Geometric constraints in the development of primate extrastriate visual cortex
Kim, H.; Arcaro, M. J.; Imam, N.
Show abstract
Although the mechanisms establishing primary sensory maps are well characterized, how multiple higher-order maps with systematic spatial relationships emerge across neocortex remains unclear. Because the probability and strength of cortical connections fall off steeply with distance, cortical geometry may centrally constrain how higher-order maps are arranged. To test this possibility in a well-characterized sensory system, we develop a growth model embedded in the folded surface geometry of the macaque visual cortex, using fMRI-defined V1 retinotopy as the sole functional anchor. Cortical organization emerges through algorithmic growth from primary visual cortex, governed by distance-dependent activity correlations and competition among developing projections. Without imposing areal boundaries, map orientations, or predefined topographic layouts, this process generates multiple retinotopic maps with systematic mirror reversals and smooth gradients that reflect key structural features of fMRI-derived extrastriate maps. Growth parameters estimated from a population template generalize across individual macaques, while individual cortical geometry shapes fine-scale map variation around a shared organizational scaffold. Together, these results show that conserved growth rules acting on realistic cortical geometry produce stereotyped higher-order retinotopic organization under minimal explicit specification.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- A conserved code for anatomy: Neurons throughout the brain embed robust signatures of their anatomical location into spike trains. 94%
- Predicting distributed working memory activity in a large-scale mouse brain: the importance of the cell type-specific connectome 94%
- An image-computable model for the stimulus selectivity of gamma oscillations 94%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.