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arXiv 2603.03037math.ATq-bio.NC
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神经对时变刺激的锯齿形持续性

Zigzag Persistence of Neural Responses to Time-Varying Stimuli

Area Science Park(地区科学园);Department of Mathematics, Informatics and Geosciences University of Trieste(的里雅斯特大学数学、信息学和地球科学系);SISSA(的里雅斯特高级研究学院)

Yuri Gardinazzi, Alessio Ansuini, Eugenio Piasini, Fabio Anselmi, Matteo Biagetti

AI总结:

该研究利用拓扑数据分析,通过锯齿形持续同调捕捉神经响应随时间变化的拓扑结构,验证了拓扑描述符在区分不同刺激神经响应中的可靠性。

摘要:

我们使用拓扑数据分析来研究Sensorium 2023数据集中的神经群体活动,该数据集记录了成千上万只小鼠视觉皮层神经元对多种视频刺激的响应。对于每个视频,我们从神经元活动构建帧级立方复形,并应用锯齿形持续同调来捕捉拓扑结构随时间的变化。这些动态通过持续景观进行总结,提供了一种紧凑的向量化时间特征表示。我们重点关注一维拓扑特征——数据中的环——这反映了神经元协同激活的协调、周期性模式。为了测试其信息量,我们通过聚类不同视频重复试验的结果来比较。我们的结果表明,这些拓扑描述符能够可靠地区分不同刺激的神经响应。这项工作强调了演变更神经活动与可解释拓扑签名之间的联系,推动了拓扑数据分析在复杂动态系统中揭示神经编码的应用。

英文摘要:

We use topological data analysis to study neural population activity in the Sensorium 2023 dataset, which records responses from thousands of mouse visual cortex neurons to diverse video stimuli. For each video, we build frame-by-frame cubical complexes from neuronal activity and apply zigzag persistent homology to capture how topological structure evolves over time. These dynamics are summarized with persistence landscapes, providing a compact vectorized representation of temporal features. We focus on one-dimensional topological features-loops in the data-that reflect coordinated, cyclical patterns of neural co-activation. To test their informativeness, we compare repeated trials of different videos by clustering their resulting topological neural representations. Our results show that these topological descriptors reliably distinguish neural responses to distinct stimuli. This work highlights a connection between evolving neuronal activity and interpretable topological signatures, advancing the use of topological data analysis for uncovering neural coding in complex dynamical systems.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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