Receptive-field-constrained stimulus optimization for human early and intermediate visual cortex
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2609.31204v1 Announce Type: cross Abstract: Recent fMRI foundation models differ substantially in the spatial scale at which they represent brain activity. ROI- and connectivity-based models ar...
arXiv:2606. 04772v1 Announce Type: cross Abstract: Understanding the relationship between deep visual representations and the human visual system is a fundamental challenge in computational neuroscience.
arXiv:2606. 00121v1 Announce Type: cross Abstract: Reconstructing visual stimuli from brain recordings has been a meaningful and challenging task in brain decoding.
Understanding the relationship between deep visual representations and the human visual system is a fundamental challenge in computational neuroscience. While modern vision models achieve strong performance in image recognition, their correspondence with the hierarchical organization of the human visual cortex remains an open question.
NEvo is a neural‑guided evolutionary video synthesis framework that generates dynamic stimuli optimized for specific brain regions in the visual cortex. It performs evolutionary search over a structured prompt space, guided by a dynamic encoding model that predicts voxel‑level responses to video inputs, thereby discovering hyper‑activating videos that outperform handcrafted localizers. The synthesized videos recover known selectivities across ventral, dorsal, and lateral pathways and reveal systematic differences in sensitivity to temporal dynamics, offering new insights into the progression of social‑dynamic features along the lateral stream.
arXiv:2503. 13212v3 Announce Type: replace Abstract: Alignment between human brain networks and artificial models has become an active research area in vision science and machine learning.