Arna Ghosh

Arna Ghosh

@arnaghosh.bsky.social

Research Scientist at Google Research, working on Bio-inspired AI • PhD at Mila & McGill University, Vanier scholar • Ex-RealityLabs, Meta AI • Comedy+Cricket enthusiast

326 followers229 following59 posts

Arna Ghosh reposted

Arna Ghosh reposted

Here's what I think everyone is missing about this issue: Prediction is how we identify understanding! If we had a model that could do OOD prediction, then we would rightly say it understands. Of course, that doesn't mean understanding *necessarily* emerges from training to predict. #NeuroAI

The Transmitter @thetransmitter.bsky.social

Prediction without understanding sustained astronomy through a thousand years of epicycles, writes @tonyzador.bsky.social. AI is now offering neuroscience the same deal. #neuroskyence www.thetransmitter.org/machine-lear...

Arna Ghosh reposted

New paper out at PNAS: www.pnas.org/doi/10.1073/... Revisiting the high-dimensional geometry of population responses in the visual cortex with @jpillowtime.bsky.social. The review took forever because a reviewer was doubtful our new estimator can infer eigenvalues beyond the rank of the data! (1/6)

pnas.orgPNASProceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans...

Arna Ghosh reposted

Are you thinking about doing neuroscience outreach but want to make it more exciting or hands on? Check out RetINaBox! (A collab led by the Trenholm lab) We tried to bring the experience of experimental neuroscience to a classroom setting: www.eneuro.org/content/13/1... #neuroscience 🧪

eneuro.orgRetINaBox: A Hands-On Learning Tool for Experimental NeuroscienceAn exciting aspect of neuroscience is developing and testing hypotheses via experimentation. However, due to logistical and financial hurdles, the experiment and discovery component of neuroscience is...

Whoaaa!! This is a fantastic effort, and an amazing resource. Huge congratulations to the authors! 🎉

Apurva Ratan Murty@apurvaratan.bsky.social

Need more fMRI data (beyond the amazing NSD)? Introducing MOSAIC! Incredible effort led expertly by Ben Lahner, with help from grad student Mayukh Deb. Work in collaboration with the amazing Aude Oliva! @neurosky.bsky.social. More below..

In San Diego attending #NeurIPS2025? Come to our poster to talk more about representation geometry in LLMs. 😃 🗓️ Friday 4:30-7:30 pm session 📍 Exhibit Hall C, D, E 🏁 Poster # 2502

Arna Ghosh@arnaghosh.bsky.social

LLMs are trained to compress data by mapping sequences to high-dim representations! How does the complexity of this mapping change across LLM training? How does it relate to the model’s capabilities? 🤔 Announcing our #NeurIPS2025 📄 that dives into this. 🧵below #AIResearch #MachineLearning #LLM

Arna Ghosh reposted

Arna Ghosh reposted

1/ Why does RL struggle with social dilemmas? How can we ensure that AI learns to cooperate rather than compete? Introducing our new framework: MUPI (Embedded Universal Predictive Intelligence) which provides a theoretical basis for new cooperative solutions in RL. Preprint🧵👇 (Paper link below.)

Population coding 🙌

The Transmitter @thetransmitter.bsky.social

“I will die on the hill that population coding is the relevant level of encoding information in the brain.” In the latest “This paper changed my life,” Nancy Padilla-Coreano discusses a paper on mixed selectivity neurons. #neuroskyence www.thetransmitter.org/this-paper-c...

This is an excellent blueprint on a very fascinating use of AI scientist! And the results and super cool and interesting! 🤩 I have been asked this when talking about our work on using powerlaws to study representation quality in deep neural networks, glad to have a more concrete answer now! 😃

Kenneth Harris@kenneth-harris.bsky.social

1. New preprint resolving a conundrum in systems neuroscience with an AI scientist, and humans Reilly Tilbury, Dabin Kwon, @haydari.bsky.social, @jacobmratliff.bsky.social, @bio-emergent.bsky.social, @carandinilab.net, @kevinjmiller.bsky.social, @neurokim.bsky.social www.biorxiv.org/content/10.1...

