Nick Blauch

well deserved, awesome paper!

To be clear, there is nothing inconsistent with 1) large scale “areas” being extremely functionally relevant and 2) them being just one scale of a locally and globally coherent map of activity!

pre-moderation is good -- but since psyarxiv historically ignored post-moderation for months, this result is not fair to authors of legitimate preprints submitted months ago, which are now unavailable at public DOIs. please restore them asap and take down only problematic preprints from pre-switch.

Very excited to be in Amsterdam for #CCN2025!

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

What makes visual processing in the brain so powerful and flexible? Very excited to share our new work where we started from SOTA models that accurately predict dynamic brain activity during hours of video watching, and investigated core computations underlying visual perception

With all due respect to AI, that pizza is way too small

A gradient of complementary learning systems emerges through meta-learning Long-term learning and memory in the primate brain rely on a series of hierarchically organized subsystems extending from early sensory neocortical areas to the hippocampus. The components differ in t...

Excited to share a new preprint w/ @annaschapiro.bsky.social! Why are there gradients of plasticity and sparsity along the neocortex–hippocampus hierarchy? We show that brain-like organization of these properties emerges in ANNs that meta-learn layer-wise plasticity and sparsity. bit.ly/4kB1yg5

Exciting new preprint from the lab: “Adopting a human developmental visual diet yields robust, shape-based AI vision”. A most wonderful case where brain inspiration massively improved AI solutions.

Work with @zejinlu.bsky.social @sushrutthorat.bsky.social and Radek Cichy

arxiv.org/abs/2507.03168

thanks Lenny! I think our 2022 PNAS paper is pretty relevant too - see Figures 5, S8-10, and S21 for sub-domain organization. we don't always get the same sub-domain clusters (except scenes: indoor vs. outdoor), but that is a cool pointer for future models!

Very cool work and very in line with how we've been thinking about category selectivity in topographic neural net models -- one scale of an inherently interactive topographic network.

Their results provide important constraints for the next generation of active, multimodal neural net models.

Can seemingly complex multi-area computations in the brain emerge from the need for energy efficient computation? In our new preprint on predictive remapping in active vision, we report on such a case.

Let us take you for a spin. 1/6 www.biorxiv.org/content/10.1...

A university profile of "Dr Amy Lloyd" who works as a research fellow on Alzheimer's research

Has nominative determinism gone too far

h/t @benjaminschneider.ch

BREAKING: A federal judge reversed National Institutes of Health's terminations of hundreds of critical research grants that were canceled because of their alleged connection to disfavored topics, including diversity, equity, inclusion, and gender identity.

This is a major win for public health.

Thanks to my Ph.D advisors Dave Plaut and Marlene Behrmann for their amazing advising on this work! And thanks to my current postdoc home in the Harvard Vision Lab for really productive discussions on presentations of this work leading to the writeup. Thanks for reading!

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If you like this sort of work -- be sure to check out the fantastic upcoming CCN workshop on biophysical modeling of the human brain. neuroailab.github.io/modeling-th...

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Retinotopic scaffolding of high-level vision Functional specialization within high-level vision is reflected in the topographic organization of the ventral temporal cortex (VTC). The presence and consistent locations of small areas responding selectively to particular visual categories – such as faces and scenes – has led to proposals of innate domain-specific modules. However, such proposals do not easily explain other aspects of an apparently multi-scale topographic organization of high-level visual features in VTC. Computational models have recently accounted for the presence of domain-selective areas and other facets of topographic organization from a basic optimization process with local topographic pressures, such as locally constrained connectivity, but fail to account for the consistent location of category-selectivity. In the current work, we extend a recent computational model to demonstrate how this consistency may emerge from wiring constraints external to VTC, focusing on the role of retinotopically organized early v

Lots more discussion of these and related ideas in the paper, if you're interested, check it out here: osf.io/preprints/p...

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Moreover, a move towards active foveated vision will allow for much more realistic modeling of viewing biases. I've been working a new model for foveated vision with my postdoc advisors @talia_konkle and @grez72 and we hope to release a pre-print and code later this summer.

Note: we are not suggesting retinotopic connectivity is the only constraint on high-level visual organization. Future models must wrestle with the role of other long-range connectivity. This will require more sophisticated interfaces between vision and cognition.

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In summary, our work highlights how the same principle of connectivity constrained task optimization that has explained the presence of topographic organization, can also explain the global topographic organization of the brain, when external connectivity is considered.

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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.

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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.

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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.

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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.

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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.

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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.

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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?

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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.)

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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:

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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

Our new story. Now also posted by the official side. :)

Activity patterns drift. Representational maps are preserved.
Even after single neuron ablations, representational maps are recovered within days.