Skip to main content

Umbrella menu

  • SfN.org
  • eNeuro
  • The Journal of Neuroscience
  • Neuronline
  • BrainFacts.org

Main menu

  • HOME
  • CONTENT
    • Early Release
    • Featured
    • Latest Articles
    • Issue Archive
    • Editorials
    • Research Highlights
  • TOPICS
    • Cognition and Behavior
    • Development
    • Disorders of the Nervous System
    • History, Teaching and Public Awareness
    • Integrative Systems
    • Neuronal Excitability
    • Novel Tools and Methods
    • Sensory and Motor Systems
  • ALERTS
  • FOR AUTHORS
  • EDITORIAL BOARD
  • BLOG
  • ABOUT
    • Overview
    • For the Media
    • Privacy Policy
    • Contact Us
    • Feedback
  • SfN.org
  • eNeuro
  • The Journal of Neuroscience
  • Neuronline
  • BrainFacts.org

User menu

  • My alerts

Search

  • Advanced search
eNeuro
  • My alerts

eNeuro

Advanced Search

Submit a Manuscript
  • HOME
  • CONTENT
    • Early Release
    • Featured
    • Latest Articles
    • Issue Archive
    • Editorials
    • Research Highlights
  • TOPICS
    • Cognition and Behavior
    • Development
    • Disorders of the Nervous System
    • History, Teaching and Public Awareness
    • Integrative Systems
    • Neuronal Excitability
    • Novel Tools and Methods
    • Sensory and Motor Systems
  • ALERTS
  • FOR AUTHORS
  • EDITORIAL BOARD
  • BLOG
  • ABOUT
    • Overview
    • For the Media
    • Privacy Policy
    • Contact Us
    • Feedback
Next
Research ArticleNew Research, Sensory and Motor Systems

The Neural Code for Motor Control in the Cerebellum and Oculomotor Brainstem

Kris S. Chaisanguanthum, Mati Joshua, Javier F. Medina, William Bialek and Stephen G. Lisberger
eNeuro 12 November 2014, 1 (1) ENEURO.0004-14.2014; DOI: https://doi.org/10.1523/ENEURO.0004-14.2014
Kris S. Chaisanguanthum
1Sloan-Swartz Center for Theoretical Neurobiology and Center for Integrative Neuroscience, Department of Physiology, University of California, San Francisco, San Francisco, California 94143
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Mati Joshua
2 Howard Hughes Medical Institute and Department of Neurobiology, Duke University School of Medicine, Durham, North Carolina 27710
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Javier F. Medina
3Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
William Bialek
4Joseph Henry Laboratories of Physics and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for William Bialek
Stephen G. Lisberger
2 Howard Hughes Medical Institute and Department of Neurobiology, Duke University School of Medicine, Durham, North Carolina 27710
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Stephen G. Lisberger
  • Article
  • Figures & Data
  • Info & Metrics
  • eLetters
  • PDF
Loading

Article Figures & Data

Figures

  • Cover Figure
    • Download figure
    • Open in new tab
    • Download powerpoint
    Cover Figure

    A single extra spike makes a difference. Here, the size of the eye velocity in the initiation of smooth eye movements in the right panel depends on whether a cerebellar Purkinje cell discharges 3 (red), 4 (green), 5 (blue), or 6 (black) spikes in the 40-ms window indicated by the gray shading in the rasters on the left.

  • Figure 1
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 1

    Example of an experimental trial and of neural and behavioral variation. A, Superimposed eye and target position for a step-ramp target motion. Dashed and continuous traces show target and eye motion. B, Eye velocity responses during the initiation of pursuit for recordings from a Purkinje cell. Gray and black traces show individual trial responses and the mean response. C, Raster from a typical Purkinje cell during pursuit initiation. Each line shows the response in one behavioral trial, and each tick shows the time of one simple-spike. D, Superimposed eye and target velocity for a step-ramp target motion. Dashed and continuous traces show eye and target motion. E, F, Distributions across the full sample of Purkinje cells of the correlation between actual and predicted magnitudes of the first (E) or second (F) principal component (PC1 and PC2, respectively) of the behavior. G, First two principal components resulting from dimension reduction of the variation in eye velocity at the initiation of pursuit. H, The amount of total behavioral variance explained by leading principal components for the variation in eye speed at the initiation of pursuit. I, J, Each trace shows the weights for an optimized linear estimator (Embedded Image in Eq. 2) relating the presence of an extra spike at time t on the horizontal axis to the amplitude of the first (I) or second (J) principal component of eye movement variation. Different colors show results for a single Purkinje cell during pursuit at three different target speeds. All times are relative to the onset of target motion.

  • Figure 2
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 2

    Prediction of eye speed by a single extra spike in four groups of cerebellar and brainstem neurons. A, B, Superimposed means of eye and target velocity for a typical recording session (A) and average firing rates (B) for the different brainstem neurons. Different colors show the mean firing rate across the different populations, and the gray ribbons show the SEM. C, Distributions across the full samples of different neuron classes of the correlation between actual and predicted magnitudes of the first principal component (PC1) of the pursuit behavior. D, Weight of spikes as a function of time for the best linear estimator. In B, C, and D, black, red, green, and blue show data for floccular Purkinje cells, FTNs, non-FTN vestibular neurons, and abducens neurons. The traces in B were taken from a figure in an earlier paper (Joshua et al., 2013).

  • Figure 3
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 3

    Comparison of the predictive value of a single extra spike in real versus simulated spike trains for a floccular Purkinje cell and an FTN. Results for Purkinje cells are in black and FTNs are in red. A, D, Predicted versus actual amplitude of the first principal component (PC) of pursuit behavior based on the optimal linear estimator from the real spike trains. B, E, Predicted versus actual amplitude of the first principal component of pursuit behavior based on the optimal linear estimator from the simulated spike trains. In all four graphs, each symbol shows the results from a single behavioral trial. C, F, Each trace shows the weights for an optimized linear decoder (Embedded Image in Eq. 2) relating the presence of an extra cell spike at time t on the horizontal axis to the amplitude of the first principal component of eye movement variation. Dark and light curves show results for real data versus simulated spike trains.

  • Figure 4
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 4

    Similarity of the predictive value of a single extra spike for real and simulated spike trains across all neurons in our sample. A, B, Each symbol summarizes the results for a single neuron and plots the correlation between the optimal predictor based on simulated spike trains against the correlation based on real spike trains. C, D, Average weights in the optimal linear estimators for the four groups of neurons, plotted as a function of the time of a spike. Continuous and dashed curves show weights based on the real and simulated spike trains, respectively. Throughout the figure, black, red, green, and blue indicate analyses for floccular Purkinje cells, FTNs, non-FTN vestibular neurons, and abducens neurons, respectively.

  • Figure 5
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 5

    Effect of discharge variability and overall signal magnitude on the predictive value of a spike train. A, B, Each graph plots the mean and standard deviation of the correlation between the real and predicted magnitude of the first principal component of eye speed based on simulated spike trains. Data are plotted as a function of the coefficient of variation (A) or the gain (B) of the artificial spike train. Red and blue symbols indicate analysis of simulations based on a representative FTN or abducens neuron. C−F, Scatter plots, where each symbol shows the results of analyzing the data of a single neuron, and the different plots show the correlation between the real and predicted magnitude of the first principal component as a function of the coefficient of variation of the spike trains (D), the amplitude of the neural signal (E), and the ratio of the signal amplitude divided by the coefficient of variation (C, F). Different colors show results for different classes of neurons.

  • Figure 6
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 6

    Demonstration that computing the best linear estimator with finite datasets causes over-fitting and underestimates the predictive value of a spike train. Each row shows data for a different set of neurons. Left, Each graph contains one symbol for each neuron and shows scatter plots of the correlation of actual and predicted magnitude of the first principal component of the data. Each point plots the correlation obtained using the same number of trials available in the real data on the y-axis and the correlation obtained using 1000 trials of simulated data on the x-axis. Middle and right, Each graph plots the time course of the optimal linear estimator when we used the actual data (middle) or 1000 simulated trials (right). The black curve in each graph shows the average across all neurons and the gray and colored traces show the individual neurons.

  • Figure 7
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 7

    Prediction of eye speed by spike counts in windows of different duration. Each graph plots the correlation between the output of different estimators and the amplitude of the first principal component of eye speed. Pink swatches are the lower bounds for the best linear estimator (Eq. 2). Black lines are from the spike count in windows of different duration centered 125 ms after the onset of target motion. The red line labeled “1000 trials” is the estimate of the upper bound of the results from the best linear estimator. Different graphs show data for different neuron types.

  • Figure 8
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 8

    Prediction of eye speed by spike counts in floccular Purkinje cells. A, Correlation between actual and predicted amplitude of the first principal component of eye speed as a function of the duration and time of the analysis interval. Different colored traces show results for analysis intervals of different durations. Pink band labeled “Linear” shows the correlation obtained with the best linear estimator. Red line labeled “1000 trials” shows the expected correlation for the best linear estimator if we had acquired 1000 repetitions of the target motion. B, Average firing rate of the population of Purkinje cells as a function of time. Gray bar shows a 20 ms analysis window centered at 140 ms. The gray bar indicates the time of the peak correlation with the first principal component (PC1) in A. C, Number of spike counts in a 5 ms analysis interval as a function of the center of the interval. Different color traces show numbers for different spike counts. D, Averages of eye velocity as a function of the time from onset of target motion for trials in one example Purkinje cell. Different colors show eye velocities for trials with three, four, five, or six spikes in the 41 ms interval centered 140 ms after the onset of target motion. E, Scatter plot showing the z-scored amplitude of the first principle component of eye speed as a function of the number of spikes in the same interval as D. Each symbol shows data from one trial. The regression line defines the sensitivity in units of standard deviations per spike. R = 0.53 for the symbols and the regression equation was y = 0.72x − 3.18. F, Distributions of sensitivity for populations of floccular Purkinje cells, FTN, and abducens neurons.

  • Figure 9
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 9

    Comparison of predictions made by spike counts over 40 ms in time versus space. Each panel shows data for a different group of neurons. Within each panel, the traces show the correlation between the actual and predicted amplitude of the first principal component of eye speed as a function of the time at the center of the analysis window. Blue traces show the results for counting spikes in a 40 ms window; the traces end at 180 ms because of the width of the analysis window. Green traces show the result for counting spikes in a 1 ms window across the simulated spike trains of 40 different neurons.

  • Figure 10
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 10

    Trade-offs between the duration of the spike counting bin and the size of the population of model neurons. Different color plots show results based on different populations of neurons. Each pixel uses color to indicate the correlation between actual and predicted magnitude of the first principle component of eye speed, for the population size and bin width on the y- and x-axes. The black lines show iso-probability contours.

Back to top

In this issue

eneuro: 1 (1)
eNeuro
Vol. 1, Issue 1
November/December 2014
  • Table of Contents
  • Index by author
Email

Thank you for sharing this eNeuro article.

NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

Enter multiple addresses on separate lines or separate them with commas.
The Neural Code for Motor Control in the Cerebellum and Oculomotor Brainstem
(Your Name) has forwarded a page to you from eNeuro
(Your Name) thought you would be interested in this article in eNeuro.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Print
View Full Page PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Citation Tools
The Neural Code for Motor Control in the Cerebellum and Oculomotor Brainstem
Kris S. Chaisanguanthum, Mati Joshua, Javier F. Medina, William Bialek, Stephen G. Lisberger
eNeuro 12 November 2014, 1 (1) ENEURO.0004-14.2014; DOI: 10.1523/ENEURO.0004-14.2014

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Respond to this article
Share
The Neural Code for Motor Control in the Cerebellum and Oculomotor Brainstem
Kris S. Chaisanguanthum, Mati Joshua, Javier F. Medina, William Bialek, Stephen G. Lisberger
eNeuro 12 November 2014, 1 (1) ENEURO.0004-14.2014; DOI: 10.1523/ENEURO.0004-14.2014
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Significance Statement
    • Introduction
    • Materials and Methods
    • Results
    • Discussion
    • Acknowledgments
    • Footnotes
    • References
    • Synthesis
    • Author Response Letter:
  • Figures & Data
  • Info & Metrics
  • eLetters
  • PDF

Keywords

  • abducen
  • Floccular Complex
  • population coding
  • rate code
  • smooth pursuit eye movements
  • temporal code

Responses to this article

Respond to this article

Jump to comment:

No eLetters have been published for this article.

Related Articles

Cited By...

More in this TOC Section

New Research

  • Food-seeking behavior is mediated by Fos-expressing neuronal ensembles formed at first learning in rats
  • Deficiency of microglial autophagy increases the density of oligodendrocytes and susceptibility to severe forms of seizures
  • Arginine Vasopressin-Containing Neurons of the Suprachiasmatic Nucleus Project to CSF
Show more New Research

Sensory and Motor Systems

  • Arginine Vasopressin-Containing Neurons of the Suprachiasmatic Nucleus Project to CSF
  • An Atoh1 CRE knock-in mouse labels motor neurons involved in fine motor control
  • Neural correlates of vocal auditory feedback processing: Unique insights from electrocorticography recordings in a human cochlear implant user
Show more Sensory and Motor Systems

Subjects

  • Sensory and Motor Systems
  • Home
  • Alerts
  • Visit Society for Neuroscience on Facebook
  • Follow Society for Neuroscience on Twitter
  • Follow Society for Neuroscience on LinkedIn
  • Visit Society for Neuroscience on Youtube
  • Follow our RSS feeds

Content

  • Early Release
  • Current Issue
  • Latest Articles
  • Issue Archive
  • Blog
  • Browse by Topic

Information

  • For Authors
  • For the Media

About

  • About the Journal
  • Editorial Board
  • Privacy Policy
  • Contact
  • Feedback
(eNeuro logo)
(SfN logo)

Copyright © 2021 by the Society for Neuroscience.
eNeuro eISSN: 2373-2822

The ideas and opinions expressed in eNeuro do not necessarily reflect those of SfN or the eNeuro Editorial Board. Publication of an advertisement or other product mention in eNeuro should not be construed as an endorsement of the manufacturer’s claims. SfN does not assume any responsibility for any injury and/or damage to persons or property arising from or related to any use of any material contained in eNeuro.