Biological and artificial vision systems both rely on hierarchical architectures, yet it remains unclear how their representational geometry evolves across processing stages, and what functional consequences may arise from potential differences. In this work, we systematically quantify and compare the linear and nonlinear dimensionality of human brain activity (fMRI) and artificial neural networks (ANNs) during natural image viewing. In the human ventral visual stream, both dimensionality measures increase along the visual hierarchy, supporting the emergence of semantic and abstract representations. For linear dimensionality, most ANNs show a similar increase, but only for pooled features, emphasizing the importance of appropriate feature readouts in brain–model comparisons. In contrast, nonlinear dimensionality shows a collapse in the later layers of ANNs, pointing at a mismatch in representational geometry between the human and artificial visual systems. This mismatch may have functional consequences: while high-dimensional brain representations support flexible generalization to abstract features, ANNs appear to lose this capacity in later layers, where their representations become overly compressed. Overall, our findings propose dimensionality alignment as a benchmark for building more flexible and biologically grounded vision models.

Advances in microscopy imaging enable researchers to visualize structures at the nanoscale level thereby unraveling intricate details of biological organization. However, challenges such as image noise, photobleaching of fluo-rophores, and low tolerability of biological samples to high light doses remain, restricting temporal resolutions and experiment durations. Reduced laser doses enable longer measurements at the cost of lower resolution and increased noise, which hinders accurate downstream analyses. Here we train a denoising diffusion probabilistic model (DDPM) to predict high-resolution images by conditioning the model on low-resolution information. Additionally, the probabilistic aspect of the DDPM allows for repeated generation of images that tend to further increase the signal-to-noise ratio. We show that our model achieves a performance that is better or similar to the previously best-performing methods, across four highly diverse datasets. Importantly, while any of the previous methods show competitive performance for some, but not all datasets, our method consistently achieves high performance across all four data sets, suggesting high generalizability. Our code and datasets are available at https://githubcom/kaschube-lab/ddpm_highres_microscopy, https://doi.org/10.5281/zenodo.14178277, https://doi.org/10.5281/zenodo.14215837.