Benchmarking spike source localization algorithms in high density probes
Fig 2
SSL algorithm performance with no electrode degradation.
A: Visualization of template and spike localization estimates on simulated dataset (MEArec). The estimated spike locations are represented by translucent dot clouds, color-coded by their corresponding neuron. The estimated template location and true neuron locations are represented by solid red and green dots, respectively, and are not color-coded by neuron. The electrode array (shown excluding the “dead” electrodes to illustrate electrode degradation) is denoted by light gray dots. B: Visualization of template and spike localization estimates on experimental dataset (SPE-1). C: Performance metrics on simulated dataset, for both templates (left) and spikes (right). Performance is assessed as (i) percentage of estimates within 30µm of true locations (accuracy), (ii) speed of algorithm on all templates/spikes (runtime), and (iii) Euclidean distance between estimates and true location (median localization error and violin plot of individual localization errors). Bars represent mean metric across multiple simulations; error bars represent standard deviation of metric. Statistical significance was assessed using one-way ANOVA followed by Tukey’s HSD post-hoc tests; all metrics exhibited significant differences (ANOVA p < 0.05). D. Performance metrics on experimental dataset, for both templates (left) and spikes (right). Performance is assessed using accuracy and runtime; since we have lower resolution of true locations in experimental data, we use a higher tolerance for accuracy (deemed correct if estimate within 50µm of estimated true location). We additionally do not include runtime, as the experimental neurons were recorded across different recording sessions (we concatenate onto one probe in Fig 2B for presentation purposes), which does not allow for localization algorithm to amortize certain overhead costs across neurons. Bars represent metric across all experimental data.