Computational Neuroethology: A Call to Action

Cross-Species Framework for Emotional Well-Being and Brain Aging: Lessons From Behavioral Neuroscience

Importance

Emotional well-being (EWB) is an emerging therapeutic target for managing and preventing symptoms associated with Alzheimer disease and related dementias (ADRD). However, more research is needed to establish causal inferences between brain changes, EWB, and behavioral changes observed in typical aging and ADRD.

Observations

This article presents a framework for using a cross-species behavioral neuroscience approach to study EWB and brain aging, adopting a well-established biobehavioral model that highlights the reciprocal roles of brain changes, EWB, and ADRD symptoms. First, the challenges and opportunities in this field are reviewed. Then, a practical solution to improve comparability between animal and human studies is proposed.

Conclusions and Relevance

The goal is to draw comprehensive parallels and distinctions that could enhance the understanding of the mechanisms linking brain aging, EWB, and ADRD symptomatic disturbances across different species.

Home-Cage Training for Non-Human Primates: An Opportunity to Reduce Stress and Study Natural Behavior in Neurophysiology Experiments

Research involving non-human primates remains a cornerstone in fields such as biomedical research and systems neuroscience. However, the daily routines of laboratory work can induce stress in these animals, potentially compromising their well-being and the reliability of experimental outcomes. To address this, many laboratories have adopted home-cage training protocols to mitigate stress caused by routine procedures such as transport and restraint-a factor that can impact both macaque physiology and experimental validity. This review explores the primary methods and experimental setups employed in home-cage training, highlighting their potential not only to address ethical concerns surrounding animal welfare but also to reduce training time and risks for the researchers. Furthermore, by combining home-cage training with wireless recordings, it becomes possible to expand research opportunities in behavioral neurophysiology with non-human primates. This approach enables the study of various cognitive processes in more naturalistic settings, thereby increasing the ecological validity of scientific findings through innovative experimental designs that thoroughly investigate the complexity of the animals' natural behavior.

Vascular and glymphatic dysfunction as drivers of cognitive impairment in Alzheimer's disease: Insights from computational approaches

Alzheimer's disease (AD) is driven by complex interactions between vascular dysfunction, glymphatic system impairment, and neuroinflammation. Vascular aging, characterized by arterial stiffness and reduced cerebral blood flow (CBF), disrupts the pulsatile forces necessary for glymphatic clearance, exacerbating amyloid-beta (Aβ) accumulation and cognitive decline. This review synthesizes insights into the mechanistic crosstalk between these systems and explores their contributions to AD pathogenesis. Emerging machine learning (ML) tools, such as DeepLabCut and Motion sequencing (MoSeq), offer innovative solutions for analyzing multimodal data and enhancing diagnostic precision. Integrating ML with imaging and behavioral analyses bridges gaps in understanding vascular-glymphatic dysfunction. Future research must prioritize these interactions to develop early diagnostics and targeted interventions, advancing our understanding of neurovascular health in AD.

An Automated AI Framework for Quantitative Measurement of Mammalian Behavior

Despite the large amount of video data captured during ethological studies of wild mammals, there is no widely accepted method available to automatically and quantitatively measure and analyze animal behavior. We developed a framework using facial recognition and deep learning to automatically track, measure, and quantify the behavior of single or multiple individuals from 10 distinct mammalian taxa, including three species of primates, three species of bovids, three species of carnivores, and one species of equid. We used spatiotemporal information based on animal skeleton models to recognize a set of distinct behaviors such as walking, feeding, grooming, and resting, and achieved an accuracy ranging from 0.82 to 0.96. Accuracies of validation videos ranged from 0.80 to 0.99. Our study offers an innovative analytical platform for the rapid and accurate evaluation of animal behavior in both captive and field settings.

Statistical signature of subtle behavioral changes in large-scale assays

The central nervous system can generate various behaviors, including motor responses, which we can observe through video recordings. Recent advances in gene manipulation, automated behavioral acquisition at scale, and machine learning enable us to causally link behaviors to their underlying neural mechanisms. Moreover, in some animals, such as the Drosophila melanogaster larva, this mapping is possible at the unprecedented scale of single neurons, allowing us to identify the neural microcircuits generating particular behaviors. These high-throughput screening efforts, linking the activation or suppression of specific neurons to behavioral patterns in millions of animals, provide a rich dataset to explore the diversity of nervous system responses to the same stimuli. However, important challenges remain in identifying subtle behaviors, including immediate and delayed responses to neural activation or suppression, and understanding these behaviors on a large scale. We here introduce several statistically robust methods for analyzing behavioral data in response to these challenges: 1) A generative physical model that regularizes the inference of larval shapes across the entire dataset. 2) An unsupervised kernel-based method for statistical testing in learned behavioral spaces aimed at detecting subtle deviations in behavior. 3) A generative model for larval behavioral sequences, providing a benchmark for identifying higher-order behavioral changes. 4) A comprehensive analysis technique using suffix trees to categorize genetic lines into clusters based on common action sequences. We showcase these methodologies through a behavioral screen focused on responses to an air puff, analyzing data from 280 716 larvae across 569 genetic lines.

FlyVISTA, an integrated machine learning platform for deep phenotyping of sleep in Drosophila

There is great interest in using genetically tractable organisms such as Drosophila to gain insights into the regulation and function of sleep. However, sleep phenotyping in Drosophila has largely relied on simple measures of locomotor inactivity. Here, we present FlyVISTA, a machine learning platform to perform deep phenotyping of sleep in flies. This platform comprises a high-resolution closed-loop video imaging system, coupled with a deep learning network to annotate 35 body parts, and a computational pipeline to extract behaviors from high-dimensional data. FlyVISTA reveals the distinct spatiotemporal dynamics of sleep and wake-associated microbehaviors at baseline, following administration of the sleep-inducing drug gaboxadol, and with dorsal fan-shaped body drivers. We identify a microbehavior ("haltere switch") exclusively seen during quiescence that indicates a deeper sleep stage. These results enable the rigorous analysis of sleep in Drosophila and set the stage for computational analyses of microbehaviors in quiescent animals.

