HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Corrigendum and corrected article All Versions of this Article: 96/2/872    most recent 00079.2006v2 00079.2006v1 Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (6) Citing Articles via Google Scholar Google Scholar Articles by Huys, Q. J. M. Articles by Paninski, L. Search for Related Content PubMed PubMed Citation Articles by Huys, Q. J. M. Articles by Paninski, L.

INNOVATIVE METHODOLOGY

Efficient Estimation of Detailed Single-Neuron Models

¹Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom; and ²Department of Statistics and Center for Theoretical Neuroscience, Columbia University, New York, New York

Submitted 24 January 2006; accepted in final form 14 April 2006

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 10⁴ 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.

This article has been cited by other articles:

				U. Picchini, S. Ditlevsen, A. De Gaetano, and P. Lansky Parameters of the Diffusion Leaky Integrate-and-Fire Neuronal Model for a Slowly Fluctuating Signal Neural Comput., November 1, 2008; 20(11): 2696 - 2714. [Abstract] [Full Text] [PDF]

				L. Badel, S. Lefort, R. Brette, C. C. H. Petersen, W. Gerstner, and M. J. E. Richardson Dynamic I-V Curves Are Reliable Predictors of Naturalistic Pyramidal-Neuron Voltage Traces J Neurophysiol, February 1, 2008; 99(2): 656 - 666. [Abstract] [Full Text] [PDF]

				S. Hong, B. Aguera y Arcas, and A. L. Fairhall Single neuron computation: from dynamical system to feature detector. Neural Comput., December 1, 2007; 19(12): 3133 - 3172. [Abstract] [Full Text] [PDF]