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Title
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A stochastic signaling network mediates the probabilistic induction of cerebellar long-term depression
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Author
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Abstract
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| Many cellular processes involve a small number of molecules and undergo stochastic fluctuations in their levels of activity. Cerebellar long-term depression (LTD) is a form of synaptic plasticity expressed as a reduction in the number of synaptic AMPA receptors (AMPARs) in Purkinje cells. We developed a stochastic model of the LTD signaling network, including a PKC-ERK-cPLA(2) positive feedback loop and mechanisms of AMPAR trafficking, and tuned the model to replicate calcium uncaging experiments. The signaling network activity in single synapses switches between two discrete stable states (LTD and non-LTD) in a probabilistic manner. The stochasticity of the signaling network causes threshold dithering and allows at the macroscopic level for many different and stable mean magnitudes of depression. The probability of LTD occurrence in a single spine is only modulated by the concentration and duration of the signal used to trigger it, and inputs with the same magnitude can give rise to two different responses; there is no threshold for the input signal. The stochasticity is intrinsic to the signaling network and not mostly dependent on noise in the calcium input signal, as has been suggested previously. The activities of the ultrasensitive ERK and of cPLA(2) undergo strong stochastic fluctuations. Conversely, PKC, which acts as a noise filter, is more constantly activated. Systematic variation of the biochemical population size demonstrates that threshold dithering and the absence of spontaneous LTD depend critically on the number of molecules in a spine, indicating constraints on spine size in Purkinje cells. |
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Language
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English
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Source (journal)
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The journal of neuroscience. - Baltimore, Md
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Publication
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Baltimore, Md
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2012
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ISSN
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0270-6474
[Print]
1529-2401
[Online]
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DOI
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10.1523/JNEUROSCI.5976-11.2012
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Volume/pages
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32
:27
(2012)
, p. 9288-9300
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ISI
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000306193900017
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Full text (Publisher's DOI)
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Full text (open access)
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