Biocompatible Acellular Dermal Matrix-Based Neuromorphic Device with Ultralow Voltage, Ion Channel Emulation, and Synaptic Forgetting Visualization Computation

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 CO<sub>2</sub> 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 × 10<sup>4</sup> 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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Publication Details

Journal
ACS Nano
Published
2024-10-31
DOI
https://doi.org/10.1021/acsnano.4c10383
Citations
15
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
3.13

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article

Biocompatible Acellular Dermal Matrix-Based Neuromorphic Device with Ultralow Voltage, Ion Channel Emulation, and Synaptic Forgetting Visualization Computation

Yu Jiang, Dar‐Jen Hsieh, Xinqing Duan, Kuan‐Chang Chang et al.
15 citations
ACS Nano
Advanced Memory and Neural Computing
3.13
article

Biocompatible Acellular Dermal Matrix-Based Neuromorphic Device with Ultralow Voltage, Ion Channel Emulation, and Synaptic Forgetting Visualization Computation

Yu Jiang, Dar‐Jen Hsieh, Xinqing Duan, Kuan‐Chang Chang, Yihua Xu, Jie Wang, Lei Li, Mingqiang Wang, Pei Huang, Srinivasan Periasamy, Qunkai Peng
article en
15 citations

Abstract

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 CO<sub>2</sub> 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 × 10<sup>4</sup> 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.

ACS NanoVol. 18(45)
Peking University (CN), Shenzhen Technology University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Guangdong Province, Shenzhen Scientific and Technological Foundation
Openalex Percentile: Top 8%
Advanced Memory and Neural Computing
3.13
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