神经形态工程学
仿真
生物相容性材料
材料科学
遗忘
计算
基质(化学分析)
计算机科学
纳米技术
生物医学工程
复合材料
人工智能
人工神经网络
心理学
工程类
社会心理学
认知心理学
算法
作者
Lei Li,Yihua Xu,Qunkai Peng,Pei Huang,Xinqing Duan,Mingqiang Wang,Yu Jiang,Jie Wang,Srinivasan Periasamy,Dar‐Jen Hsieh,Kuan‐Chang Chang
出处
期刊:ACS Nano
[American Chemical Society]
日期:2024-10-31
卷期号:18 (45): 31309-31322
被引量:1
标识
DOI:10.1021/acsnano.4c10383
摘要
Neuromorphic bioelectronics aim to integrate electronics with biological systems yet encounter challenges in biocompatibility, operating voltages, power consumption, and stability. This study presents biocompatible neuromorphic devices fabricated from acellular dermal matrix (ADM) derived from porcine dermis using low-temperature supercritical CO2 extraction. The ADM preserves the natural scaffold structure of collagen and minimizes immunogenicity by eliminating cells, fats, and noncollagenous impurities, ensuring excellent biocompatibility. The ADM-based devices emulate biological ion channels with biphasic membrane current modulation, exhibiting temperature dependency and pH sensitivity. It operates at an ultralow voltage of 1 mV and demonstrates reliable synaptic modulation exceeding 4 × 104 endurance cycles. The activation voltage can be theoretically as low as 59 μV, comparable to brainwave signals with a power of merely 7 aJ/event. Furthermore, a brain-like forgetting visualization algorithm is developed, leveraging the synaptic forgetting plasticity of ADM-based devices to achieve complex computing tasks in a highly energy-efficient manner. Neuromorphic devices based on ADM not only hold potential in implantable biointerfaces due to their exceptional biocompatibility, ultralow voltage, and power but also provide a feasible way for energy-efficient computing paradigms through a synergistic hardware-software approach.
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