Navigation strategies in Caenorhabditis elegans are differentially altered by learning

Learned olfactory-guided navigation is a powerful platform for studying how a brain generates goal-directed behaviors. However, the quantitative changes that occur in sensorimotor transformations and the underlying neural circuit substrates to generate such learning-dependent navigation is still unclear. Here we investigate learned sensorimotor processing for navigation in the nematode Caenorhabditis elegans by measuring and modeling experience-dependent odor and salt chemotaxis. We then explore the neural basis of learned odor navigation through perturbation experiments. We develop a novel statistical model to characterize how the worm employs two behavioral strategies: a biased random walk and weathervaning. We infer weights on these strategies and characterize sensorimotor kernels that govern them by fitting our model to the worm's time-varying navigation trajectories and precise sensory experiences. After olfactory learning, the fitted odor kernels reflect how appetitive and aversive trained worms up- and down-regulate both strategies, respectively. The model predicts an animal's past olfactory learning experience with  > 90% accuracy given finite observations, outperforming a classical chemotaxis metric. The model trained on natural odors further predicts the animals' learning-dependent response to optogenetically induced odor perception. Our measurements and model show that behavioral variability is altered by learning-trained worms exhibit less variable navigation than naive ones. Genetically disrupting individual interneuron classes downstream of an odor-sensing neuron reveals that learned navigation strategies are distributed in the network. Together, we present a flexible navigation algorithm that is supported by distributed neural computation in a compact brain.

Separating cognitive and motor processes in the behaving mouse

The cognitive processes supporting complex animal behavior are closely associated with movements responsible for critical processes, such as facial expressions or the active sampling of our environments. These movements are strongly related to neural activity across much of the brain and are often highly correlated with ongoing cognitive processes. A fundamental issue for understanding the neural signatures of cognition and movements is whether cognitive processes are separable from related movements or if they are driven by common neural mechanisms. Here we demonstrate how the separability of cognitive and motor processes can be assessed and, when separable, how the neural dynamics associated with each component can be isolated. We designed a behavioral task in mice that involves multiple cognitive processes, and we show that dynamics commonly taken to support cognitive processes are strongly contaminated by movements. When cognitive and motor components are isolated using a novel approach for subspace decomposition, we find that they exhibit distinct dynamical trajectories and are encoded by largely separate populations of cells. Accurately isolating dynamics associated with particular cognitive and motor processes will be essential for developing conceptual and computational models of neural circuit function.

Hierarchical behavioral analysis framework as a platform for standardized quantitative identification of behaviors

Behavior is composed of modules that operate based on inherent logic. Understanding behavior and its neural mechanisms is facilitated by clear structural behavioral analysis. Here, we developed a hierarchical behavioral analysis framework (HBAF) that efficiently reveals the organizational logic of these modules by analyzing high-dimensional behavioral data. By creating a spontaneous behavior atlas for male and female mice, we discovered that spontaneous behavior patterns are hardwired, with sniffing serving as the hub node for movement transitions. The sniffing-to-grooming ratio accurately distinguished the spontaneous behavioral states in a high-throughput manner. These states are influenced by emotional status, circadian rhythms, and lighting conditions, leading to unique behavioral characteristics, spatiotemporal features, and dynamic patterns. By implementing the straightforward and achievable spontaneous behavior paradigm, HBAF enables swift and accurate assessment of animal behavioral states and bridges the gap between a theoretical understanding of the behavioral structure and practical analysis using comprehensive multidimensional behavioral information.

The utility of animal models to inform the next generation of human space exploration

Animals have played a vital role in every stage of space exploration, from early sub-orbital flights to contemporary missions. New physiological and psychological challenges arise with plans to venture deeper into the solar system. Advances in chimeric and knockout animal models, along with genetic modification techniques have enhanced our ability to study the effects of microgravity in greater detail. However, increased investment in the purposeful design of habitats and payloads, as well as in AI-enhanced behavioral monitoring in orbit can better support the ethical and effective use of animals in deep space research.

Temporal action localisation in video data containing rabbit behavioural patterns

In this paper we present the results of a research on artificial intelligence based approaches to temporal action localisation in video recordings of rabbit behavioural patterns. When using the artificial intelligence, special attention should be paid to quality and quantity of data collected for the research. Conducting the experiments in science may take long time and involve expensive preparatory work. Artificial intelligence based approaches can be applied to different kinds of actors in the video including animals, humans, intelligent agents, etc. The peculiarities of using these approaches in specific research conditions can be of particular importance for project cost reduction. In this paper we analyze the peculiarities of using the frame-by-frame classification based approach to temporal localisation of rabbit actions in video data and propose a metric for evaluating its consistency. The analysis of existing approaches described in the literature indicates that the aforementioned approach has high accuracy (up to 99%) and F1 score of temporal action localisation (up to 0.97) thus fulfilling conditions for substantial reduction or total exclusion of manual data labeling from the process of studying actor behaviour patterns in video data collected in experimental setting. We conducted further investigation in order to determine the optimal number of manually labeled frames required to achieve 99% accuracy of automatic labeling and studied the dependence of labeling accuracy on the number of actors presented in the training data.

Building Resilience: The Stress Response as a Driving Force for Neuroplasticity and Adaptation

People exhibit an extraordinary capacity to adjust to stressful situations. Here, we argue that the acute stress response is a major driving force behind this adaptive process. In addition to immediately freeing energy reserves, facilitating a rapid and robust neurocognitive response, and helping to reinstate homeostasis, the stress response also critically regulates neuroplasticity. Therefore, understanding the healthy acute stress response is crucial for understanding stress resilience-the maintenance or rapid recovery of mental health during and after times of adversity. Contemporary resilience research differentiates between resilience factors and resilience mechanisms. Resilience factors refer to a broad array of social, psychological, or biological variables that are stable but potentially malleable and predict resilient outcomes. In contrast, resilience mechanisms refer to proximate mechanisms activated during acute stress that enable individuals to effectively navigate immediate challenges. In this article, we review literature related to how neurotransmitter and hormonal changes during acute stress regulate the activation of resilience mechanisms. We integrate literature on the timing-dependent and neuromodulator-specific regulation of neurocognition, episodic memory, and behavioral and motivational control, highlighting the distinct and often synergistic roles of catecholamines (dopamine and norepinephrine) and glucocorticoids. We conclude that stress resilience is bolstered by improved future predictions and the success-based reinforcement of effective coping strategies during acute stress. The resulting generalized memories of success, controllability, and safety constitute beneficial plasticity that lastingly improves self-control under stress. Insight into such mechanisms of resilience is critical for the development of novel interventions focused on prevention rather than treatment of stress-related disorders.

