Neuronal tuning aligns dynamically with object and texture manifolds across the visual hierarchy.

Visual neurons respond to a vast range of images, from textures to objects, but the rules linking these responses remain unclear. Although tuning to simple features is well established in the primary visual cortex, this framework breaks down in higher areas, where neurons encode diverse and unpredictable features. To ask what features neurons prioritize, we used generative models (deep networks that synthesize new images from a learned latent space), allowing neurons in V1, V4 and the posterior inferotemporal cortex (PIT) to guide image synthesis through closed-loop optimization. We compared models that emphasize texture versus those that emphasize object structure. Although V1 and V4 aligned more strongly with texture-based spaces, many PIT neurons responded equally well to both types of optimized images, revealing a focus on shared local motifs rather than whole-object templates, and this alignment to objects emerged later in their response. These findings reveal coding principles across the ventral stream and clarify the limits of current vision models.