The Argo: a high channel count recording system for neural recording in vivo.

OBJECTIVE: Decoding neural activity has been limited by the lack of tools available to record from large numbers of neurons across multiple cortical regions simultaneously with high temporal fidelity. To this end, we developed the Argo system to record cortical neural activity at high data rates. APPROACH: Here we demonstrate a massively parallel neural recording system based on platinum-iridium microwire electrode arrays bonded to a CMOS voltage amplifier array. The Argo system is the highest channel count in vivo neural recording system, supporting simultaneous recording from 65 536 channels, sampled at 32 kHz and 12-bit resolution. This system was designed for cortical recordings, compatible with both penetrating and surface microelectrodes. MAIN RESULTS: We validated this system through initial bench testing to determine specific gain and noise characteristics of bonded microwires, followed by in-vivo experiments in both rat and sheep cortex. We recorded spiking activity from 791 neurons in rats and surface local field potential activity from over 30 000 channels in sheep. SIGNIFICANCE: These are the largest channel count microwire-based recordings in both rat and sheep. While currently adapted for head-fixed recording, the microwire-CMOS architecture is well suited for clinical translation. Thus, this demonstration helps pave the way for a future high data rate intracortical implant.

X-ray Photon Counting and Two-Color X-ray Imaging Using Indirect Detection.

In this paper, we report on the design and performance of a 1 cm², 90 × 92-pixel image sensor. It is made X-ray sensitive by the use of a scintillator. Its pixels have a charge packet counting circuit topology with two channels, each realizing a different charge packet size threshold and analog domain event counting. Here, the sensor's performance was measured in setups representative of a medical X-ray environment. Further, two-energy-level photon counting performance is demonstrated, and its capabilities and limitations are documented. We then provide an outlook on future improvements.