Paper: www.nature.com/articles/s41... @natcomms.nature.com
Code: github.com/brendanjohnh... @julialang.org @makie.org
Reproducible code capsule: codeocean.com/capsule/4585...
Work with Prof. Pulin Gong @sydneyphysics.bsky.social!
At the same time, θ travels between hierarchical areas: in a feedback (higher → lower) direction after onset, and feedforward (lower → higher) direction after offset.
On the other hand, γ occurs as brief, localized packets that sharpen during stimulus viewing. These packets lock to θ phase, with preferred phases shifting systematically across layers and the visual hierarchy.
We put forward that θ traveling waves act as a flexible scaffold for local γ, carrying top-down modulation at onset and synchronizing feedforward signals at offset; a spatiotemporal θ-γ code for flexible hierarchical processing.
Spiking is jointly locked to both θ and γ: in hit trials, superficial neurons shift closer to θ troughs and fire more often, linking the nested code directly to successful change detection at the neuronal level.
θ waves travel across layers and flip direction mid-trial; they propagate upward (deep → superficial) after stimulus onset, and reverse direction (superficial → deep) after offset. This reversal is strongest in hit trials, weak during misses, and absent during flashes.
θ-γ coupling is a classical framework for cross-scale neural interactions, but has mostly been studied in time at single or paired sites. Using @alleninstitute.org Neuropixels data from six mouse visual areas during a change-detection task, we uncover the joint spatiotemporal structure of θ and γ.
How does the visual cortex coordinate neural activity over spatial and temporal scales? We found broad θ waves organize local γ bursts and spiking, forming a flexible spatiotemporal code to multiplex feedforward/feedback signals. Now out in full @natcomms.nature.com: doi.org/10.1038/s414... 🧵
New preprint: "Identifying statistical indicators of temporal asymmetry using a data-driven approach"
arxiv.org/abs/2511.15991
_Can we statistically distinguish the forward- versus reverse-time dynamics of a system from a finite time series?_
