Dynamic-Clamp pp 383-397 | Cite as

Key Factors for Improving Dynamic-Clamp Performance

Part of the Springer Series in Computational Neuroscience book series (NEUROSCI, volume 1)


The dynamic-clamp technique has been recognized and used by electrophysiologists for over 15 years. Nevertheless, only a small number of papers have been written focusing on the performance and reliability of this protocol and how the accuracy of a dynamic-clamp system can be assessed. Here we review the published literature to date, focusing on how experimental, computational, and algorithmic factors contribute to the reliability of the dynamic-clamp protocol. Several of these papers point towards a common and technologically realizable solution – the need for dynamic-clamp systems that run at computational rates much faster (100–200 kHz) than currently available. At present, dynamic-clamp rates are limited by the use of desktop computers and rationalized by the kinetics of the model simulated. Recent results show that faster and lower latency systems would result in a greater range of conductances that could be utilized, improved stability, and more accurate real-time model simulations.


Quantization Error Input Range Numerical Integration Method Computational Cycle Measured Membrane Potential 



This work was supported by grants from the National Institutes of Health (PI: Christini, R01-RR020115; PI: Canavier, R01-NS054281) and previously by the National Science Foundation (PI: Butera, DBI-9987074). Many investigators have contributed to this work and the discussions within, including Ivan Raikov, Amanda Preyer, Maeve McCarthy, John White, David Christini, and Jonathan Bettencourt.


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Laboratory for Neuroengineering, School of ECE, Georgia Institute of TechnologyAtlantaUSA

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