BehaviorDEPOT is a simple, flexible tool for automated behavioral detection based on markerless pose tracking

Social context modulates active avoidance: Contributions of the anterior cingulate cortex in male and female rats

Actively avoiding danger is necessary for survival. Most research on active avoidance has focused on the behavioral and neurobiological processes when individuals learn to avoid alone, within a solitary context. Therefore, little is known about how social context affects active avoidance. Using a modified version of the platform-mediated avoidance task in rats, we investigated whether the presence of a social partner attenuates conditioned freezing and enhances avoidance compared to avoidance in a solitary context. Rats spent a similar amount of time avoiding during either context; however, rats trained in the social context exhibited greater freezing as well as lower rates of darting and food seeking compared to rats trained in the solitary context. In addition, we observed higher levels of avoidance in females compared to males in the solitary context, but this sex difference was not present in rats trained in the social context. To gain greater mechanistic insight, we optogenetically inactivated glutamatergic projection neurons in the anterior cingulate cortex (ACC) following avoidance training in either context. After avoidance was learned in a social context, photoinactivation of ACC reduced expression of avoidance during a test when the social partner was absent, but not when the partner was present. Our findings suggest a novel contribution of the ACC in avoidance that is learned with a social partner, which has translational implications for understanding ACC dysfunction in those suffering from trauma-related disorders.

Beyond the Illusion of Controlled Environments: How to Embrace Ecological Pertinence in Research?

Through the lens of preclinical research on substance use disorders (SUD), I propose a reflection aimed at re-evaluating animal models in neuroscience, with a focus on ecological relevance. While rodent models have provided valuable insights into the neurobiology of SUD, the field currently faces a validation crisis, with findings often failing to translate into effective human treatments. Originally designed to address the lack of reproducibility in animal studies, the current global gold standard of rigorous standardization has led to increasingly controlled environments. This growing disconnection between laboratory settings and real-world scenarios exacerbates the validation crisis. Rodent models have also revealed various environmental influences on drug use and its neural mechanisms, highlighting parallels with human behaviour and underscoring the importance of ecological relevance in behavioural research. Drawing inspiration from inquiries in ethology and evolutionary biology, I advocate for incorporating greater environmental complexity into animal models. In line with this idea, the neuroethological approach involves studying spontaneous behaviours in seminatural habitats while utilizing advanced technologies to monitor neural activity. Although this framework offers new insights into human neuroscience, it does not adequately capture the complex human conditions that lead to neuropsychiatric diseases. Therefore, preclinical research should prioritize understanding the environmental factors that shape human behaviour and neural architecture, integrating these insights into animal models. By emphasizing ecological relevance, we can achieve deeper insights into neuropsychiatric disorders and develop more effective treatment strategies. This approach highlights significant benefits for both scientific inquiry and ethical considerations. The controlled environment is a chimera; it is time to rethink our models. Here, I have chosen the prism of preclinical research on SUD to present, in a nonexhaustive manner, advances enabled by the use of rodent models, the crises faced by animal experimentation, the reflections and responses provided by laboratories, to finally propose rethinking our models around questions of ecological relevance, in order to improve both ethics and scientific quality. Although my discussion is illustrated by the situation in preclinical research on SUD, the observation drawn from it and the proposals made can extend to many other domains and species.

A study of animal action segmentation algorithms across supervised, unsupervised, and semi-supervised learning paradigms

Action segmentation of behavioral videos is the process of labeling each frame as belonging to one or more discrete classes, and is a crucial component of many studies that investigate animal behavior. A wide range of algorithms exist to automatically parse discrete animal behavior, encompassing supervised, unsupervised, and semi-supervised learning paradigms. These algorithms - which include tree-based models, deep neural networks, and graphical models - differ widely in their structure and assumptions on the data. Using four datasets spanning multiple species - fly, mouse, and human - we systematically study how the outputs of these various algorithms align with manually annotated behaviors of interest. Along the way, we introduce a semi-supervised action segmentation model that bridges the gap between supervised deep neural networks and unsupervised graphical models. We find that fully supervised temporal convolutional networks with the addition of temporal information in the observations perform the best on our supervised metrics across all datasets.

