Spike separation based on symmetries analysis in phase space

Abstract

The present study introduces an approach for automatic classification of extracellularly recorded action potentials of neurons based on geometrical approach. Neuronal spikes are considered as geometrical objects, namely trajectories in phase space. It is shown that for spikes, generated by the same neuron, it is possible to find such a symmetry transformation under which their trajectories are invariant in phase space. On the other hand, the phase trajectories of spikes generated by other neurons change significantly under the action of that transformation. Thus, it is possible to define a special symmetry transformation that only typifies the spikes of the given neuron. The proposed algorithm is explained and an overview of the mathematical background is given. The method was tested on simulated data and showed good results in real experiments.