How does the brain solve visual object recognition?
Open accessAbstract
Mounting evidence suggests that 'core object recognition,' the ability to rapidly recognize objects despite substantial appearance variation, is solved in the brain via a cascade of reflexive, largely feedforward computations that culminate in a powerful neuronal representation in the inferior temporal cortex. However, the algorithm that produces this solution remains poorly understood. Here we review evidence ranging from individual neurons and neuronal populations to behavior and computational models. We propose that understanding this algorithm will require using neuronal and psychophysical data to sift through many computational models, each based on building blocks of small, canonical subnetworks with a common functional goal.
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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 [144]
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Walker, E. Y., Pohl, S., Denison, R. N., Barack, D. L., Lee, J., Block, N., Ma, W. J., & Meyniel, F. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01444-y · Cited by 56 · see citing works
Generative models for active vision
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Huber, L. S., Geirhos, R., & Wichmann, F. A. (2023) · Journal of Vision · doi:10.1167/jov.23.7.4 · Cited by 42 · see citing works
Curiosity: primate neural circuits for novelty and information seeking
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Emergence of Visual Center-Periphery Spatial Organization in Deep Convolutional Neural Networks
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Learning cortical representations through perturbed and adversarial dreaming
Deperrois, N., Petrovici, M. A., Senn, W., & Jordan, J. (2022) · eLife · doi:10.7554/elife.76384 · Cited by 28 · 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
A machine learning framework to optimize optic nerve electrical stimulation for vision restoration
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Self-citationVisual objects refine head direction coding
Siegenthaler, D., Denny, H., Carrasco, S. S., Mayer, J. L., Levenstein, D., Peyrache, A., Trenholm, S., & Macé, É. (2025) · Science · doi:10.1126/science.adu9828 · Cited by 16 · see citing works
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Better artificial intelligence does not mean better models of biology
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A 7T fMRI dataset of synthetic images for out-of-distribution modeling of vision
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Do we represent peripersonal space?
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“Given the range of tasks vision performs (e.g., see Frisby and Stone, 2010; DiCarlo et al., 2012; Poggio and Serre, 2013), this begs the question of why that particular conversion was an adaptive solution if, in consequence, the behavioral role played by all other filters was thereby reduced.”
- Authors
- DiCarlo, J. J., Zoccolan, D., & Rust, N. C.
- Affiliations
- McGovern Institute for Brain Research (US)
- Scuola Internazionale Superiore di Studi Avanzati (IT)
- University of Pennsylvania (US)
- Corresponding author
- DiCarlo, James J.
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2012-02
- Volume
- 73
- Issue
- 3
- Pages
- 415-434
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC3306444
- OpenAlex
- W2162950292
- Cited by
- 1,790
- References
- 194
- Citations by year
- 2026: 140 · 2025: 175 · 2024: 153 · 2023: 149 · 2022: 148 · 2021: 145 · 2020: 151 · 2019: 137 · 2018: 125 · 2017: 112 · 2016: 96 · 2015: 91 · 2014: 84 · 2013: 59 · 2012: 23
- FWCI
- 42.13
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 532 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- SDL (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Elsevier eBooks
- Funders
- National Eye Institute (R01EY014970)
- 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 › Advanced Image and Video Retrieval Techniques
- Keywords
- visual object recognition, inferior temporal cortex, feedforward processing, neural representations, computational models of vision, appearance variation, single neuron responses, population coding, psychophysics
- MeSH terms
- Animals, Attention, Brain, Cognition, Humans, Models, Neurological, Neurons, Pattern Recognition, Visual, Visual Pathways
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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