Principles of operation of a cerebellar learning circuit
- Duke University School of Medicine, United States
Abstract
We provide behavioral evidence using monkey smooth pursuit eye movements for four principles of cerebellar learning. Using a circuit-level model of the cerebellum, we link behavioral data to learning's neural implementation. The four principles are: (1) early, fast, acquisition driven by climbing fiber inputs to the cerebellar cortex, with poor retention; (2) learned responses of Purkinje cells guide transfer of learning from the cerebellar cortex to the deep cerebellar nucleus, with excellent retention; (3) functionally different neural signals are subject to learning in the cerebellar cortex versus the deep cerebellar nuclei; and (4) negative feedback from the cerebellum to the inferior olive reduces the magnitude of the teaching signal in climbing fibers and limits learning. Our circuit-level model, based on these four principles, explains behavioral data obtained by strategically manipulating the signals responsible for acquisition and recall of direction learning in smooth pursuit eye movements across multiple timescales.
Data availability
The data for each figure is included in a Figure Composer FYP file and can be viewed, exported, and further analyzed using the freely available Figure Composer tool (https://sites.google.com/a/srscicomp.com/datanav/figure-composer). This tool is platform agnostic and runs on Windows, Mac, and Linux systems. The source code used to generate the cerebellar model results (Figure 10) is included as a Jupyter notebook. This source code makes use of Julia but can be viewed without installing Julia.
Article and author information
Author details
Stephen G Lisberger
Funding
National Institute of Neurological Disorders and Stroke (R01NS092623)
- Stephen G Lisberger
National Institute of Neurological Disorders and Stroke (F32NS103216)
- Nathan J Hall
National Eye Institute (K99-EY030528)
- David J Herzfeld
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Jennifer L Raymond, Stanford University School of Medicine, United States
Ethics
Animal experimentation: All experimental procedures were performed in accordance with the Guide for the Care and Use of Laboratory Animals (1997) and had been approved in advance by the Institutional Animal Care and Use Committee at Duke University (Protocol A085-18-04).
Version history
- Received: January 16, 2020
- Accepted: April 29, 2020
- Accepted Manuscript published: April 30, 2020 (version 1)
- Version of Record published: May 28, 2020 (version 2)
Copyright
© 2020, Herzfeld 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
-
- 3,300
- views
-
- 611
- downloads
-
- 19
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
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)
Principles of operation of a cerebellar learning circuit
eLife 9:e55217.
https://doi.org/10.7554/eLife.55217
Further reading
-
- Genetics and Genomics
- Neuroscience
Single-cell RNA sequencing reveals the extent to which marmosets carry genetically distinct cells from their siblings.
-
- Neuroscience
We are unresponsive during slow-wave sleep but continue monitoring external events for survival. Our brain wakens us when danger is imminent. If events are non-threatening, our brain might store them for later consideration to improve decision-making. To test this hypothesis, we examined whether novel vocabulary consisting of simultaneously played pseudowords and translation words are encoded/stored during sleep, and which neural-electrical events facilitate encoding/storage. An algorithm for brain-state-dependent stimulation selectively targeted word pairs to slow-wave peaks or troughs. Retrieval tests were given 12 and 36 hr later. These tests required decisions regarding the semantic category of previously sleep-played pseudowords. The sleep-played vocabulary influenced awake decision-making 36 hr later, if targeted to troughs. The words’ linguistic processing raised neural complexity. The words’ semantic-associative encoding was supported by increased theta power during the ensuing peak. Fast-spindle power ramped up during a second peak likely aiding consolidation. Hence, new vocabulary played during slow-wave sleep was stored and influenced decision-making days later.