On growth and form of animal behavior

In this study we propose an architecture (bauplan) for the growth and form of behavior in vertebrates and arthropods. We show in what sense behavior is an extension of anatomy. Then we show that movement-based behavior shares linearity and modularity with the skeletal body plan, and with the Hox genes; that it mirrors the geometry of the physical environment; and that it reveals the animal's understanding of the animate and physical situation, with implications for perception, attention, emotion, and primordial cognition. First we define the primitives of movement in relational terms, as in comparative anatomy, yielding homological primitives. Then we define modules, generative rules and the architectural plan of behavior in terms of these primitives. In this way we expose the homology of behaviors, and establish a rigorous trans-phyletic comparative discipline of the morphogenesis of movement-based behavior. In morphogenesis, behavior builds up and narrows incessantly according to strict geometric rules. The same rules apply in moment-to-moment behavior, in ontogenesis, and partly also in phylogenesis. We demonstrate these rules in development, in neurological recovery, with drugs (dopamine-stimulated striatal modulation), in stressful situations, in locomotor behavior, and partly also in human pathology. The buildup of movement culminates in free, undistracted, exuberant behavior. It is observed in play, in superior animals during agonistic interactions, and in humans in higher states of functioning. Geometrization promotes the study of genetics, anatomy, and behavior within one and the same discipline. The geometrical bauplan portrays both already evolved dimensions, and prospective dimensional constraints on evolutionary behavioral innovations.

Machine learning and confirmatory factor analysis show that buprenorphine alters motor and anxiety-like behaviors in male, female, and obese C57BL/6J mice

Buprenorphine is an opioid approved for medication-assisted treatment of opioid use disorder. Used off-label, buprenorphine has been reported to contribute to the clinical management of anxiety. Although human anxiety is a highly prevalent disorder, anxiety is a latent construct that cannot be directly measured. The present study combined machine learning techniques and artificial intelligence with confirmatory factor analysis to evaluate the hypothesis that buprenorphine alters motor and anxiety-like behavior in C57BL/6J (B6) mice (n = 30) as a function of dose, sex, and body mass. After administration of saline (control) or buprenorphine, mice were placed on an elevated zero maze (EZM) for 5 min. Digital video of mouse behavior was uploaded to the cloud, and mouse position on the maze was tracked and analyzed with supervised machine learning and artificial intelligence. ANOVA and post hoc test showed that buprenorphine significantly altered five motor behaviors. Confirmatory factor analysis revealed that the latent construct of anxiety-like behavior accounted for a statistically significant amount of variance in all five motor behaviors.NEW & NOTEWORTHY Machine learning and pose estimation using a convolutional neural network accurately detected and objectively scored buprenorphine-induced changes in locomotor behaviors of mice on an elevated zero maze (EZM). Confirmatory factor analysis supports the interpretation that the anxiety-like construct accounted for the buprenorphine-induced changes in motor behavior. The results have noteworthy implications for the relationship between Darwin's story model of mammalian emotions and computational models of anxiety-like behavior in mice.

Assessing the effects of 5-HT2A and 5-HT5A receptor antagonists on DOI-induced head-twitch response in male rats using marker-less deep learning algorithms

BACKGROUND

Serotonergic psychedelics, which display a high affinity and specificity for 5-HT2A receptors like 2,5-dimethoxy-4-iodoamphetamine (DOI), reliably induce a head-twitch response in rodents characterized by paroxysmal, high-frequency head rotations. Traditionally, this behavior is manually counted by a trained observer. Although automation could simplify and facilitate data collection, current techniques require the surgical implantation of magnetic markers into the rodent's skull or ear.

METHODS

This study aimed to assess the feasibility of a marker-less workflow for detecting head-twitch responses using deep learning algorithms. High-speed videos were analyzed using the DeepLabCut neural network to track head movements, and the Simple Behavioral Analysis (SimBA) toolkit was employed to build models identifying specific head-twitch responses.

RESULTS

In studying DOI (0.3125-2.5 mg/kg) effects, the deep learning algorithm workflow demonstrated a significant correlation with human observations. As expected, the preferential 5-HT2A receptor antagonist ketanserin (0.625 mg/kg) attenuated DOI (1.25 mg/kg)-induced head-twitch responses. In contrast, the 5-HT5A receptor antagonists SB 699,551 (3 and 10 mg/kg), and ASP 5736 (0.01 and 0.03 mg/kg) failed to do so.

CONCLUSIONS

Previous drug discrimination studies demonstrated that the 5-HT5A receptor antagonists attenuated the interoceptive cue of a potent hallucinogen LSD, suggesting their anti-hallucinatory effects. Nonetheless, the present results were not surprising and support the head-twitch response as selective for 5-HT2A and not 5-HT5A receptor activation. We conclude that the DeepLabCut and SimBA toolkits offer a high level of objectivity and can accurately and efficiently identify compounds that induce or inhibit head-twitch responses, making them valuable tools for high-throughput research.

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.

Unsupervised discovery of family specific vocal usage in the Mongolian gerbil

In nature, animal vocalizations can provide crucial information about identity, including kinship and hierarchy. However, lab-based vocal behavior is typically studied during brief interactions between animals with no prior social relationship, and under environmental conditions with limited ethological relevance. Here, we address this gap by establishing long-term acoustic recordings from Mongolian gerbil families, a core social group that uses an array of sonic and ultrasonic vocalizations. Three separate gerbil families were transferred to an enlarged environment and continuous 20-day audio recordings were obtained. Using a variational autoencoder (VAE) to quantify 583,237 vocalizations, we show that gerbils exhibit a more elaborate vocal repertoire than has been previously reported and that vocal repertoire usage differs significantly by family. By performing gaussian mixture model clustering on the VAE latent space, we show that families preferentially use characteristic sets of vocal clusters and that these usage preferences remain stable over weeks. Furthermore, gerbils displayed family-specific transitions between vocal clusters. Since gerbils live naturally as extended families in complex underground burrows that are adjacent to other families, these results suggest the presence of a vocal dialect which could be exploited by animals to represent kinship. These findings position the Mongolian gerbil as a compelling animal model to study the neural basis of vocal communication and demonstrates the potential for using unsupervised machine learning with uninterrupted acoustic recordings to gain insights into naturalistic animal behavior.

