A computational study of synaptic mechanisms of partial memory transfer in cerebellar vestibulo-ocular-reflex learning.

Full Abstract

There is a debate regarding whether motor memory is stored in the cerebellar cortex, or the cerebellar nuclei, or both. Memory may be acquired in the cortex and then be transferred to the cerebellar nuclei. Based on a dynamical system modeling with a minimal set of variables, we theoretically investigated possible mechanisms of memory transfer and consolidation in the context of vestibulo-ocular reflex learning. We tested different plasticity rules for synapses in the cerebellar nuclei and took robustness of behavior against parameter variation as the criterion of plausibility of a model variant. In the most plausible scenarios, mossy-fiber nucleus-neuron synapses or Purkinje-cell nucleus-neuron synapses are plastic on a slow time scale and store permanent memory, whose content is passed from the cerebellar cortex storing transient memory. In these scenarios, synaptic strengths are potentiated when the mossy-fiber afferents to the nuclei are active during a pause in Purkinje-cell activities. Furthermore, assuming that mossy fibers create a limited variety of signals compared to parallel fibers, our model shows partial memory transfer from the cortex to the nuclei.

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Author information

Author/s: Masuda, Naoki (N); Amari, Shun-ichi (S);

Affiliation: Amari Research Unit, RIKEN Brain Science Institute, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan. masuda@mist.i.u-tokyo.ac.jp

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: Journal of computational neuroscience (J Comput Neurosci), published in United States. (Language: eng)

Reference: 2008-Apr; vol 24 (issue 2) : pp 137-56

Dates: Created 2008/03/19; Completed 2008/06/20;

PMID: 17616795, status: MEDLINE (last retrieval date: 11/6/2008)

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Animals
Cerebellar Cortex - physiology
Computer Simulation
Learning - physiology
Models, Neurological

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