ArticlePLoS computational biology2015
The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Emergence of brain-like mirror-symmetric viewpoint tuning in convolutional neural networks.eLife · 2024Article
- Brain-like functional specialization emerges spontaneously in deep neural networks.Science advances · 2022Article
- On the relationship between maps and domains in inferotemporal cortex.Nature reviews. Neuroscience · 2021Review
- The Thalamus as a Blackboard for Perception and Planning.Frontiers in behavioral neuroscience · 2021Article
- Generative Feedback Explains Distinct Brain Activity Codes for Seen and Mental Images.Current biology : CB · 2020Article
- Primary Cognitive Categories Are Determined by Their Invariances.Frontiers in psychology · 2020Article
- Invariant recognition drives neural representations of action sequences.PLoS computational biology · 2017Article
- View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation.Current biology : CB · 2017Article
- Toward an Integration of Deep Learning and Neuroscience.Frontiers in computational neuroscience · 2016Article
- Intelligent information loss: the coding of facial identity, head pose, and non-face information in the macaque face patch system.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2015Article
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4 authors.
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Abstract
Is visual cortex made up of general-purpose information processing machinery, or does it consist of a collection of specialized modules? If prior knowledge, acquired from learning a set of objects is only transferable to new objects that share properties with the old, then the recognition system's optimal organization must be one containing specialized modules for different object classes. Our analysis starts from a premise we call the invariance hypothesis: that the computational goal of the ventral stream is to compute an invariant-to-transformations and discriminative signature for recognition. The key condition enabling approximate transfer of invariance without sacrificing discriminability turns out to be that the learned and novel objects transform similarly. This implies that the optimal recognition system must contain subsystems trained only with data from similarly-transforming objects and suggests a novel interpretation of domain-specific regions like the fusiform face area (FFA). Furthermore, we can define an index of transformation-compatibility, computable from videos, that can be combined with information about the statistics of natural vision to yield predictions for which object categories ought to have domain-specific regions in agreement with the available data. The result is a unifying account linking the large literature on view-based recognition with the wealth of experimental evidence concerning domain-specific regions.
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