The Future Is Open: Open-Source Tools for Behavioral Neuroscience Research

A simple and affordable open-source quantitative tribometric assay and the use thereof for the analysis of a commercial water-based lubricant

Quantitative assessment of biotribological properties requires expensive specialized equipment. The aim was to: i) adapt an open-source load cell-based platform (PASTA) for biotribometric analysis; ii) study the effects of oxidation on the water-based lubricant using PASTA. Water-based lubricant was treated with 2,2'-azobis(2-amidinopropane) dihydrochloride and/or glutathione. The samples were analyzed with the ORP-146S redox microsensor and PASTA using a modified HX711 integrated circuit bord, NodeMCU ESP-32S, and an open-source Python script. PASTA can be adapted for affordable and reliable quantitative biotribometric assessment. Glutathione can prevent the loss of lubrication capacity of a water-based lubricant upon exposure to air.

A modular, cost-effective, versatile, open-source operant box solution for long-term miniscope imaging, 3D tracking, and deep learning behavioral analysis

In this procedure we have included an open-source method for a customized operant chamber optimized for long-term miniature microscope (miniscope) recordings. •The miniscope box is designed to function with custom or typical med-associates style accessories (e.g., houselights, levers, etc.).•The majority of parts can be directly purchased which minimizes the need for skilled and time-consuming labor.•We include designs and estimated pricing for a single box but it is recommended to build these in larger batches to efficiently utilize bulk ordering of certain components.

An open-source behavior controller for associative learning and memory (B-CALM)

Associative learning and memory, i.e., learning and remembering the associations between environmental stimuli, self-generated actions, and outcomes such as rewards or punishments, are critical for the well-being of animals. Hence, the neural mechanisms underlying these processes are extensively studied using behavioral tasks in laboratory animals. Traditionally, these tasks have been controlled using commercial hardware and software, which limits scalability and accessibility due to their cost. More recently, due to the revolution in microcontrollers or microcomputers, several general-purpose and open-source solutions have been advanced for controlling neuroscientific behavioral tasks. While these solutions have great strength due to their flexibility and general-purpose nature, for the same reasons, they suffer from some disadvantages including the need for considerable programming expertise, limited online visualization, or slower than optimal response latencies for any specific task. Here, to mitigate these concerns, we present an open-source behavior controller for associative learning and memory (B-CALM). B-CALM provides an integrated suite that can control a host of associative learning and memory behaviors. As proof of principle for its applicability, we show data from head-fixed mice learning Pavlovian conditioning, operant conditioning, discrimination learning, as well as a timing task and a choice task. These can be run directly from a user-friendly graphical user interface (GUI) written in MATLAB that controls many independently running Arduino Mega microcontrollers in parallel (one per behavior box). In sum, B-CALM will enable researchers to execute a wide variety of associative learning and memory tasks in a scalable, accurate, and user-friendly manner.

ReptiLearn: An automated home cage system for behavioral experiments in reptiles without human intervention

Understanding behavior and its evolutionary underpinnings is crucial for unraveling the complexities of brain function. Traditional approaches strive to reduce behavioral complexity by designing short-term, highly constrained behavioral tasks with dichotomous choices in which animals respond to defined external perturbation. In contrast, natural behaviors evolve over multiple time scales during which actions are selected through bidirectional interactions with the environment and without human intervention. Recent technological advancements have opened up new possibilities for experimental designs that more closely mirror natural behaviors by replacing stringent experimental control with accurate multidimensional behavioral analysis. However, these approaches have been tailored to fit only a small number of species. This specificity limits the experimental opportunities offered by species diversity. Further, it hampers comparative analyses that are essential for extracting overarching behavioral principles and for examining behavior from an evolutionary perspective. To address this limitation, we developed ReptiLearn-a versatile, low-cost, Python-based solution, optimized for conducting automated long-term experiments in the home cage of reptiles, without human intervention. In addition, this system offers unique features such as precise temperature measurement and control, live prey reward dispensers, engagement with touch screens, and remote control through a user-friendly web interface. Finally, ReptiLearn incorporates low-latency closed-loop feedback allowing bidirectional interactions between animals and their environments. Thus, ReptiLearn provides a comprehensive solution for researchers studying behavior in ectotherms and beyond, bridging the gap between constrained laboratory settings and natural behavior in nonconventional model systems. We demonstrate the capabilities of ReptiLearn by automatically training the lizard Pogona vitticeps on a complex spatial learning task requiring association learning, displaced reward learning, and reversal learning.

