RT Journal Article
T1 Topological analysis of sharp-wave ripple waveforms reveals input mechanisms behind feature variations
A1 Rodríguez Sebastián, Enrique
A1 Quintanilla, Juan Pablo
A1 Sánchez-Aguilera López, Alberto
A1 Esparza, Julio
A1 Cid, Elena
A1 Menéndez de la Prida, Liset
AB 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.
PB Nature
SN 1097-6256
YR 2023
FD 2023-11-09
LK https://hdl.handle.net/20.500.14352/111262
UL https://hdl.handle.net/20.500.14352/111262
LA eng
NO Sebastian, E.R., Quintanilla, J.P., Sánchez-Aguilera, A. et al. Topological analysis of sharp-wave ripple waveforms reveals input mechanisms behind feature variations. Nat Neurosci 26, 2171–2181 (2023). https://doi.org/10.1038/s41593-023-01471-9
NO Fundación La Caixa
NO Ministerio de Ciencia (España)
DS Docta Complutense
RD 5 abr 2025