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Reposted by Yash Mehta

Kelsey Han @chihyehan.bsky.social · 30/01/2026

Human visual cortex representations may be much higher-dimensional than earlier work suggested, but are these higher dimensions of cortical activity actually relevant to behavior? Our new paper tackles this by studying how different people experience the same movies. 🧵 www.cell.com/current-biol...

cell.com

High-dimensional structure underlying individual differences in naturalistic visual experience

Han and Bonner reveal that individual visual experience arises from high-dimensional neural geometry distributed across multiple representational scales. By characterizing the full dimensional spectru...

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Yash Mehta @yashsmehta.bsky.social · 12/12/2025

High dimensional representations in the visual cortex, new paper from our lab, check it out!

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Reposted by Yash Mehta

Mick Bonner @mickbonner.bsky.social · 11/12/2025

Dimensionality reduction may be the wrong approach to understanding neural representations. Our new paper shows that across human visual cortex, dimensionality is unbounded and scales with dataset size—we show this across nearly four orders of magnitude. journals.plos.org/ploscompbiol...

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Yash Mehta @yashsmehta.bsky.social · 18/11/2024

Our modeling framework would offer a new avenue for understanding the computational principles of synaptic plasticity and learning in the brain. Research at HHMI Janelia, with fantastic collaborators Danil Tyulmankov, Adithya Rajagopalan, Glenn Turner, James Fitzgerald and @janfunkey.bsky.social!

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Yash Mehta @yashsmehta.bsky.social · 18/11/2024

We applied our technique to behavioral data from Drosophila in a probabilistic reward-learning experiment. Our findings reveal an active forgetting component in reward learning in flies 🪰, improving predictive accuracy over previous models. (4/5)

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Yash Mehta @yashsmehta.bsky.social · 18/11/2024

This method uncovers complex rules inducing long nonlinear time dependencies, involving factors like postsynaptic activity and current synaptic weights. We validate it through simulations, successfully recovering known rules like Oja’s and more intricate ones. (3/5)

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Yash Mehta @yashsmehta.bsky.social · 18/11/2024

website: yashsmehta.com/plasticity-p... Our approach approximates plasticity rules using parameterized functions—either truncated Taylor series for theoretical insights or multilayer perceptrons. We optimize these parameters via gradient descent over entire trajectories to match observed data (2/5)

yashsmehta.com

NeurIPS 2024: Model-Based Inference of Synaptic Plasticity Rules

Inferring the synaptic plasticity rules that govern learning in the brain is a key challenge in neuroscience. We present a novel computational method to infer these rules from experimental data, appli...

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Yash Mehta @yashsmehta.bsky.social · 18/11/2024

🚀 Excited to share our paper has been accepted at #NeurIPS! 🎉 We developed a deep learning framework that infers local learning algorithms in the brain by fitting behavioral or neural activity trajectories during learning. We validate on synthetic data and tested on 🪰 behavioral data (1/5 🧵)

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