哥伦比亚大学Liam Paninski团队取得一项新突破。他们探明了基于模型压缩感知的全息集合刺激下神经回路的快速学习。相关论文于2025年9月17日发表在《自然—神经科学》杂志上。

在这里，研究组开发了新的计算工具，当结合起来时，可以从高速全息集合刺激中学习单突触连接。首先，该团队开发了一种基于模型的压缩感知算法，该算法可以从同时刺激多个神经元引起的突触后反应中识别连接，从而大大提高了映射效率。其次，该课题组开发了一种深度学习方法，分离每个刺激的突触后反应，允许刺激在集合之间快速切换，而无需等待突触后反应返回基线。总之，他们的系统将连接映射的吞吐量提高了一个数量级，有助于发现神经计算背后的电路。

据介绍，在单突触连接水平上发现计算是如何在大脑中实现的，需要探测突触前候选神经元的潜在需求的连接。双光子光遗传学是一种很有前途的技术，可以通过对单个神经元的顺序刺激来绘制这种连接，同时记录细胞内的突触后反应。然而，这种技术目前还不能扩展，因为一个接一个地刺激神经元需要非常长的实验时间。

附：英文原文

Title: Rapid learning of neural circuitry from holographic ensemble stimulation enabled by model-based compressed sensing

Author: Triplett, Marcus A., Gajowa, Marta, Antin, Benjamin, Sadahiro, Masato, Adesnik, Hillel, Paninski, Liam

Issue&Volume: 2025-09-17

Abstract: Discovering how computations are implemented in the brain at the level of monosynaptic connectivity requires probing for connections from potentially thousands of presynaptic candidate neurons. Two-photon optogenetics is a promising technology for mapping such connectivity via sequential stimulation of individual neurons while recording postsynaptic responses intracellularly. However, this technique is currently not scalable because stimulating neurons one by one requires prohibitively long experiments. Here we developed novel computational tools that, when combined, enable learning of monosynaptic connectivity from high-speed holographic ensemble stimulation. First, we developed a model-based compressed sensing algorithm that identifies connections from postsynaptic responses evoked by stimulating many neurons at once, greatly increasing mapping efficiency. Second, we developed a deep-learning method that isolates the postsynaptic response to each stimulus, allowing stimulation to rapidly switch between ensembles without waiting for the postsynaptic response to return to baseline. Together, our system increases the throughput of connectivity mapping by an order of magnitude, facilitating discovery of the circuitry underlying neural computations.

DOI: 10.1038/s41593-025-02053-7

Source: https://www.nature.com/articles/s41593-025-02053-7