Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Probing machine-learning classifiers using noise, bubbles, and reverse correlation

View ORCID ProfileEtienne Thoret, Thomas Andrillon, Damien Léger, Daniel Pressnitzer
doi: https://doi.org/10.1101/2020.06.22.165688
Etienne Thoret
1Laboratoire des systèmes perceptifs, Département d’études cognitives, École normale supérieure, PSL University, CNRS, 75005 Paris, France
4Aix Marseille Univ, CNRS, PRISM, LIS, ILCB, Marseille, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Etienne Thoret
  • For correspondence: etiennethoret@gmail.com
Thomas Andrillon
3Turner Institute for Brain & Mental Health and School of Psychological Sciences, Monash University, Melbourne 3168, Australia
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Damien Léger
2Université de Paris, APHP, Hotel Dieu, Centre du Sommeil et de la Vigilance & EA 7330 VIFASOM, Paris 75006, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Daniel Pressnitzer
1Laboratoire des systèmes perceptifs, Département d’études cognitives, École normale supérieure, PSL University, CNRS, 75005 Paris, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

Abstract

Many scientific fields now use machine-learning tools to assist with complex classification tasks. In neuroscience, automatic classifiers may be useful to diagnose medical images, monitor electrophysiological signals, or decode perceptual and cognitive states from neural signals. Tools such as deep neural networks regularly outperform humans with such large and high-dimensional datasets. However, such tools often remain black-boxes: they lack interpretability. A lack of interpretability has obvious ethical implications for clinical applications, but it also limits the usefulness of machine-learning tools to formulate new theoretical hypotheses. Here, we propose a simple and versatile method to help characterize and understand the information used by a classifier to perform its task. The method is inspired by the reverse correlation framework familiar to neuroscientists. Specifically, noisy versions of training samples or, when the training set is unavailable, custom-generated noisy samples are fed to the classifier. Variants of the method using uniform noise and noise focused on subspaces of the input representations, so-called “bubbles”, are presented. Reverse correlation techniques are then adapted to extract both the discriminative information used by the classifier and the canonical information for each class. We provide illustrations of the method for the classification of written numbers by a convolutional deep neural network and for the classification of speech versus music by a support vector machine. The method itself is generic and can be applied to any kind of classifier and any kind of input data. Compared to other, more specialized approaches, we argue that the noise-probing method could provide a generic and intuitive interface between machine-learning tools and neuroscientists.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/EtienneTho/proise/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
Back to top
PreviousNext
Posted June 23, 2020.
Download PDF
Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Probing machine-learning classifiers using noise, bubbles, and reverse correlation
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Probing machine-learning classifiers using noise, bubbles, and reverse correlation
Etienne Thoret, Thomas Andrillon, Damien Léger, Daniel Pressnitzer
bioRxiv 2020.06.22.165688; doi: https://doi.org/10.1101/2020.06.22.165688
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Probing machine-learning classifiers using noise, bubbles, and reverse correlation
Etienne Thoret, Thomas Andrillon, Damien Léger, Daniel Pressnitzer
bioRxiv 2020.06.22.165688; doi: https://doi.org/10.1101/2020.06.22.165688

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (3691)
  • Biochemistry (7800)
  • Bioengineering (5678)
  • Bioinformatics (21295)
  • Biophysics (10584)
  • Cancer Biology (8179)
  • Cell Biology (11948)
  • Clinical Trials (138)
  • Developmental Biology (6764)
  • Ecology (10401)
  • Epidemiology (2065)
  • Evolutionary Biology (13876)
  • Genetics (9709)
  • Genomics (13075)
  • Immunology (8151)
  • Microbiology (20022)
  • Molecular Biology (7859)
  • Neuroscience (43075)
  • Paleontology (321)
  • Pathology (1279)
  • Pharmacology and Toxicology (2261)
  • Physiology (3353)
  • Plant Biology (7232)
  • Scientific Communication and Education (1314)
  • Synthetic Biology (2008)
  • Systems Biology (5539)
  • Zoology (1128)