Deep learning-assisted comparative analysis of animal trajectories with DeepHL

A multidimensional approach to the self in non-human animals through the Pattern Theory of Self

In the last decades, research on animal consciousness has advanced significantly, fueled by interdisciplinary contributions. However, a critical dimension of animal experience remains underexplored: the self. While traditionally linked to human studies, research focused on the self in animals has often been framed dichotomously, distinguishing low-level, bodily, and affective aspects from high-level, cognitive, and conceptual dimensions. Emerging evidence suggests a broader spectrum of self-related features across species, yet current theoretical approaches often reduce the self to a derivative aspect of consciousness or prioritize narrow high-level dimensions, such as self-recognition or metacognition. To address this gap, we propose an integrated framework grounded in the Pattern Theory of Self (PTS). PTS conceptualizes the self as a dynamic, multidimensional construct arising from a matrix of dimensions, ranging from bodily and affective to intersubjective and normative aspects. We propose adopting this multidimensional perspective for the study of the self in animals, by emphasizing the graded nature of the self within each dimension and the non-hierarchical organization across dimensions. In this sense, PTS may accommodate both inter- and intra-species variability, enabling researchers to investigate the self across diverse organisms without relying on anthropocentric biases. We propose that, by integrating this framework with insights from comparative psychology, neuroscience, and ethology, the application of PTS to animals can show how the self emerges in varying degrees and forms, shaped by ecological niches and adaptive demands.

A new data-driven paradigm for the study of avian migratory navigation

Avian navigation has fascinated researchers for many years. Yet, despite a vast amount of literature on the topic it remains a mystery how birds are able to find their way across long distances while relying only on cues available locally and reacting to those cues on the fly. Navigation is multi-modal, in that birds may use different cues at different times as a response to environmental conditions they find themselves in. It also operates at different spatial and temporal scales, where different strategies may be used at different parts of the journey. This multi-modal and multi-scale nature of navigation has however been challenging to study, since it would require long-term tracking data along with contemporaneous and co-located information on environmental cues. In this paper we propose a new alternative data-driven paradigm to the study of avian navigation. That is, instead of taking a traditional theory-based approach based on posing a research question and then collecting data to study navigation, we propose a data-driven approach, where large amounts of data, not purposedly collected for a specific question, are analysed to identify as-yet-unknown patterns in behaviour. Current technological developments have led to large data collections of both animal tracking data and environmental data, which are openly available to scientists. These open data, combined with a data-driven exploratory approach using data mining, machine learning and artificial intelligence methods, can support identification of unexpected patterns during migration, and lead to a better understanding of multi-modal navigational decision-making across different spatial and temporal scales.

Improving Ocean Management Using Insights from Space

Advancements in space-based ocean observation and computational data processing techniques have demonstrated transformative value for managing living resources, biodiversity, and ecosystems of the ocean. We synthesize advancements in leveraging satellite-derived insights to better understand and manage fishing, an emerging revolution of marine industrialization, ocean hazards, sea surface dynamics, benthic ecosystems, wildlife via electronic tracking, and direct observations of ocean megafauna. We consider how diverse space-based data sources can be better coupled to modernize and improve ocean management. We also highlight examples of how data from space can be developed into tools that can aid marine decision-makers managing subjects from whales to algae. Thoughtful and prospective engagement with such technologies from those inside and outside the marine remote sensing community is, however, essential to ensure that these tools meet their full potential to strengthen the effectiveness of ocean management.

A comparison of machine learning methods for quantifying self-grooming behavior in mice

Background

As machine learning technology continues to advance and the need for standardized behavioral quantification grows, commercial and open-source automated behavioral analysis tools are gaining prominence in behavioral neuroscience. We present a comparative analysis of three behavioral analysis pipelines-DeepLabCut (DLC) and Simple Behavioral Analysis (SimBA), HomeCageScan (HCS), and manual scoring-in measuring repetitive self-grooming among mice.

Methods

Grooming behavior of mice was recorded at baseline and after water spray or restraint treatments. Videos were processed and analyzed in parallel using 3 methods (DLC/SimBA, HCS, and manual scoring), quantifying both total number of grooming bouts and total grooming duration.

