Cargando…

Real-time experimental control using network-based parallel processing

Modern neuroscience research often requires the coordination of multiple processes such as stimulus generation, real-time experimental control, as well as behavioral and neural measurements. The technical demands required to simultaneously manage these processes with high temporal fidelity is a barr...

Descripción completa

Detalles Bibliográficos
Autores principales: Kim, Byounghoon, Kenchappa, Shobha Channabasappa, Sunkara, Adhira, Chang, Ting-Yu, Thompson, Lowell, Doudlah, Raymond, Rosenberg, Ari
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6366897/
https://www.ncbi.nlm.nih.gov/pubmed/30730290
http://dx.doi.org/10.7554/eLife.40231
_version_ 1783393682583453696
author Kim, Byounghoon
Kenchappa, Shobha Channabasappa
Sunkara, Adhira
Chang, Ting-Yu
Thompson, Lowell
Doudlah, Raymond
Rosenberg, Ari
author_facet Kim, Byounghoon
Kenchappa, Shobha Channabasappa
Sunkara, Adhira
Chang, Ting-Yu
Thompson, Lowell
Doudlah, Raymond
Rosenberg, Ari
author_sort Kim, Byounghoon
collection PubMed
description Modern neuroscience research often requires the coordination of multiple processes such as stimulus generation, real-time experimental control, as well as behavioral and neural measurements. The technical demands required to simultaneously manage these processes with high temporal fidelity is a barrier that limits the number of labs performing such work. Here we present an open-source, network-based parallel processing framework that lowers this barrier. The Real-Time Experimental Control with Graphical User Interface (REC-GUI) framework offers multiple advantages: (i) a modular design that is agnostic to coding language(s) and operating system(s) to maximize experimental flexibility and minimize researcher effort, (ii) simple interfacing to connect multiple measurement and recording devices, (iii) high temporal fidelity by dividing task demands across CPUs, and (iv) real-time control using a fully customizable and intuitive GUI. We present applications for human, non-human primate, and rodent studies which collectively demonstrate that the REC-GUI framework facilitates technically demanding, behavior-contingent neuroscience research. Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).
format Online
Article
Text
id pubmed-6366897
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher eLife Sciences Publications, Ltd
record_format MEDLINE/PubMed
spelling pubmed-63668972019-02-11 Real-time experimental control using network-based parallel processing Kim, Byounghoon Kenchappa, Shobha Channabasappa Sunkara, Adhira Chang, Ting-Yu Thompson, Lowell Doudlah, Raymond Rosenberg, Ari eLife Neuroscience Modern neuroscience research often requires the coordination of multiple processes such as stimulus generation, real-time experimental control, as well as behavioral and neural measurements. The technical demands required to simultaneously manage these processes with high temporal fidelity is a barrier that limits the number of labs performing such work. Here we present an open-source, network-based parallel processing framework that lowers this barrier. The Real-Time Experimental Control with Graphical User Interface (REC-GUI) framework offers multiple advantages: (i) a modular design that is agnostic to coding language(s) and operating system(s) to maximize experimental flexibility and minimize researcher effort, (ii) simple interfacing to connect multiple measurement and recording devices, (iii) high temporal fidelity by dividing task demands across CPUs, and (iv) real-time control using a fully customizable and intuitive GUI. We present applications for human, non-human primate, and rodent studies which collectively demonstrate that the REC-GUI framework facilitates technically demanding, behavior-contingent neuroscience research. Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter). eLife Sciences Publications, Ltd 2019-02-07 /pmc/articles/PMC6366897/ /pubmed/30730290 http://dx.doi.org/10.7554/eLife.40231 Text en © 2019, Kim et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Neuroscience
Kim, Byounghoon
Kenchappa, Shobha Channabasappa
Sunkara, Adhira
Chang, Ting-Yu
Thompson, Lowell
Doudlah, Raymond
Rosenberg, Ari
Real-time experimental control using network-based parallel processing
title Real-time experimental control using network-based parallel processing
title_full Real-time experimental control using network-based parallel processing
title_fullStr Real-time experimental control using network-based parallel processing
title_full_unstemmed Real-time experimental control using network-based parallel processing
title_short Real-time experimental control using network-based parallel processing
title_sort real-time experimental control using network-based parallel processing
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6366897/
https://www.ncbi.nlm.nih.gov/pubmed/30730290
http://dx.doi.org/10.7554/eLife.40231
work_keys_str_mv AT kimbyounghoon realtimeexperimentalcontrolusingnetworkbasedparallelprocessing
AT kenchappashobhachannabasappa realtimeexperimentalcontrolusingnetworkbasedparallelprocessing
AT sunkaraadhira realtimeexperimentalcontrolusingnetworkbasedparallelprocessing
AT changtingyu realtimeexperimentalcontrolusingnetworkbasedparallelprocessing
AT thompsonlowell realtimeexperimentalcontrolusingnetworkbasedparallelprocessing
AT doudlahraymond realtimeexperimentalcontrolusingnetworkbasedparallelprocessing
AT rosenbergari realtimeexperimentalcontrolusingnetworkbasedparallelprocessing