1. Neuroscience
  2. Physics of Living Systems

Fast deep neural correspondence for tracking and identifying neurons in C. elegans using semi-synthetic training

  1. Xinwei Yu
  2. Matthew S Creamer
  3. Francesco Randi
  4. Anuj Kumar Sharma Ph.D.
  5. Scott W Linderman
  6. Andrew Michael Leifer  Is a corresponding author
  1. Princeton University, United States
  2. Stanford University, United States
https://doi.org/10.7554/eLife.66410
Share this article
Cite this article
  1. Xinwei Yu
  2. Matthew S Creamer
  3. Francesco Randi
  4. Anuj Kumar Sharma Ph.D.
  5. Scott W Linderman
  6. Andrew Michael Leifer
(2021)
Fast deep neural correspondence for tracking and identifying neurons in C. elegans using semi-synthetic training
eLife 10:e66410.
https://doi.org/10.7554/eLife.66410
  2. Download BibTeX
  3. Download .RIS

Abstract

We present an automated method to track and identify neurons in C. elegans, called 'fast Deep Neural Correspondence' or fDNC, based on the transformer network architecture. The model is trained once on empirically derived semi-synthetic data and then predicts neural correspondence across held-out real animals. The same pre-trained model both tracks neurons across time and identifies corresponding neurons across individuals. Performance is evaluated against hand-annotated datasets, including NeuroPAL [1]. Using only position information, the method achieves 79.1% accuracy at tracking neurons within an individual and 64.1% accuracy at identifying neurons across individuals. Accuracy at identifying neurons across individuals is even higher (78.2%) when the model is applied to a dataset published by another group [2]. Accuracy reaches 74.7% on our dataset when using color information from NeuroPAL. Unlike previous methods, fDNC does not require straightening or transforming the animal into a canonical coordinate system. The method is fast and predicts correspondence in 10ms making it suitable for future real-time applications.

Data availability

All datasets generated as part of this work have been deposited in a public Open Science Foundation repository DOI:10.17605/OSF.IO/T7DZU.

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Xinwei Yu

    Department of Physics, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.

  2. Matthew S Creamer

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.

  3. Francesco Randi

    Department of Physics, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.

  4. Anuj Kumar Sharma Ph.D.

    Department of Physics, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5061-9731

  5. Scott W Linderman

    Department of Statistics, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3878-9073

  6. Andrew Michael Leifer

    Department of Physics and Princeton Neuroscience Institute, Princeton University, Princeton, United States
    For correspondence
    leifer@princeton.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5362-5093

Funding

Simons Foundation (543003)

  • Andrew Michael Leifer

Simons Foundation (697092)

  • Scott W Linderman

National Science Foundation (IOS-184537)

  • Andrew Michael Leifer

National Science Foundation (PHY-1734030)

  • Andrew Michael Leifer

National Institutes of Health (R21NS101629)

  • Andrew Michael Leifer

National Institutes of Health (1R01NS113119)

  • Scott W Linderman

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

Reviewing Editor

  1. Gordon J Berman, Emory University, United States

Version history

  1. Received: January 9, 2021
  2. Accepted: July 13, 2021
  3. Accepted Manuscript published: July 14, 2021 (version 1)
  4. Version of Record published: August 16, 2021 (version 2)

Copyright

© 2021, Yu 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

  • 2,434
    Page views
  • 244
    Downloads
  • 8
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

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. Xinwei Yu
  2. Matthew S Creamer
  3. Francesco Randi
  4. Anuj Kumar Sharma Ph.D.
  5. Scott W Linderman
  6. Andrew Michael Leifer
(2021)
Fast deep neural correspondence for tracking and identifying neurons in C. elegans using semi-synthetic training
eLife 10:e66410.
https://doi.org/10.7554/eLife.66410