FreiBox: A Versatile Open-Source Behavioral Setup for Investigating the Neuronal Correlates of Behavioral Flexibility via 1-Photon Imaging in Freely Moving Mice

To survive in a complex and changing environment, animals must adapt their behavior. This ability is called behavioral flexibility and is classically evaluated by a reversal learning paradigm. During such a paradigm, the animals adapt their behavior according to a change of the reward contingencies. To study these complex cognitive functions (from outcome evaluation to motor adaptation), we developed a versatile, low-cost, open-source platform, allowing us to investigate the neuronal correlates of behavioral flexibility with 1-photon calcium imaging. This platform consists of FreiBox, a novel low-cost Arduino behavioral setup, as well as further open-source tools, which we developed and integrated into our framework. FreiBox is controlled by a custom Python interface and integrates a new licking sensor (strain gauge lickometer) for controlling spatial licking behavioral tasks. In addition to allowing both discriminative and serial reversal learning, the Arduino can track mouse licking behavior in real time to control task events in a submillisecond timescale. To complete our setup, we also developed and validated an affordable commutator, which is crucial for recording calcium imaging with the Miniscope V4 in freely moving mice. Further, we demonstrated that FreiBox can be associated with 1-photon imaging and other open-source initiatives (e.g., Open Ephys) to form a versatile platform for exploring the neuronal substrates of licking-based behavioral flexibility in mice. The combination of the FreiBox behavioral setup and our low-cost commutator represents a highly competitive and complementary addition to the recently emerging battery of open-source initiatives.

A flexible and versatile system for multi-color fiber photometry and optogenetic manipulation

Here, we present simultaneous fiber photometry recordings and optogenetic stimulation based on a multimode fused fiber coupler for both light delivery and collection without the need for dichroic beam splitters. In combination with a multi-color light source and appropriate optical filters, our approach offers remarkable flexibility in experimental design and facilitates the exploration of new molecular tools in vivo at minimal cost. We demonstrate straightforward re-configuration of the setup to operate with green, red, and near-infrared calcium indicators with or without simultaneous optogenetic stimulation and further explore the multi-color photometry capabilities of the system. The ease of assembly, operation, characterization, and customization of this platform holds the potential to foster the development of experimental strategies for multi-color fused fiber photometry combined with optogenetics far beyond its current state.

Open-source tools for behavioral video analysis: Setup, methods, and best practices

Recently developed methods for video analysis, especially models for pose estimation and behavior classification, are transforming behavioral quantification to be more precise, scalable, and reproducible in fields such as neuroscience and ethology. These tools overcome long-standing limitations of manual scoring of video frames and traditional 'center of mass' tracking algorithms to enable video analysis at scale. The expansion of open-source tools for video acquisition and analysis has led to new experimental approaches to understand behavior. Here, we review currently available open-source tools for video analysis and discuss how to set up these methods for labs new to video recording. We also discuss best practices for developing and using video analysis methods, including community-wide standards and critical needs for the open sharing of datasets and code, more widespread comparisons of video analysis methods, and better documentation for these methods especially for new users. We encourage broader adoption and continued development of these tools, which have tremendous potential for accelerating scientific progress in understanding the brain and behavior.

Multiscale imaging informs translational mouse modeling of neurological disease

Multiscale neurophysiology reveals that simple motor actions are associated with changes in neuronal firing in virtually every brain region studied. Accordingly, the assessment of focal pathology such as stroke or progressive neurodegenerative diseases must also extend widely across brain areas. To derive mechanistic information through imaging, multiple resolution scales and multimodal factors must be included, such as the structure and function of specific neurons and glial cells and the dynamics of specific neurotransmitters. Emerging multiscale methods in preclinical animal studies that span micro- to macroscale examinations fill this gap, allowing a circuit-based understanding of pathophysiological mechanisms. Combined with high-performance computation and open-source data repositories, these emerging multiscale and large field-of-view techniques include live functional ultrasound, multi- and single-photon wide-scale light microscopy, video-based miniscopes, and tissue-penetrating fiber photometry, as well as variants of post-mortem expansion microscopy. We present these technologies and outline use cases and data pipelines to uncover new knowledge within animal models of stroke, Alzheimer's disease, and movement disorders.

