Behavioral measurement and evaluation are broadly used to understand brain functions in neuroscience, especially for investigations of movement disorders, social deficits, and mental diseases. Numerous commercial software and open-source programs have been developed for tracking the movement of laboratory animals, allowing animal behavior to be analyzed digitally. In vivo optical imaging and electrophysiological recording in freely behaving animals are now widely used to understand neural functions in circuits. However, it is always a challenge to accurately track the movement of an animal under certain complex conditions due to uneven environment illumination, variations in animal models, and interference from recording devices and experimenters. To overcome these challenges, we have developed a strategy to track the movement of an animal by combining a deep learning technique, the You Only Look Once (YOLO) algorithm, with a background subtraction algorithm, a method we label DeepBhvTracking. In our method, we first train the detector using manually labeled images and a pretrained deep-learning neural network combined with YOLO, then generate bounding boxes of the targets using the trained detector, and finally track the center of the targets by calculating their centroid in the bounding box using background subtraction. Using DeepBhvTracking, the movement of animals can be tracked accurately in complex environments and can be used in different behavior paradigms and for different animal models. Therefore, DeepBhvTracking can be broadly used in studies of neuroscience, medicine, and machine learning algorithms.

Human speech and animal vocalizations are important for social communication and animal survival. Neurons in the auditory pathway are responsive to a range of sounds, from elementary sound features to complex objects such as voices. For social communication, the responses to distinct patterns of vocalization are usually highly specific to an individual conspecific call in some species, including the specificity of sound patterns and embedded biological information. To address this question, we conducted single-unit recordings in the amygdala of awake marmosets and presented calls used in marmoset communication, calls of other species and specific marmoset individuals. We found that some neurons (47/262) in the amygdala distinguished ‘Phee’ calls from vocalizations of other animals and other types of marmoset vocalizations. Interestingly, a subset of Phee-responsive neurons (22/47) also exhibited selectivity to one out of the three Phees from two different ‘caller’ marmosets. Our findings suggest that, while it has traditionally been considered as the key structure in the limbic system, the amygdala also represents a critical stage of socially relevant auditory perceptual processing.

The cerebellum is involved in encoding balance, posture, speed, and gravity during locomotion. However, most studies are carried out on flat surfaces, and little is known about cerebellar activity during free ambulation on slopes. Here, it has been imaged the neuronal activity of cerebellar molecular interneurons (MLIs) and Purkinje cells (PCs) using a miniaturized microscope while a mouse is walking on a slope. It has been found that the neuronal activity of vermal MLIs specifically enhanced during uphill and downhill locomotion. In addition, a subset of MLIs is activated during entire uphill or downhill positions on the slope and is modulated by the slope inclines. In contrast, PCs showed counter-balanced neuronal activity to MLIs, which reduced activity at the ramp peak. So, PCs may represent the ramp environment at the population level. In addition, chemogenetic inactivation of lobule V of the vermis impaired uphill locomotion. These results revealed a novel micro-circuit in the vermal cerebellum that regulates ambulatory behavior in 3D terrains.

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