Automated classification of behavioural and electrophysiological data in Neuroscience

Due to technological advances the amount of data that can be collected in modern science is increasing every day and neuroscience is no exception. Integrating large amounts of data at different spatial and temporal scales is essential for uncovering the underlying mechanisms of the brain but poses also new challenges since drawing conclusions from vast amounts of data is increasingly difficult. New automated and fast analysis methods that can make sense of large and complex data sets are therefore in need and will become increasingly important in the years and decades ahead. This work proposes new tools for the analysis of two important types of data commonly found in neuroscience. The first is behavioural data from rodent navigation tasks in the form of animal movement paths. Two novel classification methods based on machine learning algorithms are proposed here. The methods are able to automatically or semi-automatically reduce the complex movement paths of the animals to a series of stereotypical types of behaviour, leading toboth more detailed and consistent results. The second type of data considered here is electrophysiological data, in the form of extracellular multielectrode array (MEA) recordings which can record the electrical activity of thousands of neurons in parallel over long periods of time. Here a new highly-parallel data processing tool which can reduce the MEA data to a series of spike trains is presented. The tool can serve as basis for more sophisticated analyses like the reconstruction of the individual cell spike trains, for which machine learning methods are again essential. The results presented here show that machine learning algorithms and parallel processing architectures are both fundamental tools for coping with large and complex data sets, like the ones found in modern neuroscience.