Abstract
The zebra finch is a superb natural animal model to study cognition and as such could contribute to the further understanding of nicotinic intervention therapies for patients suffering from cognitive impairment as observed in neurodegenerative disorders. Manual analysis of data produced by this model is extremely labour intensive, error-prone, and typically takes weeks to complete. We designed data acquisition methods, selected analysis algorithms, and developed software to efficiently and accurately automate the detection and classification of song production (cognitive functioning) and locomotor activity (physical condition). Our custom-designed software accurately classifies song and locomotor activities. After classification, the reduced data sets can be further analysed with popular tools, such as 'R'.
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