Low-latency extracellular spike assignment for high-density electrodes at single-neuron resolution

Abstract

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 systems suitability for a broad spectrum of research and clinical applications.