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Published in PNASNews, Maryam Shanechi and her lab present a new #ML method that disentangles internal #brain patterns from sensory input patterns. The results reveal consistent intrinsic brain patterns across different subjects and tasks. viterbischool.usc.edu/news/2024/02/n…

Happy to announce that our paper has been published in PNASNews! We introduce IPSID, an #ML method for disentangling intrinsic & input-driven neural dynamics of behavior. #BCI 🧠💻 Thanks to my co-first author Omid Sani & amazing advisor Maryam Shanechi. pnas.org/doi/10.1073/pn…

📃Our paper on IPSID is out in PNASNews! It was remarkable that after accounting for task instruction dynamics, the learned intrinsic behavior-related neural dynamics were similar across subjects/tasks! 👇Summary🧵 Congrats to Maryam Shanechi & my co-first-author Parsa Vahidi🎉

Can we efficiently learn models of collective dynamics in multimodal discrete-continuous time-series, e.g. spike-LFP? We develop a new multimodal subspace identification method to do so & enable causal multimodal decoding! 👏Parima Ahmadipour Omid Sani J Neural Eng paper, code,🧵⬇️

🚀 Thrilled to announce I'll be presenting our new paper BRAID: Input-driven Nonlinear Dynamical Modeling of Neural-Behavioral Data at #ICLR2025! 🎉 Paper & more in 🧵! Huge thanks to my co-authors Omid Sani & Maryam Shanechi! Catch me at Poster Session 5 Sat Apr 26 | 10 AM