Yash Mehta

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!

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)

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)

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)

🚀 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 🧵)

Interesting approach to estimate the physiological update rules of synapses: yashsmehta.com/plasticity-p...

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Thank you, Konrad!