image classification hidden markov model matlab

Recent technological advances, such as miniaturized imaging and electrophysiological devices, have enabled the recording of neural activity in freely behaving mice ( Remedios et al., 2017 Li et al., 2017 Falkner et al., 2020)-however, to make sense of the recorded neural activity, it is also necessary to obtain a detailed characterization of the animals’ actions during recording. Gaining insight into brain systems that control these behaviors requires recording and manipulating neural activity while measuring behavior in freely moving animals. The brain evolved to guide survival-related behaviors, which frequently involve interaction with other animals.

Together, MARS and BENTO provide an end-to-end pipeline for behavior data extraction and analysis in a package that is user-friendly and easily modifiable. Finally, we introduce the Behavior Ensemble and Neural Trajectory Observatory (BENTO), a graphical user interface for analysis of multimodal neuroscience datasets. We also release the pose and annotation datasets used to train MARS to serve as community benchmarks and resources. We compare MARS’s annotations to human annotations and find that MARS’s pose estimation and behavior classification achieve human-level performance. We introduce the Mouse Action Recognition System (MARS), an automated pipeline for pose estimation and behavior quantification in pairs of freely interacting mice. However, automatically and accurately classifying complex social behaviors remains technically challenging. Recent advances in computer vision enable tracking the pose (posture) of freely behaving animals. This is generally done through manual annotation-a highly time-consuming and tedious process. The study of naturalistic social behavior requires quantification of animals’ interactions.

Image classification hidden markov model matlab