A Neural Network-Based Spike Sorting Feature Map That Resolves Spike Overlap in the Feature Space
When inserting an electrode array in the brain, its electrodes will record so-called 'spikes' which are generated by the neurons in the neighbourhood of the array. Spike sorting is the process of detecting and assigning these recorded spikes to their putative neurons. Many spike sorting pipelines rely on a clustering algorithm that groups the spikes coming from the same neuron in a pre-defined feature space. However, classical spike sorting algorithms fail when spike overlap, i.e., the near-simultaneous occurrence of two or more spikes from different neurons, is present in the recording. In such cases, the overlapping spikes segment ends up in a seemingly random position in the feature space and is not assigned to the correct cluster. This problem has been addressed before by extending the sorting algorithm with a template matching post-processor. In this work, a novel approach is presented to resolve spike overlap directly in the feature space. To this end, a neural network feature map is presented, that generates spike embeddings (feature vectors) that behave as a linear superposition in the feature space in the case of spike overlap. Its performance is quantified on semi-synthetic data obtained through a data augmentation procedure applied to real neural recordings.