Deconstruction of a memory engram reveals distinct ensembles recruited at learning

How are associative memories formed? Which cells represent a memory, and when are they engaged? By visualizing and tagging cells based on their calcium influx with unparalleled temporal precision, we identified non-overlapping dorsal CA1 neuronal ensembles that are differentially active during associative fear memory acquisition. We dissected the acquisition experience into periods during which salient stimuli were presented or certain mouse behaviors occurred and found that cells associated with specific acquisition periods are sufficient alone to drive memory expression and contribute to fear engram formation. This study delineated the different identities of the cell ensembles active during learning, and revealed, for the first time, which ones form the core engram and are essential for memory formation and recall.

Construction and Behavioral Comparison of Two Mouse Models of Cerebral Palsy

Cerebral palsy (CP) is the most common neuromuscular disorder in children with no effective therapeutic methods. To examine CP, a large variety of methods and animal models was developed, the most popular are the hypoxic-ischemic (HI) injury and/or LPS injection in mice. In the presented work, HI and LPS were applied on the postnatal day 9 to humanized immunodeficiency mouse pups, thereupon 3 behavioral tests were performed in 8 weeks later. Both HI and LPS caused significant behavioral deficits assessed in the Rotarod test. In gait dynamics and open-field tests, HI and LPS caused significant behavioral deficits reported by some parameters, and the effect of HI was more severe. Additionally, HI and LPS produced the different effects on gait dynamics of the fore and hind paws. Thus, both HI and LPS induced the behavioral disorders in mice, but HI was more suitable for the development of humanized immunodeficiency mouse model of CP.

An open-source tool for automated human-level circling behavior detection

Quantitatively relating behavior to underlying biology is crucial in life science. Although progress in keypoint tracking tools has reduced barriers to recording postural data, identifying specific behaviors from this data remains challenging. Manual behavior coding is labor-intensive and inconsistent, while automatic methods struggle to explicitly define complex behaviors, even when they seem obvious to the human eye. Here, we demonstrate an effective technique for detecting circling in mice, a form of locomotion characterized by stereotyped spinning. Despite circling's extensive history as a behavioral marker, there currently exists no standard automated detection method. We developed a circling detection technique using simple postprocessing of keypoint data obtained from videos of freely-exploring (Cib2-/-;Cib3-/-) mutant mice, a strain previously found to exhibit circling behavior. Our technique achieves statistical parity with independent human observers in matching occurrence times based on human consensus, and it accurately distinguishes between videos of wild type mice and mutants. Our pipeline provides a convenient, noninvasive, quantitative tool for analyzing circling mouse models without the need for software engineering experience. Additionally, as the concepts underlying our approach are agnostic to the behavior being analyzed, and indeed to the modality of the recorded data, our results support the feasibility of algorithmically detecting specific research-relevant behaviors using readily-interpretable parameters tuned on the basis of human consensus.

Transformations in prefrontal ensemble activity underlying rapid threat avoidance learning

The capacity to learn cues that predict aversive outcomes, and understand how to avoid those outcomes, is critical for adaptive behavior. Naturalistic avoidance often means accessing a safe location, but whether a location is safe depends on the nature of the impending threat. These relationships must be rapidly learned if animals are to survive. The prelimbic subregion (PL) of the medial prefrontal cortex (mPFC) integrates learned associations to influence these threat avoidance strategies. Prior work has focused on the role of PL activity in avoidance behaviors that are fully established, leaving the prefrontal mechanisms that drive rapid avoidance learning poorly understood. To determine when and how these learning-related changes emerge, we recorded PL neural activity using miniscope calcium imaging as mice rapidly learned to avoid a threatening cue by accessing a safe location. Over the course of learning, we observed enhanced modulation of PL activity representing intersections of a threatening cue with safe or risky locations and movements between them. We observed rapid changes in PL population dynamics that preceded changes observable in the encoding of individual neurons. Successful avoidance could be predicted from cue-related population dynamics during early learning. Population dynamics during specific epochs of the conditioned tone period correlated with the modeled learning rates of individual animals. In contrast, changes in single-neuron encoding occurred later, once an avoidance strategy had stabilized. Together, our findings reveal the sequence of PL changes that characterize rapid threat avoidance learning.

