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+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +6515 +10.21105/joss.06515 + +Pybehave: a hardware agnostic, Python-based framework for +controlling behavioral neuroscience experiments + + + +https://orcid.org/0000-0002-1428-0723 + +Rijn +Evan M. Dastin-van + + +* + + + +Nielsen +Joel + + + + +https://orcid.org/0000-0002-1669-8752 + +Sachse +Elizabeth M. + + + + + +Li +Christina + + + + + +Mensinger +Megan E. + + + + + +Simpson +Stefanie G. + + + + + +Buccini +Michelle C. + + + + + +Iacobucci +Francesca A. + + + + +https://orcid.org/0000-0001-7819-734X + +Titus +David J. + + + + +https://orcid.org/0000-0001-8510-341X + +Widge +Alik S. + + + + + +Department of Psychiatry and Behavioral Sciences, +University of Minnesota Medical Center, Minneapolis, MN 55454, United +States of America + + + + +* E-mail: + + +13 +8 +2017 + +9 +98 +6515 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +Animal behavior +Operant tasks + + + + + + Summary +

This work presents our pybehave framework + for developing behavioral tasks for use in experimental animal + neuroscience. In contrast to other platforms, + pybehave is built around a hardware-agnostic + and highly object-oriented design philosophy. + Pybehave separates code for task design from + specific hardware implementations to streamline development, + accessibility, and data sharing. This approach, combined with + task-specific graphical user interfaces, expedites and simplifies the + creation and visualization of complex behavioral tasks. User created + task definition files can interact with hardware-specific source + files, both written in Python. Any and all local configuration can be + handled separately from the underlying task code.

+
+ + Statement of need +

Operant animal behavior training and monitoring is fundamental to + scientific inquiry across fields + (Krakauer + et al., 2017). In many cases, a behavior of relevance, or its + neural substrate, is best studied through a controlled laboratory + task. These tasks require tight integration of the hardware components + with which animals interact (IR beams, levers, lights, food + dispensers, etc.) and the overarching software that coordinates these + components to elicit desired behaviors. There are a plethora of + options for systems to facilitate behavioral tasks, from commercial + solutions (Panlab, Lafayette Instruments, Med Associates) to + open-source packages + (Akam + et al., 2022; + Dastin-van + Rijn et al., 2023; + Hwang + et al., 2019) enabling a large variety of behavioral paradigms. + Many of these systems are designed for the same behavioral paradigms + with only slight differences in hardware, sensory modalities, or + geometry. However, while the actual mechanics of these paradigms + remain relatively similar, different solutions will often rely on + vastly different software interfaces + (Cardinal + & Aitken, 2010; + Lopes + et al., 2015). Especially with commercial systems, behavioral + tasks are often programmed in proprietary formats. This approach + significantly raises the barrier to entry, leads to outdated software, + and prevents sharing of tasks across labs.

+

Research in human behavior does not suffer from many of the + aforementioned issues. Human behavioral tasks are generally run + through a graphical interface implemented in a standard programming + language like Python + (Peirce + et al., 2019), Javascript + (Leeuw, + 2015), or Matlab + (Brainard, + 1997). These tasks are readily compatible with most machines + and are frequently shared between labs and used across multiple + studies + (Provenza + et al., 2021). Protocols, data, and task code can be easily + included in a manuscript and accessed and modified by future + researchers. However, unlike experiments in animal behavior, human + experiments rarely require hardware beyond a monitor and standard + input device (keyboard/mouse). Instead, most animal platforms, even + from open source developers, restrict their software to certain types + of hardware + (Akam + et al., 2022; + Hwang + et al., 2019). For example, pycontrol is + only compatible with their companion microcontroller and input devices + and MonkeyLogic can only communicate with DAQs + manufactured by National Instruments. To address these limitations, we + developed pybehave as a framework for + abstracting standard hardware components to enable an + implementation-independent format for developing and running + behavioral tasks.

+
+ + Benefits +

Pybehave is a complete framework for + building and running behavioral neuroscience experiments. It offers + the following benefits: (1) hardware independence; (2) a flexible, + programmatic system for developing tasks; (3) a highly extensible + graphical interface for configuring and executing tasks; (4) options + for task-specific visualization; (5) simultaneous control of multiple + experiments; (6) options for locally configuring task variables and + protocols; and (5) an extensive developer API, which allows users to + extend the platform with tie-ins for custom hardware, event logging, + or software connections.

