Over the past years, numerous methods have emerged to automate the quantification of animal behavior at a resolution not previously imaginable. This has opened up a new field of computational ethology and will, in the near future, make it possible to quantify in near completeness what an animal is doing as it navigates its environment. The importance of improving the techniques with which we characterize behavior is reflected in the emerging recognition that understanding behavior is an essential (or even prerequisite) step to pursuing neuroscience questions. The use of these methods, however, is not limited to studying behavior in the wild or in strictly ethological settings. Modern tools for behavioral quantification can be applied to the full gamut of approaches that have historically been used to link brain to behavior, from psychophysics to cognitive tasks, augmenting those measurements with rich descriptions of how animals navigate those tasks. Here we review recent technical advances in quantifying behavior, particularly in methods for tracking animal motion and characterizing the structure of those dynamics. We discuss open challenges that remain for behavioral quantification and highlight promising future directions, with a strong emphasis on emerging approaches in deep learning, the core technology that has enabled the markedly rapid pace of progress of this field. We then discuss how quantitative descriptions of behavior can be leveraged to connect brain activity with animal movements, with the ultimate goal of resolving the relationship between neural circuits, cognitive processes and behavior.

Quantifying behavior to understand the brain.

The rhesus macaque is an important model species in several branches of science, including neuroscience, psychology, ethology, and medicine. The utility of the macaque model would be greatly enhanced by the ability to precisely measure behavior in freely moving conditions. Existing approaches do not provide sufficient tracking. Here, we describe OpenMonkeyStudio, a deep learning-based markerless motion capture system for estimating 3D pose in freely moving macaques in large unconstrained environments. Our system makes use of 62 machine vision cameras that encircle an open 2.45 m × 2.45 m × 2.75 m enclosure. The resulting multiview image streams allow for data augmentation via 3D-reconstruction of annotated images to train a robust view-invariant deep neural network. This view invariance represents an important advance over previous markerless 2D tracking approaches, and allows fully automatic pose inference on unconstrained natural motion. We show that OpenMonkeyStudio can be used to accurately recognize actions and track social interactions. The rhesus macaque is an important model species in several branches of science, but the utility of this model would be enhanced by the ability to measure behaviour throughout pose. Here, the authors describe a deep learning-based markerless motion capture system for estimating 3D pose in freely moving macaques.

/pdf/automated-markerless-pose-estimation-in-freely-moving-m58mes2wnm.pdf

Automated markerless pose estimation in freely moving macaques with OpenMonkeyStudio.

Signal propagation in complex networks

Medial and orbital frontal cortex in decision-making and flexible behavior

The neuroscience community has witnessed a tremendous expansion of glia research. Glial cells are now on center stage with leading roles in the development, maturation, and physiology of brain circuits. Over the course of evolution, glia have highly diversified and include the radial glia, astroglia or astrocytes, microglia, oligodendrocytes, and ependymal cells, each having dedicated functions in the brain. The zebrafish, a small teleost fish, is no exception to this and recent evidences point to evolutionarily conserved roles for glia in the development and physiology of its nervous system. Due to its small size, transparency, and genetic amenability, the zebrafish has become an increasingly prominent animal model for brain research. It has enabled the study of neural circuits from individual cells to entire brains, with a precision unmatched in other vertebrate models. Moreover, its high neurogenic and regenerative potential has attracted a lot of attention from the research community focusing on neural stem cells and neurodegenerative diseases. Hence, studies using zebrafish have the potential to provide fundamental insights about brain development and function, and also elucidate neural and molecular mechanisms of neurological diseases. We will discuss here recent discoveries on the diverse roles of radial glia and astroglia in neurogenesis, in modulating neuronal activity and in regulating brain homeostasis at the brain barriers. By comparing insights made in various animal models, particularly mammals and zebrafish, our goal is to highlight the similarities and differences in glia biology among species, which could set new paradigms relevant to humans.

Radial glia in the zebrafish brain: Functional, structural, and physiological comparison with the mammalian glia

Internal state dynamics shape brain-wide activity and foraging behavior

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Internal state dynamics shape brain-wide activity and foraging behavior