Arna Ghosh reposted

Conrad Hal Waddington was born OTD in 1905. His “epigenetic landscape” is a diagrammatic representation of the constraints influencing embryonic development. On his 50th birthday, his colleagues gave him a pinball machine on the model of the epigenetic landscape. 🧪 🦫🦋 🌱🐋 #HistSTM #philsci #evobio

Arna Ghosh reposted

A tad late (announcements coming) but very happy to share the latest developments in my previous preprint! Previously, we show that neural representations for control of movement are largely distinct following supervised or reinforcement learning. The latter most closely matches NHP recordings.

Olivier Codol@oliviercodol.bsky.social

Here’s our latest work at @glajoie.bsky.social and @mattperich.bsky.social ‘s labs! Excited to see this out. We used a combination of neural recordings & modelling to show that RL yields neural dynamics closer to biology, with useful continual learning properties. www.biorxiv.org/content/10.1...

LLMs are trained to compress data by mapping sequences to high-dim representations! How does the complexity of this mapping change across LLM training? How does it relate to the model’s capabilities? 🤔 Announcing our #NeurIPS2025 📄 that dives into this. 🧵below #AIResearch #MachineLearning #LLM

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LLMs are trained to compress data by mapping sequences to high-dim representations! How does the complexity of this mapping change across LLM training? How does it relate to the model’s capabilities? 🤔 Announcing our #NeurIPS2025 📄 that dives into this. 🧵below #AIResearch #MachineLearning #LLM

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BONUS: Is task-relevant info contained in the top eigendirections? On SciQ: - Removing top 10/50 directions barely hurts accuracy.✅ - Retaining only top 10/50 directions CRUSHES accuracy.📉 As supported by our theoretical results, eigenspectrum tail encodes critical task information! 🤯 🧵7/9

Takeaway: LLM training exhibits multi-phasic information geometry changes! ✨ - Pretraining: Compress → Expand (Memorize) → Compress (Generalize). - Post-training: SFT/DPO → Expand; RLVR → Consolidate. Representation geometry offers insights into when models memorize vs. generalize! 🤓 🧵8/9

LLMs are trained to compress data by mapping sequences to high-dim representations! How does the complexity of this mapping change across LLM training? How does it relate to the model’s capabilities? 🤔 Announcing our #NeurIPS2025 📄 that dives into this. 🧵below #AIResearch #MachineLearning #LLM

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Post-training also yields distinct geometric signatures: - SFT & DPO exhibit entropy-seeking expansion, favoring instruction memorization but reducing OOD robustness.📈 - RLVR exhibits compression-seeking consolidation, learning reward-aligned behaviors at the cost of reduced exploration.📉 🧵5/9

Why do these geometric phases arise?🤔 We show, both through theory and with simulations in a toy model, that these non-monotonic spectral changes occur due to gradient descent dynamics with cross-entropy loss under 2 conditions: 1. skewed token frequencies 2. representation bottlenecks 🧵6/9

LLMs are trained to compress data by mapping sequences to high-dim representations! How does the complexity of this mapping change across LLM training? How does it relate to the model’s capabilities? 🤔 Announcing our #NeurIPS2025 📄 that dives into this. 🧵below #AIResearch #MachineLearning #LLM

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LLMs have 3 pretraining phases: Warmup: Rapid compression, collapsing representation to dominant directions. Entropy-seeking: Manifold expansion, adding info in non-dominant directions.📈 Compression-seeking: Anisotropic consolidation, selectively packing more info in dominant directions.📉 🧵3/9

How do these phases relate to LLM behavior? - Entropy-seeking: Correlates with short-sequence memorization (♾️-gram alignment). - Compression-seeking: Correlates with dramatic gains in long-context factual reasoning, e.g. TriviaQA. Curious about ♾️-grams? See: bsky.app/profile/liuj... 🧵4/9

Jiacheng Liu@liujch1998.bsky.social

As infini-gram surpasses 500 million API calls, today we're announcing two exciting updates: 1. Infini-gram is now open-source under Apache 2.0! 2. We indexed the training data of OLMo 2 models. Now you can search in the training data of these strong, fully-open LLMs. 🧵 (1/4)

LLMs are trained to compress data by mapping sequences to high-dim representations! How does the complexity of this mapping change across LLM training? How does it relate to the model’s capabilities? 🤔 Announcing our #NeurIPS2025 📄 that dives into this. 🧵below #AIResearch #MachineLearning #LLM

📐We measured representation complexity using the #eigenspectrum of the final layer representations. We used 2 spectral metrics: - Spectral Decay Rate, αReQ: Fraction of variance in non-dominant directions. - RankMe: Effective Rank; #dims truly active. ⬇️αReQ ⇒ ⬆️RankMe ⇒ More complex! 🧵1/9

Very cool study, with interesting insights about theta sequences and learning!

