Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream
Open accessAbstract
Converging evidence suggests that the primate ventral visual pathway encodes increasingly complex stimulus features in downstream areas. We quantitatively show that there indeed exists an explicit gradient for feature complexity in the ventral pathway of the human brain. This was achieved by mapping thousands of stimulus features of increasing complexity across the cortical sheet using a deep neural network. Our approach also revealed a fine-grained functional specialization of downstream areas of the ventral stream. Furthermore, it allowed decoding of representations from human brain activity at an unsurpassed degree of accuracy, confirming the quality of the developed approach. Stimulus features that successfully explained neural responses indicate that population receptive fields were explicitly tuned for object categorization. This provides strong support for the hypothesis that object categorization is a guiding principle in the functional organization of the primate ventral stream.
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Numerous studies using neuroconnectionism have identified shared representational structure across brains and ANN models, as well as factors influencing this alignment.
“Using this approach, known as neuroconnectionism, numerous studies have identified shared representational structure across brains and ANN models, as well as factors influencing this alignment [5–11]”
From The Umwelt Representation Hypothesis: rethinking Universality (2026) · cites this paper as [9]
Deep-learning algorithms share organizational overlap with the ventral visual stream of the brain.
“and share organizational overlap with the ventral visual stream of the brain [144-146].”
From How Do Expectations Shape Perception? (2018) · cites this paper as [145]
The finding that the middle LSTM layer learns highest-level representations contrasts with supervised networks where high-level cortical areas were best modeled by higher layers.
“This finding is in stark contrast to experiments that used supervised networks (7; 5; 11).”
From Incorporating Context into Language Encoding Models for fMRI (2018) · cites this paper as [7]
Different layers from supervised networks predicted different brain areas.
“However, previous fMRI studies have found that different layers from supervised networks predicted different brain areas (2; 7; 5; 11), suggesting that each layer should be tested separately.”
From Incorporating Context into Language Encoding Models for fMRI (2018) · cites this paper as [7]
Supervised network experiments found that high-level cortical areas were best modeled by higher layers of the network.
“Those experiments found that high-level cortical areas were best modeled by higher layers of the network.”
From Incorporating Context into Language Encoding Models for fMRI (2018) · cites this paper as [7]
Feature representations found in deep neural networks illustrate a similar processing hierarchy to that of the visual cortex.
“The feature representations found in deep neural networks illustrate a similar processing hierarchy to that of the visual cortex (Güçlü & van Gerven, 2015; Yamins, Hong, Cadieu, Solomon, Seibert, & DiCarlo, 2014)”
From End-to-end optimization of prosthetic vision (2022) · cites this paper as Güçlü & van Gerven, 2015
Various techniques exist to carry out comparisons of optimized receptive fields with measured receptive fields in fMRI studies, including population receptive field estimation and representational dissimilarity matrices.
“Various techniques exist to carry out such comparisons in fRMI studies, including population receptive field estimation (Dumoulin and Wandell, 2008; Güçlü and van Gerven, 2015) and representational dissimilarity matrices (Kriegeskorte et al., 2008; Khaligh-Razavi and Kriegeskorte, 2014).”
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Güçlü and van Gerven, 2015
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Deep Neural Networks for Modeling Visual Perceptual Learning
Wenliang, L. K., & Seitz, A. R. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.1620-17.2018 · Cited by 83 · see citing works
Perception science in the age of deep neural networks.
VanRullen, R. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.00142 · Cited by 82 · see citing works
How deep is the brain? The shallow brain hypothesis
Suzuki, M., Pennartz, C. M. A., & Aru, J. (2023) · Nature Reviews Neuroscience · doi:10.1038/s41583-023-00756-z · Cited by 79 · see citing works
Goldstein, A., Grinstein-Dabush, A., Schain, M., Wang, H., Hong, Z., Aubrey, B., Nastase, S. A., Zada, Z., Ham, E., Feder, A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-46631-y · Cited by 64 · see citing works
Tasks and their role in visual neuroscience
Kay, K., Bonnen, K., Denison, R. N., Arcaro, M. J., & Barack, D. L. (2023) · Neuron · doi:10.1016/j.neuron.2023.03.022 · Cited by 61 · see citing works
Unveiling functions of the visual cortex using task-specific deep neural networks
Dwivedi, K., Bonner, M. F., Cichy, R. M., & Roig, G. (2021) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009267 · Cited by 52 · see citing works
End-to-end optimization of prosthetic vision
de Ruyter van Steveninck, J., Güçlü, U., van Wezel, R., & van Gerven, M. (2022) · Journal of Vision · doi:10.1167/jov.22.2.20 · Cited by 54 · see citing works
Self-citationFrom remembering to reconstruction: The transformative neural representation of episodic memory.
Xue, G. (2022) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2022.102351 · Cited by 49 · see citing works
Development of visual object recognition
Ayzenberg, V., & Behrmann, M. (2023) · Nature Reviews Psychology · doi:10.1038/s44159-023-00266-w · Cited by 41 · see citing works
Emergence of Visual Center-Periphery Spatial Organization in Deep Convolutional Neural Networks
Mohsenzadeh, Y., Mullin, C., Lahner, B., & Oliva, A. (2020) · Scientific Reports · doi:10.1038/s41598-020-61409-0 · Cited by 31 · see citing works
Multiplicative mixing of object identity and image attributes in single inferior temporal neurons
Ratan Murty, N. A., & Arun, S. P. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1714287115 · Cited by 15 · see citing works
Golan, T., Taylor, J., Schütt, H., Peters, B., Sommers, R. P., Seeliger, K., Doerig, A., Linton, P., Konkle, T., van Gerven, M., et al. (2023) · Behavioral and Brain Sciences · doi:10.1017/s0140525x23001553 · Cited by 12 · see citing works
Self-citationEnd-to-end optimization of prosthetic vision
de Ruyter van Steveninck, J., Güçlü, U., van Wezel, R., & van Gerven, M. (2020) · doi:10.1101/2020.12.19.423601 · Cited by 54 · see citing works
“The feature representations found in deep neural networks illustrate a similar processing hierarchy to that of the visual cortex (Güçlü & van Gerven, 2015; Yamins, Hong, Cadieu, Solomon, Seibert, & DiCarlo, 2014) and boundary detection is one of these processing steps needed for segregation of…”
MethodSelf-citationThe Umwelt Representation Hypothesis: rethinking Universality
Bosch, V., Sommers, R. P., Doerig, A., & Kietzmann, T. C. (2026) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2026.07.004 · Cited by 2 · see citing works
- Authors
- Guclu, U., & van Gerven, M. A. J.
- Venue
- Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2015-07-08
- Volume
- 35
- Issue
- 27
- Pages
- 10005-10014
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6605414
- OpenAlex
- W1715013381
- Cited by
- 961
- References
- 68
- Citations by year
- 2026: 56 · 2025: 84 · 2024: 107 · 2023: 100 · 2022: 100 · 2021: 100 · 2020: 103 · 2019: 102 · 2018: 87 · 2017: 62 · 2016: 52 · 2015: 7 · 2012: 1
- FWCI
- 41.67
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Neuroscience (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- ventral stream, neural representations, object classification, population receptive field, functional specialization, primate visual cortex
- MeSH terms
- Brain, Brain Mapping, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Models, Neurological, Oxygen, Photic Stimulation, Visual Pathways
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