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Leaky Integrate-and-Fire Neuron Circuit Based on Floating-Gate Integrator

机译:基于浮栅积分器的泄漏积分与发射神经元电路

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The artificial spiking neural network (SNN) is promising and has been brought to the notice of the theoretical neuroscience and neuromorphic engineering research communities. In this light, we propose a new type of artificial spiking neuron based on leaky integrate-and-fire (LIF) behavior. A distinctive feature of the proposed FG-LIF neuron is the use of a floating-gate (FG) integrator rather than a capacitor-based one. The relaxation time of the charge on the FG relies mainly on the tunnel barrier profile, e.g., barrier height and thickness (rather than the area). This opens up the possibility of large-scale integration of neurons. The circuit simulation results offered biologically plausible spiking activity (<100 Hz) with a capacitor of merely 6 fF, which is hosted in an FG metal-oxide-semiconductor field-effect transistor. The FG-LIF neuron also has the advantage of low operation power (<30 pW/spike). Finally, the proposed circuit was subject to possible types of noise, e.g., thermal noise and burst noise. The simulation results indicated remarkable distributional features of interspike intervals that are fitted to Gamma distribution functions, similar to biological neurons in the neocortex.
机译:人工加标神经网络(SNN)很有前途,并已引起理论神经科学和神经形态工程研究界的注意。有鉴于此,我们提出了一种基于泄漏的集成并发射(LIF)行为的新型人工加标神经元。所提出的FG-LIF神经元的一个显着特征是使用浮栅(FG)积分器,而不是基于电容器的积分器。电荷在FG上的弛豫时间主要取决于隧道势垒轮廓,例如势垒高度和厚度(而不是面积)。这开辟了神经元大规模整合的可能性。电路仿真结果表明,仅用6 fF的电容器就可以提供生物学上合理的尖峰活动(<100 Hz),该电容器位于FG金属氧化物半导体场效应晶体管中。 FG-LIF神经元还具有操作功率低(<30 pW /峰值)的优势。最后,所提出的电路受到可能的噪声类型的影响,例如,热噪声和突发噪声。模拟结果表明,与新皮层中的生物神经元相似,穗间间隔的显着分布特征与Gamma分布函数拟合。

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