Population Density Methods in Large-Scale Neural Network Modelling

Daniel Tranchina

Research output: Chapter in Book/Report/Conference proceedingChapter


Population density methods have a rich history in theoretical and computational neuroscience. In earlier years, these methods were used in large part to study the statistics of spike trains. Starting in the 1990s population density function (PDF) methods have been used as an analytical and computational tool to study neural network dynamics. In this chapter, we discuss the motivation and theory underlying PDF methods and a few selected examples of computational and analytical applications in neural network modelling.

Original languageEnglish (US)
Title of host publicationStochastic Methods in Neuroscience
PublisherOxford University Press
ISBN (Electronic)9780191715778
ISBN (Print)9780199235070
StatePublished - Feb 1 2010


  • Fokker-Plank equation
  • Integrate-and-fire
  • Partial differential-integral equation
  • Poisson process
  • Random differential equation
  • Sparse connectivity
  • State space
  • Stochastic spike trains
  • Synaptic noise
  • Visual cortex

ASJC Scopus subject areas

  • General Mathematics


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