Generalization of the Dynamic Clamp Concept in Neurophysiology and Behavior

Chamorro, Pablo Palenzuela; Múñiz, Carlos; Levi, Rafael; Arroyo, David; Rodríguez, Francisco B.; Varona, Pablo

doi:10.1371/journal.pone.0040887

Cited by 28 publications

(30 citation statements)

References 66 publications

(63 reference statements)

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“…In particular, we investigated in our previous work goal-driven real-time closed-loop interactions with drug microinjectors, mechanical stimulation devices and video event driven stimulators (Muniz et al, 2008, 2011; Chamorro et al, 2009, 2012). These examples illustrate that modern activity-dependent stimulation protocols can reveal dynamics otherwise hidden under traditional stimulation techniques, provide control of regular and pathological states, induce learning processes, bridge between distinct levels of analysis and lead to a further automation of experiments.…”

Section: Introductionmentioning

confidence: 99%

Assisted closed-loop optimization of SSVEP-BCI efficiency

Fernández-Vargas¹,

Pfaff²,

Rodrı́guez³

et al. 2013

Front. Neural Circuits

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We designed a novel assisted closed-loop optimization protocol to improve the efficiency of brain-computer interfaces (BCI) based on steady state visually evoked potentials (SSVEP). In traditional paradigms, the control over the BCI-performance completely depends on the subjects' ability to learn from the given feedback cues. By contrast, in the proposed protocol both the subject and the machine share information and control over the BCI goal. Generally, the innovative assistance consists in the delivery of online information together with the online adaptation of BCI stimuli properties. In our case, this adaptive optimization process is realized by (1) a closed-loop search for the best set of SSVEP flicker frequencies and (2) feedback of actual SSVEP magnitudes to both the subject and the machine. These closed-loop interactions between subject and machine are evaluated in real-time by continuous measurement of their efficiencies, which are used as online criteria to adapt the BCI control parameters. The proposed protocol aims to compensate for variability in possibly unknown subjects' state and trait dimensions. In a study with N = 18 subjects, we found significant evidence that our protocol outperformed classic SSVEP-BCI control paradigms. Evidence is presented that it takes indeed into account interindividual variabilities: e.g., under the new protocol, baseline resting state EEG measures predict subjects' BCI performances. This paper illustrates the promising potential of assisted closed-loop protocols in BCI systems. Probably their applicability might be expanded to innovative uses, e.g., as possible new diagnostic/therapeutic tools for clinical contexts and as new paradigms for basic research.

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Section: Introductionmentioning

confidence: 99%

Assisted closed-loop optimization of SSVEP-BCI efficiency

Fernández-Vargas¹,

Pfaff²,

Rodrı́guez³

et al. 2013

Front. Neural Circuits

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“…These techniques can be successfully applied at different description levels of the nervous system, from single neurons in in vitro preparations all the way up to human rehabilitation protocols and brain computer interfaces [17,18,19,20,21,22,23]. Theoretical studies can also use these type of protocols to explore and map the model parameter space, search for specific relevant dynamics, and assess robustness and comparison with experimental results.…”

Section: Discussionmentioning

confidence: 99%

Closed-loop control of a minimal central pattern generator network

Elices

Varona

2015

Neurocomputing

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“…Hybrid circuits can also be used to characterize and control neural dynamics. For example, interactions with the living system can be used to create new elements in a circuit, to assess the functional role of existing ones, or to replace damaged elements of the living system, while characterizing or sustaining a given dynamics Chamorro et al, 2012;Sakurai and 2017). A common goal for many hybrid circuits is to reach a certain level of activity, for example a specific regular rhythmic regime (Varona et al, 2001;Hooper et al, 2015).…”

Section: Characterization and Control Of Neural Dynamicsmentioning

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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Generalization of the Dynamic Clamp Concept in Neurophysiology and Behavior

Cited by 28 publications

References 66 publications

Assisted closed-loop optimization of SSVEP-BCI efficiency

Assisted closed-loop optimization of SSVEP-BCI efficiency

Closed-loop control of a minimal central pattern generator network

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

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