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Two-compartment model for whole-cell data analysis and transient compensation

Nadeau, H. and Lester, H. A. (2000) Two-compartment model for whole-cell data analysis and transient compensation. Journal of Neuroscience Methods, 99 (1-2). pp. 25-35. ISSN 0165-0270. doi:10.1016/s0165-0270(00)00210-7.

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Recording and analysis of neuronal patch-clamp data involve many assumptions about membrane properties and cell morphology. Some of these assumptions introduce large errors or oversimplifications into the results. In particular, dendritic branching with high intracellular resistance leads to difficulty with capacitance calculation and transient subtraction, and may significantly distort measured currents. A two-compartment model, presented in detail here, provides a simple method of reducing many of these problems for the relatively simple case of cultured neurons studied with whole-cell patch electrodes. Some passive membrane properties may be accurately calculated, and the results may be used to correct recorded currents for resulting series resistance, intracellular resistance, and capacitive transient errors. The model may be tailored to particular cell types or experimental conditions. Programs to implement the algorithms are available from∼nadeau/Rscomp.html.

Item Type:Article
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Lester, H. A.0000-0002-5470-5255
Additional Information:© 2000 Elsevier. Received 12 December 1999, Revised 20 March 2000, Accepted 20 March 2000, Available online 7 August 2000.
Subject Keywords:Whole-cell recording; Series resistance; Compensation; Capacitance; Compartmental model; Cable; Voltage-clamp
Issue or Number:1-2
Record Number:CaltechAUTHORS:20181213-110051972
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Official Citation:H. Nadeau, H.A. Lester, Two-compartment model for whole-cell data analysis and transient compensation, Journal of Neuroscience Methods, Volume 99, Issues 1–2, 2000, Pages 25-35, ISSN 0165-0270, (
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:91759
Deposited By: George Porter
Deposited On:13 Dec 2018 22:18
Last Modified:16 Nov 2021 03:44

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