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yashsmehta.bsky.social
Yash Mehta
@yashsmehta.bsky.social
30 followers 32 following 7 posts
Cognitive Science PhD student, Johns Hopkins 🧠 Previously: HHMI Janelia 🇺🇸, AutoML Lab 🇩🇪, Gatsby Unit UCL 🇬🇧 www.yashsmehta.com 🇮🇳
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yashsmehta.bsky.social
Yash Mehta @yashsmehta.bsky.social · Nov 18
🚀 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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yashsmehta.bsky.social
Yash Mehta @yashsmehta.bsky.social · Nov 18
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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yashsmehta.bsky.social
Yash Mehta @yashsmehta.bsky.social · Nov 18
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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yashsmehta.bsky.social
Yash Mehta @yashsmehta.bsky.social · Nov 18
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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yashsmehta.bsky.social
Yash Mehta @yashsmehta.bsky.social · Nov 18
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)
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...
yashsmehta.com
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yashsmehta.bsky.social
Yash Mehta @yashsmehta.bsky.social · Nov 18
🚀 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
kordinglab.bsky.social
Konrad Kording @kordinglab.bsky.social · Nov 18
Interesting approach to estimate the physiological update rules of synapses: yashsmehta.com/plasticity-p...
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...
yashsmehta.com
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yashsmehta.bsky.social
Yash Mehta @yashsmehta.bsky.social · Nov 18
🙌🏼🙌🏼
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yashsmehta.bsky.social
Yash Mehta @yashsmehta.bsky.social · Nov 18
Thank you, Konrad!
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