KineWheel-DeepLabCut Automated Paw Annotation Using Alternating Stroboscopic UV and White Light Illumination

Uncovering the relationships between neural circuits, behavior, and neural dysfunction may require rodent pose tracking. While open-source toolkits such as DeepLabCut have revolutionized markerless pose estimation using deep neural networks, the training process still requires human intervention for annotating key points of interest in video data. To further reduce human labor for neural network training, we developed a method that automatically generates annotated image datasets of rodent paw placement in a laboratory setting. It uses invisible but fluorescent markers that become temporarily visible under UV light. Through stroboscopic alternating illumination, adjacent video frames taken at 720 Hz are either UV or white light illuminated. After color filtering the UV-exposed video frames, the UV markings are identified and the paw locations are deterministically mapped. This paw information is then transferred to automatically annotate paw positions in the next white light-exposed frame that is later used for training the neural network. We demonstrate the effectiveness of our method using a KineWheel-DeepLabCut setup for the markerless tracking of the four paws of a harness-fixed mouse running on top of the transparent wheel with mirror. Our automated approach, made available open-source, achieves high-quality position annotations and significantly reduces the need for human involvement in the neural network training process, paving the way for more efficient and streamlined rodent pose tracking in neuroscience research.

RECORD, a high-throughput, customizable system that unveils behavioral strategies leveraged by rodents during foraging-like decision-making

Translational studies benefit from experimental designs where laboratory organisms use human-relevant behaviors. One such behavior is decision-making, however studying complex decision-making in rodents is labor-intensive and typically restricted to two levels of cost/reward. We design a fully automated, inexpensive, high-throughput framework to study decision-making across multiple levels of rewards and costs: the REward-COst in Rodent Decision-making (RECORD) system. RECORD integrates three components: 1) 3D-printed arenas, 2) custom electronic hardware, and 3) software. We validated four behavioral protocols without employing any food or water restriction, highlighting the versatility of our system. RECORD data exposes heterogeneity in decision-making both within and across individuals that is quantifiably constrained. Using oxycodone self-administration and alcohol-consumption as test cases, we reveal how analytic approaches that incorporate behavioral heterogeneity are sensitive to detecting perturbations in decision-making. RECORD is a powerful approach to studying decision-making in rodents, with features that facilitate translational studies of decision-making in psychiatric disorders.

Simple Behavioral Analysis (SimBA) as a platform for explainable machine learning in behavioral neuroscience

The study of complex behaviors is often challenging when using manual annotation due to the absence of quantifiable behavioral definitions and the subjective nature of behavioral annotation. Integration of supervised machine learning approaches mitigates some of these issues through the inclusion of accessible and explainable model interpretation. To decrease barriers to access, and with an emphasis on accessible model explainability, we developed the open-source Simple Behavioral Analysis (SimBA) platform for behavioral neuroscientists. SimBA introduces several machine learning interpretability tools, including SHapley Additive exPlanation (SHAP) scores, that aid in creating explainable and transparent behavioral classifiers. Here we show how the addition of explainability metrics allows for quantifiable comparisons of aggressive social behavior across research groups and species, reconceptualizing behavior as a sharable reagent and providing an open-source framework. We provide an open-source, graphical user interface (GUI)-driven, well-documented package to facilitate the movement toward improved automation and sharing of behavioral classification tools across laboratories.

