Dimensionality reduction may be the wrong approach to understanding neural representations. Our new paper shows that across human visual cortex, dimensionality is unbounded and scales with dataset size—we show this across nearly four orders of magnitude. journals.plos.org/ploscompbi…

Excited to announce that this paper is now out in @ScienceAdvances science.org/doi/10.1126/scia…

Can we gain a deep understanding of neural representations through dimensionality reduction? Our new work shows that the visual representations of the human brain need to be understood in high dimensions. w/ @RajThrowaway42 & Brice Ménard. arxiv.org/abs/2409.06843#

New paper out in @PLOSCompBiol. The best deep neural network models of visual cortex do not reduce representations to low-dimensional manifolds—instead, they benefit from high-dimensionality. Led by a fantastic student, @EricElmoznino. journals.plos.org/ploscompbi…