A virtual library for behavioral performance in standard conditions-rodent spontaneous activity in an open field during repeated testing and after treatment with drugs or brain lesions

Background

Beyond their specific experiment, video records of behavior have future value—for example, as inputs for new experiments or for yet unknown types of analysis of behavior—similar to tissue or blood sample banks in life sciences where clinically derived or otherwise well-described experimental samples are stored to be available for some unknown potential future purpose.

Findings

Research using an animal model of obsessive-compulsive disorder employed a standardized paradigm where the behavior of rats in a large open field was video recorded for 55 minutes on each test. From 43 experiments, there are 19,976 such trials that amount to over 2 years of continuous recording. In addition to videos, there are 2 video-derived raw data objects: XY locomotion coordinates and plots of animal trajectory. To motivate future use, the 3 raw data objects are annotated with a general schema—one that abstracts the data records from their particular experiment while providing, at the same time, a detailed list of independent variables bearing on behavioral performance. The raw data objects are deposited as 43 datasets but constitute, functionally, a library containing 1 large dataset.

Conclusions

Size and annotation schema give the library high reuse potential: in applications using machine learning techniques, statistical evaluation of subtle factors, simulation of new experiments, or as educational resource. Ultimately, the library can serve both as the seed and as the test bed to create a machine-searchable virtual library of linked open datasets for behavioral performance in defined conditions.

Under or Absent Reporting of Light Stimuli in Testing of Anxiety-Like Behaviors in Rodents: The Need for Standardization

Behavioral neuroscience tests such as the Light/Dark Test, the Open Field Test, the Elevated Plus Maze Test, and the Three Chamber Social Interaction Test have become both essential and widely used behavioral tests for transgenic and pre-clinical models for drug screening and testing. However, as fast as the field has evolved and the contemporaneous involvement of technology, little assessment of the literature has been done to ensure that these behavioral neuroscience tests that are crucial to pre-clinical testing have well-controlled ethological motivation by the use of lighting (i.e., Lux). In the present review paper, N = 420 manuscripts were examined from 2015 to 2019 as a sample set (i.e., n = ~20–22 publications per year) and it was found that only a meager n = 50 publications (i.e., 11.9% of the publications sampled) met the criteria for proper anxiogenic and anxiolytic Lux reported. These findings illustrate a serious concern that behavioral neuroscience papers are not being vetted properly at the journal review level and are being released into the literature and public domain making it difficult to assess the quality of the science being reported. This creates a real need for standardizing the use of Lux in all publications on behavioral neuroscience techniques within the field to ensure that contributions are meaningful, avoid unnecessary duplication, and ultimately would serve to create a more efficient process within the pre-clinical screening/testing for drugs that serve as anxiolytic compounds that would prove more useful than what prior decades of work have produced. It is suggested that improving the standardization of the use and reporting of Lux in behavioral neuroscience tests and the standardization of peer-review processes overseeing the proper documentation of these methodological approaches in manuscripts could serve to advance pre-clinical testing for effective anxiolytic drugs. This report serves to highlight this concern and proposes strategies to proactively remedy them as the field moves forward for decades to come.

Minian an open-source miniscope analysis pipeline

Miniature microscopes have gained considerable traction for in vivo calcium imaging in freely behaving animals. However, extracting calcium signals from raw videos is a computationally complex problem and remains a bottleneck for many researchers utilizing single-photon in vivo calcium imaging. Despite the existence of many powerful analysis packages designed to detect and extract calcium dynamics, most have either key parameters that are hard-coded or insufficient step-by-step guidance and validations to help the users choose the best parameters. This makes it difficult to know whether the output is reliable and meets the assumptions necessary for proper analysis. Moreover, large memory demand is often a constraint for setting up these pipelines since it limits the choice of hardware to specialized computers. Given these difficulties, there is a need for a low memory demand, user-friendly tool offering interactive visualizations of how altering parameters at each step of the analysis affects data output. Our open-source analysis pipeline, Minian (Miniscope Analysis), facilitates the transparency and accessibility of single-photon calcium imaging analysis, permitting users with little computational experience to extract the location of cells and their corresponding calcium traces and deconvolved neural activities. Minian contains interactive visualization tools for every step of the analysis, as well as detailed documentation and tips on parameter exploration. Furthermore, Minian has relatively small memory demands and can be run on a laptop, making it available to labs that do not have access to specialized computational hardware. Minian has been validated to reliably and robustly extract calcium events across different brain regions and from different cell types. In practice, Minian provides an open-source calcium imaging analysis pipeline with user-friendly interactive visualizations to explore parameters and validate results.

