A tubularity trajectory-shape metric distinguishes module-specific geometry in a real trained neural-activity foundation model

Bertram, Dyballa, Keller, Kinger & Zucker build decoding manifolds (PCA of stimulus-averaged responses) and encoding manifolds (tensor factorization plus diffusion maps) from Wang et al.'s real trained FNN, a DenseNet-conv-encoder plus convLSTM plus linear-readout network fit to predict real mouse visual-cortex (MICrONS) responses [bertram-etal-2026-manifolds-and-modules-neural-foundation-model] Layer-wise decoding accuracy rises from 0.59 (early convolutional encoder) to 0.89 (recurrent module) to 0.88 (readout) [bertram-etal-2026-manifolds-and-modules-neural-foundation-model] A novel tubularity metric (trajectory tightness and self-crossings across layers) shows the recurrent module actively pushes apart stimulus trajectories, with significantly fewer crossings than the biological retina/V1 comparison data (p<0.005, Bonferroni-corrected) [bertram-etal-2026-manifolds-and-modules-neural-foundation-model]