ArticleCurrent biology : CB2021
Explaining face representation in the primate brain using different computational models.
Article in Current biology : CB, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 50 citations in OpenAlex.
- Making models disagree to learn how brains compute.Nature reviews. Neuroscience · 2026Review
- Self-supervised learning yields representational signatures of category-selective cortex.Communications biology · 2026Article
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- Higher visual areas act like domain-general filters with strong selectivity and functional specialization.Nature communications · 2026Article
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- A shared code for perceiving and imagining objects in human ventral temporal cortex.Science (New York, N.Y.) · 2026Article
- Understanding the Brain as a Mentalizing Machine with a Symbol Grounding Function.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Review
- Mesoscale functional organization of face and body areas in the macaque brain.Nature communications · 2025Article
- Reconceptualized Associative Learning.Perspectives on behavior science · 2025Review
- A Possible Neural Basis for Attentional Capture of Faces Revealed by Functional Magnetic Resonance Imaging and Causal Pharmacological Inactivation in Macaques.Journal of cognitive neuroscience · 2024Article
- Contrastive learning explains the emergence and function of visual category-selective regions.Science advances · 2024Article
- Predictions enable top-down pattern separation in the macaque face-processing hierarchy.Nature communications · 2024Article
- Predicting the temporal-dynamic trajectories of cortical neuronal responses in non-human primates based on deep spiking neural network.Cognitive neurodynamics · 2024Article
- Large-scale calcium imaging reveals a systematic V4 map for encoding natural scenes.Nature communications · 2024Article
- Emergence of brain-like mirror-symmetric viewpoint tuning in convolutional neural networks.eLife · 2024Article
- Using unsupervised capsule neural network reveal spatial representations in the human brain.Human brain mapping · 2024Article
- Information-theoretical analysis of the neural code for decoupled face representation.PloS one · 2024Article
- High-dimensional topographic organization of visual features in the primate temporal lobe.Nature communications · 2023Article
- The neural code for "face cells" is not face-specific.Science advances · 2023Article
- Behavioral signatures of face perception emerge in deep neural networks optimized for face recognition.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 3 countries.
Funding
Abstract
Understanding how the brain represents the identity of complex objects is a central challenge of visual neuroscience. The principles governing object processing have been extensively studied in the macaque face patch system, a sub-network of inferotemporal (IT) cortex specialized for face processing. A previous study reported that single face patch neurons encode axes of a generative model called the "active appearance" model, which transforms 50D feature vectors separately representing facial shape and facial texture into facial images. However, a systematic investigation comparing this model to other computational models, especially convolutional neural network models that have shown success in explaining neural responses in the ventral visual stream, has been lacking. Here, we recorded responses of cells in the most anterior face patch anterior medial (AM) to a large set of real face images and compared a large number of models for explaining neural responses. We found that the active appearance model better explained responses than any other model except CORnet-Z, a feedforward deep neural network trained on general object classification to classify non-face images, whose performance it tied on some face image sets and exceeded on others. Surprisingly, deep neural networks trained specifically on facial identification did not explain neural responses well. A major reason is that units in the network, unlike neurons, are less modulated by face-related factors unrelated to facial identification, such as illumination.
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