SAUSI: an integrative assay for measuring social aversion and motivation

Social aversion is a key feature of numerous mental health disorders such as Social Anxiety and Autism Spectrum Disorders. Nevertheless, the biobehavioral mechanisms underlying social aversion remain poorly understood. Progress in understanding the etiology of social aversion has been hindered by the lack of comprehensive tools to assess social aversion in model systems. Here, we created a new behavioral task - Selective Access to Unrestricted Social Interaction (SAUSI), which integrates elements of social motivation, hesitancy, decision-making, and free interaction to enable the wholistic assessment of social aversion in mice. Using this novel assay, we found that social isolation-induced social aversion in mice is largely driven by increases in social fear and social motivation. Deep learning analyses revealed a unique behavioral footprint underlying the socially aversive state produced by isolation, demonstrating the compatibility of modern computational approaches with SAUSI. Social aversion was further assessed using traditional assays - including the 3-chamber sociability assay and the resident intruder assay - which were sufficient to reveal fragments of a social aversion phenotype, including changes to either social motivation or social interaction, but which failed to provide a wholistic assessment of social aversion. Critically, these assays were not sufficient to reveal key components of social aversion, including social freezing and social hesitancy behaviors. Lastly, we demonstrated that SAUSI is generalizable, as it can be used to assess social aversion induced by non-social stressors, such as foot shock. Our findings debut a novel task for the behavioral toolbox - one which overcomes limitations of previous assays, allowing for both social choice as well as free interaction, and offers a new approach for assessing social aversion in rodents.

SARTAB, a scalable system for automated real-time behavior detection based on animal tracking and Region Of Interest analysis: validation on fish courtship behavior

Methods from Machine Learning (ML) and Computer Vision (CV) have proven powerful tools for quickly and accurately analyzing behavioral recordings. The computational complexity of these techniques, however, often precludes applications that require real-time analysis: for example, experiments where a stimulus must be applied in response to a particular behavior or samples must be collected soon after the behavior occurs. Here, we describe SARTAB (Scalable Automated Real-Time Analysis of Behavior), a system that achieves automated real-time behavior detection by continuously monitoring animal positions relative to behaviorally relevant Regions Of Interest (ROIs). We then show how we used this system to detect infrequent courtship behaviors in Pseudotropheus demasoni (a species of Lake Malawi African cichlid fish) to collect neural tissue samples from actively behaving individuals for multiomic profiling at single nucleus resolution. Within this experimental context, we achieve high ROI and animal detection accuracies (mAP@[.5 : .95] of 0.969 and 0.718, respectively) and 100% classification accuracy on a set of 32 manually selected behavioral clips. SARTAB is unique in that all analysis runs on low-cost, edge-deployed hardware, making it a highly scalable and energy-efficient solution for real-time experimental feedback. Although our solution was developed specifically to study cichlid courtship behavior, the intrinsic flexibility of neural network analysis ensures that our approach can be adapted to novel species, behaviors, and environments.

How can ethology inform the neuroscience of fear, aggression and dominance?

The study of behaviour is dominated by two approaches. On the one hand, ethologists aim to understand how behaviour promotes adaptation to natural contexts. On the other, neuroscientists aim to understand the molecular, cellular, circuit and psychological origins of behaviour. These two complementary approaches must be combined to arrive at a full understanding of behaviour in its natural setting. However, methodological limitations have restricted most neuroscientific research to the study of how discrete sensory stimuli elicit simple behavioural responses under controlled laboratory conditions that are only distantly related to those encountered in real life. Fortunately, the recent advent of neural monitoring and manipulation tools adapted for use in freely behaving animals has enabled neuroscientists to incorporate naturalistic behaviours into their studies and to begin to consider ethological questions. Here, we examine the promises and pitfalls of this trend by describing how investigations of rodent fear, aggression and dominance behaviours are changing to take advantage of an ethological appreciation of behaviour. We lay out current impediments to this approach and propose a framework for the evolution of the field that will allow us to take maximal advantage of an ethological approach to neuroscience and to increase its relevance for understanding human behaviour.

Integrating computer vision and molecular neurobiology to bridge the gap between behavior and the brain

The past decade of social insect research has seen rapid development in automated behavioral tracking and molecular profiling of the nervous system, two distinct but complementary lines of inquiry into phenotypic variation across individuals, colonies, populations, and species. These experimental strategies have developed largely in parallel, as automated tracking generates a continuous stream of behavioral data, while, in contrast, 'omics-based profiling provides a single 'snapshot' of the brain. Better integration of these approaches applied to studying variation in social behavior will reveal the underlying genetic and neurobiological mechanisms that shape the evolution and diversification of social life. In this review, we discuss relevant advances in both fields and propose new strategies to better elucidate the molecular and behavioral innovations that generate social life.

Analysis of behavioral flow resolves latent phenotypes

The accurate detection and quantification of rodent behavior forms a cornerstone of basic biomedical research. Current data-driven approaches, which segment free exploratory behavior into clusters, suffer from low statistical power due to multiple testing, exhibit poor transferability across experiments and fail to exploit the rich behavioral profiles of individual animals. Here we introduce a pipeline to capture each animal's behavioral flow, yielding a single metric based on all observed transitions between clusters. By stabilizing these clusters through machine learning, we ensure data transferability, while dimensionality reduction techniques facilitate detailed analysis of individual animals. We provide a large dataset of 771 behavior recordings of freely moving mice-including stress exposures, pharmacological and brain circuit interventions-to identify hidden treatment effects, reveal subtle variations on the level of individual animals and detect brain processes underlying specific interventions. Our pipeline, compatible with popular clustering methods, substantially enhances statistical power and enables predictions of an animal's future behavior.

Kinematic coding: Measuring information in naturalistic behaviour

Recent years have seen an explosion of interest in naturalistic behaviour and in machine learning tools for automatically tracking it. However, questions about what to measure, how to measure it, and how to relate naturalistic behaviour to neural activity and cognitive processes remain unresolved. In this Perspective, we propose a general experimental and computational framework - kinematic coding - for measuring how information about cognitive states is encoded in structured patterns of behaviour and how this information is read out by others during social interactions. This framework enables the design of new experiments and the generation of testable hypotheses that link behaviour, cognition, and neural activity at the single-trial level. Researchers can employ this framework to identify single-subject, single-trial encoding and readout computations and address meaningful questions about how information encoded in bodily motion is transmitted and communicated.

