Finding Ensembles of Neurons in Spike Trains by Non-linear Mapping and Statistical Testing

Finding ensembles in neural spike trains has been a vital task in neurobiology ever since D.O. Hebb's work on synaptic plasticity [15]. However, with recent advancements in multi-electrode technology, which provides means to record 100 and more spike trains simultaneously, classical ensemble detection methods became infeasible due to a combinatorial explosion and a lack of reliable statistics. To overcome this problem we developed an approach that reorders the spike trains (neurons) based on pairwise distances and Sammon's mapping to one dimension. Thus, potential ensemble neurons are placed close to each other. As a consequence we can reduce the number of statistical tests considerably over enumeration-based approaches (like e.g. [1]), since linear traversals of the neurons suffice, and thus can achieve much lower rates of falsepositives. This approach is superior to classical frequent item set mining algorithms, especially if the data itself is imperfect, e.g. if only a fraction of the items in a considered set is part of a transaction.