Nick Blauch
@nblauch.bsky.social
postdoc @ harvard | neural networks | cortical topography | learning | vision | language | neurotech
https://nblauch.github.io
https://nblauch.github.io
well deserved, awesome paper!
September 19, 2025 at 6:54 PM
well deserved, awesome paper!
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.
August 22, 2025 at 3:18 AM
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.
With all due respect to AI, that pizza is way too small
July 20, 2025 at 12:29 AM
With all due respect to AI, that pizza is way too small
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!
June 18, 2025 at 4:44 PM
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!
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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June 16, 2025 at 3:12 PM
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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June 16, 2025 at 3:12 PM
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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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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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
osf.io
June 16, 2025 at 3:12 PM
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.
June 16, 2025 at 3:12 PM
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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June 16, 2025 at 3:12 PM
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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June 16, 2025 at 3:12 PM
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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June 16, 2025 at 3:12 PM
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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June 16, 2025 at 3:12 PM
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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June 16, 2025 at 3:12 PM
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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June 16, 2025 at 3:12 PM
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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June 16, 2025 at 3:12 PM
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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June 16, 2025 at 3:12 PM
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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