A contextual fear conditioning paradigm in head-fixed mice exploring virtual reality

Contextual fear conditioning is a classical laboratory task that tests associative memory formation and recall. Techniques such as multi-photon microscopy and holographic stimulation offer tremendous opportunities to understand the neural underpinnings of these memories. However, these techniques generally require animals to be head-fixed. There are few paradigms that test contextual fear conditioning in head-fixed mice, and none where the behavioral outcome following fear conditioning is freezing, the most common measure of fear in freely moving animals. To address this gap, we developed a contextual fear conditioning paradigm in head-fixed mice using virtual reality (VR) environments. We designed an apparatus to deliver tail shocks (unconditioned stimulus, US) while mice navigated a VR environment (conditioned stimulus, CS). The acquisition of contextual fear was tested when the mice were reintroduced to the shock-paired VR environment the following day. We tested three different variations of this paradigm and, in all of them, observed an increased conditioned fear response characterized by increased freezing behavior. This was especially prominent during the first trial in the shock-paired VR environment, compared to a neutral environment where the mice received no shocks. Our results demonstrate that head-fixed mice can be fear conditioned in VR, discriminate between a feared and neutral VR context, and display freezing as a conditioned response, similar to freely behaving animals. Furthermore, using a two-photon microscope, we imaged from large populations of hippocampal CA1 neurons before, during, and following contextual fear conditioning. Our findings reconfirmed those from the literature on freely moving animals, showing that CA1 place cells undergo remapping and show narrower place fields following fear conditioning. Our approach offers new opportunities to study the neural mechanisms underlying the formation, recall, and extinction of contextual fear memories. As the head-fixed preparation is compatible with multi-photon microscopy and holographic stimulation, it enables long-term tracking and manipulation of cells throughout distinct memory stages and provides subcellular resolution for investigating axonal, dendritic, and synaptic dynamics in real-time.

Uncovering multiscale structure in the variability of larval zebrafish navigation

Animals chain movements into long-lived motor strategies, exhibiting variability across scales that reflects the interplay between internal states and environmental cues. To reveal structure in such variability, we build Markov models of movement sequences that bridge across timescales and enable a quantitative comparison of behavioral phenotypes among individuals. Applied to larval zebrafish responding to diverse sensory cues, we uncover a hierarchy of long-lived motor strategies, dominated by changes in orientation distinguishing cruising versus wandering strategies. Environmental cues induce preferences along these modes at the population level: while fish cruise in the light, they wander in response to aversive stimuli, or in search for appetitive prey. As our method encodes the behavioral dynamics of each individual fish in the transitions among coarse-grained motor strategies, we use it to uncover a hierarchical structure in the phenotypic variability that reflects exploration-exploitation trade-offs. Across a wide range of sensory cues, a major source of variation among fish is driven by prior and/or immediate exposure to prey that induces exploitation phenotypes. A large degree of variability that is not explained by environmental cues unravels hidden states that override the sensory context to induce contrasting exploration-exploitation phenotypes. Altogether, by extracting the timescales of motor strategies deployed during navigation, our approach exposes structure among individuals and reveals internal states tuned by prior experience.

AI-powered home cage system for real-time tracking and analysis of rodent behavior

Researchers in animal behavior and neuroscience devote considerable time to observing rodents behavior and physiological responses, with AI monitoring systems reducing personnel workload. This study presents the RodentWatch (RW) system, which leverages deep learning to automatically identify experimental animal behaviors in home cage environments. A single multifunctional camera and edge device are installed inside the animal's home cage, allowing continuous real-time monitoring of the animal's behavior, position, and body temperature for extended periods. We investigated identifying the drinking and resting behaviors of rats, with recognition accuracy enhanced through contextual object labeling and modified non-maximum suppression (NMS) schemes. Two tests-a light cycle change test and a sucrose preference test-were conducted to evaluate the usability of this system in rat behavioral experiments. This system enables notable advancements in image-based behavior recognition for living rodents.

Freezing of movements and its correspondence with MLG1 neuron response to looming stimuli in the crab Neohelice

Upon visually detecting a moving predator, animals often freeze, i.e. stop moving, to minimize being uncovered and to gather detailed information of the object's movements and properties. In certain conditions, the freezing behavior can be enough to avoid a predatory menace but, when the risk is high or increases to a higher level, animals switch strategy and engage in an escape response. The neural bases underlying escape responses to visual stimuli have been extensively investigated both in vertebrates and arthropods. However, those involved in freezing behaviors are much less studied. Here, we investigated the freezing behavior displayed by the crab Neohelice granulata when confronted with a variety of looming stimuli simulating objects of distinct sizes approaching on a collision course at different speeds. The experiments were performed in a treadmill-like device. Animals engaged in exploratory walks responded to the looming stimulus with freezing followed by escaping. The analysis of the stimulus optical variables shows that regardless of the looming dynamic, the freezing decision is made when the angular size of the object increases by 1.4 deg. In vivo intracellular recording responses of monostratified lobula giant neurons (MLG1) to the same looming stimuli show that the freezing times correlate with the times predicted by a hypothetical spike counter of this neuron.

Deep learning algorithms reveal increased social activity in rats at the onset of the dark phase of the light/dark cycle

The rapid decrease of light intensity is a potent stimulus of rats' activity. The nature of this activity, including the character of social behavior and the composition of concomitant ultrasonic vocalizations (USVs), is unknown. Using deep learning algorithms, this study aimed to examine the social life of rat pairs kept in semi-natural conditions and observed during the transitions between light and dark, as well as between dark and light periods. Over six days, animals were video- and audio-recorded during the transition sessions, each starting 10 minutes before and ending 10 minutes after light change. The videos were used to train and apply the DeepLabCut neural network examining animals' movement in space and time. DeepLabCut data were subjected to the Simple Behavioral Analysis (SimBA) toolkit to build models of 11 distinct social and non-social behaviors. DeepSqueak toolkit was used to examine USVs. Deep learning algorithms revealed lights-off-induced increases in fighting, mounting, crawling, and rearing behaviors, as well as 22-kHz alarm calls and 50-kHz flat and short, but not frequency-modulated calls. In contrast, the lights-on stimulus increased general activity, adjacent lying (huddling), anogenital sniffing, and rearing behaviors. The animals adapted to the housing conditions by showing decreased ultrasonic calls as well as grooming and rearing behaviors, but not fighting. The present study shows a lights-off-induced increase in aggressive behavior but fails to demonstrate an increase in a positive affect defined by hedonic USVs. We further confirm and extend the utility of deep learning algorithms in analyzing rat social behavior and ultrasonic vocalizations.

