End-to-end topographic networks as models of cortical map formation and human visual behaviour
Bibliographic Data
| ID | 4658165 |
|---|---|
| Authors | Zejin Lu (0009-0005-1942-9223, Osnabrück University, corresponding author), Adrien Doerig (0000-0001-5120-9750, Osnabrück University), Victoria Bosch (0000-0001-7454-8325, Osnabrück University), Bas Krahmer (Radboud University Nijmegen), Daniel Kaiser (0000-0002-9007-3160, Philipps University of Marburg), Radoslaw M Cichy (0000-0003-4190-6071, Freie Universität Berlin), Tim C Kietzmann (0000-0001-8076-6062, Osnabrück University, corresponding author) |
| Year | 2025 |
| Volume | 9 |
| Issue | 9 |
| Pages | 1975-1991 |
| Publication date | 2025-06-06 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Nature Human Behaviour (JOURNAL) |
| Journal identifiers | ISSN: 2397-3374 • E-ISSN: 2397-3374 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1038/s41562-025-02220-7 |
| PMID | 40481218 |
| OpenAlex | W4411099932 |
| Language | EN |
| References cited | 81 |
A prominent feature of the primate visual system is its topographic organization. For understanding its origins, its computational role and its behavioural implications, computational models are of central importance. Yet, vision is commonly modelled using convolutional neural networks, which are hard-wired to learn identical features across space and thus lack topography. Here we overcome this limitation by introducing all-topographic neural networks (All-TNNs). All-TNNs develop several features reminiscent of primate topography, including smooth orientation and category selectivity maps, and enhanced processing of regions with task-relevant information. In addition, All-TNNs operate on a low energy budget, suggesting a metabolic benefit of smooth topographic organization. To test our model against behaviour, we collected a dataset of human spatial biases in object recognition and found that All-TNNs significantly outperform control models. All-TNNs thereby offer a promising candidate for modelling primate visual topography and its role in downstream behaviour
Biology · Computational model · Computer vision · Convolutional neural network · Primate · Topographic map (neuroanatomy) · Visual cortex · Computer Science · Face Recognition and Perception · Neuroscience · Visual Attention and Saliency Detection · Visual perception and processing mechanisms · Artificial Intelligence
| Highly cited | No |
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