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高仿生神经递质通讯神经元,遵循整合-点火动力学。

Highly Bionic Neurotransmitter-Communicated Neurons Following Integrate-and-Fire Dynamics.

机构信息

State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai 200433, People's Republic of China.

Institute of Molecular Materials and Devices, Department of Material Science, Fudan University, Shanghai 200433, People's Republic of China.

出版信息

Nano Lett. 2023 Jun 14;23(11):4974-4982. doi: 10.1021/acs.nanolett.3c00799. Epub 2023 Jun 5.

Abstract

In biological neural networks, chemical communication follows the reversible integrate-and-fire (I&F) dynamics model, enabling efficient, anti-interference signal transport. However, existing artificial neurons fail to follow the I&F model in chemical communication, causing irreversible potential accumulation and neural system dysfunction. Herein, we develop a supercapacitively gated artificial neuron that mimics the reversible I&F dynamics model. Upon upstream neurotransmitters, an electrochemical reaction occurs on a graphene nanowall (GNW) gate electrode of artificial neurons. Charging and discharging the supercapacitive GNWs mimic membrane potential accumulation and recovery, realizing highly efficient chemical communication upon use of acetylcholine down to 2 × 10 M. By combining artificial chemical synapses with axon-hillock circuits, the output of neural spikes is realized. With the same neurotransmitter and I&F dynamics, the artificial neuron establishes chemical communication with other artificial neurons and living cells, holding promise as a basic unit to construct a neural network with compatibility to organisms for artificial intelligence and deep human-machine fusion.

摘要

在生物神经网络中,化学通讯遵循可逆向的整合发放(I&F)动力学模型,从而实现高效、抗干扰的信号传输。然而,现有的人工神经元在化学通讯中无法遵循 I&F 模型,导致不可逆转的势垒积累和神经功能紊乱。在此,我们开发了一种超级电容门控人工神经元,它模拟了可逆向的 I&F 动力学模型。在神经递质的上游,电化学反应发生在人工神经元的石墨烯纳米墙(GNW)门电极上。超级电容器 GNWs 的充电和放电模拟了膜电位的积累和恢复,在使用乙酰胆碱低至 2×10^-6 M 的情况下,实现了高效的化学通讯。通过将人工化学突触与轴丘电路相结合,实现了神经脉冲的输出。通过相同的神经递质和 I&F 动力学,人工神经元与其他人工神经元和活细胞建立了化学通讯,有望成为构建具有生物兼容性的神经网络的基本单元,用于人工智能和深度人机融合。

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