Abstract
During different stages of vigilance, the thalamus engages in a range of rhythmic activities from the slow (<1 Hz), delta (δ) (1–4 Hz) and spindle (7–14 Hz) waves that permeate the brain during sleep and anaesthesia to the faster oscillations in the alpha (α) and beta/gamma (β/γ) (>15 Hz) bands that occur during wakefulness. In recent years, it has been shown that several of these oscillations are associated with intrinsic rhythmic activity in individual thalamocortical (TC) neurons, with these intrinsic oscillations also being readily observable in recordings of TC neurons from thalamic slice preparations. In this chapter we will show how the dynamic-clamp technique provides an extremely useful means for studying the intricate cellular mechanisms and key properties of some of theses intrinsic oscillations. We will mainly focus on the intrinsic δ or so-called pacemaker (∼1–2 Hz) oscillation and the slow (<1 Hz) oscillation but will also briefly discuss how the dynamic-clamp technique can be utilized to study additional important oscillatory phenomena in the thalamus.
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Acknowledgements
Our ongoing work is supported by the Wellcome Trust, grants 71436, 78403 and 78311.
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Appendix
Appendix
The majority of the work (i.e. Figs. 1, 2, 3, 4, 5, 6, 7, 8, 9) described in this chapter was carried out on a personal computer with MS-DOS based software written in a combination of C++ and assembler and using an Axon Instruments Digidata 1200 ADC/DAC system (Hughes, Cope and Crunelli 1998; Hughes et al. 1999, 2002). These experiments were typically performed at sampling/update rates of 10–20 kHz although rates of up to 50 kHz could be achieved. The experiments depicted in Figs. 10 and 11 were carried out with the NeuReal software system (Hughes et al. 2008). NeuReal is an interactive system that runs on Windows XP, is also written in a combination of C++ and assembler, and uses the Microsoft DirectX application programming interface (API) to achieve high-performance graphics. Whilst not being a hard real-time system, NeuReal offers reliable performance and tolerable jitter levels up to an update rate of 50 kHz. A key feature of NeuReal is that rather than being a simple dedicated dynamic clamp, it operates as a fast simulation system within which neurons can be specified as either real or simulated. By using the Digidata 1200 hardware-based representation of membrane potential at all stages of computation and by employing simple look-up tables (see also Butera et al. 2001), on a modern personal computer (PC) NeuReal can typically simulate over 1,000 independent Hodgkin and Huxley (Hodgkin and Huxley 1952)-type conductances in real-time. For example, on a PC possessing a 2.26 GHz Intel Pentium processor and 2 GB of RAM and for an integration step size of 0.1 ms, equivalent to an update/sampling rate of 10 kHz in a dynamic-clamp experiment, NeuReal can compute 2,070 independent activation/inactivation variables and, therefore, simulate 690 inactivating and 690 non-inactivating conductances at sub-real-time speeds.
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Hughes, S.W., Lörincz, M., Cope, D.W., Crunelli, V. (2009). Using the Dynamic Clamp to Dissect the Properties and Mechanisms of Intrinsic Thalamic Oscillations. In: Bal, T., Destexhe, A. (eds) Dynamic-Clamp. Springer Series in Computational Neuroscience, vol 1. Springer, New York, NY. https://doi.org/10.1007/978-0-387-89279-5_15
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