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Related Concept Videos

Neural Regulation01:37

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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Related Experiment Videos

POCO: Scalable Neural Forecasting through Population Conditioning.

Yu Duan1,2, Hamza Tahir Chaudhry3, Misha B Ahrens4

  • 1EECS, MIT.

Advances in Neural Information Processing Systems
|May 11, 2026
PubMed
Summary
This summary is machine-generated.

We developed POCO, a novel forecasting model for predicting neural activity in calcium imaging data. POCO achieves high accuracy across multiple species and sessions, advancing brain dynamics modeling.

Related Experiment Videos

Area of Science:

  • Neuroscience
  • Computational Biology
  • Machine Learning

Background:

  • Predicting future neural activity is crucial for understanding brain dynamics and developing neurotechnologies.
  • Existing models often focus on interpretability or decoding, leaving neural forecasting, especially across multiple sessions of spontaneous calcium recordings, underexplored.

Purpose of the Study:

  • To introduce POCO, a unified model for accurate and generalizable neural forecasting in calcium imaging data.
  • To capture both neuron-specific and brain-wide dynamics for improved prediction accuracy.

Main Methods:

  • Developed POCO, a unified forecasting model combining a lightweight univariate forecaster with a population-level encoder.
  • Trained and validated POCO across five diverse calcium imaging datasets from zebrafish, mice, and C. elegans.
  • Analyzed factors influencing performance, including context length, session diversity, and preprocessing.

Main Results:

  • POCO achieved state-of-the-art accuracy at the cellular resolution for spontaneous behaviors.
  • The model demonstrated rapid adaptation to new recordings with minimal fine-tuning after pre-training.
  • Learned unit embeddings in POCO recovered biologically meaningful structures, like brain region clustering, without anatomical labels.

Conclusions:

  • POCO offers a scalable and adaptable approach for cross-session neural forecasting in calcium imaging.
  • The findings provide insights for designing future neural forecasting models.
  • POCO facilitates the development of adaptive neurotechnologies and large-scale neural foundation models.