Margaret Marie Henderson — accepted AI-conference papers

2025

Brain Mapping with Dense Features: Grounding Cortical Semantic Selectivity in Natural Images With Vision Transformers

Andrew Luo, Jacob Yeung, Rushikesh Zawar, Shaurya Rajat Dewan, Margaret Marie Henderson, Leila Wehbe +1

ICLR 2025poster

We introduce BrainSAIL (Semantic Attribution and Image Localization), a method for linking neural selectivity with spatially distributed semantic visual concepts in natural scenes. BrainSAIL leverages recent advances in large-scale artificial neural networks, using them to provide insights into the…

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2025

Meta-Learning an In-Context Transformer Model of Human Higher Visual Cortex

Muquan Yu, Mu Nan, Hossein Adeli, Jacob S. Prince, John A. Pyles, Leila Wehbe +3

NeurIPS 2025poster

Understanding functional representations within higher visual cortex is a fundamental question in computational neuroscience. While artificial neural networks pretrained on large-scale datasets exhibit striking representational alignment with human neural responses, learning image-computable models…

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2024

BrainSCUBA: Fine-Grained Natural Language Captions of Visual Cortex Selectivity

Andrew Luo, Margaret Marie Henderson, Michael J. Tarr, Leila Wehbe

ICLR 2024poster

Understanding the functional organization of higher visual cortex is a central focus in neuroscience. Past studies have primarily mapped the visual and semantic selectivity of neural populations using hand-selected stimuli, which may potentially bias results towards pre-existing hypotheses of visual…

Cited by 12 Source ↗PDF ↗Scholar ↗

2023

Brain Diffusion for Visual Exploration: Cortical Discovery using Large Scale Generative Models

Andrew Luo, Margaret Marie Henderson, Leila Wehbe, Michael J. Tarr

NeurIPS 2023oral

A long standing goal in neuroscience has been to elucidate the functional organization of the brain. Within higher visual cortex, functional accounts have remained relatively coarse, focusing on regions of interest (ROIs) and taking the form of selectivity for broad categories such as faces, places,…

Cited by 22 Source ↗PDF ↗Scholar ↗