Results

Both treatment conditions (water spray and restraint) resulted in significant elevation in both total grooming duration and number of grooming bouts. HCS measures of grooming duration were significantly elevated relative to those derived from manual scoring: specifically, HCS tended to overestimate duration at low levels of grooming. DLC/SimBA duration measurements were not significantly different than those derived from manual scoring. However, both SimBA and HCS measures of the number of grooming bouts were significantly different than those derived from manual scoring; the magnitude and direction of the difference depended on treatment condition.

Conclusion

DLC/SimBA provides a high-throughput pipeline for quantifying grooming duration that correlates well with manual scoring. However, grooming bout data derived from both DLC/SimBA and HCS did not reliably estimate measures obtained via manual scoring.

Ponds as experimental arenas for studying animal movement: current research and future prospects

Animal movement is a multifaceted process that occurs for multiple reasons with powerful consequences for food web and ecosystem dynamics. New paradigms and technical innovations have recently pervaded the field, providing increasingly powerful means to deliver fine-scale movement data, attracting renewed interest. Specifically in the aquatic environment, tracking with acoustic telemetry now provides integral spatiotemporal information to follow individual movements in the wild. Yet, this technology also holds great promise for experimental studies, enhancing our ability to truly establish cause-and-effect relationships. Here, we argue that ponds with well-defined borders (i.e. "islands in a sea of land") are particularly well suited for this purpose. To support our argument, we also discuss recent experiences from studies conducted in an innovative experimental infrastructure, composed of replicated ponds equipped with modern aquatic telemetry systems that allow for unparalleled insights into the movement patterns of individual animals.

Advancements in Genomic and Behavioral Neuroscience Analysis for the Study of Normal and Pathological Brain Function

Psychiatric and neurological disorders are influenced by an undetermined number of genes and molecular pathways that may differ among afflicted individuals. Functionally testing and characterizing biological systems is essential to discovering the interrelationship among candidate genes and understanding the neurobiology of behavior. Recent advancements in genetic, genomic, and behavioral approaches are revolutionizing modern neuroscience. Although these tools are often used separately for independent experiments, combining these areas of research will provide a viable avenue for multidimensional studies on the brain. Herein we will briefly review some of the available tools that have been developed for characterizing novel cellular and animal models of human disease. A major challenge will be openly sharing resources and datasets to effectively integrate seemingly disparate types of information and how these systems impact human disorders. However, as these emerging technologies continue to be developed and adopted by the scientific community, they will bring about unprecedented opportunities in our understanding of molecular neuroscience and behavior.

Autism-associated protein POGZ controls ESCs and ESC neural induction by association with esBAF

Background

The POGZ gene has been found frequently mutated in neurodevelopmental disorders (NDDs), particularly autism spectrum disorder (ASD) and intellectual disability (ID). However, little is known about its roles in embryonic stem cells (ESCs), neural development and diseases.

Methods

We generated Pogz−/− ESCs and directed ESC differentiation toward a neural fate. We performed biochemistry, ChIP-seq, ATAC-seq, and bioinformatics analyses to understand the role of POGZ.

Results

We show that POGZ is required for the maintenance of ESC identity and the up-regulation of neural genes during ESC differentiation toward a neural fate. Genome-wide binding analysis shows that POGZ is primarily localized to gene promoter and enhancer regions. POGZ functions as both a transcriptional activator and repressor, and its loss leads to deregulation of differentiation genes, including neural genes. POGZ physically associates with the SWI-SNF (esBAF) chromatin remodeler complex, and together they modulate enhancer activities via epigenetic modifications such as chromatin remodeling and histone modification. During ESC neural induction, POGZ-mediated recruitment of esBAF/BRG1 and H3K27ac are important for proper expression of neural progenitor genes.

Limitations

The genotype and allele relevant to human neurodevelopmental disorders is heterozygous loss of function. This work is designed to study the effects of loss of POGZ function on ESCs and during ESC neural induction. Also, this work lacks of in vivo validation using animal models.

Conclusions

The data suggest that POGZ is both a transcription factor and a genome regulator, and its loss leads to defects in neural induction and neurogenesis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13229-022-00502-9.

