.png){kind=icon}
.png){kind=icon}
Return
A likelihood-based method for identifying replay from spike sequences
.png){kind=icon}
OA
AI
DOI：10.1038/s41467-026-74822-2.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Hippocampal replay—sequential reactivation of place cells—has been implicated in memory recall, consolidation and planning, but traditional analyses rely on spatial tuning and exclude non-place cells, limiting their scope. Model-based analyses without spatial templates often depend on predefined parameters and constrained pattern definitions, making them parameter-sensitive and potentially biased. To address these limitations, we developed an analysis that estimates the likelihood of spike sequences based on pairwise firing order probabilities observed during active behavior regardless of their behavioral correlates. We validated the method using simulations, rat single-unit recordings, and mouse calcium imaging, showing strong agreement with conventional replay metrics and broad applicability. When applied to head-fixed mice passively viewing naturalistic movie stimuli, the method detected significant replay in both hippocampus and visual cortex, demonstrating its utility in uncovering structured replay across brain regions and sensory domains. How the brain replays past experiences beyond spatial memories remains unclear. Here the authors introduce a new method based on neural firing order, revealing structured replay in both the hippocampus and visual cortex.
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
15.7
Papers:
9.3W
Citations:
91.2W