Toward the explainability, transparency, and universality of machine learning for behavioral classification in neuroscience

The use of rigorous ethological observation via machine learning techniques to understand brain function (computational neuroethology) is a rapidly growing approach that is poised to significantly change how behavioral neuroscience is commonly performed. With the development of open-source platforms for automated tracking and behavioral recognition, these approaches are now accessible to a wide array of neuroscientists despite variations in budget and computational experience. Importantly, this adoption has moved the field toward a common understanding of behavior and brain function through the removal of manual bias and the identification of previously unknown behavioral repertoires. Although less apparent, another consequence of this movement is the introduction of analytical tools that increase the explainabilty, transparency, and universality of the machine-based behavioral classifications both within and between research groups. Here, we focus on three main applications of such machine model explainabilty tools and metrics in the drive toward behavioral (i) standardization, (ii) specialization, and (iii) explainability. We provide a perspective on the use of explainability tools in computational neuroethology, and detail why this is a necessary next step in the expansion of the field. Specifically, as a possible solution in behavioral neuroscience, we propose the use of Shapley values via Shapley Additive Explanations (SHAP) as a diagnostic resource toward explainability of human annotation, as well as supervised and unsupervised behavioral machine learning analysis.

Custom-Built Operant Conditioning Setup for Calcium Imaging and Cognitive Testing in Freely Moving Mice

Operant chambers are widely used in animal research to study cognition, motivation, and learning processes. Paired with the rapidly developing technologies for brain imaging and manipulations of brain activity, operant conditioning chambers are a powerful tool for neuroscience research. The behavioral testing and imaging setups that are commercially available are often quite costly. Here, we present a custom-built operant chamber that can be constructed in a few days by an unexperienced user with relatively inexpensive, widely available materials. The advantages of our operant setup compared with other open-source and closed-source solutions are its relatively low cost, its support of complex behavioral tasks, its user-friendly setup, and its validated functionality with video imaging of behavior and calcium imaging using the UCLA Miniscope. Using this setup, we replicate our previously published findings showing that mice exposed to social defeat stress in adolescence have inhibitory control impairments in the Go/No-Go task when they reach adulthood. We also present calcium imaging data of medial prefrontal cortex (mPFC) neuronal activity acquired during Go/No-Go testing in freely moving mice and show that neuronal population activity increases from day 1 to day 14 of the task. We propose that our operant chamber is a cheaper alternative to its commercially available counterparts and offers a better balance between versatility and user-friendly setup than other open-source alternatives.

A Kiosk Station for the Assessment of Multiple Cognitive Domains and Cognitive Enrichment of Monkeys

Nonhuman primates (NHP's) are self-motivated to perform cognitive tasks on touchscreens in their animal housing setting. To leverage this ability, fully integrated hardware and software solutions are needed that work within housing and husbandry routines while also spanning cognitive task constructs of the Research Domain Criteria (RDoC). Here, we detail such an integrated robust hardware and software solution for running cognitive tasks in cage-housed NHP's with a cage-mounted Kiosk Station (KS-1). KS-1 consists of a frame for mounting flexibly on housing cages, a touchscreen animal interface with mounts for receptables, reward pumps, and cameras, and a compact computer cabinet with an interface for controlling behavior. Behavioral control is achieved with a Unity3D program that is virtual-reality capable, allowing semi-naturalistic visual tasks to assess multiple cognitive domains.KS-1 is fully integrated into the regular housing routines of monkeys. A single person can operate multiple KS-1's. Monkeys engage with KS-1 at high motivation and cognitive performance levels at high intra-individual consistency. KS-1 is optimized for flexible mounting onto standard apartment cage systems and provides a new design variation complementing existing cage-mounted touchscreen systems. KS-1 has a robust animal interface with options for gaze/reach monitoring. It has an integrated user interface for controlling multiple cognitive tasks using a common naturalistic object space designed to enhance task engagement. All custom KS-1 components are open-sourced.In summary, KS-1 is a versatile new tool for cognitive profiling and cognitive enrichment of cage-housed monkeys. It reliably measures multiple cognitive domains which promises to advance our understanding of animal cognition, inter-individual differences, and underlying neurobiology in refined, ethologically meaningful behavioral foraging contexts.

