@article {69009,
	title = {Low-latency extracellular spike assignment for high-density electrodes at single-neuron resolution},
	journal = {bioRxiv},
	year = {2023},
	month = {2023 Sep 15},
	abstract = {<p>Real-time neural signal processing is essential for brain-machine interfaces and closed-loop neuronal perturbations. However, most existing applications sacrifice cell-specific identity and temporal spiking information for speed. We developed a hybrid hardware-software system that utilizes a Field Programmable Gate Array (FPGA) chip to acquire and process data in parallel, enabling individual spikes from many simultaneously recorded neurons to be assigned single-neuron identities with 1-millisecond latency. The FPGA assigns labels, validated with ground-truth data, by comparing multichannel spike waveforms from tetrode or silicon probe recordings to a spike-sorted model generated offline in software. This platform allowed us to rapidly inactivate a region in vivo based on spikes from an upstream neuron before these spikes could excite the downstream region. Furthermore, we could decode animal location within 3 ms using data from a population of individual hippocampal neurons. These results demonstrate our system{\textquoteright}s suitability for a broad spectrum of research and clinical applications.</p>
},
	doi = {10.1101/2023.09.14.557854},
	url = {https://doi.org/10.1101/2023.09.14.557854},
	author = {Chongxi Lai and Dohoung Kim and Brian Lustig and Shinsuke Tanaka and Brian Barbarits and Lakshmi Narayan and Jennifer Colonell and Ole Paulsen and Albert K. Lee and Timothy D. Harris}
}
