A Λ‐type neuron model using enhancement‐mode MOSFETs

A Λ‐type neuron model using enhancement‐mode MOSFETs
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使用增强型 MOSFET 的 Λ 型神经模型

DOI:
10.1002/ecjb.10020
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发表时间:
2003
期刊:
Electronics and Communications in Japan Part Ii-electronics
影响因子:
--
通讯作者:
K. Aihara
K. Aihara
中科院分区:
--
文献类型:
--
作者:
Y. Sekine;Masami Sumiyama;K. Saeki;K. Aihara

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近来,以工程应用为目标,积极地进行了以硬件实现神经元的研究,神经元是构建神经网络的基本单元。具体而言,脉冲型硬件神经元模型,其近似地复制脉冲信号,已经被检查作为大脑中的信息传输阶段。然而,脉冲型硬件神经元模型是复杂的电路并且包括电感器。因此,几乎没有实用的模型。在本文中,我们提出了Λ型神经元模型,这是一个脉冲型硬件神经元模型,仅由增强型MOSFET(E-MOSFET),它可以在标准的CMOS工艺处理,和电容器,是有用的,作为一个硬件神经元模型,用于大规模神经网络。首先,我们展示了具有随时间变化的负电阻的Λ型负电阻电路可以由E-MOSFET制造,并解释了其原理。接下来,我们表明这个负阻电路可用于从E-MOSFET制造Λ型神经元模型,该模型可以在标准CMOS工艺中处理。© 2002 Wiley Periodicals,Inc. Electron Comm Jpn Pt 2,86(1):18-25,2003;在线发表于Wiley InterScience(www.interscience.wiley.com)。DOI 10.1002/ecjb.10020
Recently, research has been actively pursued to implement a neuron, which is the basic unit for building neural networks, in hardware with engineering applications as the objective. Specifically, pulse-type hardware neuron models, which approximately replicate pulse signals, have been examined as the information transmission stage in the brain. However, a pulse-type hardware neuron model is a complex circuit and includes inductors. Consequently, there are few practical models. In this paper, we present the Λ-type neuron model, which is a pulse-type hardware neuron model built from only enhancement-mode MOSFETs (E-MOSFETs), which can be handled in a standard CMOS process, and capacitors, and is useful as a hardware neuron model intended for large-scale neural networks. First, we show that a Λ-type negative resistance circuit having a negative resistance that varies over time can be fabricated from E-MOSFETs, and explain its principle. Next, we show that this negative resistance circuit can be used to fabricate a Λ-type neuron model from E-MOSFETs, which can be handled in a standard CMOS process. © 2002 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 86(1): 18–25, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.10020