Spike sorting biases and information loss in a detailed cortical model

Abstract

Sorting action potentials (spikes) from extracellular recordings of large groups of connected neurons is essential to understanding brain function. Simulations with known spike times have driven significant advances in spike sorting, but present models do not account for neuronal heterogeneity and its effect on sorting accuracy. Here, we used a large-scale detailed cortical microcircuit model to simulate recordings, evaluate modern spike sorters, and link their performance to neuronal heterogeneity. We also exposed the network to various stimuli to investigate how sorting errors affect stimulus discrimination. Spike sorters successfully isolated about 10% of neurons within 50 {micro}m of the electrode shank. This undersampling had no impact on stimulus discrimination ability. However, sorting biases related to firing rate, spike extent, synaptic type, and layer reduced its discrimination ability by nearly half. These findings show realistic models are a complementary method to evaluate and improve spike sorting and, hence, improve our understanding of neural activity.