Demixed principal component analysis of neural population data

  1. Dmitry Kobak  Is a corresponding author
  2. Wieland Brendel
  3. Christos Constantinidis
  4. Claudia E Feierstein
  5. Adam Kepecs
  6. Zachary F Mainen
  7. Ranulfo Romo
  8. Xue-Lian Qi
  9. Naoshige Uchida
  10. Christian K Machens
  1. Champalimaud Centre for the Unknown, Portugal
  2. Wake Forest University School of Medicine, United States
  3. Cold Spring Harbor Laboratory, United States
  4. Universidad Nacional Autónoma de México, Mexico
  5. Harvard University, United States

Abstract

Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure.

Article and author information

Author details

  1. Dmitry Kobak

    Champalimaud Neuroscience Program, Champalimaud Centre for the Unknown, Lisbon, Portugal
    For correspondence
    dmitry.kobak@neuro.fchampalimaud.org
    Competing interests
    No competing interests declared.
  2. Wieland Brendel

    Champalimaud Neuroscience Program, Champalimaud Centre for the Unknown, Lisbon, Portugal
    Competing interests
    No competing interests declared.
  3. Christos Constantinidis

    Wake Forest University School of Medicine, Winston-Salem, United States
    Competing interests
    No competing interests declared.
  4. Claudia E Feierstein

    Champalimaud Neuroscience Program, Champalimaud Centre for the Unknown, Lisbon, Portugal
    Competing interests
    No competing interests declared.
  5. Adam Kepecs

    Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
    Competing interests
    No competing interests declared.
  6. Zachary F Mainen

    Champalimaud Neuroscience Program, Champalimaud Centre for the Unknown, Lisbon, Portugal
    Competing interests
    No competing interests declared.
  7. Ranulfo Romo

    Instituto de Fisiología Celular-Neurociencias, Universidad Nacional Autónoma de México, Ciudad de México, Mexico
    Competing interests
    No competing interests declared.
  8. Xue-Lian Qi

    Wake Forest University School of Medicine, Winston-Salem, United States
    Competing interests
    No competing interests declared.
  9. Naoshige Uchida

    Harvard University, Cambridge, United States
    Competing interests
    Naoshige Uchida, Reviewing editor, eLife.
  10. Christian K Machens

    Champalimaud Neuroscience Program, Champalimaud Centre for the Unknown, Lisbon, Portugal
    Competing interests
    No competing interests declared.

Copyright

© 2016, Kobak 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

  • 41,136
    views
  • 6,900
    downloads
  • 488
    citations

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

Download links

Share this article

https://doi.org/10.7554/eLife.10989

Further reading

    1. Neuroscience
    Yisi Liu, Pu Wang ... Hongwei Zhou
    Short Report

    The increasing use of tissue clearing techniques underscores the urgent need for cost-effective and simplified deep imaging methods. While traditional inverted confocal microscopes excel in high-resolution imaging of tissue sections and cultured cells, they face limitations in deep imaging of cleared tissues due to refractive index mismatches between the immersion media of objectives and sample container. To overcome these challenges, the RIM-Deep was developed to significantly improve deep imaging capabilities without compromising the normal function of the confocal microscope. This system facilitates deep immunofluorescence imaging of the prefrontal cortex in cleared macaque tissue, extending imaging depth from 2 mm to 5 mm. Applied to an intact and cleared Thy1-EGFP mouse brain, the system allowed for clear axonal visualization at high imaging depth. Moreover, this advancement enables large-scale, deep 3D imaging of intact tissues. In principle, this concept can be extended to any imaging modality, including existing inverted wide-field, confocal, and two-photon microscopy. This would significantly upgrade traditional laboratory configurations and facilitate the study of connectomes in the brain and other tissues.

    1. Neuroscience
    Damian Koevoet, Laura Van Zantwijk ... Christoph Strauch
    Research Article

    What determines where to move the eyes? We recently showed that pupil size, a well-established marker of effort, also reflects the effort associated with making a saccade (‘saccade costs’). Here, we demonstrate saccade costs to critically drive saccade selection: when choosing between any two saccade directions, the least costly direction was consistently preferred. Strikingly, this principle even held during search in natural scenes in two additional experiments. When increasing cognitive demand experimentally through an auditory counting task, participants made fewer saccades and especially cut costly directions. This suggests that the eye-movement system and other cognitive operations consume similar resources that are flexibly allocated among each other as cognitive demand changes. Together, we argue that eye-movement behavior is tuned to adaptively minimize saccade-inherent effort.