Sharing an Open Stimulation System for Auditory EEG Experiments Using Python, Raspberry Pi, and HifiBerry

In auditory behavioral and EEG experiments, the variability of stimulation solutions, for both software and hardware, adds unnecessary technical constraints. Currently, there is no easy to use, inexpensive, and shareable solution that could improve collaborations and data comparisons across different sites and contexts. This article outlines a system composed by a Raspberry Pi coupled with Python programming and associated with a HifiBerry sound card. We compare its sound performances with those of a wide variety of materials and configurations. This solution achieves the high timing accuracy and sound quality important in auditory cognition experiments, while being simple to use and open source. The present system shows high performances and results along with excellent feedback from users. It is inexpensive, easy to build, share, and improve on. Working with such low-cost, powerful, and collaborative hardware and software tools allows people to create their own specific, adapted, and shareable system that can be standardized across different collaborative sites, while being extremely simple and robust in use.

The Cocaine and Oxycodone Biobanks, Two Repositories from Genetically Diverse and Behaviorally Characterized Rats for the Study of Addiction

The rat oxycodone and cocaine biobanks contain samples that vary by genotypes (by using genetically diverse genotyped HS rats), phenotypes (by measuring addiction-like behaviors in an advanced SA model), timepoints (samples are collected longitudinally before, during, and after SA, and terminally at three different timepoints in the addiction cycle: intoxication, withdrawal, and abstinence or without exposure to drugs through age-matched naive rats), samples collected (organs, cells, biofluids, feces), preservation (paraformaldehyde-fixed, snap-frozen, or cryopreserved) and application (proteomics, transcriptomics, microbiomics, metabolomics, epigenetics, anatomy, circuitry analysis, biomarker discovery, etc.Substance use disorders (SUDs) are pervasive in our society and have substantial personal and socioeconomical costs. A critical hurdle in identifying biomarkers and novel targets for medication development is the lack of resources for obtaining biological samples with a detailed behavioral characterization of SUD. Moreover, it is nearly impossible to find longitudinal samples. As part of two ongoing large-scale behavioral genetic studies in heterogeneous stock (HS) rats, we have created two preclinical biobanks using well-validated long access (LgA) models of intravenous cocaine and oxycodone self-administration (SA) and comprehensive characterization of addiction-related behaviors. The genetic diversity in HS rats mimics diversity in the human population and includes individuals that are vulnerable or resilient to compulsive-like responding for cocaine or oxycodone. Longitudinal samples are collected throughout the experiment, before exposure to the drug, during intoxication, acute withdrawal, and protracted abstinence, and include naive, age-matched controls. Samples include, but are not limited to, blood plasma, feces and urine, whole brains, brain slices and punches, kidney, liver, spleen, ovary, testis, and adrenal glands. Three preservation methods (fixed in formaldehyde, snap-frozen, or cryopreserved) are used to facilitate diverse downstream applications such as proteomics, metabolomics, transcriptomics, epigenomics, microbiomics, neuroanatomy, biomarker discovery, and other cellular and molecular approaches. To date, >20,000 samples have been collected from over 1000 unique animals and made available free of charge to non-profit institutions through https://www.cocainebiobank.org/ and https://www.oxycodonebiobank.org/.

An Open Source Platform for Presenting Dynamic Visual Stimuli

Operant behavior procedures often rely on visual stimuli to cue the initiation or secession of a response, and to provide a means for discriminating between two or more simultaneously available responses. While primate and human studies typically use Liquid-Crystal Display (LCD) or Organic Light-Emitting Diode (OLED) monitors and touch screens, rodent studies use a variety of methods to present visual cues ranging from traditional incandescent light bulbs, single LEDs, and, more recently, touch screen monitors. Commercially available systems for visual stimulus presentation are costly, challenging to customize, and are typically closed source. We developed an open-source, highly-modifiable visual stimulus presentation platform that can be combined with a 3D-printed operant response device. The device uses an 8 × 8 matrix of LEDs, and can be expanded to control much larger LED matrices. Implementing the platform is low-cost (<$70 USD per device in the year 2020). Using the platform, we trained rats to make nosepoke responses and discriminate between two distinct visual cues in a location-independent manner. This visual stimulus presentation platform is a cost-effective way to implement complex visually-guided operant behavior, including the use of moving or dynamically changing visual stimuli.

