Emulation of brain‐like signal processing with thin‐film devices can lay the foundation for building artificially intelligent learning circuitry in future. Encompassing higher functionalities into single artificial neural elements will allow the development of robust neuromorphic circuitry emulating biological adaptation mechanisms with drastically lesser neural elements, mitigating strict process challenges and high circuit density requirements necessary to match the computational complexity of the human brain. Here, 2D transition metal di‐chalcogenide (MoS₂) neuristors are designed to mimic intracellular ion endocytosis–exocytosis dynamics/neurotransmitter‐release in chemical synapses using three approaches: (i) electronic‐mode: a defect modulation approach where the traps at the semiconductor–dielectric interface are perturbed; (ii) ionotronic‐mode: where electronic responses are modulated via ionic gating; and (iii) photoactive‐mode: harnessing persistent photoconductivity or trap‐assisted slow recombination mechanisms. Exploiting a novel multigated architecture incorporating electrical and optical biases, this incarnation not only addresses different charge‐trapping probabilities to finely modulate the synaptic weights, but also amalgamates neuromodulation schemes to achieve “plasticity of plasticity–metaplasticity” via dynamic control of Hebbian spike‐time dependent plasticity and homeostatic regulation. Coexistence of such multiple forms of synaptic plasticity increases the efficacy of memory storage and processing capacity of artificial neuristors, enabling design of highly efficient novel neural architectures.

中文翻译：

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电离子光敏二维硫族化物神经元的协同门控：赫比族和稳态突触代谢的共存。

利用薄膜设备模拟类脑信号处理可以为将来构建人工智能学习电路奠定基础。将更高的功能包含到单个人工神经元中将允许开发健壮的神经形态电路，以更少的神经元来模拟生物适应机制，从而减轻了严格的过程挑战和与人脑的计算复杂度相匹配的电路密度要求。在这里，二维过渡金属二硫属元素化物（MoS ₂）神经学家被设计为使用三种方法来模拟化学突触中的胞内离子内吞-胞吐动力学/神经递质-释放：（i）电子模式：一种缺陷调制方法，其中干扰了半导体-介电界面的陷阱；（ii）ionotronic模式：通过离子门控调节电子响应的模式；（iii）光敏模式：利用持久的光电导或陷阱辅助的慢速重组机制。利用化为一体的新颖结构，结合了电和光偏置，这种化身不仅解决了不同的电荷捕获概率，可以精细地调节突触权重，而且还通过动态控制Hebbian峰值时间相关的可塑性和稳态调节，将神经调节方案合并起来，以实现“可塑性-可塑性”。这种多种形式的突触可塑性的共存增加了人工神经变种的记忆存储和处理能力，从而能够设计出高效的新型神经结构。