behaviorMate: An Intranet of Things Approach for Adaptable Control of Behavioral and Navigation-Based Experiments

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    This work represents a new toolkit for implementing virtual reality experiments in head-fixed animals. It is a valuable contribution to the field and the evidence for its utility and performance is solid. Some minor improvements in the material presented - including clarifying design decisions and providing more details about design features - would improve the readability and thereby potentially increase its impact.

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Abstract

Investigators conducting behavioral experiments often need precise control over the timing of the delivery of stimuli to subjects and to collect the precise times of the subsequent behavioral responses. Furthermore, investigators want fine-tuned control over how various multi-modal cues are presented. behaviorMate takes an “Intranet of Things” approach, using a networked system of hardware and software components for achieving these goals. The system outputs a file with integrated timestamp-event pairs that investigators can then format and process using their own analysis pipelines. We present an overview of the electronic components and GUI application that make up behaviorMate as well as mechanical designs for compatible experimental rigs to provide the reader with the ability to set up their own system. A wide variety of paradigms are supported, including goal-oriented learning, random foraging, and context switching. We demonstrate behaviorMate’s utility and reliability with a range of use cases from several published studies and benchmark tests. Finally, we present experimental validation demonstrating different modalities of hippocampal place field studies. Both treadmill with burlap belt and virtual reality with running wheel paradigms were performed to confirm the efficacy and flexibility of the approach. Previous solutions rely on proprietary systems that may have large upfront costs or present frameworks that require customized software to be developed. behaviorMate uses open-source software and a flexible configuration system to mitigate both concerns. behaviorMate has a proven record for head-fixed imaging experiments and could be easily adopted for task control in a variety of experimental situations.

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  1. Author response:

    We thank the reviewers for their comments and will revise the manuscript to provide more comprehensive clarifications to aide readers’ understanding of behaviorMate. Additionally, we intend to take several steps which could provide further insights and improve the ease of use for new behaviorMate users: (1) to release an expanded and annotated library of existing settings and VR scene files, (2) improve the online documentation of context lists and decorators which allow behaviorMate to run custom experimental paradigms without writing code, and (3) release online API details of the JSON messaging protocol that is used between behaviorMate, the Arduinos, and the VRMate program which could be especially helpful to developers interested in expanding or modifying the system. Here we provide a few brief points of clarification to some of the concerns raised by the reviewers.

    Firstly, we clarify the system’s focus on modularity and flexibility. behaviorMate leverages the “Intranet of Things” framework to provide a low-cost platform that relies on asynchronous message passing between independent networked devices. While our current VR implementation typically involves a PC, 2 Arduinos, and an Android device per VR display, the behaviorMate GUI can be configured without editing any source code to listen on additional ports for UDP messages which will be automatically timestamped and logged. Since the current implementation of the behaviorMate GUI can be configured through the settings file to send and receive JSON-formatted messages on arbitrary ports, third-party devices could be configured to listen and respond to these messages also without editing the UI source code. More specialized responsibilities or tasks that require higher temporal precision (such as position tracking) are handled by dedicated circuits so as to not overload the general purpose one. This provides a level of encapsulation/separation of concerns since components can be optimized for performance of a single tasks—a feature that is especially desirable given resource limitations on the most common commercially available microcontrollers.

    A number of methods exist for synchronizing recording devices like microscopes or electrophysiology recordings with behaviorMate’s time-stamped logs of actuators and sensors. For example, the GPIO circuit can be configured to send sync triggers, or receive timing signals as input, alternatively a dedicated circuit could record frame start signals and relay them to the PC to be logged indecently of the GPIO (enabling a high-resolution post-hoc alignment of the time stamps). The optimal method to use varies based on the needs of the experiment. For example, if very high temporal precision is needed, such as during electrophysiology experiments, a high-speed data acquisition (DAQ) circuit to capture a fixed interval readout might be beneficial. behaviorMate could still be set up as normal to provide closed and open-loop task control at behaviorally relevant timescales alongside a DAQ circuit recording events at a consistent temporal resolution. While this would increase the relative cost of the recording setup, identical rigs for training animals could still be configured without the DAQ circuit avoiding the additional cost and complexity.

    VRMate provides the interface between Unity and behaviorMate—therefore using the two systems together mean that no Unity or C# programming is necessary. VRMate provides a prespecified set of visual cues that can be scaled in 3 dimensions and have textures applied to them, permitting a wide variety of different scenes to be displayed. All VRMate scene details are additionally logged by behaviorMate to allow for consistency checks across experiments. The VRMate project also includes “editor scripts” that provide a drag-and-drop utility in Unity Editor for developing new scenes. Since the details pertaining to specific scenes and view angle are loaded at runtime via JSON-formatted UDP messages, it is not necessary to recompile VRMate in order to use this feature. Since we send individual position updates to VRMate from the PC, any issues with clock drift would be limited to the refresh rate of the Unity program that fast enough to be perceived as instantaneous and we have thoroughly tested the timing differences between displays using high-speed cameras and found them to be negligible. While we find using 5 separate Android computers to render scenes as described an optimal solution to maximize flexibility, it would also be possible to render all scenes on a single PC to further mitigate this concern depending on experimental demands. Finally, our treadmill implementations of behaviorMate use no monitor displays, however due to the modular design of behaviorMate virtual cues could be seamlessly added by added to any such setup by a VR context to the settings files.