Active avoidance recruits the anterior cingulate cortex regardless of social context in male and female rats

Actively avoiding danger is necessary for survival. Most research has focused on the behavioral and neurobiological processes when individuals avoid danger alone, under solitary conditions. Therefore, little is known about how social context affects active avoidance. Using a modified version of the platform-mediated avoidance task in rats, we investigated whether the presence of a social partner attenuates conditioned freezing and enhances avoidance learning compared to avoidance learned under solitary conditions. Rats spent a similar percentage of time avoiding during the tone under both conditions; however, rats trained under social conditions exhibited greater freezing during the tone as well as lower rates of darting and food seeking compared to solitary rats. Under solitary conditions, we observed higher levels of avoidance in females compared to males, which was not present in rats trained under social conditions. To gain greater mechanistic insight, we optogenetically inactivated glutamatergic projection neurons in the anterior cingulate cortex (ACC) following avoidance training. Photoinactivation of ACC neurons reduced expression of avoidance under social conditions both in the presence and absence of the partner. Under solitary conditions, photoinactivation of ACC delayed avoidance in males but blocked avoidance in females. Our findings suggest that avoidance is mediated by the ACC, regardless of social context, and may be dysfunctional in those suffering from trauma-related disorders. Furthermore, sex differences in prefrontal circuits mediating active avoidance warrant further investigation, given that females experience a higher risk of developing anxiety disorders.

Hippocampal Engrams Generate Variable Behavioral Responses and Brain-Wide Network States

Freezing is a defensive behavior commonly examined during hippocampal-mediated fear engram reactivation. How these cellular populations engage the brain and modulate freezing across varying environmental demands is unclear. To address this, we optogenetically reactivated a fear engram in the dentate gyrus subregion of the hippocampus across three distinct contexts in male mice. We found that there were differential amounts of light-induced freezing depending on the size of the context in which reactivation occurred: mice demonstrated robust light-induced freezing in the most spatially restricted of the three contexts but not in the largest. We then utilized graph theoretical analyses to identify brain-wide alterations in cFos expression during engram reactivation across the smallest and largest contexts. Our manipulations induced positive interregional cFos correlations that were not observed in control conditions. Additionally, regions spanning putative "fear" and "defense" systems were recruited as hub regions in engram reactivation networks. Lastly, we compared the network generated from engram reactivation in the small context with a natural fear memory retrieval network. Here, we found shared characteristics such as modular composition and hub regions. By identifying and manipulating the circuits supporting memory function, as well as their corresponding brain-wide activity patterns, it is thereby possible to resolve systems-level biological mechanisms mediating memory's capacity to modulate behavioral states.

Assessment of orofacial nociceptive behaviors of mice with the sheltering tube method: Oxaliplatin-induced mechanical and cold allodynia in orofacial regions

Preclinical studies on pathological pain rely on the von Frey test to examine changes in mechanical thresholds and the acetone spray test to determine alterations in cold sensitivity in rodents. These tests are typically conducted on rodent hindpaws, where animals with pathological pain show reliable nocifensive responses to von Frey filaments and acetone drops applied to the hindpaws. Pathological pain in orofacial regions is also an important clinical problem and has been investigated with rodents. However, performing the von Frey and acetone spray tests in the orofacial region has been challenging, largely due to the high mobility of the head of testing animals. To solve this problem, we implemented a sheltering tube method to assess orofacial nociception in mice. In experiments, mice were sheltered in elevated tubes, where they were well accommodated because the tubes provided safe shelters for mice. Examiners could reliably apply mechanical stimuli with von Frey filament, cold stimuli with acetone spray, and light stimuli with a laser beam to the orofacial regions. We validated this method in Nav1.8-ChR2 mice treated with oxaliplatin that induced peripheral neuropathy. Using the von Frey test, orofacial response frequencies and nociceptive response scores were significantly increased in Nav1.8-ChR2 mice treated with oxaliplatin. In the acetone spray test, the duration of orofacial responses was significantly prolonged in oxaliplatin-treated mice. The response frequencies to laser light stimulation were significantly increased in Nav1.8-ChR2 mice treated with oxaliplatin. Our sheltering tube method allows us to reliably perform the von Frey, acetone spray, and optogenetic tests in orofacial regions to investigate orofacial pain.

