Comparison of the geometry estimate on A) measured fMRI data and B) on predicted fMRI data.

<p>We notice the overall reduction in SNR, Signal and, counter-intuitively, Overlap, while Dimensionality increases for manifold geometry estimated directly on data. On the other hand, the overall pattern appears very similar, with the same antagonistic variation in geometric components.</p...

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Bibliographic Details
Main Author:	Ghislain St-Yves (22238830) (author)
Other Authors:	Kendrick Kay (7901603) (author), Thomas Naselaris (103861) (author)
Published:	2025
Subjects:	Neuroscience Biotechnology Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified large fmri dataset g ., objects deep neural networks support accurate classification shot classification performance single geometric property simple &# 8220 shot &# 8221 brain area depends brain activity patterns estimated geometric properties accurate linear classification mid dnn layers concept manifolds differs geometric properties linear classification dnn layers &# 8217 concept manifolds linear classifiers visual cortex vice versa scenes ). recent theory negatively correlated even though encode images effective number directly determine
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Comparison of the geometry estimate on A) measured fMRI data and B) on predicted fMRI data.

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