In COM we trust: Feasibility of USB-based event marking

Modern experimental research often relies on the synchronization of different events prior to data analysis. One way of achieving synchronization involves marking distinct events with electrical pulses (event markers or “TTL pulses”), which are continuously recorded with research hardware, and can later be temporally aligned. Traditionally, this event marking was often performed using the parallel port in standard personal computers. However, the parallel port is disappearing from the landscape of computer hardware, being replaced by a serial (COM) port, namely the USB port. To find an adequate replacement for the parallel port, we evaluated four microcontroller units (MCUs) and the LabJack U3, an often-used USB data acquisition device, in terms of their latency and jitter for sending event markers in a simulated experiment on both Windows and Linux. Our results show that all four MCUs were comparable to the parallel port in terms of both latency and jitter, and consistently achieved latencies under 1 ms. With some caveats, the LabJack U3 can also achieve comparable latencies. In addition to the collected data, we share extensive documentation on how to build and use MCUs for event marking, including code examples. MCUs are a cost-effective, flexible, and performant replacement for the disappearing parallel port, enabling event marking and synchronization of data streams.

New Open-Source Tools: Using Bonsai for Behavioral Tracking and Closed-Loop Experiments

The ability to dynamically control a behavioral task based on real-time animal behavior is an important feature for experimental neuroscientists. However, designing automated boxes for behavioral studies requires a coordinated combination of mechanical, electronic, and software design skills which can challenge even the best engineers, and for that reason used to be out of reach for the majority of experimental neurobiology and behavioral pharmacology researchers. Due to parallel advances in open-source hardware and software developed for neuroscience researchers, by neuroscience researchers, the landscape has now changed significantly. Here, we discuss powerful approaches to the study of behavior using examples and tutorials in the Bonsai visual programming language, towards designing simple neuroscience experiments that can help researchers immediately get started. This language makes it easy for researchers, even without programming experience, to combine the operation of several open-source devices in parallel and design their own integrated custom solutions, enabling unique and flexible approaches to the study of behavior, including video tracking of behavior and closed-loop electrophysiology.

Repurposing a digital kitchen scale for neuroscience research: a complete hardware and software cookbook for PASTA

Widely available low-cost electronics encourage the development of open-source tools for neuroscientific research. In recent years, many neuroscientists recognized the open science movement for its potential to stimulate and encourage science that is less focused on money, and more on robustness, validity, questioning and understanding. Here, we wanted to contribute to this global community by creating a research platform based on a common digital kitchen scale. This everyday ordinary kitchen tool is sometimes used in neuroscience research in various ways; however, its use is limited by sampling rate and inability to store and analyze data. To tackle this problem we developed a Platform for Acoustic STArtle or PASTA. This robust and simple platform enables users to obtain data from kitchen scale load cells at a high sampling rate, store it and analyze it. Here, we used it to analyze acoustic startle and prepulse inhibition sensorimotor gating in rats treated intracerebroventricularly with streptozotocin, but the system can be easily modified and upgraded for other purposes. In accordance with open science principles, we shared complete hardware design with instructions. Furthermore, we also disclose our software codes written for PASTA data acquisition (C++, Arduino) and acoustic startle experimental protocol (Python) and analysis (ratPASTA R package-R-based Awesome Toolbox for PASTA, and pastaWRAP-Python wrapper package for ratPASTA). To further encourage the development of our PASTA platform we demonstrate its sensitivity by using PASTA-gathered data to extract breathing patterns during rat freezing behavior in our experimental protocol.

