Speakers
Description
How mouse primary visual cortex (V1) represents time-varying stimuli is typically studied either through pairwise correlations or through black-box decoders that generalise poorly across animals. We ask whether the temporal reconfiguration of higher-order co-activation patterns carries stimulus information that these approaches miss, and whether topological data analysis can summarise it compactly.
We apply zigzag persistent homology to frame-by-frame population activity from the Sensorium-2023 movie dataset. We interpolate single-neuron responses onto a spatial grid and track loop-like (H₁) co-activation structures as they are born and die over time through a compressed summary statistic.
Two findings stand out. First, this topological summary transfers across animals: with only a linear classifier it matches or beats a black box model (3D-CNN trained on the raw grid) in cross-mouse decoding, even though the CNN wins within-animal. We run ablations to confirm the different source of signal: shuffling frame order or Fourier phase destroys the zigzag signal but not the CNN, while spatial shuffling does the reverse, thus identifying zigzag as a temporal, animal-invariant code.
Second, we confirm that the cross-mouse signal adds decoding power beyond standard descriptors: when the same decoder is already given firing rates, pairwise correlations, and higher-order summaries (co-active triplet and quadruplet counts, graph-theoretic measures) computed from the same activity, turnover still contributes a unique, reliable increment that none of those descriptors accounts for. This information lives in the temporal coherence of multiway co-activation, not in per-frame or lower-order summaries.
| Preferred Presentation | Oral Presentation |
|---|