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Poster Session C: Friday, August 15, 2:00 – 5:00 pm, de Brug & E‑Hall

Inverse receptive field attention for naturalistic image reconstruction from the brain

Lynn Le¹, Thirza Dado², K. Seeliger³, Paolo Papale⁴, Antonio Lozano⁴, Pieter R. Roelfsema⁵, Yağmur Güçlütürk⁶, Marcel van Gerven⁷, Umut Güçlü⁶; ¹Radboud University, ²Donders Institute for Brain, Cognition and Behaviour, ³Martin-Luther Universität Halle-Wittenberg, ⁴Netherlands Institute for Neuroscience, ⁵AT&T, ⁶Radboud University Nijmegen, ⁷Donders Institute for Brain, Cognition and Behaviour, Radboud University

Presenter: K. Seeliger

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.

Topic Area: Visual Processing & Computational Vision

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