Deep inference of seabird dives from GPS-only records: Performance and generalization properties

At-sea behaviour of seabirds have received significant attention in ecology over the last decades as it is a key process in the ecology and fate of these populations. It is also, through the position of top predator that these species often occupy, a relevant and integrative indicator of the dynamics of the marine ecosystems they rely on. Seabird trajectories are recorded through the deployment of GPS, and a variety of statistical approaches have been tested to infer probable behaviours from these location data. Recently, deep learning tools have shown promising results for the segmentation and classification of animal behaviour from trajectory data. Yet, these approaches have not been widely used and investigation is still needed to identify optimal network architecture and to demonstrate their generalization properties. From a database of about 300 foraging trajectories derived from GPS data deployed simultaneously with pressure sensors for the identification of dives, this work has benchmarked deep neural network architectures trained in a supervised manner for the prediction of dives from trajectory data. It first confirms that deep learning allows better dive prediction than usual methods such as Hidden Markov Models. It also demonstrates the generalization properties of the trained networks for inferring dives distribution for seabirds from other colonies and ecosystems. In particular, convolutional networks trained on Peruvian boobies from a specific colony show great ability to predict dives of boobies from other colonies and from distinct ecosystems. We further investigate accross-species generalization using a transfer learning strategy known as ‘fine-tuning’. Starting from a convolutional network pre-trained on Guanay cormorant data reduced by two the size of the dataset needed to accurately predict dives in a tropical booby from Brazil. We believe that the networks trained in this study will provide relevant starting point for future fine-tuning works for seabird trajectory segmentation.

Over the last decades, the use of miniaturized electronic devices enabled the tracking of many wide-ranging animal species. The deployment of GPS has notably informed on migratory, habitat and foraging strategies of numerous seabird species. A key challenge in movement ecology is to identify specific behavioural patterns (e.g. travelling, resting, foraging) through the observed movement data. In this work, we address the inference of seabird diving behaviour from GPS data using deep learning methods. We demonstrate the performance of deep networks to accurately identify movement patterns from GPS data over state-of-the-art tools, and we illustrate their great accross-species generalization properties (i.e. the ability to generalize prediction from one seabird species to aother). Our results further supports the relevance of deep learning schemes as ‘ready-to-use’ tools which could be used by ecologists to segmentate animal trajectories on new (small) datasets, including when these datasets do not include groundtruthed labelled data for a supervised training.

Big-data approaches lead to an increased understanding of the ecology of animal movement

Understanding animal movement is essential to elucidate how animals interact, survive, and thrive in a changing world. Recent technological advances in data collection and management have transformed our understanding of animal "movement ecology" (the integrated study of organismal movement), creating a big-data discipline that benefits from rapid, cost-effective generation of large amounts of data on movements of animals in the wild. These high-throughput wildlife tracking systems now allow more thorough investigation of variation among individuals and species across space and time, the nature of biological interactions, and behavioral responses to the environment. Movement ecology is rapidly expanding scientific frontiers through large interdisciplinary and collaborative frameworks, providing improved opportunities for conservation and insights into the movements of wild animals, and their causes and consequences.

A predictive flight-altitude model for avoiding future conflicts between an emblematic raptor and wind energy development in the Swiss Alps

Deployment of wind energy is proposed as a mechanism to reduce greenhouse gas emissions. Yet, wind energy and large birds, notably soaring raptors, both depend on suitable wind conditions. Conflicts in airspace use may thus arise due to the risks of collisions of birds with the blades of wind turbines. Using locations of GPS-tagged bearded vultures, a rare scavenging raptor reintroduced into the Alps, we built a spatially explicit model to predict potential areas of conflict with future wind turbine deployments in the Swiss Alps. We modelled the probability of bearded vultures flying within or below the rotor-swept zone of wind turbines as a function of wind and environmental conditions, including food supply. Seventy-four per cent of the GPS positions were collected below 200 m above ground level, i.e. where collisions could occur if wind turbines were present. Flight activity at potential risk of collision is concentrated on south-exposed mountainsides, especially in areas where ibex carcasses have a high occurrence probability, with critical areas covering vast expanses throughout the Swiss Alps. Our model provides a spatially explicit decision tool that will guide authorities and energy companies for planning the deployment of wind farms in a proactive manner to reduce risk to emblematic Alpine wildlife.

