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. 

RT @sandervanbree: What does it mean when neural network dimensions converge? In this blog post I explore possible…

4/4 The ED that is measured in the Elmoznino & Bonner paper is the ED of channels, with spatial dimensionality factored out. This tells us about the richness of the features that a model learned, not simply the dimensionality of the regressors in an encoding model.

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This work reveals a vast space of uncharted dimensions that have been out of reach for conventional methods but may be critical for understanding the cortical code of human vision.

RT @martin_hebart: How does our brain allow us to make sense of our visual world? Work led by @OliverContier w/ @Chris_I_Baker now out in @…

@ShahabBakht Yes, we discuss this a bit in the manuscript. An open question is whether universal visual dimensions are due to natural image statistics or “Platonic” representations of the generative factors in scenes (e.g., surfaces, objects, lighting).

RT @ChuyanQu: Thrilled to share this new paper in JEP: General! w/@michaelfbonner @DewindNick and Liz Brannon @liz07435499 Visual coheren…

RT @NauMatt: #CogNeuro seeks unified theories of behavioral, physiological, and mental states. To this aim, our @NatureNeuro Perspective pr…