    One last point to mention is that while our project is not affected by the recent changes in pricing structure of the Unity project, since the compiled software does not need to be regenerated to update VR scenes, or implement new task logic since this is handled by the behaviorMate GUI. This means the current state of the VRMate program is robust to any future pricing changes or other restructuring of the Unity program and does not rely on continued support of Unity. Additionally, the solution presented in VRMate has many benefits, however, a developer could easily adapt any open-source VR Maze project to receive the UDP-based position updates from behaviorMate or develop their own novel VR solutions. We intend to update the VR section of the manuscript to make all of this information clearer in the document as well as to provide the additional online documentation in the materials linked in the supplemental information.

  2. eLife assessment

    This work represents a new toolkit for implementing virtual reality experiments in head-fixed animals. It is a valuable contribution to the field and the evidence for its utility and performance is solid. Some minor improvements in the material presented - including clarifying design decisions and providing more details about design features - would improve the readability and thereby potentially increase its impact.

  3. Reviewer #1 (Public Review):

    Summary:

    Bowler et al. present a thoroughly tested system for modularized behavioral control of navigation-based experiments, particularly suited for pairing with 2-photon imaging but applicable to a variety of techniques. This system, which they name behaviorMate, represents a valuable contribution to the field. As the authors note, behavioral control paradigms vary widely across laboratories in terms of hardware and software utilized and often require specialized technical knowledge to make changes to these systems. Having a standardized, easy-to-implement, and flexible system that can be used by many groups is therefore highly desirable. This work will be of interest to systems neuroscientists looking to integrate flexible head-fixed behavioral control with neural data acquisition.

    Strengths:

    The present manuscript provides compelling evidence of the functionality and applicability of behaviorMate. The authors report benchmark tests for real-time update speed between the animal's movement and the behavioral control, on both the treadmill-based and virtual reality (VR) setups. Further, they nicely demonstrate and quantify reliable hippocampal place cell coding in both setups, using synchronized 2-photon imaging. This place cell characterization also provides a concrete comparison between the place cell properties observed in treadmill-based navigation vs. visual VR in a single study, which itself is a helpful contribution to the field.

    Documentation for installing and operating behaviorMate is available via the authors' lab website and linked in the manuscript.

    Weaknesses:

    The following comments are mostly minor suggestions intended to add clarity to the paper and provide context for its significance.

    (1) As VRMate (a component of behaviorMate) is written using Unity, what is the main advantage of using behaviorMate/VRMate compared to using Unity alone paired with Arduinos (e.g. Campbell et al. 2018), or compared to using an existing toolbox to interface with Unity (e.g. Alsbury-Nealy et al. 2022, DOI: 10.3758/s13428-021-01664-9)? For instance, one disadvantage of using Unity alone is that it requires programming in C# to code the task logic. It was not entirely clear whether VRMate circumvents this disadvantage somehow -- does it allow customization of task logic and scenery in the GUI? Does VRMate add other features and/or usability compared to Unity alone? It would be helpful if the authors could expand on this topic briefly.

    (2) The section on "context lists", lines 163-186, seemed to describe an important component of the system, but this section was challenging to follow and readers may find the terminology confusing. Perhaps this section could benefit from an accompanying figure or flow chart, if these terms are important to understand.

    (2a) Relatedly, "context" is used to refer to both when the animal enters a particular state in the task like a reward zone ("reward context", line 447) and also to describe a set of characteristics of an environment (Figure 3G), akin to how "context" is often used in the navigation literature. To avoid confusion, one possibility would be to use "environment" instead of "context" in Figure 3G, and/or consider using a word like "state" instead of "context" when referring to the activation of different stimuli.

    (3) Given the authors' goal of providing a system that is easily synchronizable with neural data acquisition, especially with 2-photon imaging, I wonder if they could expand on the following features:

    (3a) The authors mention that behaviorMate can send a TTL to trigger scanning on the 2P scope (line 202), which is a very useful feature. Can it also easily generate a TTL for each frame of the VR display and/or each sample of the animal's movement? Such TTLs can be critical for synchronizing the imaging with behavior and accounting for variability in the VR frame rate or sampling rate.

    (3b) Is there a limit to the number of I/O ports on the system? This might be worth explicitly mentioning.