SaLSa: A Combinatory Approach of Semi-Automatic Labeling and Long Short-Term Memory to Classify Behavioral Syllables

Accurately and quantitatively describing mouse behavior is an important area. Although advances in machine learning have made it possible to track their behaviors accurately, reliable classification of behavioral sequences or syllables remains a challenge. In this study, we present a novel machine learning approach, called SaLSa (a combination of semi-automatic labeling and long short-term memory-based classification), to classify behavioral syllables of mice exploring an open field. This approach consists of two major steps. First, after tracking multiple body parts, spatial and temporal features of their egocentric coordinates are extracted. A fully automated unsupervised process identifies candidates for behavioral syllables, followed by manual labeling of behavioral syllables using a graphical user interface (GUI). Second, a long short-term memory (LSTM) classifier is trained with the labeled data. We found that the classification performance was marked over 97%. It provides a performance equivalent to a state-of-the-art model while classifying some of the syllables. We applied this approach to examine how hyperactivity in a mouse model of Alzheimer's disease develops with age. When the proportion of each behavioral syllable was compared between genotypes and sexes, we found that the characteristic hyperlocomotion of female Alzheimer's disease mice emerges between four and eight months. In contrast, age-related reduction in rearing is common regardless of genotype and sex. Overall, SaLSa enables detailed characterization of mouse behavior.

Elephants and algorithms: a review of the current and future role of AI in elephant monitoring

Artificial intelligence (AI) and machine learning (ML) present revolutionary opportunities to enhance our understanding of animal behaviour and conservation strategies. Using elephants, a crucial species in Africa and Asia's protected areas, as our focal point, we delve into the role of AI and ML in their conservation. Given the increasing amounts of data gathered from a variety of sensors like cameras, microphones, geophones, drones and satellites, the challenge lies in managing and interpreting this vast data. New AI and ML techniques offer solutions to streamline this process, helping us extract vital information that might otherwise be overlooked. This paper focuses on the different AI-driven monitoring methods and their potential for improving elephant conservation. Collaborative efforts between AI experts and ecological researchers are essential in leveraging these innovative technologies for enhanced wildlife conservation, setting a precedent for numerous other species.

Chronotate: An open-source tool for manual timestamping and quantification of animal behavior

A core necessity to behavioral neuroscience research is the ability to accurately measure performance on behavioral assays, such as the novel object location and novel object recognition tasks. These tasks are widely used in neuroscience research and measure a rodent's instinct for investigating novel features as a proxy to test their memory of a previous experience. Automated tools for scoring behavioral videos can be cost prohibitive and often have difficulty distinguishing between active investigation of an object and simply being in close proximity to an object. As such, many experimenters continue to rely on hand scoring interactions using stopwatches, which makes it difficult to review scoring after-the-fact and results in the loss of temporal information. Here, we introduce Chronotate, a free, open-source tool to aid in manually scoring novel object behavior videos. The software consists of an interactive video player with keyboard integration for marking timestamps of behavioral events during video playback, making it simple to quickly score and review bouts of rodent-object interaction. In addition, Chronotate outputs detailed interaction bout data, allowing for nuanced behavioral performance analyses. Using this detailed temporal information, we demonstrate that novel object location performance peaks within the first 3 s of interaction time and preference for the novel location becomes reduced across the test session. Thus, Chronotate can be used to determine the temporal structure of interactions on this task and can provide new insight into the memory processes that drive this behavior. Chronotate is available for download at: https://github.com/ShumanLab/Chronotate.

VoDEx: a Python library for time annotation and management of volumetric functional imaging data

SUMMARY

In functional imaging studies, accurately synchronizing the time course of experimental manipulations and stimulus presentations with resulting imaging data is crucial for analysis. Current software tools lack such functionality, requiring manual processing of the experimental and imaging data, which is error-prone and potentially non-reproducible. We present VoDEx, an open-source Python library that streamlines the data management and analysis of functional imaging data. VoDEx synchronizes the experimental timeline and events (e.g. presented stimuli, recorded behavior) with imaging data. VoDEx provides tools for logging and storing the timeline annotation, and enables retrieval of imaging data based on specific time-based and manipulation-based experimental conditions.

