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...
Autores principales: | , , , , , , |
---|---|
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 |