Neuropixels Data-Acquisition System: A Scalable Platform for Parallel Recording of 10 000+ Electrophysiological Signals

Although CMOS fabrication has enabled a quick evolution in the design of high-density neural probes and neural-recording chips, the scaling and miniaturization of the complete data-acquisition systems has happened at a slower pace. This is mainly due to the complexity and the many requirements that change depending on the specific experimental settings. In essence, the fundamental challenge of a neural-recording system is getting the signals describing the largest possible set of neurons out of the brain and down to data storage for analysis. This requires a complete system optimization that considers the physical, electrical, thermal and signal-processing requirements, while accounting for available technology, manufacturing constraints and budget. Here we present a scalable and open-standards-based open-source data-acquisition system capable of recording from over 10,000 channels of raw neural data simultaneously. The components and their interfaces have been optimized to ensure robustness and minimum invasiveness in small-rodent electrophysiology.

[1]  Srinjoy Mitra,et al.  A Neural Probe With Up to 966 Electrodes and Up to 384 Configurable Channels in 0.13 $\mu$m SOI CMOS , 2017, IEEE Transactions on Biomedical Circuits and Systems.

[2]  Henrik Jeldtoft Jensen,et al.  Self-Organized Criticality: Emergent Complex Behavior in Physical and Biological Systems , 1998 .

[3]  Refet Firat Yazicioglu,et al.  Time Multiplexed Active Neural Probe with 1356 Parallel Recording Sites , 2017, 2016 46th European Solid-State Device Research Conference (ESSDERC).

[4]  Karl Deisseroth,et al.  Next-generation probes, particles, and proteins for neural interfacing , 2017, Science Advances.

[5]  Hassan Sepehrian,et al.  A Survey of Neural Front End Amplifiers and Their Requirements toward Practical Neural Interfaces , 2014 .

[6]  Yiannos Manoli,et al.  Fully Immersible Subcortical Neural Probes With Modular Architecture and a Delta-Sigma ADC Integrated Under Each Electrode for Parallel Readout of 144 Recording Sites , 2018, IEEE Journal of Solid-State Circuits.

[7]  G. Buzsáki,et al.  Tools for probing local circuits: high-density silicon probes combined with optogenetics , 2015, Neuron.

[8]  W. Reichert Indwelling Neural Implants : Strategies for Contending with the In Vivo Environment , 2007 .

[9]  A. Sayed Herbawi,et al.  High-density CMOS neural probe implementing a hierarchical addressing scheme for 1600 recording sites and 32 output channels , 2017, 2017 19th International Conference on Solid-State Sensors, Actuators and Microsystems (TRANSDUCERS).

[10]  G. Lazzi,et al.  Thermal effects of bioimplants , 2005, IEEE Engineering in Medicine and Biology Magazine.

[11]  Edward S. Boyden,et al.  A direct-to-drive neural data acquisition system , 2015, Front. Neural Circuits.

[12]  Gian Nicola Angotzi,et al.  SiNAPS: An implantable active pixel sensor CMOS-probe for simultaneous large-scale neural recordings. , 2019, Biosensors & bioelectronics.

[13]  Konrad P Kording,et al.  How advances in neural recording affect data analysis , 2011, Nature Neuroscience.

[14]  Sergey L. Gratiy,et al.  Fully integrated silicon probes for high-density recording of neural activity , 2017, Nature.

[15]  Shay Ohayon,et al.  Open Ephys: an open-source, plugin-based platform for multichannel electrophysiology , 2017, Journal of neural engineering.

[16]  S. Herculano‐Houzel The Human Brain in Numbers: A Linearly Scaled-up Primate Brain , 2009, Front. Hum. Neurosci..

[17]  Nicholas A. Steinmetz,et al.  Spontaneous behaviors drive multidimensional, brainwide activity , 2019, Science.

[18]  Eran Stark,et al.  Large-scale, high-density (up to 512 channels) recording of local circuits in behaving animals. , 2014, Journal of neurophysiology.

[19]  John A Rogers,et al.  Recent Advances in Materials, Devices, and Systems for Neural Interfaces , 2018, Advanced materials.

[20]  Nicholas A. Steinmetz,et al.  Distinct contributions of mouse cortical areas to visual discrimination , 2018 .

[21]  Jihyun Cho,et al.  Toward 1024-channel parallel neural recording: Modular Δ-ΔΣ analog front-end architecture with 4.84fJ/C-s·mm2 energy-area product , 2015, 2015 Symposium on VLSI Circuits (VLSI Circuits).

[22]  R. Normann,et al.  Thermal Impact of an Active 3-D Microelectrode Array Implanted in the Brain , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[23]  Sergey L. Gratiy,et al.  Real-time spike sorting platform for high-density extracellular probes with ground-truth validation and drift correction , 2017, bioRxiv.

[24]  Song Luan,et al.  Compact standalone platform for neural recording with real-time spike sorting and data logging. , 2018, Journal of neural engineering.

[25]  Jun Liu,et al.  512-Channel and 13-Region Simultaneous Recordings Coupled with Optogenetic Manipulation in Freely Behaving Mice , 2016, Front. Syst. Neurosci..

[26]  Kenneth D. Harris,et al.  High-dimensional geometry of population responses in visual cortex , 2019, Nat..

[27]  Yu-Wei Wu,et al.  Massively parallel microwire arrays integrated with CMOS chips for neural recording , 2019, Science Advances.

[28]  Refet Firat Yazicioglu,et al.  An Implantable 455-Active-Electrode 52-Channel CMOS Neural Probe , 2014, IEEE Journal of Solid-State Circuits.

[29]  T. Blanche,et al.  Polytrodes: high-density silicon electrode arrays for large-scale multiunit recording. , 2005, Journal of neurophysiology.

[30]  Nitish V. Thakor,et al.  Implantable neurotechnologies: a review of integrated circuit neural amplifiers , 2015, Medical & Biological Engineering & Computing.

[31]  Matteo Carandini,et al.  Kilosort: realtime spike-sorting for extracellular electrophysiology with hundreds of channels , 2016, bioRxiv.

[32]  Kenneth D Harris,et al.  Distinct Structure of Cortical Population Activity on Fast and Infraslow Timescales , 2018, bioRxiv.