Electrode pooling can boost the yield of extracellular recordings with switchable silicon probes

A fully automatic multichannel neural spike sorting algorithm with spike reduction and positional feature

Objective: The sorting of neural spike data recorded by multichannel and high channel neural probes such as Neuropixels, especially in real-time, remains a significant technical challenge. Most neural spike sorting algorithms focus on sorting neural spikes post-hoc for high sorting accuracy-but reducing the processing delay for fast sorting, potentially even live sorting, is generally not possible with these algorithms.Approach: Here we report our Graph nEtwork Multichannel sorting (GEMsort) algorithm, which is largely based on graph network, to allow rapid neural spike sorting for multiple neural recording channels. This was accomplished by two innovations: In GEMsort, duplicated neural spikes recorded from multiple channels were eliminated from duplicate channels by only selecting the highest amplitude neural spike in any channel for subsequent processing. In addition, the channel from which the representative neural spike was recorded was used as an additional feature to differentiate between neural spikes recorded from different neurons having similar temporal features.Main results: Synthetic and experimentally recorded multichannel neural recordings were used to evaluate the sorting performance of GEMsort. The sorting results of GEMsort were also compared with two other state-of-the-art sorting algorithms (Kilosort and Mountainsort) in sorting time and sorting agreements.Significance: GEMsort allows rapidly sort neural spikes and is highly suitable to be implemented with digital circuitry for high processing speed and channel scalability.

A mechanically adaptive hydrogel neural interface based on silk fibroin for high-efficiency neural activity recording

A flexible non-transient electrical platform that can realize bidirectional neural communication from living tissues is of great interest in neuroscience to better understand basic neuroscience and the nondrug therapy of diseases or disorders. The development of soft, biocompatible, and conductive neural interface with mechanical coupling and efficient electrical exchange is a new trend but remains a challenge. Herein, we designed a multifunctional neural electrical communication platform in the form of a mechanically compliant, electrically conductive, and biocompatible hydrogel electrode. Silk fibroin (SF) obtained from Bombyx Mori cocoons was compounded with aldehyde-hyaluronic acid (HA-CHO) with a dynamic network to delay or interrupt the β-sheet-induced hardening of SF chains, resulting in the fabrication of a hydrogel matrix that is mechanically matched to biological tissues. Moreover, the incorporation of functionalized carbon nanotubes (CNTs) facilitated interaction and dispersion and enabled the formation of a hydrogel electrode with a high-current percolation network, thus contributing toward improving the electrical properties in terms of conductivity, impedance, and charge storage capabilities. These advances allow high-efficiency stimulation and the recording of neural signals during in vivo implantation. Overall, a wide range of animal experiments demonstrate that the platform exhibits minimal foreign body responses, thus showing it to be a promising electrophysiology interface for potential applications in neuroscience.

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

The meaning behind neural single unit activity has constantly been a challenge, so it will persist in the foreseeable future. As one of the most sourced strategies, detecting neural activity in high-resolution neural sensor recordings and then attributing them to their corresponding source neurons correctly, namely the process of spike sorting, has been prevailing so far. Support from ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information and abundance in clustering procedures turned spike sorting into an indispensable tool in electrophysiological analysis. This review attempts to illustrate that in all stages of spike sorting algorithms, the past 5 years innovations' brought about concepts, results, and questions worth sharing with even the non-expert user community. By thoroughly inspecting latest innovations in the field of neural sensors, recording procedures, and various spike sorting strategies, a skeletonization of relevant knowledge lays here, with an initiative to get one step closer to the original objective: deciphering and building in the sense of neural transcript.