Quantitative analysis of animal behaviour is a requirement to understand the task solving strategies of animals and the\r\nunderlying control mechanisms. The identification of repeatedly occurring behavioural components is thereby a key\r\nelement of a structured quantitative description. However, the complexity of most behaviours makes the identification of\r\nsuch behavioural components a challenging problem. We propose an automatic and objective approach for determining\r\nand evaluating prototypical behavioural components. Behavioural prototypes are identified using clustering algorithms and\r\nfinally evaluated with respect to their ability to represent the whole behavioural data set. The prototypes allow for a\r\nmeaningful segmentation of behavioural sequences. We applied our clustering approach to identify prototypical\r\nmovements of the head of blowflies during cruising flight. The results confirm the previously established saccadic gaze\r\nstrategy by the set of prototypes being divided into either predominantly translational or rotational movements,\r\nrespectively. The prototypes reveal additional details about the saccadic and intersaccadic flight sections that could not be\r\nunravelled so far. Successful application of the proposed approach to behavioural data shows its ability to automatically\r\nidentify prototypical behavioural components within a large and noisy database and to evaluate these with respect to their\r\nquality and stability. Hence, this approach might be applied to a broad range of behavioural and neural data obtained from\r\ndifferent animals and in different contexts.
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