Very excited to be in Amsterdam for #CCN2025!

See below for my two presentations -- a talk today and a poster Friday. Come say hi!

However, when we trained on less broad distributions of viewing size, the topographic responses became less invariant to retinotopic variation. At small sizes, scenes responded more like objects, and at large sizes, objects responded more like scenes.

9/n

Domain-level responses were highly invariant to the input size, suggesting the model wasn't merely recapitulating the retinotopic responses of the input areas, but had learned to efficiently organize its representations given the viewing biases and retinotopic connectivity.

8/n

Critically, this systematic organization is consistent across model runs (B), as in the human brain (A). When we remove the retinotopic connectivity constraint (C), we see topographic selectivity, but without any group-level consistency, as expected.

7/n

This produces a systematic organization of domains in the medial-lateral axis, putting face representations closer to foveal inputs, and scene representations closer to peripheral inputs, as in human VTC. The organization is functionally relevant, as confirmed lesions.

6/n

We implement this retinotopic constraint as a connectivity cost on V4 feature map inputs into our topographic "ventral temporal cortex" (VTC) layers, w/ viewing biases. Faces and objects are viewed at smaller sizes than scenes, with overlapping distributions.

5/n

Here, we build on the Eccentricity Bias theory, which states that the retinotopic organization of early visual cortex constrains the organization of higher-level visual cortex, since stimuli like faces and words are foveated, while scenes take up the full periphery.

4/n

In all these models, when we change the random initialization, selectivity moves around.

In contrast, the consistency of the topographic layout in humans has been argued to suggest innate pre-specification.

What could explain the consistent global organization?

3/n

And work from many others ( @dyamins.bksy.social, @eshedmargalit, @kalanitgs.bsky.social, @adriendoerig.bsky.social, @timkietzmann.bsky.social, @apurvaratan.bsky.social, @andykeller.bsky.social, @taliakonkle.bsky.social, @bashivan.bsky.social, etc.)

3/n

Recent spatially-constrained deep neural networks have shown how task-optimized learning under local constraints in high-level vision gives rise to smooth organization of representations and functionally relevant clusters of category-selectivity. Ours:

2/n

What shapes the topography of high-level visual cortex?

Excited to share a new pre-print addressing this question with connectivity-constrained interactive topographic networks, titled "Retinotopic scaffolding of high-level vision", w/ Marlene Behrmann & David Plaut.

🧵 ↓ 1/n

Just back from an awesome visit to Georgia Tech to speak at their Computational Cognition Postdoc Day. Very impressed by their community. And really happy to finally have met my good friend and student @neurotaha.bsky.social in person after knowing each other remotely for over 3 years!