Categories and tags

Research organism

Further reading

    1. Neuroscience

    ACC neural ensemble dynamics are structured by strategy prevalence

    Mikhail Proskurin, Maxim Manakov, Alla Karpova
    Research Article Updated

    Medial frontal cortical areas are thought to play a critical role in the brain’s ability to flexibly deploy strategies that are effective in complex settings, yet the underlying circuit computations remain unclear. Here, by examining neural ensemble activity in male rats that sample different strategies in a self-guided search for latent task structure, we observe robust tracking during strategy execution of a summary statistic for that strategy in recent behavioral history by the anterior cingulate cortex (ACC), especially by an area homologous to primate area 32D. Using the simplest summary statistic – strategy prevalence in the last 20 choices – we find that its encoding in the ACC during strategy execution is wide-scale, independent of reward delivery, and persists through a substantial ensemble reorganization that accompanies changes in global context. We further demonstrate that the tracking of reward by the ACC ensemble is also strategy-specific, but that reward prevalence is insufficient to explain the observed activity modulation during strategy execution. Our findings argue that ACC ensemble dynamics is structured by a summary statistic of recent behavioral choices, raising the possibility that ACC plays a role in estimating – through statistical learning – which actions promote the occurrence of events in the environment.

    1. Neuroscience

    Postsynaptic mitochondria are positioned to support functional diversity of dendritic spines

    Connon I Thomas, Melissa A Ryan ... Benjamin Scholl
    Research Article

    Postsynaptic mitochondria are critical for the development, plasticity, and maintenance of synaptic inputs. However, their relationship to synaptic structure and functional activity is unknown. We examined a correlative dataset from ferret visual cortex with in vivo two-photon calcium imaging of dendritic spines during visual stimulation and electron microscopy reconstructions of spine ultrastructure, investigating mitochondrial abundance near functionally and structurally characterized spines. Surprisingly, we found no correlation to structural measures of synaptic strength. Instead, we found that mitochondria are positioned near spines with orientation preferences that are dissimilar to the somatic preference. Additionally, we found that mitochondria are positioned near groups of spines with heterogeneous orientation preferences. For a subset of spines with a mitochondrion in the head or neck, synapses were larger and exhibited greater selectivity to visual stimuli than those without a mitochondrion. Our data suggest mitochondria are not necessarily positioned to support the energy needs of strong spines, but rather support the structurally and functionally diverse inputs innervating the basal dendrites of cortical neurons.

    1. Neuroscience

    Multiple NTS neuron populations cumulatively suppress food intake

    Weiwei Qui, Chelsea R Hutch ... Darleen Sandoval
    Research Article

    Several discrete groups of feeding-regulated neurons in the nucleus of the solitary tract (nucleus tractus solitarius; NTS) suppress food intake, including avoidance-promoting neurons that express Cck (NTSCck cells) and distinct Lepr- and Calcr-expressing neurons (NTSLepr and NTSCalcr cells, respectively) that suppress food intake without promoting avoidance. To test potential synergies among these cell groups we manipulated multiple NTS cell populations simultaneously. We found that activating multiple sets of NTS neurons (e.g., NTSLepr plus NTSCalcr (NTSLC), or NTSLC plus NTSCck (NTSLCK)) suppressed feeding more robustly than activating single populations. While activating groups of cells that include NTSCck neurons promoted conditioned taste avoidance (CTA), NTSLC activation produced no CTA despite abrogating feeding. Thus, the ability to promote CTA formation represents a dominant effect but activating multiple non-aversive populations augments the suppression of food intake without provoking avoidance. Furthermore, silencing multiple NTS neuron groups augmented food intake and body weight to a greater extent than silencing single populations, consistent with the notion that each of these NTS neuron populations plays crucial and cumulative roles in the control of energy balance. We found that silencing NTSLCK neurons failed to blunt the weight-loss response to vertical sleeve gastrectomy (VSG) and that feeding activated many non-NTSLCK neurons, however, suggesting that as-yet undefined NTS cell types must make additional contributions to the restraint of feeding.