NEvo: Neural-Guided Evolutionary Video Synthesis for Dynamic Visual Selectivity

2026-07-02

AI Summary

Existing brain encoding models are largely confined to static images, limiting insights into the brain’s selective responses to dynamic visual stimuli. This work proposes a neural-guided evolutionary video synthesis framework that performs evolutionary search within a structured text-prompt space, leveraging a dynamic brain encoding model to predict fMRI voxel responses and generate videos that maximize activation in specific visual cortical areas. For the first time, this approach enables efficient synthesis of dynamic stimuli tailored to visual selectivity, systematically revealing differential temporal sensitivity across the ventral, dorsal, and lateral visual pathways. Notably, it uncovers a progressive sensitivity in the lateral pathway to complex social dynamics. The synthesized videos substantially outperform handcrafted stimuli, not only recapitulating known functional preferences of brain regions but also validating novel dynamic feature selectivities through abstract, non-naturalistic stimuli.

Abstract

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

Problem

• dynamic visual selectivity
• brain encoding
• video synthesis
• visual cortex
• temporal dynamics

Innovation

• neural-guided evolution
• dynamic visual selectivity
• video synthesis
• brain encoding model
• evolutionary optimization