Abhilasha Joshi, PhD@rhythmicspikes.bsky.social

1/ 🚨 New preprint! 🚨 Excited and proud (& a little nervous 😅) to share our latest work on the importance of #theta-timescale spiking during #locomotion in #learning. If you care about how organisms learn, buckle up. 🧵👇 📄 www.biorxiv.org/content/10.1... 💻 code + data 🔗 below 🤩 #neuroskyence

Arna Ghosh reposted

This looks like a very cool result! 😀 Can't wait to read in detail.

Samuel Liebana@samuel-liebana.bsky.social

Does the brain learn by gradient descent? It's a pleasure to share our paper at @cp-cell.bsky.social, showing how mice learning over long timescales display key hallmarks of gradient descent (GD). The culmination of my PhD supervised by @laklab.bsky.social, @saxelab.bsky.social and Rafal Bogacz!

Also, big shoutout to @quentin-garrido.bsky.social+gang and @aggieinca.bsky.social+gang for developing Rankme and Lidar, respectively. Reptrix incorporates these representation quality metrics. 🚀 Let's make it easier to select good SSL/foundation models. 💪

Arna Ghosh@arnaghosh.bsky.social

Are you training self-supervised/foundation models, and worried if they are learning good representations? We got you covered! 💪 🦖Introducing Reptrix, a #Python library to evaluate representation quality metrics for neural nets: github.com/BARL-SSL/rep... 🧵👇[1/6] #DeepLearning

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✨ Key Features of Reptrix: - 📈 Suite of metrics to assess representation quality: α-ReQ, RankMe, LiDAR, and more! - 🤝 Seamless PyTorch integration for minimal setup, maximum insights - 💻 Open Source: Contribute and enhance! [3/6]

Metrics for evaluating representation quality: - α-ReQ: Measures discriminativeness. Lower alpha = better! - RankMe: Assesses representation capacity. Higher rank = higher capacity! - LiDAR: Evaluates separability among object manifolds. Higher rank = better separability! [4/6]

Inspired by conversations after our α-ReQ paper (NeurIPS 2022) and subsequent work, we created Reptrix as an open-source library for assessing representation quality across models of vision, language… and more. Check out our @mila-quebec.bsky.social blogpost: mila.quebec/en/article/a... [2/6]

mila.quebecα-ReQ: Assessing Representation Quality in SSL | MilaThe success of self-supervised learning algorithms has drastically changed the landscape of training deep neural networks. With well-engineered architectures and training objectives, SSL models learn ...

Super cool paper! It formalizes a lot of ideas I have been mulling over the past year, and connects tons of historical ideas neatly. Definitely worth a read if you are working/interested in mechanistic interp and neural representations.

David Klindt@david-klindt.bsky.social

🔵 New paper! We explore sparse coding, superposition, and the Linear Representation Hypothesis (LRH) through identifiability theory, compressed sensing, and interpretability. If you’re curious about lifting neural reps out of superposition, this might interest you! 🤓 arxiv.org/abs/2503.01824

Arna Ghosh reposted

Just giving this a boost for those who may not have seen it yet... we have a PI position (molecular and cellular basis of cognition) at The Hospital for Sick Children (Toronto). The position comes with an appointment at Assist/Assoc Prof level at U of T. Share widely! can-acn.org/scientist-se...

can-acn.orgScientist/Senior Scientist – Research Institute, Hospital for Sick Children, University of Toronto – Canadian Association for Neuroscience

Come say hi at the noon poster session today in the East hall, poster #2201. 🚀 #NeurIPS2024

Arna Ghosh@arnaghosh.bsky.social

The problem with current SSL? It's hungry. Very hungry. 🤖 Training time: Weeks Dataset size: Millions of images Compute costs: 💸💸💸 Our #NeurIPS2024 poster makes SSL pipelines 2x faster and achieves similar accuracy at 50% pretraining cost! 💪🏼✨ 🧵 1/8