Format

Send to

Choose Destination
J Neurophysiol. 2006 Aug;96(2):872-90. Epub 2006 Apr 19.

Efficient estimation of detailed single-neuron models.

Author information

1
Gatsby Computational Neuroscience Unit, University College London, UK. qhuys.ahrens@gatsby.ucl.ac.uk

Abstract

Biophysically accurate multicompartmental models of individual neurons have significantly advanced our understanding of the input-output function of single cells. These models depend on a large number of parameters that are difficult to estimate. In practice, they are often hand-tuned to match measured physiological behaviors, thus raising questions of identifiability and interpretability. 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. Recent experimental advances, notably in voltage-sensitive imaging, motivate us to assume access to: i) the spatiotemporal voltage signal in the dendrite and ii) an approximate description of the channel kinetics of interest. We show here that, given i and ii, parameters 1-3 can be inferred simultaneously by nonnegative linear regression; that this optimization problem possesses a unique solution and is guaranteed to converge despite the large number of parameters and their complex nonlinear interaction; and that standard optimization algorithms efficiently reach this optimum with modest computational and data requirements. We demonstrate that the method leads to accurate estimations on a wide variety of challenging model data sets that include up to about 10(4) 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.

PMID:
16624998
DOI:
10.1152/jn.00079.2006
[Indexed for MEDLINE]
Free full text

Supplemental Content

Full text links

Icon for Atypon Icon for ModelDB
Loading ...
Support Center