ArticleCurrent biology : CB2017
View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation.
Article in Current biology : CB, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Unraveling the complexity of rat object vision requires a full convolutional network and beyond.Patterns (New York, N.Y.) · 2025Article
- Emergence of brain-like mirror-symmetric viewpoint tuning in convolutional neural networks.eLife · 2024Article
- A Unifying Model for Discordant and Concordant Results in Human Neuroimaging Studies of Facial Viewpoint Selectivity.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Specialized Networks for Social Cognition in the Primate Brain.Annual review of neuroscience · 2023Review
- Encoding of dynamic facial information in the middle dorsal face area.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Local features drive identity responses in macaque anterior face patches.Nature communications · 2022Article
- Symmetry-Based Representations for Artificial and Biological General Intelligence.Frontiers in computational neuroscience · 2022Review
- Explaining face representation in the primate brain using different computational models.Current biology : CB · 2021Article
- Social interaction networks in the primate brain.Current opinion in neurobiology · 2020Review
- The macaque face patch system: a turtle's underbelly for the brain.Nature reviews. Neuroscience · 2020Review
- A new no-report paradigm reveals that face cells encode both consciously perceived and suppressed stimuli.eLife · 2020Article
- Convolutional neural networks explain tuning properties of anterior, but not middle, face-processing areas in macaque inferotemporal cortex.Communications biology · 2020Article
- Efficient inverse graphics in biological face processing.Science advances · 2020Article
- The neural mechanisms of face processing: cells, areas, networks, and models.Current opinion in neurobiology · 2020Review
- Nonlinear Processing of Shape Information in Rat Lateral Extrastriate Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2019Article
- Towards deep learning with segregated dendrites.eLife · 2017Article
- Invariant recognition drives neural representations of action sequences.PLoS computational biology · 2017Article
- Computational Models of Face Perception.Current directions in psychological science · 2017Article
- Computational Foundations of Natural Intelligence.Frontiers in computational neuroscience · 2017Review
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Authors and funding
5 authors.
Funding
Abstract
The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and robust against identity-preserving transformations, like depth rotations [1, 2]. Current computational models of object recognition, including recent deep-learning networks, generate these properties through a hierarchy of alternating selectivity-increasing filtering and tolerance-increasing pooling operations, similar to simple-complex cells operations [3-6]. Here, we prove that a class of hierarchical architectures and a broad set of biologically plausible learning rules generate approximate invariance to identity-preserving transformations at the top level of the processing hierarchy. However, all past models tested failed to reproduce the most salient property of an intermediate representation of a three-level face-processing hierarchy in the brain: mirror-symmetric tuning to head orientation [7]. Here, we demonstrate that one specific biologically plausible Hebb-type learning rule generates mirror-symmetric tuning to bilaterally symmetric stimuli, like faces, at intermediate levels of the architecture and show why it does so. Thus, the tuning properties of individual cells inside the visual stream appear to result from group properties of the stimuli they encode and to reflect the learning rules that sculpted the information-processing system within which they reside.
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