Semi-supervised K-Means clustering for trajectory analysis in behavioural experiments

Behavioural neuroscience uses a variety of animals to model human diseases, test novel drugs and study how certain factors affect or alter natural behaviour. In its arsenal, it contains a number of different experimental procedures involving navigation and locomotion tasks inside constrained environments and a number of analysis techniques to draw conclusions about various aspects of neuroscience such as the development of learning and memory. With the advancements in technology and the rise of artificial intelligence in many areas of our society, machine learning algorithms and applications are commonly used to draw, with limited user interaction and in a speedy manner, as much intelligence as possible from collections of data. Machine learning has greatly boosted behavioural neuroscience research but in many cases it provides experiment-specific analysis methods requiring domain knowledge in order to be used. This work addresses the first limitation of experiment-specific analysis methods by bringing an integration of common metrics used in different experimental procedures involving path analysis. For the second limitation it proposes a machine learning agnostic framework for data analysis in a common experimental procedure called Morris Water Maze which can also be used to other experiments involving behavioural categorisation tasks. In addition, it proposes a novel machine learning method for detailed analysis of locomotion that can be applied to any navigation task for both automatic categorisation and pattern recognition tasks. Other objectives of this study are to present detailed benchmarks of machine learning techniques that can be used for data analytics in behavioural neuroscience and to expand the usability of the methods it presents by making them easy to use by the research community. For this reason, all the source codes of the presented algorithms and pipelines is publicly available and, when applicable, graphical user interfaces or software tools have been engineered to help executing them.