Representing the dynamics of natural marmoset vocal behaviors in frontal cortex

Here, we tested the respective contributions of primate premotor and prefrontal cortex to support vocal behavior. We applied a model-based generalized linear model (GLM) analysis that better accounts for the inherent variance in natural, continuous behaviors to characterize the activity of neurons throughout the frontal cortex as freely moving marmosets engaged in conversational exchanges. While analyses revealed functional clusters of neural activity related to the different processes involved in the vocal behavior, these clusters did not map to subfields of prefrontal or premotor cortex, as has been observed in more conventional task-based paradigms. Our results suggest a distributed functional organization for the myriad neural mechanisms underlying natural social interactions and have implications for our concepts of the role that frontal cortex plays in governing ethological behaviors in primates.

Dynamic modulation of social gaze by sex and familiarity in marmoset dyads

Social communication relies on the ability to perceive and interpret the direction of others' attention, and is commonly conveyed through head orientation and gaze direction in humans and nonhuman primates. However, traditional social gaze experiments in nonhuman primates require restraining head movements, significantly limiting their natural behavioral repertoire. Here, we developed a novel framework for accurately tracking facial features and three-dimensional head gaze orientations of multiple freely moving common marmosets (Callithrix jacchus). By combining deep learning-based computer vision tools with triangulation algorithms, we were able to track the facial features of marmoset dyads within an arena. This method effectively generates dynamic 3D geometrical facial frames while overcoming common challenges like occlusion. To detect the head gaze direction, we constructed a virtual cone, oriented perpendicular to the facial frame. Using this pipeline, we quantified different types of interactive social gaze events, including partner-directed gaze and joint gaze to a shared spatial location. We observed clear effects of sex and familiarity on both interpersonal distance and gaze dynamics in marmoset dyads. Unfamiliar pairs exhibited more stereotyped patterns of arena occupancy, more sustained levels of social gaze across social distance, and increased social gaze monitoring. On the other hand, familiar pairs exhibited higher levels of joint gazes. Moreover, males displayed significantly elevated levels of gazes toward females' faces and the surrounding regions, irrespective of familiarity. Our study reveals the importance of two key social factors in driving the gaze behaviors of a prosocial primate species and lays the groundwork for a rigorous quantification of primate behaviors in naturalistic settings.

Characterizing the structure of mouse behavior using Motion Sequencing

Spontaneous mouse behavior is composed from repeatedly used modules of movement (e.g., rearing, running or grooming) that are flexibly placed into sequences whose content evolves over time. By identifying behavioral modules and the order in which they are expressed, researchers can gain insight into the effect of drugs, genes, context, sensory stimuli and neural activity on natural behavior. Here we present a protocol for performing Motion Sequencing (MoSeq), an ethologically inspired method that uses three-dimensional machine vision and unsupervised machine learning to decompose spontaneous mouse behavior into a series of elemental modules called 'syllables'. This protocol is based upon a MoSeq pipeline that includes modules for depth video acquisition, data preprocessing and modeling, as well as a standardized set of visualization tools. Users are provided with instructions and code for building a MoSeq imaging rig and acquiring three-dimensional video of spontaneous mouse behavior for submission to the modeling framework; the outputs of this protocol include syllable labels for each frame of the video data as well as summary plots describing how often each syllable was used and how syllables transitioned from one to the other. In addition, we provide instructions for analyzing and visualizing the outputs of keypoint-MoSeq, a recently developed variant of MoSeq that can identify behavioral motifs from keypoints identified from standard (rather than depth) video. This protocol and the accompanying pipeline significantly lower the bar for users without extensive computational ethology experience to adopt this unsupervised, data-driven approach to characterize mouse behavior.

X-Tracker: Automated Analysis of Xenopus Tadpole Visual Avoidance Behavior

Xenopus laevis tadpoles exhibit an avoidance behavior when they encounter a moving visual stimulus. A visual avoidance event occurs when a moving object approaches the eye of a free-swimming animal at an approximately 90-degree angle and the animal turns in response to the encounter. Analysis of this behavior requires tracking both the free-swimming animal and the moving visual stimulus both prior to and after the encounter. Previous automated tracking software does not discriminate the moving animal from the moving stimulus, requiring time-consuming manual analysis. Here we present X-Tracker, an automated behavior tracking code that can detect and discriminate moving visual stimuli and free-swimming animals and score encounters and avoidance events. X-Tracker is as accurate as human analysis without the human time commitment. We also present software improvements to our previous visual stimulus presentation and image capture that optimize videos for automated analysis, and hardware improvements that increase the number of animal-stimulus encounters. X-Tracker is a high throughput, unbiased, and significant time-saving analysis system that will greatly facilitate visual avoidance behavior analysis of Xenopus laevis tadpoles, and potentially other free-swimming organisms. The tool is available at https://github.com/ClineLab/Tadpole-Behavior-Automation.

A behavioral analysis system MCFBM enables objective inference of songbirds' attention during social interactions

Understanding animal behavior is crucial in behavioral neuroscience, aiming to unravel the mechanisms driving these behaviors. A significant milestone in this field is the analysis of behavioral reactions during social interactions. Despite their importance in social learning, the behavioral aspects of these interaction are not well understood in detail due to the lack of appropriate tools. We introduce a high-precision, marker-based motion-capture system for analyzing behavior in songbirds, accurately tracking body location and head direction in multiple freely moving finches during social interaction. Focusing on zebra finches, our analysis revealed variations in eye use based on individuals presented. We also observed behavioral changes during virtual and live presentations and a conditioned-learning paradigm. Additionally, the system effectively analyzed social interactions among mice. This system provides an efficient tool for advanced behavioral analysis in small animals and offers an objective method to infer their focus of attention.

