Kavli Affiliate: Jia Liu
| First 5 Authors: Hao Zhao, Hao Zhao, , ,
| Summary:
Estimating neuron location from extracellular recordings is essential for
developing advanced brain-machine interfaces. Accurate neuron localization
improves spike sorting, which involves detecting action potentials and
assigning them to individual neurons. It also helps monitor probe drift, which
affects long-term probe reliability. Although several localization algorithms
are currently in use, the field is nascent and arguments for using one
algorithm over another are largely theoretical or based on visual analysis of
clustering results. We present a first-of-its-kind benchmarking of commonly
used neuron localization algorithms. We tested these algorithms using two
ground truth datasets: a biophysically realistic simulated dataset, and
experimental data combining patch-clamp and Neuropixels probes. We
systematically evaluate the accuracy, robustness, and runtime of these
algorithms in ideal conditions and long-term recording conditions with
electrode decay. Our findings highlight significant performance differences;
while more complex and physically realistic models perform better in ideal
situations, simple heuristics demonstrate superior robustness to noise and
electrode degradation in experimental datasets, making them more suitable for
long-term neural recordings. This work provides a framework for assessing
localization algorithms and developing robust, biologically grounded algorithms
to advance the development of brain-machine interfaces.
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