Estimation of nonuniform quantal parameters with multiple-probability fluctuation analysis: theory, application and limitations

J Neurosci Methods. 2003 Dec 15;130(2):127-41. doi: 10.1016/j.jneumeth.2003.09.030.


Synapses are a key determinant of information processing in the central nervous system. Investigation of the mechanisms underlying synaptic transmission at central synapses is complicated by the inaccessibility of synaptic contacts and the fact that their temporal dynamics are governed by multiple parameters. Multiple-probability fluctuation analysis (MPFA) is a recently developed method for estimating quantal parameters from the variance and mean amplitude of evoked steady-state synaptic responses recorded under a range of release probability conditions. This article describes the theoretical basis and the underlying assumptions of MPFA, illustrating how a simplified multinomial model can be used to estimate mean quantal parameters at synapses where quantal size and release probability are nonuniform. Interpretations of the quantal parameter estimates are discussed in relation to uniquantal and multiquantal models of transmission. Practical aspects of this method are illustrated including a new method for estimating quantal size and variability, approaches for optimising data collection, error analysis and a method for identifying multivesicular release. The advantages and limitations of investigating synaptic function with MPFA are explored and contrasted with those for traditional quantal analysis and more recent optical quantal analysis methods.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Central Nervous System / physiology
  • Models, Neurological*
  • Models, Statistical*
  • Neurotransmitter Agents / metabolism*
  • Presynaptic Terminals / metabolism*
  • Synaptic Transmission / physiology*
  • Synaptic Vesicles / metabolism*


  • Neurotransmitter Agents