Application of spike sorting algorithm to neuronal signals originated from boron doped diamond micro-electrode arrays

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.

Enhancing electroanalytical performance of porous boron-doped diamond electrodes by increasing thickness for dopamine detection

Novel porous boron-doped diamond (BDD_porous)-based materials have attracted lots of research interest due to their enhanced detection ability and biocompatibility, favouring them for use in neuroscience. This study reports on morphological, spectral, and electrochemical characterisation of three BDD_porous electrodes of different thickness given by a number of deposited layers (2, 3 and 5). These were prepared using microwave plasma-enhanced chemical vapour deposition on SiO₂ nanofiber-based scaffolds. Further, the effect of number of layers and poly-l-lysine coating, commonly employed in neuron cultivation experiments, on sensing properties of the neurotransmitter dopamine in a pH 7.4 phosphate buffer media was investigated. The boron doping level of ∼2 × 10²¹ atoms cm^-3 and increased content of non-diamond (sp²) carbon in electrodes with more layers was evaluated by Raman spectroscopy. Cyclic voltammetric experiments revealed reduced working potential windows (from 2.4 V to 2.2 V), higher double-layer capacitance values (from 405 μF cm^-2 to 1060 μF cm^-2), enhanced rates of electron transfer kinetics and larger effective surface areas (from 5.04 mm² to 7.72 mm²), when the number of porous layers increases. For dopamine, a significant boost in analytical performance was recognized with increasing number of layers using square-wave voltammetry: the highest sensitivity of 574.1 μA μmol^-1 L was achieved on a BDD_porous electrode with five layers and dropped to 35.9 μA μmol^-1 L when the number of layers decreased to two. Consequently, the lowest detection limit of 0.20 μmol L^-1 was obtained on a BDD_porous electrode with five layers. Moreover, on porous electrodes, enhanced selectivity for dopamine detection in the presence of ascorbic acid and uric acid was demonstrated. The application of poly-l-lysine coating on porous electrode surface resulted in a decrease in dopamine peak currents by 17% and 60% for modification times of 1 h and 15 h, respectively. Hence, both examined parameters, the number of deposited porous layers and the presence of poly-l-lysine coating, were proved to considerably affect the characteristics and performance of BDD_porous electrodes.

Non-linear dimensionality reduction on extracellular waveforms reveals cell type diversity in premotor cortex

Cortical circuits are thought to contain a large number of cell types that coordinate to produce behavior. Current in vivo methods rely on clustering of specified features of extracellular waveforms to identify putative cell types, but these capture only a small amount of variation. Here, we develop a new method (WaveMAP) that combines non-linear dimensionality reduction with graph clustering to identify putative cell types. We apply WaveMAP to extracellular waveforms recorded from dorsal premotor cortex of macaque monkeys performing a decision-making task. Using WaveMAP, we robustly establish eight waveform clusters and show that these clusters recapitulate previously identified narrow- and broad-spiking types while revealing previously unknown diversity within these subtypes. The eight clusters exhibited distinct laminar distributions, characteristic firing rate patterns, and decision-related dynamics. Such insights were weaker when using feature-based approaches. WaveMAP therefore provides a more nuanced understanding of the dynamics of cell types in cortical circuits.