Neuron· Matthew G. Perich, Charlotte Arlt, Sofia Soares, Siyan Zhou, Manuel Beiran, Aaron S. Andalman, Tyler Benster, Megan E. Young, Clayton P. Mosher, Juri Minxha, Eugene Carter, Ueli Rutishauser, Peter H. Rudebeck, Christopher D. Harvey, Karl Deisseroth, Kanaka Rajan·2026-08-07 08:00· 2026-08-07AI 评分62

Perich 等提出 CURBD：用数据约束的循环神经网络模型推断全脑相互作用

Inferring brain-wide interactions using data-constrained recurrent neural network models

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Perich 等人提出 CURBD，一种从大规模神经记录中提取多脑区相互作用结构的方法，其核心是用数据约束的循环神经网络模型在单细胞分辨率上推断相互作用。依据摘要，作者在斑马鱼幼体、小鼠、猴和人类的钙成像与电生理数据集上展示了 CURBD 的用途。摘要未报告具体样本量、验证指标、效应量或代码与数据的获取条件，方法细节与适用范围需查阅全文。

摘要

Perich et al. introduce CURBD, a method for extracting multi-regional interaction structure from large-scale neural recordings. CURBD uses data-constrained recurrent neural network models to extract interactions at single-cell resolution. Perich et al. show the utility of CURBD with calcium and electrophysiology datasets from larval zebrafish, mice, monkeys, and humans.

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来源：Neuron · cell.com

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