Cited by 1,621Visual perception and processing mechanismsAdvanced Neural Network ApplicationsNeural dynamics and brain function

Using goal-driven deep learning models to understand sensory cortex

Yamins, Daniel L K, DiCarlo, James J·2016·Nature Neuroscience

Abstract

Fueled by innovation in the computer vision and artificial intelligence communities, recent developments in computational neuroscience have used goal-driven hierarchical convolutional neural networks (HCNNs) to make strides in modeling neural single-unit and population responses in higher visual cortical areas. In this Perspective, we review the recent progress in a broader modeling context and describe some of the key technical innovations that have supported it. We then outline how the goal-driven HCNN approach can be used to delve even more deeply into understanding the development and organization of sensory cortical processing.

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Crossref · 10.1038/nn.4244

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72 of 72 references verified0 citation errors · 0 not checkable

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  • A shortcoming of the DNN approach is that it does not predict how an individual neuron causally contributes to behaviour.

    “A shortcoming of this approach is that the DNN does not predict how an individual neuron causally contributes to behaviour, making it difficult to interpret the role of the neuron in the sensorimotor transformation.”
  • This is a stronger criterion than typical evaluations of DNN models and neural activity, where a linear mapping from DNN features (~10,000 feature variables) to neural responses is fit.

    “This is a stronger criterion than typical evaluations of DNN models and neural activity, where a linear mapping from DNN features (~10,000 feature variables) to neural responses is fit1.”
  • Computational models that mimic brain information processing during perceptual, cognitive and control tasks are beginning to be developed and tested with brain and behavioral data.

    “Computational models that mimic brain information processing during perceptual, cognitive and control tasks are beginning to be developed and tested with brain and behavioral data.”

    From Cognitive computational neuroscience (2018) · cites this paper as [38]

  • Computational neuroscience has begun to test complex computational models that can explain high-level sensory and cognitive brain representations.

    “Computational neuroscience has also begun to test complex computational models that can explain high-level sensory and cognitive brain representations37,38.”

    From Cognitive computational neuroscience (2018) · cites this paper as [38]

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Authors
Yamins, D. L. K., & DiCarlo, J. J.
Affiliations
McGovern Institute for Brain Research (US)
Corresponding author
Yamins, Daniel L K
Venue
Nature Neuroscience
Published
2016-02-23
Volume
19
Issue
3
Pages
356-365
ISSN
  • 1097-6256 (ISSN-L)
  • 1546-1726
OpenAlex
W2274405424
Cited by
1,621
References
72
Citations by year
2026: 133 · 2025: 193 · 2024: 188 · 2023: 164 · 2022: 167 · 2021: 183 · 2020: 197 · 2019: 173 · 2018: 115 · 2017: 82 · 2016: 26
FWCI
13.86
Type
journal article
Language
en
Open access
closed
Locations
  • Nature Neuroscience (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Keywords
hierarchical convolutional neural network, sensory cortex, visual cortex, neural population responses, single-unit responses, cortical development, cortical organization, computer vision algorithms
MeSH terms
Animals, Goals, Humans, Learning, Models, Neurological, Somatosensory Cortex, Visual Cortex, Neural Networks, Computer

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