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Lei, T. C.

Publications and source records attributed to Lei, T. C..

2 recordsLinked to original sources

Sapphire-Based Optrode for Low Noise Neural Recording and Optogenetic Manipulation

Electrophysiological recordings of neurons in deep brain regions using optogenetic stimulation are essential to understanding and regulating the role of complex neural activity in biological behavior and cognitive function. Optogenetic techniques have significantly advanced neuroscience research by enabling the optical manipulation of neural activities. Because of the significance of the technique, constant advancements in implantable optrodes that integrate optical stimulation with low-noise, large-scale electrophysiological recording are in demand to improve the spatiotemporal resolution for various experimental designs and future clinical applications. However, robust and easy-to-use neural optrodes that integrate neural recording arrays with high-intensity light emitting diodes (LEDs) are still lacking. Here, we propose a neural optrode based on Gallium Nitride (GaN) on sapphire technology, which integrates a high-intensity blue LED with a 5x2 recording array monolithically for simultaneous neural recording and optogenetic manipulation. To reduce the noise interference between the recording electrodes and the LED, which is in close physical proximity, three metal grounding interlayers were incorporated within the optrode, and their ability to reduce LED-induced artifacts during neural recording was confirmed through both electromagnetic simulations and experimental demonstrations. The capability of the sapphire optrode to record action potentials has been demonstrated by recording the firing of mitral/tuft cells in the olfactory bulbs of mice in vivo. Additionally, the elevation of action potential firing due to optogenetic stimulation observed using the sapphire probe in medial superior olive (MSO) neurons of the gerbil auditory brainstem confirms the capability of this sapphire optrode to precisely access neural activities in deep brain regions under complex experimental designs.

neuroscience↗

Multichannel neural spike sorting with spike reduction and positional feature

Sorting neural voltages measured from a multichannel neural probe to extract the single unit activities of neuronal firing, especially in real-time, remains a significant technical challenge, largely due to the large amount of acquired data and the technical difficulties involved in processing and classifying these neural spikes promptly. Most neural spike sorting algorithms focus on sorting neural spikes post hoc for high sorting accuracy, and reducing the processing time generally is not the chief concern. Here we report on two signal processing modifications to our previously developed single-channel real-time spike sorting (Enhanced Growing Neural Gas) algorithm, which is largely based on graph network. Duplicated neural spikes were eliminated and represented by the neural spike with the strongest signal profile, significantly reducing the amount of neural data to be processed. In addition, the channel from which the representing neural spike was recorded was used as an additional feature to differentiate between neural spikes recorded from different neurons having similar temporal features. With these two modifications, the Graph nEtwork Multichannel (GEMsort) neural spike sorting algorithm can rapidly sort neural spikes without requiring significant computer processing power and system memory storage. The parallel processing architecture of GEMsort is particularly suitable for digital hardware implementation to improve processing speed and recording channel scalability. Multichannel synthetic neural spikes and actual neural recordings with Neuropixels probes were used to evaluate the sorting accuracies of the GEMsort algorithm.

neuroscience↗