In S1–M1 recordings 🧠, BiXformer splits M1 activity into S1→M1 and M1→S1 streams, revealing M1 neurons driven almost entirely by S1 input and active before movement onset, reflecting top-down signals, not just sensory feedback.
BiXformer separates them using directionally masked attention 🤿, decomposing inter-regional communication into causal & acausal streams. It recovers low-dimensional, directed latent dynamics and estimates communication delays 🛰️ — no linearity or stationarity assumptions needed!
Applied to simultaneous medulla recordings + orofacial tracking during licking 😛, BiXformer separates a motor stream that precedes movement (feedforward command) 🏃 from a sensory stream that follows it (ascending feedback) — pulled from the same neural population. 🔁
Work by amazing PhD student Omar El Sayed (depasquale-lab.github.io/members/omar...), with data and insights from lab of @mikeeconomo.bsky.social collected by @dragoitudor.bsky.social and Yujin Han
🚨New Preprint🚨
from amazing PhD student @ryguy.io!
We augmented the classic DDM to account for state-dependent changes in decision strategy and found a clear improvement. Applied to a novel 24 hour dataset we found support for circadian influence on decisions and more! Check out thread and paper!
National Academy of Sciences experts denounce Trump’s NSF board purge www.scientificamerican.com/article/nati...
🚨 New preprint from the lab! 🚨 We introduce BiXformer, a bidirectional cross-attention transformer for disentangling inter-regional neural dynamics. Recordings from multiple regions superimpose feedforward & feedback signals 🔁, offset in time, making it difficult to tease these signals apart. 🧠 🔁
FY27 Focused Research Programs Spotlight: "AI for Characterizing and Designing Biomolecular Interactions"
Learn more about this FRP: spr.ly/63323B8Y3sz
I had no idea men played soccer, too! Good for them! Go boy soccer!! ⚽️
Computational models are a key part of science but discovering new ones is hard!
DataDIVER discovers concise models from data, which surface new mechanistic ideas and clear predictions for future experiments
From Google Deepmind Neuroscience Lab + collaborators
www.biorxiv.org/content/10.6...