Inverse receptive field attention for naturalistic image reconstruction from the brain

TL;DR

This paper introduces an inverse receptive field attention model that reconstructs naturalistic images from brain data, revealing insights into neuronal representations and their dynamics across visual cortex regions.

Contribution

The study presents a novel end-to-end IRFA model that accurately reconstructs images from neurophysiological data without relying on pre-trained models, and explores neuronal tuning properties.

Findings

High-accuracy image reconstructions from brain data.

Consistent spatial receptive fields across stimuli in macaque visual areas.

Significant variation in feature selectivity of neuronal responses.

Abstract

Visual perception in the brain largely depends on the organization of neuronal receptive fields. Although extensive research has delineated the coding principles of receptive fields, most studies have been constrained by their foundational assumptions. Moreover, while machine learning has successfully been used to reconstruct images from brain data, this approach faces significant challenges, including inherent feature biases in the model and the complexities of brain structure and function. In this study, we introduce an inverse receptive field attention (IRFA) model, designed to reconstruct naturalistic images from neurophysiological data in an end-to-end fashion. This approach aims to elucidate the tuning properties and representational transformations within the visual cortex. The IRFA model incorporates an attention mechanism that determines the inverse receptive field for each pixel, weighting neuronal responses across the visual field and feature spaces. This method allows for an examination of the dynamics of neuronal representations across stimuli in both spatial and feature dimensions. Our results show highly accurate reconstructions of naturalistic data, independent of pre-trained models. Notably, IRF models trained on macaque V1, V4, and IT regions yield remarkably consistent spatial receptive fields across different stimuli, while the features to which neuronal representations are selective exhibit significant variation. Additionally, we propose a data-driven method to explore representational clustering within various visual areas, further providing testable hypotheses.