Improved signaling as a result of randomness in synaptic vesicle release

Calvin Zhang, Charles S. Peskin

Research output: Contribution to journalArticlepeer-review

Abstract

The probabilistic nature of neurotransmitter release in synapses is believed to be one of the most significant sources of noise in the central nervous system. We show how p0, the probability of release per docked vesicle when an action potential arrives, affects the dynamics of the rate of vesicle release in response to changes in the rate of arrival of action potentials. Furthermore, we examine the theoretical capability of a synapse in the estimation of desired signals using information from the stochastic vesicle release events under the framework of optimal linear filter theory. We find that a small p0, such as 0.1, reduces the error in the reconstruction of the input, or in the reconstruction of the time derivative of the input, from the time series of vesicle release events. Our results imply that the probabilistic nature of synaptic vesicle release plays a direct functional role in synaptic transmission.

Original languageEnglish (US)
Pages (from-to)14954-14959
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume112
Issue number48
DOIs
StatePublished - Dec 1 2015

Keywords

  • Optimal filter
  • Release probability
  • Stochastic vesicle release
  • Synaptic transmission

ASJC Scopus subject areas

  • General

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