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Implementing an artificial synapse and neuron using a Si nanowire ion- sensitive field-effect transistor and indium-gallium-zinc-oxide memristors

机译:使用Si纳米线离子敏感场效应晶体管和铟 - 镓 - 锌 - 氧化物存储器来实现人工突触和神经元

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In this study, we implement an artificial synapse and neuron in a single platform by combining a silicon nanowire (SiNW) ion-sensitive field-effect transistor (ISFET), an indium-gallium-zinc-oxide (IGZO) memristor, and a voltage-controlled oscillator (VCO). The chemical and electrical operations of the synapse are emulated using the pH sensor operation of the ISFET and long-term potentiation/short-term plasticity of the IGZO memristor, respectively. The concentration of hydrogen ions in an electrolyte is successfully transformed via a VCO-based neuron into modulation of synaptic strength, i.e., the current of the memristor. It mimics the strength of the synaptic connection modulated by the concentration of the neurotransmitter. Thus, the chemical-electrical signal conversion in chemical synapses is clearly demonstrated. Furthermore, the proposed artificial platform can discriminate the chemical synapse from the electrical synapse and the path of the neuro-signal propagation and that of memorization/update of synaptic strength. This can potentially provide a new insight into the principles of brain-inspired computing that can overcome the bottleneck of the state-of-the-art von-Neumann computing systems.
机译:在这项研究中,通过将硅纳米线(SINW)离子敏感场效应晶体管(ISFET),镓 - 氧化锌(IGZO)忆阻器和电压组合,在单个平台中实施人工突触和神经元。 - 控制振荡器(VCO)。使用ISFET的pH传感器操作和IGZO忆耳的长期电位/短期可塑性模拟突触的化学和电气操作。通过VCO的神经元成功地将电解质中的氢离子浓度成功转化为突触强度的调节,即椎间盘的电流。它模仿由神经递质的浓度调节的突触连接的强度。因此,清楚地证明了化学突触中的化学电信号转换。此外,所提出的人工平台可以区分从电突触和神经信号传播的路径和突触强度的记忆/更新的化学突触和突触强度的路径。这可能会对脑激发计算原理提供新的洞察,这可以克服最先进的von-neumann计算系统的瓶颈。

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