Johannes Roth

Experience

Publications

• 2025 How to sample the world for understanding the visual system Roth & Hebart / CCN Oral
  Vision neuroscience runs on large fMRI datasets, but nobody had checked whether the stimulus images in these datasets actually cover what humans see in the real world. We built LAION-natural -a reference distribution of ~120M naturalistic photographs filtered from 2 billion LAION images using a CLIP-based classifier trained on 25k actively sampled labels. Then we measured coverage: ~50% of the visual-semantic space is missing from the two most widely used datasets (NSD and THINGS).

  The good news: you don't need millions of images to fix this. In both simulations and real fMRI data, out-of-distribution generalization saturates at 5-10k samples - as long as you draw them from a diverse enough pool. We compared seven sampling strategies (random, stratified, k-Means, Core-Set, effective dimensionality optimization, active learning) and found that pool diversity matters far more than which algorithm you use to sample from it.

  The pipeline processes billions of images using CLIP embeddings, Annoy indices for nearest-neighbor search, mini-batch k-Means clustering, and Ridge regression encoding models - all at a scale that runs on a university HPC cluster, not a cloud budget.

• 2025 Ten principles for reliable, efficient, and adaptable coding Roth et al. / Communications Psychology
  Most scientists learn to code informally - picking things up as they go, optimizing for "does it run?" over "will anyone else understand this?" This paper introduces a structured framework for writing better research code, built around the idea that researchers naturally switch between quick prototyping and careful development - and that being deliberate about which mode you're in makes all the difference.

  The ten principles span three tiers: organizing code (standardized project structures, version control, automation), writing reusable code (testing, documentation, clean interfaces), and collaborating (code review systems, shared knowledge bases, lab-wide standards). Already at 22k+ accesses, it clearly hit a nerve - these are problems every computational lab deals with but rarely talks about explicitly.

• 2025 Fine-grained image and category information in ventral visual pathway Badwal, Bergmann, Roth et al. / J Neuroscience
• 2023 High stimulus presentation rates for fMRI Roth, Miyawaki & Hebart / VSS Poster
• 2021 Preferred stimuli for individual voxels in the human visual system Roth, Seeliger, Schmid & Hebart / Cosyne Poster
  You can't show the brain every possible image, so how do you figure out what a specific patch of visual cortex actually responds to? We trained a convolutional neural network end-to-end on fMRI data from a subject watching naturalistic movies - no ImageNet pretraining, just raw stimulus-response pairs. Then we used BigGAN to synthesize images that maximally activate individual voxels via gradient ascent through the model.

  Early visual areas (V1-V3) preferred gratings in small receptive fields, as expected. More interesting: FFA showed preference for faces but also oval shapes and vertical symmetry, while PPA preferred places plus horizontal lines and high spatial frequencies. An SVM classifier could distinguish FFA vs. PPA preferred stimuli from their GAN latent vectors at 87% accuracy, confirming the approach produces meaningfully different outputs per region. This was one of the first demonstrations of GAN-based preferred stimulus synthesis for the human visual system.

• 2021 Multi-plane UNet++ Ensemble for Glioblastoma Segmentation Roth, Keller et al. / MICCAI BraTS

Datasets & Tools

Get in Touch

Happy to chat about research, potential collaborations, or opportunities. Email is best. Also on LinkedIn, GitHub, Hugging Face, and Google Scholar.