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Yash Mehta

@yashsmehta.bsky.social
41 followers 37 following 8 posts

Cognitive Science PhD student, Johns Hopkins 🧠 Previously: HHMI Janelia 🇺🇸, AutoML Lab 🇩🇪, Gatsby Unit UCL 🇬🇧 www.yashsmehta.com 🇮🇳

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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
🚀 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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Reposted by Yash Mehta
Konrad Kording @kordinglab.bsky.social · 18/11/2024
Interesting approach to estimate the physiological update rules of synapses: yashsmehta.com/plasticity-p...
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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