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Amaducci R, Reyes-Sanchez M, Elices I, Rodriguez FB, Varona P. RTHybrid: A Standardized and Open-Source Real-Time Software Model Library for Experimental Neuroscience. Front Neuroinform 2019; 13:11. [PMID: 30914940 PMCID: PMC6423167 DOI: 10.3389/fninf.2019.00011] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 02/14/2019] [Indexed: 12/05/2022] Open
Abstract
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, user-friendly and open-source software tools developed for open- and closed-loop experimental neuroscience.
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Affiliation(s)
- Rodrigo Amaducci
- Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, Madrid, Spain
| | | | | | | | - Pablo Varona
- Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, Madrid, Spain
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Patel YA, George A, Dorval AD, White JA, Christini DJ, Butera RJ. Hard real-time closed-loop electrophysiology with the Real-Time eXperiment Interface (RTXI). PLoS Comput Biol 2017; 13:e1005430. [PMID: 28557998 PMCID: PMC5469488 DOI: 10.1371/journal.pcbi.1005430] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Revised: 06/13/2017] [Accepted: 02/10/2017] [Indexed: 01/24/2023] Open
Abstract
The ability to experimentally perturb biological systems has traditionally been limited to static pre-programmed or operator-controlled protocols. In contrast, real-time control allows dynamic probing of biological systems with perturbations that are computed on-the-fly during experimentation. Real-time control applications for biological research are available; however, these systems are costly and often restrict the flexibility and customization of experimental protocols. The Real-Time eXperiment Interface (RTXI) is an open source software platform for achieving hard real-time data acquisition and closed-loop control in biological experiments while retaining the flexibility needed for experimental settings. RTXI has enabled users to implement complex custom closed-loop protocols in single cell, cell network, animal, and human electrophysiology studies. RTXI is also used as a free and open source, customizable electrophysiology platform in open-loop studies requiring online data acquisition, processing, and visualization. RTXI is easy to install, can be used with an extensive range of external experimentation and data acquisition hardware, and includes standard modules for implementing common electrophysiology protocols.
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Affiliation(s)
- Yogi A. Patel
- Bioengineering Graduate Program, Georgia Institute of Technology, Atlanta, Georgia, United States of America
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States of America
| | - Ansel George
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, United States of America
| | - Alan D. Dorval
- Department of Bioengineering, University of Utah, Salt Lake City, Utah, United States of America
| | - John A. White
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
| | - David J. Christini
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, United States of America
- * E-mail: (DJC); (RJB)
| | - Robert J. Butera
- Bioengineering Graduate Program, Georgia Institute of Technology, Atlanta, Georgia, United States of America
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
- * E-mail: (DJC); (RJB)
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Kocaturk M, Gulcur HO, Canbeyli R. Toward Building Hybrid Biological/in silico Neural Networks for Motor Neuroprosthetic Control. Front Neurorobot 2015; 9:8. [PMID: 26321943 PMCID: PMC4531252 DOI: 10.3389/fnbot.2015.00008] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2015] [Accepted: 07/15/2015] [Indexed: 11/13/2022] Open
Abstract
In this article, we introduce the Bioinspired Neuroprosthetic Design Environment (BNDE) as a practical platform for the development of novel brain–machine interface (BMI) controllers, which are based on spiking model neurons. We built the BNDE around a hard real-time system so that it is capable of creating simulated synapses from extracellularly recorded neurons to model neurons. In order to evaluate the practicality of the BNDE for neuroprosthetic control experiments, a novel, adaptive BMI controller was developed and tested using real-time closed-loop simulations. The present controller consists of two in silico medium spiny neurons, which receive simulated synaptic inputs from recorded motor cortical neurons. In the closed-loop simulations, the recordings from the cortical neurons were imitated using an external, hardware-based neural signal synthesizer. By implementing a reward-modulated spike timing-dependent plasticity rule, the controller achieved perfect target reach accuracy for a two-target reaching task in one-dimensional space. The BNDE combines the flexibility of software-based spiking neural network (SNN) simulations with powerful online data visualization tools and is a low-cost, PC-based, and all-in-one solution for developing neurally inspired BMI controllers. We believe that the BNDE is the first implementation, which is capable of creating hybrid biological/in silico neural networks for motor neuroprosthetic control and utilizes multiple CPU cores for computationally intensive real-time SNN simulations.