AVAILABILITY AND IMPLEMENTATION

VoDEx is an open-source Python library and can be installed via the "pip install" command. It is released under a BSD license, and its source code is publicly accessible on GitHub (https://github.com/LemonJust/vodex). A graphical interface is available as a napari-vodex plugin, which can be installed through the napari plugins menu or using "pip install." The source code for the napari plugin is available on GitHub (https://github.com/LemonJust/napari-vodex). The software version at the time of submission is archived at Zenodo (version v1.0.18, https://zenodo.org/record/8061531).

Brain-Wide Projections and Differential Encoding of Prefrontal Neuronal Classes Underlying Learned and Innate Threat Avoidance

To understand how the brain produces behavior, we must elucidate the relationships between neuronal connectivity and function. The medial prefrontal cortex (mPFC) is critical for complex functions including decision-making and mood. mPFC projection neurons collateralize extensively, but the relationships between mPFC neuronal activity and brain-wide connectivity are poorly understood. We performed whole-brain connectivity mapping and fiber photometry to better understand the mPFC circuits that control threat avoidance in male and female mice. Using tissue clearing and light sheet fluorescence microscopy (LSFM), we mapped the brain-wide axon collaterals of populations of mPFC neurons that project to nucleus accumbens (NAc), ventral tegmental area (VTA), or contralateral mPFC (cmPFC). We present DeepTraCE (deep learning-based tracing with combined enhancement), for quantifying bulk-labeled axonal projections in images of cleared tissue, and DeepCOUNT (deep-learning based counting of objects via 3D U-net pixel tagging), for quantifying cell bodies. Anatomical maps produced with DeepTraCE aligned with known axonal projection patterns and revealed class-specific topographic projections within regions. Using TRAP2 mice and DeepCOUNT, we analyzed whole-brain functional connectivity underlying threat avoidance. PL was the most highly connected node with functional connections to subsets of PL-cPL, PL-NAc, and PL-VTA target sites. Using fiber photometry, we found that during threat avoidance, cmPFC and NAc-projectors encoded conditioned stimuli, but only when action was required to avoid threats. mPFC-VTA neurons encoded learned but not innate avoidance behaviors. Together our results present new and optimized approaches for quantitative whole-brain analysis and indicate that anatomically defined classes of mPFC neurons have specialized roles in threat avoidance.SIGNIFICANCE STATEMENT Understanding how the brain produces complex behaviors requires detailed knowledge of the relationships between neuronal connectivity and function. The medial prefrontal cortex (mPFC) plays a key role in learning, mood, and decision-making, including evaluating and responding to threats. mPFC dysfunction is strongly linked to fear, anxiety and mood disorders. Although mPFC circuits are clear therapeutic targets, gaps in our understanding of how they produce cognitive and emotional behaviors prevent us from designing effective interventions. To address this, we developed a high-throughput analysis pipeline for quantifying bulk-labeled fluorescent axons [DeepTraCE (deep learning-based tracing with combined enhancement)] or cell bodies [DeepCOUNT (deep-learning based counting of objects via 3D U-net pixel tagging)] in intact cleared brains. Using DeepTraCE, DeepCOUNT, and fiber photometry, we performed detailed anatomic and functional mapping of mPFC neuronal classes, identifying specialized roles in threat avoidance.

An Open-Source Tool for Automated Human-Level Circling Behavior Detection

Quantifying behavior and relating it to underlying biological states is of paramount importance in many life science fields. Although barriers to recording postural data have been reduced by progress in deep-learning-based computer vision tools for keypoint tracking, extracting specific behaviors from this data remains challenging. Manual behavior coding, the present gold standard, is labor-intensive and subject to intra- and inter-observer variability. Automatic methods are stymied by the difficulty of explicitly defining complex behaviors, even ones which appear obvious to the human eye. Here, we demonstrate an effective technique for detecting one such behavior, a form of locomotion characterized by stereotyped spinning, termed 'circling'. Though circling has an extensive history as a behavioral marker, at present there exists no standard automated detection method. Accordingly, we developed a technique to identify instances of the behavior by applying simple postprocessing to markerless keypoint data from videos of freely-exploring (Cib2-/-;Cib3-/-) mutant mice, a strain we previously found to exhibit circling. Our technique agrees with human consensus at the same level as do individual observers, and it achieves >90% accuracy in discriminating videos of wild type mice from videos of mutants. As using this technique requires no experience writing or modifying code, it also provides a convenient, noninvasive, quantitative tool for analyzing circling mouse models. Additionally, as our approach was agnostic to the underlying behavior, these results support the feasibility of algorithmically detecting specific, research-relevant behaviors using readily-interpretable parameters tuned on the basis of human consensus.