Deep learning-based scoring method of the three-chamber social behaviour test in a mouse model of alcohol intoxication. A comparative analysis of DeepLabCut, commercial automatic tracking and manual scoring

Background

Alcohol consumption and withdrawal alter social behaviour in humans in a sex-dependent manner. The three-chamber test is a widely used paradigm to assess rodents' social behaviour, including sociability and social novelty. Automatic tracking systems are commonly used to score time spent with conspecifics, despite failing to score direct interaction time with conspecifics rather than time in the nearby zone. Thereby, the automatically scored results are usually inaccurate and need manual corrections.

New method

New advances in artificial intelligence (AI) have been used recently to analyze complex behaviours. DeepLabCat is a pose-estimation toolkit that allows the tracking of animal body parts. Thus, we used DeepLabCut, to introduce a scoring model of the three-chamber test to investigate alcohol withdrawal effects on social behaviour in mice considering sex and withdrawal periods. We have compared the results of two automatic pose estimation methods: automatic tracking (AnyMaze) and DeepLabCut considering the manual scoring method, the current gold standard.

Results

We have found that the automatic tracking method (AnyMaze) has failed to detect the significance of social deficits in female mice during acute withdrawal. However, tracking the animal's nose using DeepLabCut showed a significant social deficit in agreement with manual scoring. Interestingly, this social deficit was shown only in females during acute and recovered by the protracted withdrawal. DLC and manually scored results showed a higher Spearman correlation coefficient and a lower bias in the Bland-Altman analysis.

Conclusion

our approach helps improve the accuracy of scoring the three-chamber test while outperforming commercial automatic tracking systems.

Unsupervised discovery of family specific vocal usage in the Mongolian gerbil

In nature, animal vocalizations can provide crucial information about identity, including kinship and hierarchy. However, lab-based vocal behavior is typically studied during brief interactions between animals with no prior social relationship, and under environmental conditions with limited ethological relevance. Here, we address this gap by establishing long-term acoustic recordings from Mongolian gerbil families, a core social group that uses an array of sonic and ultrasonic vocalizations. Three separate gerbil families were transferred to an enlarged environment and continuous 20-day audio recordings were obtained. Using a variational autoencoder (VAE) to quantify 583,237 vocalizations, we show that gerbils exhibit a more elaborate vocal repertoire than has been previously reported and that vocal repertoire usage differs significantly by family. By performing gaussian mixture model clustering on the VAE latent space, we show that families preferentially use characteristic sets of vocal clusters and that these usage preferences remain stable over weeks. Furthermore, gerbils displayed family-specific transitions between vocal clusters. Since gerbils live naturally as extended families in complex underground burrows that are adjacent to other families, these results suggest the presence of a vocal dialect which could be exploited by animals to represent kinship. These findings position the Mongolian gerbil as a compelling animal model to study the neural basis of vocal communication and demonstrates the potential for using unsupervised machine learning with uninterrupted acoustic recordings to gain insights into naturalistic animal behavior.

Centering cognitive neuroscience on task demands and generalization

Cognitive neuroscience seeks generalizable theories explaining the relationship between behavioral, physiological and mental states. In pursuit of such theories, we propose a theoretical and empirical framework that centers on understanding task demands and the mutual constraints they impose on behavior and neural activity. Task demands emerge from the interaction between an agent's sensory impressions, goals and behavior, which jointly shape the activity and structure of the nervous system on multiple spatiotemporal scales. Understanding this interaction requires multitask studies that vary more than one experimental component (for example, stimuli and instructions) combined with dense behavioral and neural sampling and explicit testing for generalization across tasks and data modalities. By centering task demands rather than mental processes that tasks are assumed to engage, this framework paves the way for the discovery of new generalizable concepts unconstrained by existing taxonomies, and moves cognitive neuroscience toward an action-oriented, dynamic and integrated view of the brain.

Behavioral neuroscience's inevitable SABV growing pains

The field of rodent behavioral neuroscience is undergoing two major sea changes: an ever-growing technological revolution, and worldwide calls to consider sex as a biological variable (SABV) in experimental design. Both have enormous potential to improve the precision and rigor with which the brain can be studied, but the convergence of these shifts in scientific practice has exposed critical limitations in classic and widely used behavioral paradigms. While our tools have advanced, our behavioral metrics - mostly developed in males and often allowing for only binary outcomes - have not. This opinion article explores how this disconnect has presented challenges for the accurate depiction and interpretation of sex differences in brain function, arguing for the expansion of current behavioral constructs to better account for behavioral diversity.

Comprehensive ethological analysis of fear expression in rats using DeepLabCut and SimBA machine learning model

Introduction

Defensive responses to threat-associated cues are commonly evaluated using conditioned freezing or suppression of operant responding. However, rats display a broad range of behaviors and shift their defensive behaviors based on immediacy of threats and context. This study aimed to systematically quantify the defensive behaviors that are triggered in response to threat-associated cues and assess whether they can accurately be identified using DeepLabCut in conjunction with SimBA.

Methods

We evaluated behavioral responses to fear using the auditory fear conditioning paradigm. Observable behaviors triggered by threat-associated cues were manually scored using Ethovision XT. Subsequently, we investigated the effects of diazepam (0, 0.3, or 1 mg/kg), administered intraperitoneally before fear memory testing, to assess its anxiolytic impact on these behaviors. We then developed a DeepLabCut + SimBA workflow for ethological analysis employing a series of machine learning models. The accuracy of behavior classifications generated by this pipeline was evaluated by comparing its output scores to the manually annotated scores.

Results

Our findings show that, besides conditioned suppression and freezing, rats exhibit heightened risk assessment behaviors, including sniffing, rearing, free-air whisking, and head scanning. We observed that diazepam dose-dependently mitigates these risk-assessment behaviors in both sexes, suggesting a good predictive validity of our readouts. With adequate amount of training data (approximately > 30,000 frames containing such behavior), DeepLabCut + SimBA workflow yields high accuracy with a reasonable transferability to classify well-represented behaviors in a different experimental condition. We also found that maintaining the same condition between training and evaluation data sets is recommended while developing DeepLabCut + SimBA workflow to achieve the highest accuracy.

Discussion

Our findings suggest that an ethological analysis can be used to assess fear learning. With the application of DeepLabCut and SimBA, this approach provides an alternative method to decode ongoing defensive behaviors in both male and female rats for further investigation of fear-related neurobiological underpinnings.

