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Analysis of Parallel Spike Trains with Clustering Methods
| Type of publication: | Mastersthesis |
| Citation: | |
| Year: | 2012 |
| Month: | June |
| School: | Otto von Guericke University Magdeburg, School of Computer Science, Department of Knowledge and Language Processing |
| Address: | Universitätsplatz 2, 39106 Magdeburg |
| Abstract: | In neurobiology one of the greatest challenges is to explain higher order brain functioning. For this it is necessary to know how the brain encodes information such as a given stimulus. In his seminal work Donald Hebb introduced the notion of cell assemblies/ensembles that, presented with the same stimulus, will react in the same way. This leads to the hypothesis that neurons processing the same information should somehow behave similarly by showing activity (spikes) at the same time. This temporal coincidence hypothesis will be in the focus of this work where we present a way to find a metric space representation of the recorded spike trains such that standard clustering methods become applicable. The resulting clustering, or – more precisely – the labels assigned to each data point by the clustering algorithm, can then be used as a binary classifier that either labels neurons as belonging to a present ensemble or not. Using this, different clustering algorithms can be evaluated on respective measures of goodness of fit. Especially the misclassification rate (i.e. the number of false positive and false negative classifications) is of interest here. We show that our method still gives us favourable results, even if we have to deal with temporal jitter and selective participation. |
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| Added by: | [] |
| Total mark: | 0 |
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