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Cerebellar Motor Learning: When Is Cortical Plasticity Not Enough?

Overview of attention for article published in PLoS Computational Biology, October 2007
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Title
Cerebellar Motor Learning: When Is Cortical Plasticity Not Enough?
Published in
PLoS Computational Biology, October 2007
DOI 10.1371/journal.pcbi.0030197
Pubmed ID
Authors

John Porrill, Paul Dean

Abstract

Classical Marr-Albus theories of cerebellar learning employ only cortical sites of plasticity. However, tests of these theories using adaptive calibration of the vestibulo-ocular reflex (VOR) have indicated plasticity in both cerebellar cortex and the brainstem. To resolve this long-standing conflict, we attempted to identify the computational role of the brainstem site, by using an adaptive filter version of the cerebellar microcircuit to model VOR calibration for changes in the oculomotor plant. With only cortical plasticity, introducing a realistic delay in the retinal-slip error signal of 100 ms prevented learning at frequencies higher than 2.5 Hz, although the VOR itself is accurate up to at least 25 Hz. However, the introduction of an additional brainstem site of plasticity, driven by the correlation between cerebellar and vestibular inputs, overcame the 2.5 Hz limitation and allowed learning of accurate high-frequency gains. This "cortex-first" learning mechanism is consistent with a wide variety of evidence concerning the role of the flocculus in VOR calibration, and complements rather than replaces the previously proposed "brainstem-first" mechanism that operates when ocular tracking mechanisms are effective. These results (i) describe a process whereby information originally learnt in one area of the brain (cerebellar cortex) can be transferred and expressed in another (brainstem), and (ii) indicate for the first time why a brainstem site of plasticity is actually required by Marr-Albus type models when high-frequency gains must be learned in the presence of error delay.

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Geographical breakdown

Country Count As %
Germany 4 3%
United Kingdom 4 3%
United States 4 3%
Brazil 1 <1%
India 1 <1%
Belgium 1 <1%
Switzerland 1 <1%
Spain 1 <1%
Poland 1 <1%
Other 0 0%
Unknown 113 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 33 25%
Researcher 33 25%
Student > Master 12 9%
Professor 10 8%
Professor > Associate Professor 9 7%
Other 26 20%
Unknown 8 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 34 26%
Neuroscience 22 17%
Engineering 16 12%
Psychology 15 11%
Medicine and Dentistry 14 11%
Other 19 15%
Unknown 11 8%