ArticleeNeuro
A Balanced Comparison of Object Invariances in Monkey IT Neurons.
Article in eNeuro. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 16 citations in OpenAlex.
- Rotation-tolerant representations elucidate the time-course of high-level object processing.PloS one · 2026Article
- Relationships between the degrees of freedom in the affine Gaussian derivative model for visual receptive fields and 2-D affine image transformations with application to covariance properties of simple cells in the primary visual cortex.Biological cybernetics · 2025Article
- Unraveling the complexity of rat object vision requires a full convolutional network and beyond.Patterns (New York, N.Y.) · 2025Article
- Understanding transformation tolerant visual object representations in the human brain and convolutional neural networks.NeuroImage · 2022Article
- Invariance of object detection in untrained deep neural networks.Frontiers in computational neuroscience · 2022Article
- Object discrimination performance and dynamics evaluated by inferotemporal cell population activity.IBRO neuroscience reports · 2021Article
- Stable readout of observed actions from format-dependent activity of monkey's anterior intraparietal neurons.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Characterisation of nonlinear receptive fields of visual neurons by convolutional neural network.Scientific reports · 2019Article
- Three-stage processing of category and variation information by entangled interactive mechanisms of peri-occipital and peri-frontal cortices.Scientific reports · 2018Article
- Multiplicative mixing of object identity and image attributes in single inferior temporal neurons.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Our ability to recognize objects across variations in size, position, or rotation is based on invariant object representations in higher visual cortex. However, we know little about how these invariances are related. Are some invariances harder than others? Do some invariances arise faster than others? These comparisons can be made only upon equating image changes across transformations. Here, we targeted invariant neural representations in the monkey inferotemporal (IT) cortex using object images with balanced changes in size, position, and rotation. Across the recorded population, IT neurons generalized across size and position both stronger and faster than to rotations in the image plane as well as in depth. We obtained a similar ordering of invariances in deep neural networks but not in low-level visual representations. Thus, invariant neural representations dynamically evolve in a temporal order reflective of their underlying computational complexity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.