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.

Neurobiology and systems biology of stress resilience

Stress resilience is the phenomenon that some people maintain their mental health despite exposure to adversity or show only temporary impairments followed by quick recovery. Resilience research attempts to unravel the factors and mechanisms that make resilience possible and to harness its insights for the development of preventative interventions in individuals at risk for acquiring stress-related dysfunctions. Biological resilience research has been lagging behind the psychological and social sciences but has seen a massive surge in recent years. At the same time, progress in this field has been hampered by methodological challenges related to finding suitable operationalizations and study designs, replicating findings, and modeling resilience in animals. We embed a review of behavioral, neuroimaging, neurobiological, and systems biological findings in adults in a critical methods discussion. We find preliminary evidence that hippocampus-based pattern separation and prefrontal-based cognitive control functions protect against the development of pathological fears in the aftermath of singular, event-type stressors [as found in fear-related disorders, including simpler forms of posttraumatic stress disorder (PTSD)] by facilitating the perception of safety. Reward system-based pursuit and savoring of positive reinforcers appear to protect against the development of more generalized dysfunctions of the anxious-depressive spectrum resulting from more severe or longer-lasting stressors (as in depression, generalized or comorbid anxiety, or severe PTSD). Links between preserved functioning of these neural systems under stress and neuroplasticity, immunoregulation, gut microbiome composition, and integrity of the gut barrier and the blood-brain barrier are beginning to emerge. On this basis, avenues for biological interventions are pointed out.

Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics

Keypoint tracking algorithms can flexibly quantify animal movement from videos obtained in a wide variety of settings. However, it remains unclear how to parse continuous keypoint data into discrete actions. This challenge is particularly acute because keypoint data are susceptible to high-frequency jitter that clustering algorithms can mistake for transitions between actions. Here we present keypoint-MoSeq, a machine learning-based platform for identifying behavioral modules ('syllables') from keypoint data without human supervision. Keypoint-MoSeq uses a generative model to distinguish keypoint noise from behavior, enabling it to identify syllables whose boundaries correspond to natural sub-second discontinuities in pose dynamics. Keypoint-MoSeq outperforms commonly used alternative clustering methods at identifying these transitions, at capturing correlations between neural activity and behavior and at classifying either solitary or social behaviors in accordance with human annotations. Keypoint-MoSeq also works in multiple species and generalizes beyond the syllable timescale, identifying fast sniff-aligned movements in mice and a spectrum of oscillatory behaviors in fruit flies. Keypoint-MoSeq, therefore, renders accessible the modular structure of behavior through standard video recordings.

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.

Uncovering 2-D toroidal representations in grid cell ensemble activity during 1-D behavior

Minimal experiments, such as head-fixed wheel-running and sleep, offer experimental advantages but restrict the amount of observable behavior, making it difficult to classify functional cell types. Arguably, the grid cell, and its striking periodicity, would not have been discovered without the perspective provided by free behavior in an open environment. Here, we show that by shifting the focus from single neurons to populations, we change the minimal experimental complexity required. We identify grid cell modules and show that the activity covers a similar, stable toroidal state space during wheel running as in open field foraging. Trajectories on grid cell tori correspond to single trial runs in virtual reality and path integration in the dark, and the alignment of the representation rapidly shifts with changes in experimental conditions. Thus, we provide a methodology to discover and study complex internal representations in even the simplest of experiments.

FlyVISTA, an Integrated Machine Learning Platform for Deep Phenotyping of Sleep in Drosophila

Animal behavior depends on internal state. While subtle movements can signify significant changes in internal state, computational methods for analyzing these "microbehaviors" are lacking. Here, we present FlyVISTA, a machine-learning platform to characterize microbehaviors in freely-moving flies, which we use to perform deep phenotyping of sleep. This platform comprises a high-resolution closed-loop video imaging system, coupled with a deep-learning network to annotate 35 body parts, and a computational pipeline to extract behaviors from high-dimensional data. FlyVISTA reveals the distinct spatiotemporal dynamics of sleep-associated microbehaviors in flies. We further show that stimulation of dorsal fan-shaped body neurons induces micromovements, not sleep, whereas activating R5 ring neurons triggers rhythmic proboscis extension followed by persistent sleep. Importantly, we identify a novel microbehavior ("haltere switch") exclusively seen during quiescence that indicates a deeper sleep stage. These findings enable the rigorous analysis of sleep in Drosophila and set the stage for computational analyses of microbehaviors.

SuperAnimal pretrained pose estimation models for behavioral analysis

Quantification of behavior is critical in diverse applications from neuroscience, veterinary medicine to animal conservation. A common key step for behavioral analysis is first extracting relevant keypoints on animals, known as pose estimation. However, reliable inference of poses currently requires domain knowledge and manual labeling effort to build supervised models. We present SuperAnimal, a method to develop unified foundation models that can be used on over 45 species, without additional manual labels. These models show excellent performance across six pose estimation benchmarks. We demonstrate how to fine-tune the models (if needed) on differently labeled data and provide tooling for unsupervised video adaptation to boost performance and decrease jitter across frames. If fine-tuned, SuperAnimal models are 10-100× more data efficient than prior transfer-learning-based approaches. We illustrate the utility of our models in behavioral classification and kinematic analysis. Collectively, we present a data-efficient solution for animal pose estimation.

Measuring the replicability of our own research

In the study of transgenic mouse models of neurodevelopmental and neurodegenerative disorders, we use batteries of tests to measure deficits in behaviour and from the results of these tests, we make inferences about the mental states of the mice that we interpret as deficits in "learning", "memory", "anxiety", "depression", etc. This paper discusses the problems of determining whether a particular transgenic mouse is a valid mouse model of disease X, the problem of background strains, and the question of whether our behavioural tests are measuring what we say they are. The problem of the reliability of results is then discussed: are they replicable between labs and can we replicate our results in our own lab? This involves the study of intra- and inter- experimenter reliability. The variables that influence replicability and the importance of conducting a complete behavioural phenotype: sensory, motor, cognitive and social emotional behaviour are discussed. Then the thorny question of failure to replicate is examined: Is it a curse or a blessing? Finally, the role of failure in research and what it tells us about our research paradigms is examined.