Literature DB >> 18076999

Effects of imperfect dynamic clamp: computational and experimental results.

Jonathan C Bettencourt1, Kyle P Lillis, Laura R Stupin, John A White.   

Abstract

In the dynamic clamp technique, a typically nonlinear feedback system delivers electrical current to an excitable cell that represents the actions of "virtual" ion channels (e.g., channels that are gated by local membrane potential or by electrical activity in neighboring biological or virtual neurons). Since the conception of this technique, there have been a number of different implementations of dynamic clamp systems, each with differing levels of flexibility and performance. Embedded hardware-based systems typically offer feedback that is very fast and precisely timed, but these systems are often expensive and sometimes inflexible. PC-based systems, on the other hand, allow the user to write software that defines an arbitrarily complex feedback system, but real-time performance in PC-based systems can be deteriorated by imperfect real-time performance. Here, we systematically evaluate the performance requirements for artificial dynamic clamp knock-in of transient sodium and delayed rectifier potassium conductances. Specifically, we examine the effects of controller time step duration, differential equation integration method, jitter (variability in time step), and latency (the time lag from reading inputs to updating outputs). Each of these control system flaws is artificially introduced in both simulated and real dynamic clamp experiments. We demonstrate that each of these errors affect dynamic clamp accuracy in a way that depends on the time constants and stiffness of the differential equations being solved. In simulations, time steps above 0.2ms lead to catastrophic alteration of spike shape, but the frequency-current relationship is much more robust. Latency (the part of the time step that occurs between measuring membrane potential and injecting re-calculated membrane current) is a crucial factor as well. Experimental data are substantially more sensitive to inaccuracies than simulated data.

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Year:  2007        PMID: 18076999      PMCID: PMC2387131          DOI: 10.1016/j.jneumeth.2007.10.009

Source DB:  PubMed          Journal:  J Neurosci Methods        ISSN: 0165-0270            Impact factor:   2.390


  13 in total

1.  Role of an A-type K+ conductance in the back-propagation of action potentials in the dendrites of hippocampal pyramidal neurons.

Authors:  M Migliore; D A Hoffman; J C Magee; D Johnston
Journal:  J Comput Neurosci       Date:  1999 Jul-Aug       Impact factor: 1.621

2.  Extended dynamic clamp: controlling up to four neurons using a single desktop computer and interface.

Authors:  R D Pinto; R C Elson; A Szücs; M I Rabinovich; A I Selverston; H D Abarbanel
Journal:  J Neurosci Methods       Date:  2001-07-15       Impact factor: 2.390

3.  A methodology for achieving high-speed rates for artificial conductance injection in electrically excitable biological cells.

Authors:  R J Butera; C G Wilson; C A Delnegro; J C Smith
Journal:  IEEE Trans Biomed Eng       Date:  2001-12       Impact factor: 4.538

4.  Real-Time linux dynamic clamp: a fast and flexible way to construct virtual ion channels in living cells.

Authors:  A D Dorval; D J Christini; J A White
Journal:  Ann Biomed Eng       Date:  2001-10       Impact factor: 3.934

5.  MRCI: a flexible real-time dynamic clamp system for electrophysiology experiments.

Authors:  Ivan Raikov; Amanda Preyer; Robert J Butera
Journal:  J Neurosci Methods       Date:  2004-01-30       Impact factor: 2.390

6.  Implementation of a fast 16-Bit dynamic clamp using LabVIEW-RT.

Authors:  Paul H M Kullmann; Diek W Wheeler; Joshua Beacom; John P Horn
Journal:  J Neurophysiol       Date:  2003-09-24       Impact factor: 2.714

Review 7.  The dynamic clamp comes of age.

Authors:  Astrid A Prinz; L F Abbott; Eve Marder
Journal:  Trends Neurosci       Date:  2004-04       Impact factor: 13.837

8.  StdpC: a modern dynamic clamp.

Authors:  Thomas Nowotny; Attila Szucs; Reynaldo D Pinto; Allen I Selverston
Journal:  J Neurosci Methods       Date:  2006-07-18       Impact factor: 2.390

9.  Injection of digitally synthesized synaptic conductance transients to measure the integrative properties of neurons.

Authors:  H P Robinson; N Kawai
Journal:  J Neurosci Methods       Date:  1993-09       Impact factor: 2.390

10.  Dynamic clamp: computer-generated conductances in real neurons.

Authors:  A A Sharp; M B O'Neil; L F Abbott; E Marder
Journal:  J Neurophysiol       Date:  1993-03       Impact factor: 2.714

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  34 in total

1.  Real-time experiment interface for biological control applications.

Authors:  Risa J Lin; Jonathan Bettencourt; John Wha Ite; David J Christini; Robert J Butera
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2010

2.  Dynamic clamp in cardiac and neuronal systems using RTXI.

Authors:  Francis A Ortega; Robert J Butera; David J Christini; John A White; Alan D Dorval
Journal:  Methods Mol Biol       Date:  2014

3.  High-speed dynamic-clamp interface.

Authors:  Yang Yang; Timothy Adowski; Bina Ramamurthy; Andreas Neef; Matthew A Xu-Friedman
Journal:  J Neurophysiol       Date:  2015-01-28       Impact factor: 2.714

4.  Spike resonance properties in hippocampal O-LM cells are dependent on refractory dynamics.

Authors:  Tilman J Kispersky; Fernando R Fernandez; Michael N Economo; John A White
Journal:  J Neurosci       Date:  2012-03-14       Impact factor: 6.167

5.  Voltage and calcium dynamics both underlie cellular alternans in cardiac myocytes.

Authors:  Willemijn Groenendaal; Francis A Ortega; Trine Krogh-Madsen; David J Christini
Journal:  Biophys J       Date:  2014-05-20       Impact factor: 4.033

6.  Reduction of spike afterdepolarization by increased leak conductance alters interspike interval variability.

Authors:  Fernando R Fernandez; John A White
Journal:  J Neurosci       Date:  2009-01-28       Impact factor: 6.167

7.  MATLAB implementation of a dynamic clamp with bandwidth of >125 kHz capable of generating I Na at 37 °C.

Authors:  Chris Clausen; Virginijus Valiunas; Peter R Brink; Ira S Cohen
Journal:  Pflugers Arch       Date:  2012-12-09       Impact factor: 3.657

8.  Tracking and control of neuronal Hodgkin-Huxley dynamics.

Authors:  Ghanim Ullah; Steven J Schiff
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2009-04-13

9.  Gain control in CA1 pyramidal cells using changes in somatic conductance.

Authors:  Fernando R Fernandez; John A White
Journal:  J Neurosci       Date:  2010-01-06       Impact factor: 6.167

10.  Pacemaker rate and depolarization block in nigral dopamine neurons: a somatic sodium channel balancing act.

Authors:  Kristal R Tucker; Marco A Huertas; John P Horn; Carmen C Canavier; Edwin S Levitan
Journal:  J Neurosci       Date:  2012-10-17       Impact factor: 6.167

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