    (3c) In the VR version, if each display is run by a separate Android computer, is there any risk of clock drift between displays? Or is this circumvented by centralized control of the rendering onset via the "real-time computer"?

  4. Reviewer #2 (Public Review):

    Summary:

    The authors present behaviorMate, an open-source behavior recording and control system including a central GUI and compatible treadmill and display components. Notably, the system utilizes the "Intranet of things" scheme and the components communicate through a local network, making the system modular, which in turn allows user to easily configure the setup to suit their experimental needs. Overall, behaviorMate is a valuable resource for researchers performing head-fixed imaging studies, as the commercial alternatives are often expensive and inflexible to modify.

    Strengths and Weaknesses:

    The manuscript presents two major utilities of behaviorMate: (1) as an open-source alternative to commercial behavior apparatus for head-fixed imaging studies, and (2) as a set of generic schema and communication protocols that allows the users to incorporate arbitrary recording and stimulation devices during a head-fixed imaging experiment. I found the first point well-supported and demonstrated in the manuscript. Indeed, the documentation, BOM, CAD files, circuit design, source, and compiled software, along with the manuscript, create an invaluable resource for neuroscience researchers looking to set up a budget-friendly VR and head-fixed imaging rig. Some features of behaviorMate, including the computer vision-based calibration of the treadmill, and the decentralized, Android-based display devices, are very innovative approaches and can be quite useful in practical settings. However, regarding the second point, my concern is that there is not adequate documentation and design flexibility to allow the users to incorporate arbitrary hardware into the system. In particular:

    (1) The central controlling logic is coupled with GUI and an event loop, without a documented plugin system. It's not clear whether arbitrary code can be executed together with the GUI, hence it's not clear how much the functionality of the GUI can be easily extended without substantial change to the source code of the GUI. For example, if the user wants to perform custom real-time analysis on the behavior data (potentially for closed-loop stimulation), it's not clear how to easily incorporate the analysis into the main GUI/control program.

    (2) The JSON messaging protocol lacks API documentation. It's not clear what the exact syntax is, supported key/value pairs, and expected response/behavior of the JSON messages. Hence, it's not clear how to develop new hardware that can communicate with the behaviorMate system.

    (3) It seems the existing control hardware and the JSON messaging only support GPIO/TTL types of input/output, which limits the applicability of the system to more complicated sensor/controller hardware. The authors mentioned that hardware like Arduino natively supports serial protocols like I2C or SPI, but it's not clear how they are handled and translated to JSON messages.

    Additionally, because it's unclear how easy to incorporate arbitrary hardware with behaviorMate, the "Intranet of things" approach seems to lose attraction. Since currently, the manuscript focuses mainly on a specific set of hardware designed for a specific type of experiment, it's not clear what are the advantages of implementing communication over a local network as opposed to the typical connections using USB.

    In summary, the manuscript presents a well-developed open-source system for head-fixed imaging experiments with innovative features. The project is a very valuable resource to the neuroscience community. However, some claims in the manuscript regarding the extensibility of the system and protocol may require further development and demonstration.

  5. Reviewer #3 (Public Review):

    In this work, the authors present an open-source system called behaviourMate for acquiring data related to animal behavior. The temporal alignment of recorded parameters across various devices is highlighted as crucial to avoid delays caused by electronics dependencies. This system not only addresses this issue but also offers an adaptable solution for VR setups. Given the significance of well-designed open-source platforms, this paper holds importance.

    Advantages of behaviorMate:

    The cost-effectiveness of the system provided.

    The reliability of PCBs compared to custom-made systems.

    Open-source nature for easy setup.

    Plug & Play feature requiring no coding experience for optimizing experiment performance (only text-based Json files, 'context List' required for editing).

    Points to clarify:

    While using UDP for data transmission can enhance speed, it is thought that it lacks reliability. Are there error-checking mechanisms in place to ensure reliable communication, given its criticality alongside speed?

    Considering this year's price policy changes in Unity, could this impact the system's operations?

    Also, does the Arduino offer sufficient precision for ephys recording, particularly with a 10ms check?

    Could you clarify the purpose of the Sync Pulse? In line 291, it suggests additional cues (potentially represented by the Sync Pulse) are needed to align the treadmill screens, which appear to be directed towards the Real-Time computer. Given that event alignment occurs in the GPIO, the connection of the Sync Pulse to the Real-Time Controller in Figure 1 seems confusing. Additionally, why is there a separate circuit for the treadmill that connects to the UI computer instead of the GPIO? It might be beneficial to elaborate on the rationale behind this decision in line 260. Moreover, should scenarios involving pupil and body camera recordings connect to the Analog input in the PCB or the real-time computer for optimal data handling and processing?

    Given that all references, as far as I can see, come from the same lab, are there other labs capable of implementing this system at a similar optimal level?