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Large-scale neural recordings with high spatial and temporal accuracy are instrumental to understand how the brain works. To this end, it is of key importance to develop probes that can be conveniently scaled up to a high number of recording channels. Despite recent achievements in complementary metal-oxide semiconductor (CMOS) multi-electrode arrays probes, in current circuit architectures an increase in the number of simultaneously recording channels would significantly increase the total chip area. A promising approach for overcoming this scaling issue consists in the use of the modular Active Pixel Sensor (APS) concept, in which a small front-end circuit is located beneath each electrode. However, this approach imposes challenging constraints on the area of the in-pixel circuit, power consumption and noise. Here, we present an APS CMOS-probe technology for Simultaneous Neural recording that successfully addresses all these issues for whole-array read-outs at 25 kHz/channel from up to 1024 electrode-pixels. To assess the circuit performances, we realized in a 0.18 μm CMOS technology an implantable single-shaft probe with a regular array of 512 electrode-pixels with a pitch of 28 μm. Extensive bench tests showed an in-pixel gain of 45.4 ± 0.4 dB (low pass, F = 4 kHz), an input referred noise of 7.5 ± 0.67 μV (300 Hz to 7.5 kHz) and a power consumption <6 μW/pixel. In vivo acute recordings demonstrate that our SiNAPS CMOS-probe can sample full-band bioelectrical signals from each electrode, with the ability to resolve and discriminate activity from several packed neurons both at the spatial and temporal scale. These results pave the way to new generations of compact and scalable active single/multi-shaft brain recording systems.
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http://dx.doi.org/10.1016/j.bios.2018.10.032 | DOI Listing |
Biosens Bioelectron
September 2025
Microtechnology for Neuroelectronics Unit (NetS(3) lab), Fondazione Istituto Italiano di Tecnologia, Genova, Italy.
Achieving stable and continuous monitoring of signals of numerous single neurons in the brain faces the conflicting challenge of increasing the microelectrode count while minimizing cross-sectional shank dimensions to reduce tissue damage, foreign-body-reaction and maintain signal quality. Passive probes need to route each microelectrode individually to external electronics, thus increasing shank size and tissue-damage as the number of electrodes grows. Active complementary metal-oxide-semiconductor (CMOS) probes overcome the limitation in electrode count and density with on-probe frontend, addressing and multiplexing circuits, but current probes have relatively large shank widths of 70 - 100 μm.
View Article and Find Full Text PDFThe brain is a metabolically demanding organ as it orchestrates and stabilizes neuronal network activity through plasticity. This mechanism imposes enormous and prolonged energetic demands at synapses, yet it is unclear how these needs are met in a sustained manner. Mitochondria serve as a local energy supply for dendritic spines, providing instant and sustained energy during synaptic plasticity.
View Article and Find Full Text PDFAdv Healthc Mater
September 2025
School of Chemistry and Chemical Engineering, Guangdong Provincial Key Lab of Green Chemical Product Technology, South China University of Technology, Guangzhou, 510640, P. R. China.
Photonic crystal (PC) hydrogel-based sensors with visual signal outputs have attracted attention for wearable motion monitoring, but current devices suffer from low spatial resolution, small-scale design, and poor signal consistency. Herein, we present a combined and scalable PC sensing platform that includes a single-point sensor (100 × 400 mm) and an 8 × 8 multipixel array (100 × 100 mm) for dual-mode visual-electrical feedback. The array achieves 2D strain mapping on the arm with a spatial resolution of 0.
View Article and Find Full Text PDFbioRxiv
August 2025
Department of Biology, McGill University, Montreal, Quebec, Canada.
Quantitative phenotyping of is essential across numerous fields, yet data extraction remains a significant analytical bottleneck. Traditional segmentation methods, typically reliant on pixel-intensity thresholding, are highly sensitive to variations in imaging conditions and often fail in the presence of noise, overlaps, or uneven illumination. These failures necessitate meticulous experimental setups, expensive hardware, or extensive manual curation, which reduces throughput and introduces bias.
View Article and Find Full Text PDFQuant Imaging Med Surg
September 2025
Institute for Surgical Pathology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Background: Images obtained from stimulated Raman scattering can be used to identify histomorphologically relevant information intraoperatively. In order to leverage deep learning algorithms for distinguishing tumoral and non-tumoral tissue, data preprocessing remains a crucial task and may affect the classification performance. To date, the effect of different preprocessing techniques on deep learning algorithm performance is unclear.
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