Measuring Behavior
Organiser: Loes Ottink (Noldus Information Technology, The Netherlands) and Lucas Noldus (Radboud University, Nijmegen, The Netherlands and Noldus Information Technology, The Netherlands).
Description:
As advances in AI development and deployment are rapidly evolving, the implementations in behavioral neuroscience are increasing as well. Behavioral research in laboratory animals such as rodents, flies and zebrafish has benefited a lot from developments in machine learning and computer vision algorithms and models and their performance. Accurate analysis of behavior is important in research areas such as neuroscience, psychology and pharmacology. It is essential in for instance preclinical research to investigate treatment efficacy in animal models for diseases and disorders, and for other advancements in biomedical and genetic research. Nevertheless, robust video tracking, pose estimation and automatic recognition of complex behaviors, especially in group settings, still remain challenges that the field is confronted with.
Robust video tracking and pose estimation are prerequisites for accurate behavior recognition in laboratory animals, especially in social and other complex behaviors, and in semi-natural environments or cages with occluding objects. Deep learning algorithms and computer vision are being deployed for body point detection, pose estimation and tracking over time, and subsequently, AI models are trained to recognize behaviors from this data. Automating tracking and behavior recognition reduces the role of human labeling of behavior in videos, making it less time-consuming. Furthermore, it decreases variation between researchers and in annotations of ambiguous behaviors, leading to more reliable results. These methods face challenges when involving for instance semi-natural environments or situations with a lot of social interaction. However, with the rapid rise of AI, machine learning methods for overcoming such challenges are advancing as well.
This symposium addresses challenges in video tracking, pose estimation and behavior recognition in laboratory animals. It presents AI and computer vision developments of the past couple of years and promising advances in the near future, focusing on machine learning methods and implementations for behavioral researchers.
