'Artiphysiology' reveals V4-like shape tuning in a deep network trained for image classification

  1. Dean A Pospisil  Is a corresponding author
  2. Anitha Pasupathy
  3. Wyeth Bair
  1. University of Washington, United States

Abstract

Deep networks provide a potentially rich interconnection between neuroscientific and artificial approaches to understanding visual intelligence, but the relationship between artificial and neural representations of complex visual form has not been elucidated at the level of single-unit selectivity. Taking the approach of an electrophysiologist to characterizing single CNN units, we found many units exhibit translation-invariant boundary curvature selectivity approaching that of exemplar neurons in the primate mid-level visual area V4. For some V4-like units, particularly in middle layers, the natural images that drove them best were qualitatively consistent with selectivity for object boundaries. Our results identify a novel image-computable model for V4 boundary curvature selectivity and suggest that such a representation may begin to emerge within an artificial network trained for image categorization, even though boundary information was not provided during training. This raises the possibility that single-unit selectivity in CNNs will become a guide for understanding sensory cortex.

Data availability

No new datasets were generated in the course of this research. The model this research is based on is openly available from the Berkeley Artificial Intelligence Lab.

The following previously published data sets were used

Article and author information

Author details

  1. Dean A Pospisil

    Department of Biological Structure, University of Washington, Seattle, United States
    For correspondence
    deanp3@uw.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5793-2517
  2. Anitha Pasupathy

    Department of Biological Structure, University of Washington, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3808-8063
  3. Wyeth Bair

    Department of Biological Structure, University of Washington, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.

Funding

National Science Foundation (Graduate Research Fellowship)

  • Dean A Pospisil

National Science Foundation (CRCNS Grant IIS-1309725)

  • Anitha Pasupathy
  • Wyeth Bair

Google (Google Faculty Research Award)

  • Wyeth Bair

National Institutes of Health (Grant R01 EY-018839)

  • Anitha Pasupathy

National Institutes of Health Office of Research Infrastructure Programs (Grant RR-00166 to the Washington National Primate Research Center)

  • Anitha Pasupathy

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Eilon Vaadia, The Hebrew University of Jerusalem, Israel

Ethics

Animal experimentation: All animal procedures for this study, including implants, surgeries and behavioral training, conformed to NIH and USDA guidelines and were performed under an institutionally approved protocol at the Johns Hopkins University (Pasupathy and Connor, 2001) protocol #PR98A63 and the University of Washington (El-Shamayleh and Pasupathy, 2016) UW protocol #4133-01.

Version history

  1. Received: May 14, 2018
  2. Accepted: December 17, 2018
  3. Accepted Manuscript published: December 20, 2018 (version 1)
  4. Version of Record published: January 16, 2019 (version 2)
  5. Version of Record updated: February 1, 2021 (version 3)

Copyright

© 2018, Pospisil et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 2,775
    views
  • 379
    downloads
  • 55
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Dean A Pospisil
  2. Anitha Pasupathy
  3. Wyeth Bair
(2018)
'Artiphysiology' reveals V4-like shape tuning in a deep network trained for image classification
eLife 7:e38242.
https://doi.org/10.7554/eLife.38242

Share this article

https://doi.org/10.7554/eLife.38242

Further reading

    1. Neuroscience
    Mischa Vance Bandet, Ian Robert Winship
    Research Article

    Despite substantial progress in mapping the trajectory of network plasticity resulting from focal ischemic stroke, the extent and nature of changes in neuronal excitability and activity within the peri-infarct cortex of mice remains poorly defined. Most of the available data have been acquired from anesthetized animals, acute tissue slices, or infer changes in excitability from immunoassays on extracted tissue, and thus may not reflect cortical activity dynamics in the intact cortex of an awake animal. Here, in vivo two-photon calcium imaging in awake, behaving mice was used to longitudinally track cortical activity, network functional connectivity, and neural assembly architecture for 2 months following photothrombotic stroke targeting the forelimb somatosensory cortex. Sensorimotor recovery was tracked over the weeks following stroke, allowing us to relate network changes to behavior. Our data revealed spatially restricted but long-lasting alterations in somatosensory neural network function and connectivity. Specifically, we demonstrate significant and long-lasting disruptions in neural assembly architecture concurrent with a deficit in functional connectivity between individual neurons. Reductions in neuronal spiking in peri-infarct cortex were transient but predictive of impairment in skilled locomotion measured in the tapered beam task. Notably, altered neural networks were highly localized, with assembly architecture and neural connectivity relatively unaltered a short distance from the peri-infarct cortex, even in regions within ‘remapped’ forelimb functional representations identified using mesoscale imaging with anaesthetized preparations 8 weeks after stroke. Thus, using longitudinal two-photon microscopy in awake animals, these data show a complex spatiotemporal relationship between peri-infarct neuronal network function and behavioral recovery. Moreover, the data highlight an apparent disconnect between dramatic functional remapping identified using strong sensory stimulation in anaesthetized mice compared to more subtle and spatially restricted changes in individual neuron and local network function in awake mice during stroke recovery.

    1. Neuroscience
    Renbo Mao, Jianjun Yu ... Yi Rao
    Tools and Resources

    Dissection of neural circuitry underlying behaviors is a central theme in neurobiology. We have previously proposed the concept of chemoconnectome (CCT) to cover the entire chemical transmission between neurons and target cells in an organism and created tools for studying it (CCTomics) by targeting all genes related to the CCT in Drosophila. Here we have created lines targeting the CCT in a conditional manner after modifying GFP RNA interference, Flp-out, and CRISPR/Cas9 technologies. All three strategies have been validated to be highly effective, with the best using chromatin-peptide fused Cas9 variants and scaffold optimized sgRNAs. As a proof of principle, we conducted a comprehensive intersection analysis of CCT genes expression profiles in the clock neurons, uncovering 43 CCT genes present in clock neurons. Specific elimination of each from clock neurons revealed that loss of the neuropeptide CNMa in two posterior dorsal clock neurons (DN1ps) or its receptor (CNMaR) caused advanced morning activity, indicating a suppressive role of CNMa-CNMaR on morning anticipation, opposite to the promoting role of PDF-PDFR on morning anticipation. These results demonstrate the effectiveness of conditional CCTomics and its tools created here and establish an antagonistic relationship between CNMa-CNMaR and PDF-PDFR signaling in regulating morning anticipation.