Heuristic Spike Sorting Tuner (HSST), a framework to determine optimal parameter selection for a generic spike sorting algorithm

Abstract

Bjånes DA, Fisher LE, Gaunt RA, Weber DJ. Heuristic Spike Sorting Tuner (HSST), a framework to determine optimal parameter selection for a generic spike sorting algorithm. bioRxiv First published May 21, 2020. Extracellular microelectrodes frequently record neural activity from more than one neuron in the vicinity of the electrode. The process of labeling each recorded spike waveform with the identity of its source neuron is called spike sorting and is often approached from an abstracted statistical perspective. However, these approaches do not consider neurophysiological realities and may ignore important features that could improve the accuracy of these methods. Further, standard algorithms typically require selection of at least one free parameter, which can have significant effects on the quality of the output. We describe a Heuristic Spike Sorting Tuner (HSST) that determines the optimal choice of the free parameters for a given spike sorting algorithm based on the neurophysiological qualification of unit isolation and signal discrimination. A set of heuristic metrics are used to score the output of a spike sorting algorithm over a range of free parameters resulting in optimal sorting quality. We demonstrate that these metrics can be used to tune parameters in several spike sorting algorithms. The HSST algorithm shows robustness to variations in signal to noise ratio, number and relative size of units per channel. Moreover, the HSST algorithm is computationally efficient, operates unsupervised, and is parallelizable for batch processing. NEW & NOTEWORTHYHSST incorporates known neurophysiological priors of extracellular neural recordings while simultaneously taking advantage of powerful abstract mathematical tools. Rather than simply selecting free parameters prior to running a sorting algorithm, HSST executes a sorting algorithm across a range of input parameters, using heuristic metrics to detect which spike-sorting output is most physiologically plausible. This novel approach enables unsupervised spike-sorting exceeding the performance of previous methods, thereby enabling the processing of large data sets with confidence.

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

“…Learning-based methods in the pay of automated spike sorting benefit a lot from additional remarks and optimization strategies ( Table 1 ). If artificial neural networks perpetrate clustering, the optimal number of clusters may be estimated by Gap statistics (Tariq et al, 2019 ), and a method called Heuristic Spike Sorting Tuner even helps in selecting spatial or temporal features that ensure precise clustering (Bjånes et al, 2020 ). Regarding ideal input dimensionality, that is to say the number of specific features under analysis, studying four features are mostly sufficient for clustering (Hilgen et al, 2017 ).…”