  The Morris Water Maze is commonly used in behavioural neuroscience for the
study of spatial learning with rodents. Over the years, various methods of
analysing rodent data collected in this task have been proposed. These methods
span from classical performance measurements (e.g. escape latency, rodent
speed, quadrant preference) to more sophisticated methods of categorisation
which classify the animal swimming path into behavioural classes known as
strategies. Classification techniques provide additional insight in relation to
the actual animal behaviours but still only a limited amount of studies utilise
them mainly because they highly depend on machine learning knowledge. We have
previously demonstrated that the animals implement various strategies and by
classifying whole trajectories can lead to the loss of important information.
In this work, we developed a generalised and robust classification methodology
which implements majority voting to boost the classification performance and
successfully nullify the need of manual tuning. Based on this framework, we
built a complete software, capable of performing the full analysis described in
this paper. The software provides an easy to use graphical user interface (GUI)
through which users can enter their trajectory data, segment and label them and
finally generate reports and figures of the results.
