小柯机器人

西班牙卡哈尔研究所Liset M. de la Prida研究组利用尖波涟漪（SWRs）拓扑分析揭示了特征变化背后的输入机制。相关论文发表在2023年11月9日出版的《自然-神经科学》杂志上。

研究人员使用拓扑和降维技术来分析在CA1锥体层中记录到的波纹波形。研究表明，SWR波形在低维空间中沿一连续体分布，传达了底层特定突触输入的信息。在这一空间中训练的解码器成功地将单个波纹与其预期的汇和源联系起来，证明了形成SWR变异的生理机制。

此外，研究还发现，在一系列认知任务前后，清醒和睡眠时的SWR波形分离不同，新奇感和学习效果显著。因此，该研究结果表明对波纹波形的拓扑分析有助于人们在生理学对SWR有更深入的理解。

据介绍，海马体中与经验相关神经活动模式的再激活对于学习和记忆至关重要。这些再激活模式及其相关的尖波涟漪变化很大。然而，常用的频谱方法却忽略了这种可变性。

附：英文原文

Title: Topological analysis of sharp-wave ripple waveforms reveals input mechanisms behind feature variations

Author: Sebastian, Enrique R., Quintanilla, Juan P., Snchez-Aguilera, Alberto, Esparza, Julio, Cid, Elena, de la Prida, Liset M.

Issue&Volume: 2023-11-09

Abstract: The reactivation of experience-based neural activity patterns in the hippocampus is crucial for learning and memory. These reactivation patterns and their associated sharp-wave ripples (SWRs) are highly variable. However, this variability is missed by commonly used spectral methods. Here, we use topological and dimensionality reduction techniques to analyze the waveform of ripples recorded at the pyramidal layer of CA1. We show that SWR waveforms distribute along a continuum in a low-dimensional space, which conveys information about the underlying layer-specific synaptic inputs. A decoder trained in this space successfully links individual ripples with their expected sinks and sources, demonstrating how physiological mechanisms shape SWR variability. Furthermore, we found that SWR waveforms segregated differently during wakefulness and sleep before and after a series of cognitive tasks, with striking effects of novelty and learning. Our results thus highlight how the topological analysis of ripple waveforms enables a deeper physiological understanding of SWRs. This study applies topological analysis to hippocampal ripple waveforms, uncovering a low-dimensional continuum that encodes layer-specific synaptic input information. It also reveals how ripple waveforms vary during wakefulness, sleep and learning.

DOI: 10.1038/s41593-023-01471-9

Source: https://www.nature.com/articles/s41593-023-01471-9

Nature Neuroscience：《自然—神经科学》，创刊于1998年。隶属于施普林格·自然出版集团，最新IF：28.771
官方网址：https://www.nature.com/neuro/
投稿链接：https://mts-nn.nature.com/cgi-bin/main.plex