Realistic Stimulation Through Advanced Dynamic-Clamp Protocols

Múñiz, Carlos; Arganda, Sara; Rodrı́guez, Francisco; Polavieja, Gonzalo G. de

doi:10.1007/11499220_10

Cited by 14 publications

(13 citation statements)

References 16 publications

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“…In spite of the large applicability of hybrid circuits to study neuron and network dynamics, including plasticity and learning mechanisms, their use has been somehow limited by the difficulty of their implementation. Hybrid circuit construction often requires specific hardware and/or soft or hard real-time software technology to accurately implement the associated recording and stimulation cycles (Christini et al, 1999;Pinto et al, 2001;Muñiz et al, 2005;Arsiero et al, 2007; Fig. 1 General closed-loop approach proposed for the assisted automatic adaptation in hybrid circuits.…”

Section: Introductionmentioning

confidence: 99%

Automatic adaptation of model neurons and connections to build hybrid circuits with living networks

Reyes-Sanchez

Amaducci

Elices

et al. 2018

Preprint

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Hybrid circuits built by creating mono-or bi-directional interactions among living cells and model neurons and synapses are an effective way to study neuron, synaptic and neural network dynamics. However, hybrid circuit technology has been largely underused in the context of neuroscience studies mainly because of the inherent difficulty in implementing and tuning this type of interactions. In this paper, we present a set of algorithms for the automatic adaptation of model neurons and connections in the creation of hybrid circuits with living neural networks. The algorithms perform model time and amplitude scaling, drift compensation, goal-driven synaptic and model tuning/calibration and also automatic parameter mapping. These algorithms have been implemented in RTHybrid, an open-source library that works with hard real-time constraints. We provide validation examples by building hybrid circuits in a central pattern generator. The results of the validation experiments show that the proposed dynamic adaptation facilitates building hybrid circuits and closedloop communication among living and artificial model neurons and connections. Furthermore contributes to characterize system dynamics, achieve control, automate experimental protocols and extend the lifespan of the preparations.

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Section: Introductionmentioning

confidence: 99%

Automatic adaptation of model neurons and connections to build hybrid circuits with living networks

Reyes-Sanchez

Amaducci

Elices

et al. 2018

Preprint

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“…While closed-loop technology allows researchers to conduct online observation, control and interaction with neural elements, it also presents some drawbacks in its implementation. Some of these difficulties are related to the complexity of their experimental design, but also to the accomplishment of precise temporal restrictions, in the scale of milliseconds or lower, which often are required during data acquisition and stimulation in biological experiments (Christini et al, 1999;Muñiz et al, 2005Muñiz et al, , 2008Muñiz et al, , 2009). The capacity of a system to perform periodic tasks and respond to asynchronous external events in an strict time slot (neither sooner nor later) is known as real-time (Furht et al, 1991).…”

Section: Introductionmentioning

confidence: 99%

“…Some of them are hardwarebased (Franke et al, 2012;Tessadori et al, 2012;Müller et al, 2013;Desai et al, 2017). Several software tools have been designed, particularly for dynamic-clamp electrophysiology experiments, both following soft- (Pinto et al, 2001;Nowotny et al, 2006;Linaro et al, 2014;Ciliberti and Kloosterman, 2017;Hazan and Ziv, 2017) and hard real-time (Christini et al, 1999;Dorval et al, 2001;Muñiz et al, 2005Muñiz et al, , 2009Biró and Giugliano, 2015;Patel et al, 2017) approaches, using distinct platforms and RTOS, which have diverse purposes and architectures, hence presenting different advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

RTHybrid: a standardized and open-source real-time software model library for experimental neuroscience

Amaducci

Reyes-Sanchez

Elices

et al. 2018

Preprint

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Closed-loop technologies provide novel ways of online observation, control and bidirectional interaction with the nervous system, which help to study complex non-linear and partially observable neural dynamics. These protocols are often difficult to implement due to the temporal precision required when interacting with biological components, which in many cases can only be achieved using real-time technology. In this paper we introduce RTHybrid (www.github.com/GNB-UAM/RTHybrid), a free and open-source software that includes a neuron and synapse model library to build hybrid circuits with living neurons in a wide variety of experimental contexts. In an effort to encourage the standardization of real-time software technology in neuroscience research, we compared different open-source real-time operating system patches, RTAI, Xenomai 3 and Preempt-RT, according to their performance and usability. RTHybrid has been developed to run over Linux operating systems supporting both Xenomai 3 and Preempt-RT real-time patches, and thus allowing an easy implementation in any laboratory. We report a set of validation tests and latency benchmarks for the construction of hybrid circuits using this library. With this work we want to promote the dissemination of standardized, userfriendly and open-source software tools developed for open-and closed-loop experimental neuroscience.

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“…So far, it has mainly been exploited in the form of the dynamic clamp in electrophysiological preparations. The dynamic clamp uses RT technology to introduce artificial membrane or synaptic conductances into biological neurons and to create hybrid circuits of real, model and electronic neurons (Robinson and Kawai, 1993;Sharp et al, 1993;Harsch and Robinson, 2000;Pinto et al, 2001;Butera et al, 2001;Dorval et al, 2001;Muniz et al, 2005;Nowotny et al, 2006). This technique has become a widely used tool for studying neural systems at the cellular and circuit levels (Szücs et al, 2000;Pinto et al, 2001;Varona et al, 2001;Masson et al, 2002;Kullmann et al, 2004;Prinz et al, 2004).…”

Section: Introductionmentioning

confidence: 99%