Fig. 3
From: Machine learning-based model for behavioural analysis in rodents applied to the forced swim test
The model architecture used for behaviour classification. The model is composed of a cascade of 3D convolutions organized in residual blocks to extract the spatio-temporal features of the data. Then, after an average global pooling, a series of dense layers was used to learn non-linear combination of the extracted features. Finally, a three-node softmax layer is used to produce a probability distribution of the possible behaviours label. The behaviour corresponding to the highest probability is the output of the model. We set the kernel size of the 3D convolutions to (3,3,3), the kernel size of the 3D average pooling to (2,2,2), and the dropout value to 0.1. The variable n in the convolutional neural layer is the number of its output channel.