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Affiliation(s)
- Mehmet Kocaturk
- Institute of Biomedical Engineering, Bogazici University , Istanbul , Turkey ; Department of Biomedical Engineering, Istanbul Medipol University , Istanbul , Turkey
| | - Halil Ozcan Gulcur
- Institute of Biomedical Engineering, Bogazici University , Istanbul , Turkey
| | - Resit Canbeyli
- Department of Psychology, Bogazici University , Istanbul , Turkey
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Linaro D, Couto J, Giugliano M. Command-line cellular electrophysiology for conventional and real-time closed-loop experiments. J Neurosci Methods 2014; 230:5-19. [PMID: 24769169 DOI: 10.1016/j.jneumeth.2014.04.003] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2014] [Revised: 02/25/2014] [Accepted: 04/05/2014] [Indexed: 11/27/2022]
Abstract
BACKGROUND Current software tools for electrophysiological experiments are limited in flexibility and rarely offer adequate support for advanced techniques such as dynamic clamp and hybrid experiments, which are therefore limited to laboratories with a significant expertise in neuroinformatics. NEW METHOD We have developed lcg, a software suite based on a command-line interface (CLI) that allows performing both standard and advanced electrophysiological experiments. Stimulation protocols for classical voltage and current clamp experiments are defined by a concise and flexible meta description that allows representing complex waveforms as a piece-wise parametric decomposition of elementary sub-waveforms, abstracting the stimulation hardware. To perform complex experiments lcg provides a set of elementary building blocks that can be interconnected to yield a large variety of experimental paradigms. RESULTS We present various cellular electrophysiological experiments in which lcg has been employed, ranging from the automated application of current clamp protocols for characterizing basic electrophysiological properties of neurons, to dynamic clamp, response clamp, and hybrid experiments. We finally show how the scripting capabilities behind a CLI are suited for integrating experimental trials into complex workflows, where actual experiment, online data analysis and computational modeling seamlessly integrate. COMPARISON WITH EXISTING METHODS We compare lcg with two open source toolboxes, RTXI and RELACS. CONCLUSIONS We believe that lcg will greatly contribute to the standardization and reproducibility of both simple and complex experiments. Additionally, on the long run the increased efficiency due to a CLI will prove a great benefit for the experimental community.
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Affiliation(s)
- Daniele Linaro
- Theoretical Neurobiology and Neuroengineering Laboratory, Department of Biomedical Sciences, University of Antwerp, B-2610 Wilrijk, Belgium; Neuro-Electronics Research Flanders (NERF), B-3001 Leuven, Belgium.
| | - João Couto
- Theoretical Neurobiology and Neuroengineering Laboratory, Department of Biomedical Sciences, University of Antwerp, B-2610 Wilrijk, Belgium; Neuro-Electronics Research Flanders (NERF), B-3001 Leuven, Belgium
| | - Michele Giugliano
- Theoretical Neurobiology and Neuroengineering Laboratory, Department of Biomedical Sciences, University of Antwerp, B-2610 Wilrijk, Belgium; Neuro-Electronics Research Flanders (NERF), B-3001 Leuven, Belgium; Department of Computer Science, University of Sheffield, S1 4DP Sheffield, UK; Brain Mind Institute, EPFL, CH-1015 Lausanne, Switzerland
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Bauer JA, Lambert KM, White JA. The past, present, and future of real-time control in cellular electrophysiology. IEEE Trans Biomed Eng 2014; 61:1448-56. [PMID: 24710815 DOI: 10.1109/tbme.2014.2314619] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
For over 60 years, real-time control has been an important technique in the study of excitable cells. Two such control-based technologies are reviewed here. First, voltage-clamp methods revolutionized the study of excitable cells. In this family of techniques, membrane potential is controlled, allowing one to parameterize a powerful class of models that describe the voltage-current relationship of cell membranes simply, flexibly, and accurately. Second, dynamic-clamp methods allow the addition of new, "virtual" membrane mechanisms to living cells. Dynamic clamp allows researchers unprecedented ways of testing computationally based hypotheses in biological preparations. The review ends with predictions of how control-based technologies will be improved and adapted for new uses in the near future.
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Newman JP, Zeller-Townson R, Fong MF, Arcot Desai S, Gross RE, Potter SM. Closed-Loop, Multichannel Experimentation Using the Open-Source NeuroRighter Electrophysiology Platform. Front Neural Circuits 2013; 6:98. [PMID: 23346047 PMCID: PMC3548271 DOI: 10.3389/fncir.2012.00098] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2012] [Accepted: 11/18/2012] [Indexed: 11/25/2022] Open
Abstract
Single neuron feedback control techniques, such as voltage clamp and dynamic clamp, have enabled numerous advances in our understanding of ion channels, electrochemical signaling, and neural dynamics. Although commercially available multichannel recording and stimulation systems are commonly used for studying neural processing at the network level, they provide little native support for real-time feedback. We developed the open-source NeuroRighter multichannel electrophysiology hardware and software platform for closed-loop multichannel control with a focus on accessibility and low cost. NeuroRighter allows 64 channels of stimulation and recording for around US $10,000, along with the ability to integrate with other software and hardware. Here, we present substantial enhancements to the NeuroRighter platform, including a redesigned desktop application, a new stimulation subsystem allowing arbitrary stimulation patterns, low-latency data servers for accessing data streams, and a new application programming interface (API) for creating closed-loop protocols that can be inserted into NeuroRighter as plugin programs. This greatly simplifies the design of sophisticated real-time experiments without sacrificing the power and speed of a compiled programming language. Here we present a detailed description of NeuroRighter as a stand-alone application, its plugin API, and an extensive set of case studies that highlight the system’s abilities for conducting closed-loop, multichannel interfacing experiments.
