Wingel, K. | Search bioRxiv

bioRxiv2026

Scalable hardware and software for high-density, multi-regional electrophysiology in non-human primates

Understanding how distributed neural circuits generate complex behaviors requires long-duration recordings from populations of individual neurons across multiple brain regions. High-density Neuropixels probes have revolutionized rodent systems neuroscience. Extending these capabilities to non-human primates (NHPs) for chronic, multi-regional recordings requires compact, implantable microdrive hardware and reliable automated spike-sorting software. NHPs provide access to complex and flexible behaviors that are difficult to study in other model systems. Extending the Neuropixels revolution to NHPs therefore depends on integrating hardware and software for long-duration, multi-regional recordings. Here, we present a scalable hardware-software platform co-designed for high-density neural data acquisition and reliable single-unit identification in NHPs. The Multi-regional Implantable NeuroDrive (MIND) is a lightweight, implantable microdrive that provides stable recordings from multiple independently positioned Neuropixels probes and is designed to support chronic, ambulatory, head-free recordings. We paired MIND with UnitAssay, an automated post-sorting curation framework that identifies and curates characteristic spike-sorting errors with minimal computational cost. Together, MIND and UnitAssay provide an integrated platform for implanted, multi-regional recordings in NHPs performing complex and naturalistic behaviors. We tested MIND and UnitAssay across parietal, prefrontal, and motor cortices in rhesus macaques performing reaching and eye-movement tasks. MIND enabled stable multi-probe recordings across cortical regions and durations. UnitAssay enabled automated curation of large-scale spike-sorting output and improved reliable single-unit identification relative to Kilosort 4 labels and standard SpikeInterface quality control. Together, MIND and UnitAssay provide a scalable solution for high-density, multi-regional, long-duration electrophysiology in NHPs.

neuroscience↗

bioRxiv2020

Optimal Adaptive Electrode Selection to Maximize Simultaneously Recorded Neuron Yield

Neural-Matrix style, high-density electrode arrays for brain-machine interfaces (BMIs) and neuroscientific research require the use of multiplexing: Each recording channel can be routed to one of several electrode sites on the array. This capability allows the user to flexibly distribute recording channels to the locations where the most desirable neural signals can be resolved. For example, in the Neuropixel probe, 960 electrodes can be addressed by 384 recording channels. However, currently no adaptive methods exist to use recorded neural data to optimize/customize the electrode selections per recording context. Here, we present an algorithm called classification-based selection (CBS) that optimizes the joint electrode selections for all recording channels so as to maximize isolation quality of detected neurons. We show, in experiments using Neuropixels in non-human primates, that this algorithm yields a similar number of isolated neurons as would be obtained if all electrodes were recorded simultaneously. Neuron counts were 41-85% improved over previously published electrode selection strategies. The neurons isolated from electrodes selected by CBS were a 73% match, by spike timing, to the complete set of recordable neurons around the probe. The electrodes selected by CBS exhibited higher average per-recording-channel signal-to-noise ratio. CBS, and selection optimization in general, could play an important role in development of neurotechnologies for BMI, as signal bandwidth becomes an increasingly limiting factor. Code and experimental data have been made available1.

neuroscience↗