Allen School researchers develop machine learning technique to capture the chatter between brain regions - Allen School News Understanding how different parts of the brain communicate is like trying to follow conversations at a crowded party. Neuroscientists face a similar challenge: even when they can record signals from m...

(7/7) We see MR-LFADS as a promising new tool for discovering principles of brain-wide information processing from large-scale neural datasets. Feedback, questions, or ideas are welcome!

And here’s a great blog post overview by Kristine White @uwcse.bsky.social : tinyurl.com/3h9yd6uu

(6/7) 🧪 In real electrophysiology data (Neuropixels in mice), MR-LFADS predicts brain-wide effects of circuit perturbations (ALM photoinhibition) that it had never seen during training!

(5/7) 🔍 We evaluate on 37 synthetic multi-region datasets, covering challenging and diverse scenarios. MR-LFADS reliably recovers both communication pathways (“effectomes”) and content, outperforming prior methods.

(4/7)
Communication from data-constrained inferred firing rates, rather than from overly flexible latent factors
Unsupervised inference of unobserved influences
Region-specific nonlinear dynamics modules
Structured KL bottlenecks support disentangling of all above

(3/7) Our contribution: Multi-Region LFADS (MR-LFADS), a sequential-VAE built on the powerful LFADS framework (Nature Methods, 2018), with 4 key design features that support accurate identification of single-trial communication:

(2/7) When modeling multi-region neural recordings, we found it difficult yet critically important to disentangle communication between recorded regions, unobserved influences (e.g., unrecorded regions), and local-region neural population dynamics.

Accurate identification of communication between multiple interacting neural populations Neural recording technologies now enable simultaneous recording of population activity across many brain regions, motivating the development of data-driven models of communication between brain region...

(1/n) How can we infer single-trial communication between 🧠 regions? Check out our ICML 2025 paper: “Accurate Identification of Communication Between Multiple Interacting Neural Populations,” by Belle Liu and @jsacks.bsky.social in my lab: arxiv.org/abs/2506.19094