In this study, we developed and validated a single-shank silicon-based neural probe with 128 closely-packed microelectrodes suitable for high-resolution extracellular recordings. The 8-mm-long, 100-µm-wide and 50-µm-thick implantable shank of the probe fabricated using a 0.13-µm complementary metal-oxide-semiconductor (CMOS) metallization technology contains square-shaped (20 × 20 µm), low-impedance (~ 50 kΩ at 1 kHz) recording sites made of rough and porous titanium nitride which are arranged in a 32 × 4 dense array with an inter-electrode pitch of 22.5 µm. The electrophysiological performance of the probe was tested in in vivo experiments by implanting it acutely into neocortical areas of anesthetized animals (rats, mice and cats). We recorded local field potentials, single- and multi-unit activity with superior quality from all layers of the neocortex of the three animal models, even after reusing the probe in multiple (> 10) experiments. The low-impedance electrodes monitored spiking activity with high signal-to-noise ratio; the peak-to-peak amplitude of extracellularly recorded action potentials of well-separable neurons ranged from 0.1 mV up to 1.1 mV. The high spatial sampling of neuronal activity made it possible to detect action potentials of the same neuron on multiple, adjacent recording sites, allowing a more reliable single unit isolation and the investigation of the spatiotemporal dynamics of extracellular action potential waveforms in greater detail. Moreover, the probe was developed with the specific goal to use it as a tool for the validation of electrophysiological data recorded with high-channel-count, high-density neural probes comprising integrated CMOS circuitry.
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http://dx.doi.org/10.1016/j.bios.2018.01.060 | DOI Listing |
Commun Eng
July 2024
EPIC, Large Area Thin-film Transistor Electronics, imec, Kapeldreef 75, 3001, Leuven, Belgium.
Spiking neural network algorithms require fine-tuned neuromorphic hardware to increase their effectiveness. Such hardware, mainly digital, is typically built on mature silicon nodes. Future artificial intelligence applications will demand the execution of tasks with increasing complexity and over timescales spanning several decades.
View Article and Find Full Text PDFAdv Mater
December 2024
State Key Laboratory of Silicon and Advanced Semiconductor Materials, Cyrus Tang Center for Sensor Materials and Applications, School of Materials Science and Engineering, Zhejiang University, Hangzhou, 310058, China.
2D-layered materials are recognized as up-and-coming candidates to overcome the intrinsic physical limitation of silicon-based devices. Herein, the coexistence of positive persistent photoconductivity (PPPC) and negative persistent photoconductivity (NPPC) in SnSe thin films prepared by pulsed laser deposition provides an excellent avenue for engineering novel devices. It is determined that surface oxygen is co-regulated by physisorption and chemisorption, and the NPPC is attributed to the photo-controllable oxygen desorption behavior.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
December 2024
Department of Materials Science and Institute of Optoelectronics, Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception, Fudan University, Shanghai 200438, China.
Continuous monitoring and closed-loop therapy of soft wound tissues is of particular interest in biomedical research and clinical practices. An important focus is on the development of implantable bioelectronics that can measure time-dependent temperature distribution related to localized inflammation over large areas of wound and offer in situ treatment. Existing approaches such as thermometers/thermocouples provide limited spatial resolution, inapplicable to a wearable/implantable format.
View Article and Find Full Text PDFAdv Sci (Weinh)
November 2024
Department of Materials Science and Engineering, Stanford University, 350 Jane Stanford Way, Stanford, CA, 94305, USA.
Silicon-based microelectronics can scalably record and modulate neural activity at high spatiotemporal resolution, but their planar form factor poses challenges in targeting 3D neural structures. A method for fabricating tissue-penetrating 3D microelectrodes directly onto planar microelectronics using high-resolution 3D printing via 2-photon polymerization and scalable microfabrication technologies are presented. This approach enables customizable electrode shape, height, and positioning for precise targeting of neuron populations distributed in 3D.
View Article and Find Full Text PDFOptical computing has become an important way to achieve low power consumption and high computation speed. Optical neural network (ONN) is one of the key branches of optical computing due to its wide range of applications. However, the integrated ONN schemes proposed in previous works have some disadvantages, such as fixed network structure, complex matrix-vector multiplication (MVM) unit, and few all-optical nonlinear activation function (NAF) methods.
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