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This study introduces NEvo, a neural-guided video synthesis framework that employs evolutionary search to generate dynamic visual stimuli optimized for specific regions of the visual cortex. By leveraging a dynamic encoding model to predict voxel-level responses, the framework outperforms traditional handcrafted localizer videos, revealing not only established selectivities but also nuanced differences in sensitivity to temporal dynamics across various visual pathways. The findings provide valuable insights into the complexity of social-dynamic features in visual processing, paving the way for future in vivo experiments.
Synthesized videos reveal systematic differences in how the brain's visual pathways respond to dynamic stimuli, surpassing traditional methods.
The human brain processes dynamic visual input through hierarchically organized, functionally specialized regions. While recent in silico brain encoding models can synthesize optimal stimuli to probe selectivity in different brain regions, prior work has been largely limited to static images, leaving dynamic visual processing underexplored. We introduce a novel neural-guided video synthesis framework that generates stimuli optimized for target brain regions across visual cortex. Our method performs evolutionary search over a structured prompt space, guided by a dynamic encoding model that predicts voxel-level responses to video inputs. By maximizing predicted activity for a target ROI, the framework efficiently discovers hyper-activating dynamic stimuli that consistently surpass handcrafted localizer videos. The synthesized videos recover known selectivities across ventral, dorsal, and lateral pathways, and further reveal systematic differences in sensitivity to temporal dynamics. A searchlight analysis provides new insight into the progression toward increasingly complex social-dynamic features along the lateral stream, further supported by probing with synthesized abstract, non-naturalistic stimuli. Taken together, our framework enables in silico exploration of dynamic visual selectivity, with new predictions for in vivo experiments
