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Memristive Circuit Implementation of Biological Nonassociative Learning Mechanism and its Applications.
IEEE Transactions on Biomedical Circuits and Systems ( IF 5.1 ) Pub Date : 2020-08-24 , DOI: 10.1109/tbcas.2020.3018777
Qinghui Hong , Renao Yan , Chunhua Wang , Jingru Sun

Biological nonassociative learning is one of the simplest forms of unsupervised learning in animals and can be categorized into habituation and sensitization according to mechanism. This paper proposes a memristive circuit that is based on nonassociative learning and can adapt to repeated inputs, reduce power consumption (habituation), and be sensitive to harmful inputs (sensitization). The circuit includes 1) synapse module, 2) neuron module, 3) feedback module. The first module mainly consists of memristors representing synapse weights that vary with corresponding inputs. Memristance is automatically reduced when a harmful stimulus is input, and climbs at the input interval according to the feedback input when repeated stimuli are input. The second module produces spiking voltage when the total input is above the given threshold. The third module can provide feedback voltage according to the frequency and quantity of input stimuli. Simulation results show that the proposed circuit can generate output signals with biological nonassociative learning characteristics, with varying amplitudes depending on the characteristics of input signals. When the frequency and quantity of the input stimuli are high, the degree of habituation and sensitization intensifies. The proposed circuit has good robustness; can reduce the influence of noise, circuit parasitics and circuit aging during nonassociative learning; and simulate the afterimages caused by visual fatigue for application in automatic exposure compensation.

中文翻译:

生物非联想学习机制的忆阻电路实现及其应用。

生物非联想学习是动物无监督学习的最简单形式之一,根据机制可分为习惯化和敏化。本文提出了一种基于非关联学习的忆阻电路,可以适应重复输入,降低功耗(习惯化),对有害输入敏感(敏化)。该电路包括1)突触模块,2)神经元模块,3)反馈模块。第一个模块主要由代表突触权重的忆阻器组成,这些突触权重随相应的输入而变化。当输入有害刺激时,忆阻会自动降低,并在输入重复刺激时根据反馈输入以输入间隔攀升。当总输入高于给定阈值时,第二个模块会产生尖峰电压。第三个模块可以根据输入刺激的频率和数量提供反馈电压。仿真结果表明,所提出的电路可以生成具有生物非关联学习特性的输出信号,其幅度取决于输入信号的特性。当输入刺激的频率和数量都很高时,习惯和敏感的程度就会加强。所提出的电路具有良好的鲁棒性;可以减少非联想学习过程中噪声、电路寄生和电路老化的影响;并模拟视觉疲劳造成的残像,用于自动曝光补偿。仿真结果表明,所提出的电路可以生成具有生物非关联学习特性的输出信号,其幅度取决于输入信号的特性。当输入刺激的频率和数量都很高时,习惯和敏感的程度就会加强。所提出的电路具有良好的鲁棒性;可以减少非联想学习过程中噪声、电路寄生和电路老化的影响;并模拟视觉疲劳造成的残像,用于自动曝光补偿。仿真结果表明,所提出的电路可以生成具有生物非关联学习特性的输出信号,其幅度取决于输入信号的特性。当输入刺激的频率和数量都很高时,习惯和敏感的程度就会加强。所提出的电路具有良好的鲁棒性;可以减少非联想学习过程中噪声、电路寄生和电路老化的影响;并模拟视觉疲劳造成的残像,用于自动曝光补偿。所提出的电路具有良好的鲁棒性;可以减少非联想学习过程中噪声、电路寄生和电路老化的影响;并模拟视觉疲劳造成的残像,用于自动曝光补偿。所提出的电路具有良好的鲁棒性;可以减少非联想学习过程中噪声、电路寄生和电路老化的影响;并模拟视觉疲劳造成的残像,用于自动曝光补偿。
更新日期:2020-10-16
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