Hard real-time closed-loop electrophysiology with the Real-Time eXperiment Interface (RTXI)

Patel, Yogi A.; George, Ansel; Dorval, Alan D.; White, John A.; Christini, David J.; Butera, Robert J.

doi:10.1371/journal.pcbi.1005430

Cited by 59 publications

(45 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…including OpenEphys [11] , BONSAI [10] , NeuroRighter [68] and RTXI [69] . PolyTouch compliments the functionality of these platforms by providing rapid (within, on average, 5.7 ms) closed-loop feedback based on animal behavior.…”

Section: Comparison To Existing Tracking Systemsmentioning

confidence: 99%

Real-time contextual feedback for closed-loop control of navigation

Lim

Celikel

2018

Preprint

View full text Add to dashboard Cite

Objective: Close-loop control of brain and behavior will benefit from real-time detection of behavioral events to enable low-latency communication with peripheral devices. In animal experiments, this is typically achieved by using sparsely distributed (embedded) sensors that detect animal presence in select regions of interest. High-speed cameras provide high-density sampling across large arenas, capturing the richness of animal behavior, however, the image processing bottleneck prohibits real-time feedback in the context of rapidly evolving behaviors.Approach: Here we developed an open-source software, named PolyTouch, to track animal behavior in large arenas and provide rapid close-loop feedback in~5.7 ms, ie. average latency from the detection of an event to analog stimulus delivery, e.g. auditory tone, TTL pulse, when tracking a single body. This stand-alone software is written in JAVA. The included wrapper for MATLAB provides experimental flexibility for data acquisition, analysis and visualization.Main results: As a proof-of-principle application we deployed the PolyTouch for place awareness training. A user-defined portion of the arena was used as a virtual target; visit (or approach) to the target triggered auditory feedback. We show that mice develop awareness to virtual spaces, tend to stay shorter and move faster when they reside in the virtual target zone if their visits are coupled to relatively high stimulus intensity (≥49dB). Thus, close-loop presentation of perceived aversive feedback is sufficient to condition mice to avoid virtual targets within the span of a single session (~20min).Significance: Neuromodulation techniques now allow control of neural activity in a cell-type specific manner in spiking resolution. Using animal behavior to drive closed-loop control of neural activity would help to address the neural basis of behavioral state and environmental context-dependent information processing in the brain.

show abstract

Section: Comparison To Existing Tracking Systemsmentioning

confidence: 99%

Real-time contextual feedback for closed-loop control of navigation

Lim

Celikel

2018

Preprint

View full text Add to dashboard Cite

show abstract

“…2009; Kemenes et al, 2011;Nowotny and Varona, 2012;Linaro et al, 2015;Patel et al, 2017). Additionally, the construction of hybrid circuits involves handling the different time and amplitude scales of living and model neurons and synapses.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

Hard real-time closed-loop electrophysiology with the Real-Time eXperiment Interface (RTXI)

Cited by 59 publications

References 49 publications

Real-time contextual feedback for closed-loop control of navigation

Real-time contextual feedback for closed-loop control of navigation

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

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

Contact Info

Product

Resources

About