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.
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References
Abraham WC, Robins A (2005) Memory retention: the synaptic stability versus plasticity dilemma. Trends Neurosci 28:73–78
Masugi-Tokita M, Tarusawa E, Watanabe M, Molnar E, Fujimoto K, Shigemoto R (2007) Number and density of AMPA receptors in individual synapses in the rat cerebellum as revealed by SDS-digested freeze-fracture replica labeling. J Neurosci 27:2135–2144
Marr D (1969) A theory of cerebellar cortex. J Physiol 202:437–470
Ito M (1970) Neurophysiological aspects of the cerebellar motor control system. Int J Neurol 7:162–176
Albus JS (1971) A theory of cerebellar function. Math Biosci 10:25–61
Ito M (2001) Cerebellar long-term depression: characterization, signal transduction, and functional roles. Physiol Rev 81:1143–1195
Ito M (2002) The molecular organization of cerebellar long-term depression. Nat Rev Neurosci 3:896–902
Hartell NA (2002) Parallel fiber plasticity. Cerebellum 1:3–18
Ito M (2006) Cerebellar circuitry as a neuronal machine. Prog Neurobiol 78:272–303
Jörntell H, Hansel C (2006) Synaptic memories upside down: bidirectional plasticity at cere-bellar parallel fiber-Purkinje cell synapses. Neuron 52:227–238
Iino M (2006) Ca2+-dependent inositol 1,4,5-trisphosphate and nitric oxide signaling in cere-bellar neurons. J Pharmacol Sci 100:538–544
Ferrell JE Jr, Machleder EM (1998) The biochemical basis of an all-or-none cell fate switch in Xenopus oocytes. Science 280:895–898
Kuroda S, Schweighofer N, Kawato M (2001) Exploration of signal transduction pathways in cerebellar long-term depression by kinetic simulation. J Neurosci 21:5693–5702
Doi T, Kuroda S, Michikawa T, Kawato M (2005) Inositol 1,4,5-trisphosphate-dependent Ca2+ threshold dynamics detect spike timing in cerebellar Purkinje cells. J Neurosci 25:950–961
Ogasawara H, Doi T, Kawato M (2008) Systems biology perspectives on cerebellar long-term depression. NeuroSignals 16:300–317
Sarkisov DV, Wang SS (2008) Order-dependent coincidence detection in cerebellar Purkinje neurons at the inositol trisphosphate receptor. J Neurosci 28:133–142
Ogasawara H (2008) The calcium kinetics and inositol trisphosphate receptor properties shape the asymmetric timing window of coincidence detection. J. Neurosci 28:4293–4294
Tanaka K, Khiroug L, Santamaria F, Doi T, Ogasawara H, Ellis-Davies GC, Kawato M, Augustine GJ (2007) Ca2+ requirements for cerebellar long-term synaptic depression: role for a postsynaptic leaky integrator. Neuron 54:787–800
Tanaka K, Augustine GJ (2008) A positive feedback signal transduction loop determines timing of cerebellar long-term depression. Neuron 59:608–620
Ramakrishnan N, Bhalla US (2008) Memory switches in chemical reaction space. PLoS Comput Biol 4:e1000122
Kawato M (2008) From ‘understanding the brain by creating the brain’ towards manipulative neuroscience. Philos Trans R Soc Lond B Biol Sci 363:2201–2214
Sacktor TC (2008) PKMζ, LTP maintenance, and the dynamic molecular biology of memory storage. Prog Brain Res 169:27–40
Shema R, Sacktor TC, Dudai Y (2007) Rapid erasure of long-term memory associations in the cortex by an inhibitor of PKMζ. Science 317:951–953
Hernandez AI, Blace N, Crary JF, Serrano PA, Leitges M, Libien JM, Weinstein G, Tcherapanov A, Sacktor TC (2003) Protein kinase Mζ synthesis from a brain mRNA encoding an independent protein kinase Cζ catalytic domain. Implications for the molecular mechanism of memory. J Biol Chem 278:40305–40316
Oster H, Eichele G, Leitges M (2004) Differential expression of atypical PKCs in the adult mouse brain. Brain Res Mol Brain Res 127:79–88
Lynch MA (2004) Long-term potentiation and memory. Physiol Rev 84:87–136
Silva AJ, Stevens CF, Tonegawa S, Wang Y (1992) Deficient hippocampal long-term potentia-tion in alpha-calcium-calmodulin kinase II mutant mice. Science 257:201–206
Hansel C, de Jeu M, Belmeguenai A, Houtman SH, Buitendijk GH, Andreev D, De Zeeuw CI, Elgersma Y (2006) αCaMKII Is essential for cerebellar LTD and motor learning. Neuron 51:835–843
Goldbeter A, Koshland DE Jr (1981) An amplified sensitivity arising from covalent modification in biological systems. Proc Natl Acad Sci USA 78:6840–6844
Ferrell JE Jr (1999) Building a cellular switch: more lessons from a good egg. Bioessays 21:866–870
Shibuki K, Kimura S (1997) Dynamic properties of nitric oxide release from parallel fibres in rat cerebellar slices. J Physiol 498(pt 2):443–452
Lev-Ram V, Wong ST, Storm DR, Tsien RY (2002) A new form of cerebellar long-term potentiation is postsynaptic and depends on nitric oxide but not cAMP. Proc Natl Acad Sci USA 99:8389–8393
Kakegawa W, Yuzaki M (2005) A mechanism underlying AMPA receptor trafficking during cerebellar long-term potentiation. Proc Natl Acad Sci USA 102:17846–17851
Gillespie DT (1977) Exact stochastic simulation of coupled chemical reactions. J Phys Chem 81:2340–2361
Brandman O, Ferrell JE Jr, Li R, Meyer T (2005) Interlinked fast and slow positive feedback loops drive reliable cell decisions. Science 310:496–498
Nimchinsky EA, Sabatini BL, Svoboda K (2002) Structure and function of dendritic spines. Annu Rev Physiol 64:313–353
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Ogasawara, H., Kawato, M. (2009). Computational Models of Cerebellar Long-Term Memory. In: Nakanishi, S., Kageyama, R., Watanabe, D. (eds) Systems Biology. Springer, Tokyo. https://doi.org/10.1007/978-4-431-87704-2_18
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