+
+ + Software Design Principles +

To ensure flexibility while maintaining low-latency, + pybehave is optimized through a combination of + multiprocessing and multithreading along with separation of its + features (events, hardware sources, tasks, etc.) into a modular + software architecture. Additionally, pybehave + uses two different GUI frameworks (QT and pygame) for user interfacing + and task visualization/stimulus display respectively + ([fig:framework]).

+ +

Framework diagram showing the information exchange + between the pybehave threads and processes. + The workstation process handles the interface and task GUIs. When + Tasks are added from the workstation, they are initialized in the + task process. Each Source with a connection to an external hardware + or software system communicates with their + pybehave equivalent in the Task process. All + events sent between processes are mediated via inter-process + communication over + Pipes.

+ +
+
+ + Tutorials and ongoing usage +

A variety of tutorials are included in the repository aimed at all + levels of usage, from technicians running tasks or analyzing + behavioral data to developers aiming to build new tasks or integrate + additional hardware. Pybehave has already been + applied to implement a variety of behavioral tasks which have been + included in a separate repository for users to pull from directly or + modify. These tasks are being run in a number of ongoing studies + spanning standard operant conditioning + (Dastin-van + Rijn et al., 2023; + Mensinger + et al., 2023), evoked responses + (Sachse + et al., 2023), and video assays.

+
+ + Acknowledgements +

Testing of pybehave was carried out with + substantial support from many members of the Translational + Neuroengineering lab. Evan Dastin-van Rijn was supported by a National + Science Foundation Graduate Research Fellowship under award number + 2237827. Aspects of the research were supported by grants R01MH123634, + R01NS120851, R01NS113804 and R01MH119384, as well as by the Minnesota + Medical Discovery Team on Addiction and the MnDRIVE Brain Conditions + Initiative. The opinions presented herein are not those of any funding + body.

+
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2024 + + + 9 + + 98 + + + + Pybehave: a hardware agnostic, Python-based framework +for controlling behavioral neuroscience experiments + + + + Evan M. Dastin-van + Rijn + https://orcid.org/0000-0002-1428-0723 + + + Joel + Nielsen + + + Elizabeth M. + Sachse + https://orcid.org/0000-0002-1669-8752 + + + Christina + Li + + + Megan E. + Mensinger + + + Stefanie G. + Simpson + + + Michelle C. + Buccini + + + Francesca A. + Iacobucci + + + David J. + Titus + https://orcid.org/0000-0001-7819-734X + + + Alik S. + Widge + https://orcid.org/0000-0001-8510-341X + + + + 06 + 05 + 2024 + + + 6515 + + + 10.21105/joss.06515 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.11244351 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/6515 + + + + 10.21105/joss.06515 + https://joss.theoj.org/papers/10.21105/joss.06515 + + + https://joss.theoj.org/papers/10.21105/joss.06515.pdf + + + + + + Open-source, Python-based, hardware and +software for controlling behavioural neuroscience +experiments + Akam + eLife + 11 + 10.7554/eLife.67846 + 2050-084X + 2022 + Akam, T., Lustig, A., Rowland, J. M., +Kapanaiah, S. K., Esteve-Agraz, J., Panniello, M., Márquez, C., Kohl, M. +M., Kätzel, D., Costa, R. M., & Walton, M. E. (2022). Open-source, +Python-based, hardware and software for controlling behavioural +neuroscience experiments. eLife, 11, e67846. +https://doi.org/10.7554/eLife.67846 + + + NIMH MonkeyLogic: Behavioral control and data +acquisition in MATLAB + Hwang + Journal of Neuroscience +Methods + 323 + 10.1016/j.jneumeth.2019.05.002 + 0165-0270 + 2019 + Hwang, J., Mitz, A. R., & Murray, +E. A. (2019). NIMH MonkeyLogic: Behavioral control and data acquisition +in MATLAB. Journal of Neuroscience Methods, 323, 13–21. +https://doi.org/10.1016/j.jneumeth.2019.05.002 + + + jsPsych: A JavaScript library for creating +behavioral experiments in a Web browser + Leeuw + Behavior Research Methods + 1 + 47 + 10.3758/s13428-014-0458-y + 1554-3528 + 2015 + Leeuw, J. R. de. 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Whisker: A client–server high-performance multimedia research +control system. 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