Stochastic Neural Field Theory
One of the major challenges in neuroscience is to determine how noise that is present at the molecular and cellular levels affects dynamics and information processing at the macroscopic level of synaptically coupled neuronal populations. Often noise is incorporated into deterministic neural network models using extrinsic noise sources. An alternative approach is to assume that noise arises intrinsically as a collective population effect, which has led to a master equation formulation of stochastic neural networks. Stochastic neural fields are obtained by taking a continuum limit of a stochastic neural network with spatially structured synaptic weights.
The spike trains of individual cortical neurons in vivo tend to be very noisy, having interspike interval (ISI) distributions that are close to Poisson (Softky and Koch 1993). The main source of intrinsic fluctuations at the single-cell level is channel noise, which arises from the variability in the...
- Faugeras O, Touboul J, Cessac B (2009) A constructive mean-field analysis of multi-population neural networks with random synaptic weights and stochastic inputs. Front Comp Neurosci 3:1–28Google Scholar