Can we efficiently learn models of collective dynamics in multimodal discrete-continuous time-series, e.g., spike-LFP? We develop a multimodal subspace identification method to do so & enable causal multimodal decoding! 👏P Ahmadipour! 📜J Neural Eng Paper: iopscience.iop.org/article/10.1... Code+🧵⬇️ - ThreadSky

maryamshanechi.bsky.social • 75 days ago

Can we efficiently learn models of collective dynamics in multimodal discrete-continuous time-series, e.g., spike-LFP?

We develop a multimodal subspace identification method to do so & enable causal multimodal decoding!

👏P Ahmadipour!
📜J Neural Eng Paper: https://iopscience.iop.org/article/10.1088/1741-2552/ad1053
Code+🧵⬇️

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maryamshanechi.bsky.social•75 days ago

Learning dynamical models of multimodal time-series (e.g., spike-LFP neural data) can reveal their collective dynamics & enable multimodal fusion to improve decoding (e.g., of behavior). But this learning often relies on expectation-maximization (EM), which is iterative & slow.

maryamshanechi.bsky.social•75 days ago

We develop multiscale SID, a computationally efficient learning method that extends subspace identification (SID) to multimodal time-series. We also introduce a constrained optimization to learn valid noise statistics, which enables multimodal statistical inference. Inference can be done causally.

maryamshanechi.bsky.social•75 days ago

Using neural data recorded during arm movements, we show that multiscale SID can fuse information across spiking & field potential neural modalities. This results in improved learning of dynamical modes & better behavior (movement) prediction compared to using a single modality.

maryamshanechi.bsky.social•75 days ago

Also, compared to multiscale EM, multiscale SID has a much lower training time, coupled with a better accuracy in dynamical mode identification and a better or similar accuracy in predicting neural activity and behavior.

maryamshanechi.bsky.social•75 days ago

Overall, multiscale SID is particularly beneficial when efficient & accurate multimodal learning and fusion are desired.

👏 Congrats Parima Ahmadipour & Omid Sani. Thanks to collaborator Bijan Pesaran.

📜Paper: https://iopscience.iop.org/article/10.1088/1741-2552/ad1053
💻Code: https://github.com/ShanechiLab/MultiscaleSID

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