(2/8) POCO was trained on spontaneous & task-specific behavior data from zebrafish, mice, & C. elegans. It combines a local forecaster with a population encoder capturing brain-wide patterns, so we track each neuron individually AND how the whole brain affects each cell 🧠

(3/8) POCO forecasts how the brain will behave up to ~15 seconds into the future across behavioral data & species 🔮 After pre-training, POCO’s speed & flexibility allow it to adapt to new recordings with minimal fine-tuning, opening the door for real-time applications.

(4/8) Beyond neural predictions, POCO's learned unit embeddings independently reproduce brain region clustering without any anatomical labels. That means at single-cell resolution across entire brains, POCO mimics biological organization purely from neural activity patterns ✨

(5/8) Other time-series forecasting models perform well on synthetic/simulated data 🤖 POCO dominates in context-dense predictions based on REAL neural data 🧠

(6/8) Combined with its prediction speed and steady improvement from longer recordings/more sessions, POCO shows enormous potential for usage in larger brains & real-time neurotechnologies like “neuro-foundation models” for brain-computer interfaces (BCI).

(7/8) Thanks to @deisseroth.bsky.social‬, @mishaahrens.bsky.social & Chris Harvey for their contributions, and to @kempnerinstitute.bsky.social & @harvardmed.bsky.social‬ for supporting computational neuroscience research. Read the paper here: arxiv.org/abs/2506.14957