美国埃默里大学Chethan Pandarinath和美国芝加哥大学Matthew T. Kaufman合作搭建出基于子帧时间分辨率的钙成像单次试验神经种群动态推理的深度学习框架。相关论文发表在2022年11月24日出版的《自然—神经科学》杂志上。

他们描述了用于发现成像钙电位(RADICaL)的循环自编码器，这是一种深度学习方法，可以在群体水平上克服很多限制。RADICaL扩展了利用峰值活性动态的方法，将其应用于反卷积钙信号，其统计和时间动态与电生理学记录的峰值截然不同。它结合了一种新的网络训练策略，利用双光子(2p)采样的时间来恢复具有高时间精度的网络动态。

在合成测试中，RADICaL比以前的方法更准确地推断出网络状态，特别是对于高频组件。在执行前肢完成任务的小鼠感觉运动区域的2p记录中，RADICaL推断出的网络状态与行为的单一试验变化密切对应，即使在神经元数量大幅减少时也能保持高质量的推断。

据介绍，在大脑的许多区域，神经群就像一个协调的网络，其状态与行为的关系以毫秒为尺度。2p钙成像是探测这种网络尺度现象的有力工具。然而，由于噪声、固有非线性和时间分辨率的限制，从2p测量估计网络状态和动态具有挑战性。

附：英文原文

Title: A deep learning framework for inference of single-trial neural population dynamics from calcium imaging with subframe temporal resolution

Author: Zhu, Feng, Grier, Harrison A., Tandon, Raghav, Cai, Changjia, Agarwal, Anjali, Giovannucci, Andrea, Kaufman, Matthew T., Pandarinath, Chethan

Issue&Volume: 2022-11-24

Abstract: In many areas of the brain, neural populations act as a coordinated network whose state is tied to behavior on a millisecond timescale. Two-photon (2p) calcium imaging is a powerful tool to probe such network-scale phenomena. However, estimating the network state and dynamics from 2p measurements has proven challenging because of noise, inherent nonlinearities and limitations on temporal resolution. Here we describe Recurrent Autoencoder for Discovering Imaged Calcium Latents (RADICaL), a deep learning method to overcome these limitations at the population level. RADICaL extends methods that exploit dynamics in spiking activity for application to deconvolved calcium signals, whose statistics and temporal dynamics are quite distinct from electrophysiologically recorded spikes. It incorporates a new network training strategy that capitalizes on the timing of 2p sampling to recover network dynamics with high temporal precision. In synthetic tests, RADICaL infers the network state more accurately than previous methods, particularly for high-frequency components. In 2p recordings from sensorimotor areas in mice performing a forelimb reach task, RADICaL infers network state with close correspondence to single-trial variations in behavior and maintains high-quality inference even when neuronal populations are substantially reduced.

DOI: 10.1038/s41593-022-01189-0

Source: https://www.nature.com/articles/s41593-022-01189-0