Beyond Single Discrete Responses: An Integrative and Multidimensional Analysis of Behavioral Dynamics Assisted by Machine Learning

Understanding behavioral systems as emergent systems comprising the environment and organism subsystems, include spatial dynamics as a primary dimension in natural settings. Nevertheless, under the standard approaches, the experimental analysis of behavior is based on the single response paradigm and the temporal distribution of discrete responses. Thus, the continuous analysis of spatial behavioral dynamics is a scarcely studied field. The technological advancements in computer vision have opened new methodological perspectives for the continuous sensing of spatial behavior. With the application of such advancements, recent studies suggest that there are multiple features embedded in the spatial dynamics of behavior, such as entropy, and that they are affected by programmed stimuli (e.g., schedules of reinforcement) at least as much as features related to discrete responses. Despite the progress, the characterization of behavioral systems is still segmented, and integrated data analysis and representations between discrete responses and continuous spatial behavior are exiguous in the experimental analysis of behavior. Machine learning advancements, such as t-distributed stochastic neighbor embedding and variable ranking, provide invaluable tools to crystallize an integrated approach for analyzing and representing multidimensional behavioral data. Under this rationale, the present work (1) proposes a multidisciplinary approach for the integrative and multilevel analysis of behavioral systems, (2) provides sensitive behavioral measures based on spatial dynamics and helpful data representations to study behavioral systems, and (3) reveals behavioral aspects usually ignored under the standard approaches in the experimental analysis of behavior. To exemplify and evaluate our approach, the spatial dynamics embedded in phenomena relevant to behavioral science, namely, water-seeking behavior and motivational operations, are examined, showing aspects of behavioral systems hidden until now.

Cross-species behavior analysis with attention-based domain-adversarial deep neural networks

Since the variables inherent to various diseases cannot be controlled directly in humans, behavioral dysfunctions have been examined in model organisms, leading to better understanding their underlying mechanisms. However, because the spatial and temporal scales of animal locomotion vary widely among species, conventional statistical analyses cannot be used to discover knowledge from the locomotion data. We propose a procedure to automatically discover locomotion features shared among animal species by means of domain-adversarial deep neural networks. Our neural network is equipped with a function which explains the meaning of segments of locomotion where the cross-species features are hidden by incorporating an attention mechanism into the neural network, regarded as a black box. It enables us to formulate a human-interpretable rule about the cross-species locomotion feature and validate it using statistical tests. We demonstrate the versatility of this procedure by identifying locomotion features shared across different species with dopamine deficiency, namely humans, mice, and worms, despite their evolutionary differences.

Learning and Animal Movement

Integrating diverse concepts from animal behavior, movement ecology, and machine learning, we develop an overview of the ecology of learning and animal movement. Learning-based movement is clearly relevant to ecological problems, but the subject is rooted firmly in psychology, including a distinct terminology. We contrast this psychological origin of learning with the task-oriented perspective on learning that has emerged from the field of machine learning. We review conceptual frameworks that characterize the role of learning in movement, discuss emerging trends, and summarize recent developments in the analysis of movement data. We also discuss the relative advantages of different modeling approaches for exploring the learning-movement interface. We explore in depth how individual and social modalities of learning can matter to the ecology of animal movement, and highlight how diverse kinds of field studies, ranging from translocation efforts to manipulative experiments, can provide critical insight into the learning process in animal movement.

Unraveling hidden interactions in complex systems with deep learning

Rich phenomena from complex systems have long intrigued researchers, and yet modeling system micro-dynamics and inferring the forms of interaction remain challenging for conventional data-driven approaches, being generally established by scientists with human ingenuity. In this study, we propose AgentNet, a model-free data-driven framework consisting of deep neural networks to reveal and analyze the hidden interactions in complex systems from observed data alone. AgentNet utilizes a graph attention network with novel variable-wise attention to model the interaction between individual agents, and employs various encoders and decoders that can be selectively applied to any desired system. Our model successfully captured a wide variety of simulated complex systems, namely cellular automata (discrete), the Vicsek model (continuous), and active Ornstein-Uhlenbeck particles (non-Markovian) in which, notably, AgentNet's visualized attention values coincided with the true variable-wise interaction strengths and exhibited collective behavior that was absent in the training data. A demonstration with empirical data from a flock of birds showed that AgentNet could identify hidden interaction ranges exhibited by real birds, which cannot be detected by conventional velocity correlation analysis. We expect our framework to open a novel path to investigating complex systems and to provide insight into general process-driven modeling.