Real-time spike sorting with 3D neural probe and triangulation localization

Abstract

A key challenge in correlating neuronal activity with brain function is the limited sampling probability of neuronal activity in real time. It is crucial to increase the sampling probability substantially and in real time. We hypothesized that 3-dimensional (3D) neural probes offer faster and stronger prospects for cell yield than 2D electrode arrays. We simulated a 1000-neuron neuronal network, mimicking the granular layer of the barrel cortical column, recorded signals from inserted 384 electrodes (organized in 3D or 2D), and sorted units using Kilosrt or triangulation localization. We demonstrated that 3D electrode arrays converge more space for triangulation spike sorting than 2D probes do. 3D neural probes, together with triangulation, could isolate up to 80% of the simulated 1000 neurons (as ground truth) and have a cell yield of up to 5, which is, to the best of our knowledge, significantly higher than standard 2D electrodes with Kilosort or triangulation. With a signal-to-noise ratio (SNR) of 10, which is close to the real world, the simulation data suggest that 3D electrode arrays in a face-centric cubic (FCC) arrangement provide a better cell yield. However, larger background noise (e.g. an SNR of 1, which can be improved with lower electrode impedance) has a stronger impact on the triangulation spike sorting. Since only the peak value of spikes are required for triangulation localization, the computing loading is much less than spike waveform-based spike sorting approach. Thus, combining 3D electrode arrays with triangulation localization is ideal for real-time spike sorting. Thus, we demonstrated that adding one more dimension in designing neural probes can dramatically increase cell yield and speed up isolating neuronal unit activity. We, for the first time, provide a tool for utilizing computer simulations to optimize the design of neural electrode arrays before time-consuming probe fabrication.