Efficient estimation of detailed single-neuron models (Huys et al. 2006)

"Biophysically accurate multicompartmental models of individual neurons ... depend on a large number of parameters that are difficult to estimate. ... We propose a statistical approach to the automatic estimation of various biologically relevant parameters, including 1) the distribution of channel densities, 2) the spatiotemporal pattern of synaptic input, and 3) axial resistances across extended dendrites. ... We demonstrate that the method leads to accurate estimations on a wide variety of challenging model data sets that include up to about 10,000 parameters (roughly two orders of magnitude more than previously feasible) and describe how the method gives insights into the functional interaction of groups of channels."

Model Type: Neuron or other electrically excitable cell

Model Concept(s): Methods

Simulation Environment: MATLAB


Huys QJ, Ahrens MB, Paninski L. (2006). Efficient estimation of detailed single-neuron models. Journal of neurophysiology 96 [PubMed]