Big behavior: challenges and opportunities in a new era of deep behavior profiling

The assessment of rodent behavior forms a cornerstone of preclinical assessment in neuroscience research. Nonetheless, the true and almost limitless potential of behavioral analysis has been inaccessible to scientists until very recently. Now, in the age of machine vision and deep learning, it is possible to extract and quantify almost infinite numbers of behavioral variables, to break behaviors down into subcategories and even into small behavioral units, syllables or motifs. However, the rapidly growing field of behavioral neuroethology is experiencing birthing pains. The community has not yet consolidated its methods, and new algorithms transfer poorly between labs. Benchmarking experiments as well as the large, well-annotated behavior datasets required are missing. Meanwhile, big data problems have started arising and we currently lack platforms for sharing large datasets-akin to sequencing repositories in genomics. Additionally, the average behavioral research lab does not have access to the latest tools to extract and analyze behavior, as their implementation requires advanced computational skills. Even so, the field is brimming with excitement and boundless opportunity. This review aims to highlight the potential of recent developments in the field of behavioral analysis, whilst trying to guide a consensus on practical issues concerning data collection and data sharing.

Efficient training of mice on the 5-choice serial reaction time task in an automated rodent training system

Experiments aiming to understand sensory-motor systems, cognition and behavior necessitate training animals to perform complex tasks. Traditional training protocols require lab personnel to move the animals between home cages and training chambers, to start and end training sessions, and in some cases, to hand-control each training trial. Human labor not only limits the amount of training per day, but also introduces several sources of variability and may increase animal stress. Here we present an automated training system for the 5-choice serial reaction time task (5CSRTT), a classic rodent task often used to test sensory detection, sustained attention and impulsivity. We found that full automation without human intervention allowed rapid, cost-efficient training, and decreased stress as measured by corticosterone levels. Training breaks introduced only a transient drop in performance, and mice readily generalized across training systems when transferred from automated to manual protocols. We further validated our automated training system with wireless optogenetics and pharmacology experiments, expanding the breadth of experimental needs our system may fulfill. Our automated 5CSRTT system can serve as a prototype for fully automated behavioral training, with methods and principles transferrable to a range of rodent tasks.

The lab on lockdown: thinking back and looking ahead

The shuttering of labs across nations during the COVID-19 pandemic created significant complexities related to remote lab management and ongoing research. The lessons learned may provide opportunities for new ways of working in the post-pandemic era.

Gina Poe and Denise Cai reflect on the challenges to research team management presented by the widespread closure of laboratories during the COVID-19 pandemic and consider the unexpected opportunities that have arisen from the lockdown

PiDose: an open-source system for accurate and automated oral drug administration to group-housed mice

Drug treatment studies in laboratory mice typically employ manual administration methods such as injection or gavage, which can be time-consuming to perform over long periods and cause substantial stress in animals. These stress responses may mask or enhance treatment effects, increasing the risk of false positive or negative results and decreasing reliability. To address the lack of an automated method for drug treatment in group-housed mice, we have developed PiDose, a home-cage attached device that weighs individual animals and administers a daily dosage of drug solution based on each animal’s bodyweight through their drinking water. Group housed mice are identified through the use of RFID tagging and receive both regular water and drug solution drops by licking at a spout within the PiDose module. This system allows animals to be treated over long periods (weeks to months) in a fully automated fashion, with high accuracy and minimal experimenter interaction. PiDose is low-cost and fully open-source and should prove useful for researchers in both translational and basic research.

A Custom Microcontrolled and Wireless-Operated Chamber for Auditory Fear Conditioning

Animal behavioral paradigms, such as classical conditioning and operant conditioning, are an important tool to study the neural basis of cognition and behavior. These paradigms involve manipulating sensory stimuli in a way that learning processes are induced under controlled experimental conditions. However, the majority of the commercially available equipment did not offer flexibility to manipulate stimuli. Therefore, the development of most versatile devices would allow the study of more complex cognitive functions. The purpose of this work is to present a low-cost, customized and wireless-operated chamber for animal behavior conditioning, based on the joint operation of two microcontroller modules: Arduino Due and ESP8266-12E. Our results showed that the auditory stimulation system allows setting the carrier frequency in the range of 1 Hz up to more than 100 kHz and the sound stimulus can be modulated in amplitude, also over a wide range of frequencies. Likewise, foot-shock could be precisely manipulated regarding its amplitude (from ∼200 μA to ∼1500 μA) and frequency (up to 20 pulses per second). Finally, adult rats exposed to a protocol of cued fear conditioning in our device showed consistent behavioral response and electrophysiological evoked responses in the midbrain auditory pathway. Furthermore, the device developed in the current study represents an open source alternative to develop customized protocols to study fear memory under conditions of varied sensory stimuli.