Synthesizing images to map neural networks to the human brain.

ABSTRACT: Computational models can be used to generate hypotheses about the brain. In the visual system, this approach has revealed similarities between how natural images are represented in convolutional neural networks and in cortical regions. However, natural images generate highly correlated representations across the hierarchy of model layers, meaning that each brain region will not correspond selectively to a single layer. Because each model layer performs additional image transformations, gaining a more selective mapping between individual layers and regions could reveal how specific algorithmic stages of processing are instantiated in the brain. To enable this mapping, we developed a generative framework for image synthesis that minimizes the similarity of image representational similarity m

SUBMITTER: Peng K 

PROVIDER: S-EPMC12697728 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature