Evidence map›Paper›PMID 26464956›Full record

ArticleeNeuro

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

Open access · goldAbstract read
In one paragraph

Article in eNeuro. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.5field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 20 citations in OpenAlex.

  1. Article
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  11. How and why neural and motor variation are related.Current opinion in neurobiology · 2015
    Review
  12. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 4 institutions in 1 country.

Kris S ChaisanguanthumSloan-Swartz Center for Theoretical Neurobiology and Center for Integrative Neuroscience, Department of Physiology, University of California , San Francisco, San Francisco, California 94143.
Mati JoshuaHoward Hughes Medical Institute and Department of Neurobiology, Duke University School of Medicine, Durham, North Carolina 27710.
Javier F MedinaDepartment of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania 19104.
William BialekJoseph Henry Laboratories of Physics and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544.
Stephen G LisbergerHoward Hughes Medical Institute and Department of Neurobiology, Duke University School of Medicine, Durham, North Carolina 27710.
Howard Hughes Medical Institute · USPrinceton University · USUniversity of California, San Francisco · USUniversity of Pennsylvania · US

Funding

Collaborative research: CRCNS: Precision and coding in smooth pursuitR01EY017210 · NEI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI LISBERGER, STEPHEN G · 2005 to 2010
$1.7M
NEI NIH HHS R01 EY017210
6 · The paper itself

Abstract

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. Spike trains are rich in information that can be extracted to guide behaviors at millisecond time resolution or across longer time intervals. In sensory systems, the information usually is defined with respect to the stimulus. Especially in motor systems, however, it is equally critical to understand how spike trains predict behavior. Thus, our goal was to compare systematically spike trains in the oculomotor system with eye movement behavior on single movements. We analyzed the discharge of Purkinje cells in the floccular complex of the cerebellum, floccular target neurons in the brainstem, other vestibular neurons, and abducens neurons. We find that an extra spike in a brief analysis window predicts a substantial fraction of the trial-by-trial variation in the initiation of smooth pursuit eye movements. For Purkinje cells, a single extra spike in a 40 ms analysis window predicts, on average, 0.5 SDs of the variation in behavior. An optimal linear estimator predicts behavioral variation slightly better than do spike counts in brief windows. Simulations reveal that the ability of single spikes to predict a fraction of behavior also emerges from model spike trains that have the same statistics as the real spike trains, as long as they are driven by shared sensory inputs. We think that the shared sensory estimates in their inputs create correlations in neural spiking across time and across each population. As a result, one or a small number of spikes in a brief time interval can predict a substantial fraction of behavioral variation.

Indexed as

abducenfloccular complexpopulation codingrate codesmooth pursuit eye movementstemporal code

Identifiers

PMID26464956
PMCPMC4596133
OpenAlexW2149384040

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.