Of Mice and Mates: Automated Classification and Modelling of Mouse Behaviour in Groups using a Single Model across Cages

TL;DR

The novel Global Behaviour Model (GBM) is presented, which summarises the joint behaviour of groups of mice across cages, using a permutation matrix to match the mouse identities in each cage to the model.

Abstract

Behavioural experiments often happen in specialised arenas, but this may confound the analysis. To address this issue, we provide tools to study mice in the home-cage environment, equipping biologists with the possibility to capture the temporal aspect of the individual's behaviour and model the interaction and interdependence between cage-mates with minimal human intervention. Our main contribution is the novel Group Behaviour Model (GBM) which summarises the joint behaviour of groups of mice across cages, using a permutation matrix to match the mouse identities in each cage to the model. In support of the above, we also (a) developed the Activity Labelling Module (ALM) to automatically classify mouse behaviour from video, and (b) released two datasets, ABODe for training behaviour classifiers and IMADGE for modelling behaviour.

Figures (14)

• Figure 1: An example video frame from our data, showing the raw video (left) and an enhanced visual (right) using CLAHE MISC_038. In the latter, the hopper is marked in yellow and the water spout in purple, while the (rfid) mouse positions are projected into image space and overlaid as red, green and blue dots.
• Figure 2: The for classifying observability and behaviour per mouse. The input signal comes from three modalities: (i) coarse position (), (ii) identified (using the as implemented in VL_083) and (iii) video frames. An oc (iv) determines whether the mouse is observable and its behaviour can be classified. If this is the case, then the bc (v) is activated to generate a probability distribution over behaviours for the mouse. Further architectural details appear in the text.
• Figure 3: Graphical representation of our gbm. '$\times$' refers to standard matrix multiplication. To reduce clutter, the model is not shown unrolled in time.
• Figure 4: ROC curves for various architectures of the evaluated on the Validation split. Each coloured line shows the rate against the rate for various operating thresholds: the 'default' 0.5 threshold in each case is marked with a cross '$\times$'. The baseline (worst-case) model is shown as a dotted line.
• Figure 5: Behaviour confusion matrix of the End-to-End model as a Hinton plot. The area of each square represents the numerical value, and each row is normalized to sum to 1.