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Affiliation(s)
- Jonathan P Newman
- Laboratory for Neuroengineering, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine Atlanta, GA, USA
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Bot CT, Kherlopian AR, Ortega FA, Christini DJ, Krogh-Madsen T. Rapid genetic algorithm optimization of a mouse computational model: benefits for anthropomorphization of neonatal mouse cardiomyocytes. Front Physiol 2012; 3:421. [PMID: 23133423 PMCID: PMC3488799 DOI: 10.3389/fphys.2012.00421] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2012] [Accepted: 10/17/2012] [Indexed: 11/13/2022] Open
Abstract
While the mouse presents an invaluable experimental model organism in biology, its usefulness in cardiac arrhythmia research is limited in some aspects due to major electrophysiological differences between murine and human action potentials (APs). As previously described, these species-specific traits can be partly overcome by application of a cell-type transforming clamp (CTC) to anthropomorphize the murine cardiac AP. CTC is a hybrid experimental-computational dynamic clamp technique, in which a computationally calculated time-dependent current is inserted into a cell in real-time, to compensate for the differences between sarcolemmal currents of that cell (e.g., murine) and the desired species (e.g., human). For effective CTC performance, mismatch between the measured cell and a mathematical model used to mimic the measured AP must be minimal. We have developed a genetic algorithm (GA) approach that rapidly tunes a mathematical model to reproduce the AP of the murine cardiac myocyte under study. Compared to a prior implementation that used a template-based model selection approach, we show that GA optimization to a cell-specific model results in a much better recapitulation of the desired AP morphology with CTC. This improvement was more pronounced when anthropomorphizing neonatal mouse cardiomyocytes to human-like APs than to guinea pig APs. CTC may be useful for a wide range of applications, from screening effects of pharmaceutical compounds on ion channel activity, to exploring variations in the mouse or human genome. Rapid GA optimization of a cell-specific mathematical model improves CTC performance and may therefore expand the applicability and usage of the CTC technique.
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Affiliation(s)
- Corina T. Bot
- Greenberg Division of Cardiology, Weill Cornell Medical CollegeNew York, NY, USA
| | - Armen R. Kherlopian
- Greenberg Division of Cardiology, Weill Cornell Medical CollegeNew York, NY, USA
- Department of Physiology, Biophysics and Systems Biology, Weill Cornell Medical CollegeNew York, NY, USA
| | - Francis A. Ortega
- Greenberg Division of Cardiology, Weill Cornell Medical CollegeNew York, NY, USA
| | - David J. Christini
- Greenberg Division of Cardiology, Weill Cornell Medical CollegeNew York, NY, USA
- Department of Physiology, Biophysics and Systems Biology, Weill Cornell Medical CollegeNew York, NY, USA
- Institute for Computational Biomedicine, Weill Cornell Medical CollegeNew York, NY, USA
| | - Trine Krogh-Madsen
- Greenberg Division of Cardiology, Weill Cornell Medical CollegeNew York, NY, USA
- Institute for Computational Biomedicine, Weill Cornell Medical CollegeNew York, NY, USA
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Membrane properties and the balance between excitation and inhibition control gamma-frequency oscillations arising from feedback inhibition. PLoS Comput Biol 2012; 8:e1002354. [PMID: 22275859 PMCID: PMC3261914 DOI: 10.1371/journal.pcbi.1002354] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2011] [Accepted: 12/03/2011] [Indexed: 11/19/2022] Open
Abstract
Computational studies as well as in vivo and in vitro results have shown that many cortical neurons fire in a highly irregular manner and at low average firing rates. These patterns seem to persist even when highly rhythmic signals are recorded by local field potential electrodes or other methods that quantify the summed behavior of a local population. Models of the 30-80 Hz gamma rhythm in which network oscillations arise through 'stochastic synchrony' capture the variability observed in the spike output of single cells while preserving network-level organization. We extend upon these results by constructing model networks constrained by experimental measurements and using them to probe the effect of biophysical parameters on network-level activity. We find in simulations that gamma-frequency oscillations are enabled by a high level of incoherent synaptic conductance input, similar to the barrage of noisy synaptic input that cortical neurons have been shown to receive in vivo. This incoherent synaptic input increases the emergent network frequency by shortening the time scale of the membrane in excitatory neurons and by reducing the temporal separation between excitation and inhibition due to decreased spike latency in inhibitory neurons. These mechanisms are demonstrated in simulations and in vitro current-clamp and dynamic-clamp experiments. Simulation results further indicate that the membrane potential noise amplitude has a large impact on network frequency and that the balance between excitatory and inhibitory currents controls network stability and sensitivity to external inputs.
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