Great thread! Fun fact related to this (maybe a bit): we find that the smaller PCs in the representations of deep neural networks are actually a better predictor of their task performance than the large ones.
Adel Halawa
@ahalawa.bsky.social
Neuroscience PhD student at McGill Co-supervised by Adrien Peyrache & Blake Richards
After a long journey through review, our paper is finally out in Nature Communications. We started with a simple problem: The visual cortex is strongly modulated by movement. So how can it represent the visual world without movement interfering with everything? www.nature.com/articles/s41...
New paper from the Neurosurgery Research Team at BCM! “Neural geometry in the human hippocampus enables generalization across spatial position and gaze” led by @assiachericoni.bsky.social! 🧵 www.cell.com/neuron/abstr...
Neural geometry in the human hippocampus enables generalization across spatial position and gaze
Chericoni et al. show that neurons in the human hippocampus track the positions of multiple agents in a simple pursuit-based video game. They identified place codes for each agent and for gaze. The co...
cell.com
Excited to share our new paper accepted at ICML 2026 with @tyrellturing.bsky.social and Doina Precup! 🇰🇷 See you in Seoul. A major challenge in continual reinforcement learning is balancing: • plasticity (learning new things) • stability (not forgetting old ones) 🧵 1/15
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.
The Transmitter @thetransmitter.bsky.social · 6mo agoNeuroscience has become increasingly concerned with prediction, and machine learning with causal explanation, with each field adopting methods from the other, writes @gershbrain.bsky.social. Will this bring us closer to understanding neural systems? www.thetransmitter.org/the-big-pict...
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
Inhibitory normalization of error signals improves learning in neural circuits
Normalization is a critical operation in neural circuits. In the brain, there is evidence that normalization is implemented via inhibitory interneurons and allows neural populations to adjust to chang...
arxiv.org
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...
Neuronal spiking in the mammalian forebrain is dominated by a heterogeneous ground state
Neuronal firing patterns have significant spatiotemporal variability with no agreed-upon theoretical framework. Using a combined experimental and mode…
sciencedirect.com
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
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...
Seeing the world as animals do: How to leverage generative AI for ecological neuroscience
Generative artificial intelligence will offer a new way to see, simulate and hypothesize about how animals experience their worlds. In doing so, it could help bridge the long-standing gap between…
thetransmitter.org
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...
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...
biorxiv.org
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... 🧠📈 🧪
RetINaBox: A hands-on learning tool for experimental neuroscience
An exciting aspect of neuroscience research is developing and testing hypotheses via experimentation. However, due to logistical and financial hurdles, this compelling part of neuroscience research is...
biorxiv.org
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/...
Visual objects refine head direction coding
Animals use visual objects to guide navigation-related behaviors. However, visual object–preferring areas have yet to be described in the mouse brain, limiting our understanding of how visual objects ...
science.org
Emilie Macé@mace-lab.bsky.social · last yr.Thrilled to share that our work is now published in Science! ✨ We found a preference for visual objects in the mouse spatial navigation system where they dynamically refine head-direction coding. In short, objects boost our inner compass! 🧭 www.science.org/doi/10.1126/... 🧵1/
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.
Mice navigate scent trails using predictive policies
Animals actively sense their environment to extract features of interest to guide behaviors. For mammals, odors are prominent environmental features which are sampled by active modulation of sniffing ...
biorxiv.org
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
Neurodata Without Borders@nwb.org · last yr.NWB just turned 10 years old! Researchers worldwide have downloaded 1.9 PB of NWB data from @dandiarchive.org. This animation shows the reach of NWB, facilitating collaboration across the globe. What impact has open neurophysiology data had on your science? Share your stories! 🧠 @openscience
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:
![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)
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
A neural manifold view of the brain - Nature Neuroscience
Recent advances in neuroscience have revealed how neural population activity underlying behavior can be well described by topological objects called neural manifolds. Understanding how nature, nurture...
nature.com
Attractors are usually not mechanisms - new blog post: open.substack.com/pub/kording/...
Attractors are usually not mechanisms
The mathematical objects can not be. And the "attractor models" have not been established as mechanisms in mammals
open.substack.com
Self supervised learning of spatial representations from episodic memories. Led by undergraduate student in my lab. Very proud!
Herbie(Zizhan) He@herbiehe.bsky.social · last yr.🧠 Can a neural network build a spatial map from scattered episodic experiences like humans do? We introduce the Episodic Spatial World Model (ESWM)—a model that constructs flexible internal world models from sparse, disjoint memories. 🧵👇 [1/12]
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
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
My first-ever 1st author paper is now on bioRxiv 🙂 www.biorxiv.org/content/10.1... Special thanks to my masters supervisor Steve Prescott! Also with @lauramedlock.bsky.social , @cdedek.bsky.social & more
Adaptation in somatosensory afferents improves rate and temporal coding of vibrotactile stimulus features
Adaptation is a common neural phenomenon wherein sustained stimulation evokes fewer action potentials (spikes) over time. Rather than simply reduce firing rate, adaptation may help neurons form better...
biorxiv.org
Many recent posts on free energy. Here is a summary from my class “Statistical mechanics of learning and computation” on the many relations between free energy, KL divergence, large deviation theory, entropy, Boltzmann distribution, cumulants, Legendre duality, saddle points, fluctuation-response…
Neural manifolds: more than the sum of their neurons — a Journal Club article by Juan Alvaro Gallego www.nature.com/articles/s41... @juangallego.bsky.social #neuroscience #neuroskyence
Neural manifolds: more than the sum of their neurons - Nature Reviews Neuroscience
In this Journal Club, Juan Gallego discusses a 2014 article that provided a first causal hint that neural manifolds may not only be a convenient way to interpret neural population activity.
nature.com
1/7: Super excited to share our new paper! This one should be of interest to neuroscientists and deep learning theory folks. This paper was a collaboration with Alexandre Payeur, @averyryoo.bsky.social, Thomas Jiralerspong, @mattperich.bsky.social, Luca Mazzucato, @glajoie.bsky.social
Basic pain researchers Steven Prescott and Stéphanie Ratté critique the clinical relevance of preclinical studies in the field and highlight areas for improvement. By @sydneywyatt.bsky.social #neuroskyence www.thetransmitter.org/pain/basic-p...
Basic pain research ‘is not working’: Q&A with Steven Prescott and Stéphanie Ratté
Prescott and Ratté critique the clinical relevance of preclinical studies in the field and highlight areas for improvement.
thetransmitter.org