Evolving images for visual neurons using a deep generative network reveals coding principles and neuronal preferences
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Abstract
What specific features should visual neurons encode, given the infinity of real-world images and the limited number of neurons available to represent them? We investigated neuronal selectivity in monkey inferotemporal cortex via the vast hypothesis space of a generative deep neural network, avoiding assumptions about features or semantic categories. A genetic algorithm searched this space for stimuli that maximized neuronal firing. This led to the evolution of rich synthetic images of objects with complex combinations of shapes, colors, and textures, sometimes resembling animals or familiar people, other times revealing novel patterns that did not map to any clear semantic category. These results expand our conception of the dictionary of features encoded in the cortex, and the approach can potentially reveal the internal representations of any system whose input can be captured by a generative model.
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The organization of the cerebral cortex is characterized by extraordinary specialization of neural units at all levels.
“Notably, the organization of the cerebral cortex, widely considered as the likely substrate of human consciousness, is characterized by extraordinary specialization of neural units at all levels [38–40].”
From Integrated information theory (IIT) 4.0: Formulating the properties of phenomenal existence in physical terms (2023) · cites this paper as [39]
The Introduction (page unavailable) describes visual cortical neurons selective for features including motion, color, curvature, and faces. This supports specialization in visual cortex, but the paper does not establish that neural units are specialized throughout the cerebral cortex or at all levels. (LLM, conf=0.85)
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- Authors
- Ponce, C. R., Xiao, W., Schade, P. F., Hartmann, T. S., Kreiman, G., & Livingstone, M. S.
- Affiliations
- Harvard University (US)
- Boston Children's Hospital (US)
- Corresponding author
- Ponce, Carlos R.
- Livingstone, Margaret S.
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2019-05
- Volume
- 177
- Issue
- 4
- Pages
- 999-1009.e10
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC6718199
- OpenAlex
- W2943083682
- Cited by
- 245
- References
- 52
- Citations by year
- 2026: 40 · 2025: 21 · 2024: 35 · 2023: 27 · 2022: 28 · 2021: 35 · 2020: 37 · 2019: 22
- FWCI
- 17.73
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation
- National Institutes of Health
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Keywords
- inferotemporal cortex, visual neurons, neuronal selectivity, coding principles, genetic algorithm, feature dictionary, object recognition, monkey visual cortex, internal representations
- MeSH terms
- Algorithms, Animals, Cerebral Cortex, Macaca mulatta, Male, Nerve Net, Neurons, Temporal Lobe, Visual Perception
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