DLATA: Deep Learning-Assisted transformation alignment of 2D brain slice histology.

Accurate alignment of brain slices is crucial for the classification of neuron populations by brain region, and for quantitative analysis in in vitro brain studies. Current semi-automated alignment workflows require labor intensive labeling of feature points on each slice image, which is time-consuming. To speed up the process in large-scale studies, we propose a method called Deep Learning-Assisted Transformation Alignment (DLATA), which uses deep learning to automatically identify feature points in images after training on a few labeled samples. DLATA only requires approximately 10% of the sample size of other semi-automated alignment workflows. Following feature point recognition, local weighted mean method is used as a geometrical transformation to align slice images for registration, achieving better results with about 4 fewer pixels of error than other semi-automated alignment workflows. DLATA can be retrained and successfully applied to the alignment of other biological tissue slices with different stains, including the typically challenging fluorescent stains. Reference codes and trained models for Nissl-stained coronal brain slices of mice can be found at https://github.com/ALIGNMENT2023/DLATA.

Mice learn from the predator-attack experience to accelerate flight behavior via optimizing the strategy of environment exploration.

Efficiently avoiding predators is critical for animal survival. However, little is known about how the experience of predator attack affects behaviors in predator defense. Here, we caught mice by tail to simulate a predator attack. We found that the experienced mice accelerated the flight in response to the visual threaten cue. Single predator attack didn't induce anxiety but increased the activity of innate fear or learning related nucleus. The predator attack induced acceleration of flight was partly rescued when we used drug to block protein synthesis which is critical in the learning process. The experienced mice significantly reduced the focused exploration on the floor during the environment exploration, which might facilitate the discovery of predator. These results suggest that mice could learn from the experience of predator attack to optimize their behavioral pattern to detect the predator cue immediately and response intensely, and therefore increase the probability of survival.

An iterative neural processing sequence orchestrates feeding.

Feeding requires sophisticated orchestration of neural processes to satiate appetite in natural, capricious settings. However, the complementary roles of discrete neural populations in orchestrating distinct behaviors and motivations throughout the feeding process are largely unknown. Here, we delineate the behavioral repertoire of mice by developing a machine-learning-assisted behavior tracking system and show that feeding is fragmented and divergent motivations for food consumption or environment exploration compete throughout the feeding process. An iterative activation sequence of agouti-related peptide (AgRP)-expressing neurons in arcuate (ARC) nucleus, GABAergic neurons in the lateral hypothalamus (LH), and in dorsal raphe (DR) orchestrate the preparation, initiation, and maintenance of feeding segments, respectively, via the resolution of motivational conflicts. The iterative neural processing sequence underlying the competition of divergent motivations further suggests a general rule for optimizing goal-directed behaviors.

Morphological and distributional properties of SMI-32 immunoreactive ganglion cells in the rat retina.

SMI-32 is widely used to identify entire populations of alpha retinal ganglion cells (RGCs), and several SMI-32+ RGC subsets have been studied thoroughly in rodents. However, due to the thick cover of SMI-32+ neurofilaments, the morphology of SMI-32+ RGCs in the central retinal region is obscured and rarely described. Moreover, SMI-32 labels more than one morphological RGC type and the full morphological characteristics and distribution of SMI-32+ RGCs have yet to be discovered. Here, using intracellular neurobiotin injections combined with SMI-32 antibody staining, we investigated morphological and distributional properties of the entire SMI-32+ RGCs population in the rat retina. We found that SMI-32+ RGCs were evenly distributed throughout the rat retina. We compared the morphological features of SMI-32+ ON and OFF cells in the central, middle, and peripheral retinal regions. We found that SMI-32+ RGCs in different regions have distinct characteristics, such as the soma area and the dendritic field area, and Sholl analysis of ON cells and OFF cells revealed significant differences between each region. We classified SMI-32+ RGCs into five clusters based on morphological features and found that a majority of SMI-32+ RGCs belong to alpha-like cells; however, a small proportion of SMI-32+ RGCs had small soma and small dendritic fields. Together, we present a full description of the morphology and distribution of SMI-32 immunoreactive RGCs in the rat retina.

An Infrared Touch System for Automatic Behavior Monitoring.

Key requirements of successful animal behavior research in the laboratory are robustness, objectivity, and high throughput, which apply to both the recording and analysis of behavior. Many automatic methods of monitoring animal behavior meet these requirements. However, they usually depend on high-performing hardware and sophisticated software, which may be expensive. Here, we describe an automatic infrared behavior-monitor (AIBM) system based on an infrared touchscreen frame. Using this, animal positions can be recorded and used for further behavioral analysis by any PC supporting touch events. This system detects animal behavior in real time and gives closed-loop feedback using relatively low computing resources and simple algorithms. The AIBM system automatically records and analyzes multiple types of animal behavior in a highly efficient, unbiased, and low-cost manner.