to think through these ideas over the past couple of years, and I'm excited it's finally out in the world!
🔗: www.nature.com/articles/s41...
📄: rdcu.be/ex8hW
Self supervised learning of spatial representations from episodic memories. Led by undergraduate student in my lab. Very proud!
What's the best neural evidence that the brain does in-context learning? In other words, learning through activity dynamics rather than through synaptic plasticity.
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. 🧵
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
Manitokan are images set up where one can bring a gift or receive a gift. 1930s Rocky Boy Reservation, Montana, Montana State University photograph. Colourized with AI
Preprint Alert 🚀
Multi-agent reinforcement learning (MARL) often assumes that agents know when other agents cooperate with them. But for humans, this isn’t always the case. For example, plains indigenous groups used to leave resources for others to use at effigies called Manitokan.
1/8
I don’t think this is a misapplication. MI calculations assume ideal observers ; they’re agnostic about which decoder is actually used.
…..that makes mutual information a not-so-useful metric in neuroscience. Agreed on that. But using diff decoders than the brain isnt a misapplication of the term
This is the best one yet
Let’s talk more about it offline sometime
There is a famous paper by physicist Seth Lloyd who argues that there is a finite amount of information in the universe: 10^90
It’s based on the maximum entropy that the observable universe can contain, given its energy and volume.
Mutual Info is relative, but info/entropy isn’t. It’s super cool
