Abstract
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 language | English (US) |
|---|---|
| Title of host publication | Stochastic Methods in Neuroscience |
| Publisher | Oxford University Press |
| ISBN (Electronic) | 9780191715778 |
| ISBN (Print) | 9780199235070 |
| DOIs | |
| State | Published - Feb 1 2010 |
Keywords
- 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