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arXiv · 2610.05419

Recurrent network dynamics explain the time course of perceptual grouping in natural scenes

Sami Mollard·Alekh K. Ashok·Lore Goetschalckx·Drew Linsley·Thomas Serre·Sander M. Bohte·Pieter R. Roelfsema
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Abstract

How the brain groups image elements into coherent objects in natural scenes remains unclear. Existing models for grouping in biological vision rely on simplified stimuli with explicit boundaries and cannot explain human behavior in naturalistic settings. We propose a mechanistic framework in which recurrent interactions propagate enhanced neuronal activity within and between cortical areas. The framework links computational principles, neural circuitry and perceptual psychology. Local boundary signals govern early grouping, whereas later stages integrate top-down feedback carrying information about object identity, grouping features across internal edges into coherent object representations. We instantiated this framework as a brain-inspired recurrent neural network trained to group features in natural images. The network learned to propagate enhanced activity across a cued object's representation, mirroring the brain's perceptual grouping mechanisms. The network's dynamics also predicted human reaction times. The framework links cortical dynamics to human vision and generates testable predictions for neuro-physiological and psychophysical experiments.

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Sami Mollard, Alekh K. Ashok, Lore Goetschalckx, Drew Linsley, Thomas Serre, Sander M. Bohte, Pieter R. Roelfsema. 2026-10-04. Recurrent network dynamics explain the time course of perceptual grouping in natural scenes. https://arxiv.org/abs/2610.05419

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