Real-time Control

 
 

Real-Time Control of Biological Experiments via dynamic clamp

 Controlled virtual realities are extremely powerful ways to study the behavior of any complex system. Over the last several years, we have developed open-source, Linux-based methods for hard real-time control of biological experiments. Our first version of this system is described in Dorval et al. (2001). The newest version of the system is freely downloadable at rtxi.org. RTXI is a collaboration of our group with that of David Christini (Weill College of Medicine, Cornell) and Robert Butera (Georgia Tech).

 We have used real-time control for a number of applications:

  • Introduction of user-controllable, virtual voltage-gated ion channels into neurons (Dorval and White 2005)

  • Study of neuronal input-output relationships (Dorval and White 2006; Fernandez and White, 2008, 2009, 2010; Fernandez et al. 2011; Kispersky et al. 2010)

  • Immersion of living neurons into virtual neuronal networks running in real-time (Netoff et al. 2005b)

  • Building nonlinear dynamics-inspired descriptions of neuronal synchronization (Netoff et al. 2005a,b; Pervouchine et al. 2006)

  • Virtual acoustic environments for studying the use of dynamic cues in sound localization (Scarpaci et al. 2005)

Our system operates in “hard” real time, limiting the maximal timing inaccuracy to only a few microseconds. Other systems operate in “soft” real time. In general, these systems perform well, but occasionally they have temporal inaccuracies of milliseconds. In Bettencourt et al. (2007), we studied the consequences of temporal inaccuracy in dynamic clamp simulations and experiments. We found that even small inaccuracies distort action potential shape substantially, but that average spiking rate was much more fault-tolerant.

For a review of dynamic clamp technology and its uses, see Economo et al. (2010). For a review of RTXI, see Lin et al. (2010).

References

Bauer JA*, Lambert KM*, and White JA (2014) The past, present, and future of real-time control in cellular electrophysiology. IEEE Transactions on Biomedical Engineering 61: 1448-1456. (*: co-first authors).

Bettencourt JC, Lillis KP, Stupin L, and White JA (2008) Effects of imperfect dynamic clamp: computational and experimental results. Journal of Neuroscience Methods 169: 282-289

Dorval AD and White JA (2005) Channel noise is essential for perithreshold oscillations in entorhinal stellate neurons. Journal of Neuroscience 25: 10025-10028.

Dorval AD and White JA (2006) Synaptic input statistics tune the variability and reproducibility of neuronal responses. Chaos 16: 026105-1 – 026105-16.

Economo MN, Fernandez FR, and White JA (2010) Dynamic clamp: Alteration of response properties and creation of virtual realities in neurophysiology. Journal of Neuroscience 30: 2407-2413.

Fernandez FR and White JA (2008) Artificial synaptic conductances reduce subthreshold oscillations and periodic firing in stellate cells of the entorhinal cortex. Journal of Neuroscience 28: 3790-3803.

Fernandez FR and White JA (2009) Reduction of spike afterdepolarization by increased leak conductance alters interspike interval variability. Journal of Neuroscience 29: 973-986.

Fernandez FR and White JA (2010) Gain control in CA1 pyramidal cells using changes in somatic conductance. Journal of Neuroscience 30: 230-241.

Fernandez FR*, Broicher T*, Truong A, and White JA (2011) Membrane voltage fluctuations reduce spike frequency adaptation and preserve output gain in CA1 pyramidal neurons in a high conductance state. Journal of Neuroscience 31: 3880-3893. (*: these authors contributed equally to the work)

Fernandez FR, Malerba P, Bressloff PC, and White JA (2013) Entorhinal stellate cells show preferred spike phase-locking to theta inputs that is enhanced by correlations in synaptic activity. Journal of Neuroscience 33: 6027-6040. PMID: 23554484. PMCID: PMC3680114.

Fernandez FR, Malerba P, and White JA (2015) Non-linear membrane properties in entorhinal cortical stellate cells reduce modulation of input-output responses by voltage fluctuations. PloS Computational Biology. 11: e1004188. PMID: 25909971. PMCID: PMC440931.

Kispersky TJ, White JA, and Rotstein HG (2010) The mechanism of abrupt transition between theta and hyper-excitable spiking activity in medial entorhinal cortex layer II stellate cells. PLoS One 5: e13697.

Lin RJ, Bettencourt J, White JA, Christini DJ, Butera RJ (2010) Real-time experiment interface for biological control applications. Proceedings of the 32nd Annual Conference of IEEE EMBS. pp. 4160-4163.

Netoff TI, Acker CD, Bettencourt JC, and White JA (2005) Beyond two-cell networks: experimental measurement of neuronal responses to multiple synaptic inputs. Journal of Computational Neuroscience 18: 287-295.

Netoff TI, Banks MI, Dorval AD, Acker CD, Haas JS, Kopell N, and White JA (2005) Synchronization in hybrid neuronal networks of the hippocampal formation. Journal of Neurophysiology 93: 1197-1208.

Pervouchine DD, Netoff TI, Rotstein HG, White JA, Cunningham MO, Whittington MA, and Kopell NJ (2006) Low-dimensional maps encoding dynamics in entorhinal cortex and hippocampus. Neural Computation 18: 2617-2650.