VoDEx: a Python library for time annotation and management of volumetric functional imaging data

In functional imaging studies, accurately synchronizing the time course of experimental manipulations and stimulus presentations with resulting imaging data is crucial for analysis. Current software tools lack such functionality, requiring manual processing of the experimental and imaging data, which is error-prone and potentially non-reproducible. We present VoDEx, an open-source Python library that streamlines the data management and analysis of functional imaging data. VoDEx synchronizes the experimental timeline and events (eg. presented stimuli, recorded behavior) with imaging data. VoDEx provides tools for logging and storing the timeline annotation, and enables retrieval of imaging data based on specific time-based and manipulation-based experimental conditions. Availability and Implementation: VoDEx is an open-source Python library and can be installed via the "pip install" command. It is released under a BSD license, and its source code is publicly accessible on GitHub https://github.com/LemonJust/vodex. A graphical interface is available as a napari-vodex plugin, which can be installed through the napari plugins menu or using "pip install." The source code for the napari plugin is available on GitHub https://github.com/LemonJust/napari-vodex.

Open-source software for automated rodent behavioral analysis

Rodent behavioral analysis is a major specialization in experimental psychology and behavioral neuroscience. Rodents display a wide range of species-specific behaviors, not only in their natural habitats but also under behavioral testing in controlled laboratory conditions. Detecting and categorizing these different kinds of behavior in a consistent way is a challenging task. Observing and analyzing rodent behaviors manually limits the reproducibility and replicability of the analyses due to potentially low inter-rater reliability. The advancement and accessibility of object tracking and pose estimation technologies led to several open-source artificial intelligence (AI) tools that utilize various algorithms for rodent behavioral analysis. These software provide high consistency compared to manual methods, and offer more flexibility than commercial systems by allowing custom-purpose modifications for specific research needs. Open-source software reviewed in this paper offer automated or semi-automated methods for detecting and categorizing rodent behaviors by using hand-coded heuristics, machine learning, or neural networks. The underlying algorithms show key differences in their internal dynamics, interfaces, user-friendliness, and the variety of their outputs. This work reviews the algorithms, capability, functionality, features and software properties of open-source behavioral analysis tools, and discusses how this emergent technology facilitates behavioral quantification in rodent research.

High-throughput automated methods for classical and operant conditioning of Drosophila larvae

Learning which stimuli (classical conditioning) or which actions (operant conditioning) predict rewards or punishments can improve chances of survival. However, the circuit mechanisms that underlie distinct types of associative learning are still not fully understood. Automated, high-throughput paradigms for studying different types of associative learning, combined with manipulation of specific neurons in freely behaving animals, can help advance this field. The Drosophila melanogaster larva is a tractable model system for studying the circuit basis of behaviour, but many forms of associative learning have not yet been demonstrated in this animal. Here, we developed a high-throughput (i.e. multi-larva) training system that combines real-time behaviour detection of freely moving larvae with targeted opto- and thermogenetic stimulation of tracked animals. Both stimuli are controlled in either open- or closed-loop, and delivered with high temporal and spatial precision. Using this tracker, we show for the first time that Drosophila larvae can perform classical conditioning with no overlap between sensory stimuli (i.e. trace conditioning). We also demonstrate that larvae are capable of operant conditioning by inducing a bend direction preference through optogenetic activation of reward-encoding serotonergic neurons. Our results extend the known associative learning capacities of Drosophila larvae. Our automated training rig will facilitate the study of many different forms of associative learning and the identification of the neural circuits that underpin them.