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Computational Models of Cerebellar Long-Term Memory

  • Hideaki Ogasawara
  • Mitsuo Kawato

Our brain is capable of learning new things while maintaining old memory. Retention of information requires stability, and new learning requires plasticity. As a memory device, neurons have to meet these contradictory requirements (the “stability versus plasticity dilemma” [1]), but stochastic noise makes this duty still more difficult. The dendritic spine, the key unit of neuronal information processing, is very small (̃1 μm or less in diameter) and contains only a limited number of each molecular species. For instance, the number of α-amino-3-hydroxy-5-methylisoxazole-4-propionic acid (AMPA)-type glutamate receptors (AMPARs) in a parallel fiber (PF)—Purkinje cell (PC) synapse is as small as 4 to 73 [2]. In such a minute environment, stochastic fluctuations come into play and, affect the signaling pathways underlying memory formation and maintenance. How do neurons handle the stability versus plasticity dilemma without being overwhelmed by the noise? In this chapter, we address this issue by reviewing several theoretical studies of cerebellar long-term depression (LTD) and simulating simple models.

Keywords

Dendritic Spine Stable Steady State Bistable Switch Inositol Trisphosphate Receptor Fast Loop 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer 2009

Authors and Affiliations

  • Hideaki Ogasawara
    • 1
    • 2
  • Mitsuo Kawato
    • 2
  1. 1.National Institute of Information and Communications TechnologySeikaJapan
  2. 2.ATR Computational Neuroscience LaboratoriesSeikaJapan

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