Optimal inter-electrode distances for maximizing single unit yield per electrode in neural recordings

Journal article

State-of-the-art high-density multielectrode arrays enable the recording of simultaneous spiking activity from hundreds of neurons. Although significant efforts have been dedicated to enhancing neural recording devices and developing more efficient sorting algorithms, there has been relatively less focus on the allocation of microelectrodes–a factor that undeniably affects spike sorting effectiveness and ultimately the total number of detected neurons. Here, we systematically examined the relationship between optimal electrode spacing and spike sorting efficiency by creating virtual sparser layouts from high-density recordings through spatial downsampling. We assessed spike sorting performance by comparing the quantity of well-isolated single units per electrode in sparse configurations across various brain regions (neocortex and thalamus), species (rat, mouse, and human) and various spike-sorting algorithms. Enabling the theoretical estimation of optimal electrode arrangements, we complement experimental results with a geometrical modeling framework. Contrary to the general assumption that higher electrode density inherently leads to more efficient sorting, both our theoretical and experimental results reveal a clear optimum for electrode spacing specific to species and regions. We demonstrate that carefully choosing optimal electrode distances could yield a total of 1.7–3.75 times increase in spike sorting efficiency. These findings emphasize the necessity of species- and region-specific microelectrode design optimization.

Authors: Meszéna, D; Fadel, W; Tóth, R; Paulk, AC; Cash, SS

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer Nature [academic journals on nature.com]
• Journal: Microsystems & Nanoengineering
• Volume: 12
• Issue: 1
• Article number: 41
• Publication date: 2026-01-26
• Acceptance date: 2025-11-09
• DOI: 10.1038/s41378-025-01115-x
• EISSN: 2055-7434
• ISSN: 2096-1030
• Language: English