Published in Computational and Systems Neuroscience (CoSyNe), Mar 2026.
Neurons in visual cortex represent the complex features that occur in natural environments. Characterization of their responses has been limited by our inability to create stimuli that are both naturalistic and precisely controlled. Traditional stimuli - like gratings, textures, or noise - capture only specialized elements, while photographic images are too unconstrained for systematic study. Here, we leverage a generative diffusion model to create stimuli, and use high-density electrode arrays to measure responses of populations of neurons in anesthetized macaque V2. We trained a diffusion model, using a denoising objective, to capture a prior probability distribution over a natural image set (Tka\u{c}ik et al, 2011) , from which naturalistic images can be sampled. In the vicinity of any given natural image, this distribution may be approximately described as a low-dimensional manifold. We generated images both within and off this manifold, with controlled pixel-level distances (MSE) relative to a set of base natural images. Specifically, off-manifold images were generated by adding Gaussian white noise to base images, while on-manifold images were generated by adding noise and then forcing the noisy images back onto the learned manifold. On-manifold images drove population responses that were more diverse than distance-matched off-manifold images, without greatly changing the overall mean response. By analyzing responses to images that varied in distance from the base image, we found that the cosine similarity of the population response relative to the base image declined more steeply on-manifold than off-manifold. This occurred because some neurons increased while others decreased their firing along on-manifold trajectories, forming heterogeneous encoding axes. Our results suggest that V2 neurons provide an orderly representation of natural image structure. Diffusion models thus provide a powerful new stimulus engine to explore population coding in visual cortex.