Behavioral Modeling and STDP Learning Characteristics of a Memristive Synapse

Hajiabadi, Zohreh; Shalchian, Majid

doi:10.1109/icee50131.2020.9260770

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…The analysis of learning behaviour features includes two parts -group feature analysis and individual feature analysis. The latter is the basis and refinement of the former, and the two types of features analyses support each other and develop together [19][20][21][22][23][24][25]. Luckily, with the increase of online open course learning platforms, online open courses and online learners, the learning behaviour data generated in the learning process of learners are gradually accumulating, further supporting the exploration of the learning behaviour features and patterns of learners in the online learning environment.…”

Section: Introductionmentioning

confidence: 90%

Features of Group Online Learning Behaviours Based on Data Mining

Zhu¹,

Wang²,

Wang³

2022

Int. J. Emerg. Technol. Learn.

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With the development of information technology, how to scientifically and properly organizing and guiding learners to learn actively and efficiently has become a research subject for domestic and foreign scholars. However, existing research on online learning behaviours studied little about learning attitudes, learning preferences, student-student interaction, teacher-student interaction and so on. To this end, this paper studies the features of group online learning behaviours based on data mining. In this paper, a K-means-based group online learning behaviour feature selection model and an AdaBoost-based group online learning behaviour classification model were constructed, and the processing methods, execution processes and algorithm functions of the two models were described in detail. Finally, the effectiveness of the constructed models was verified through an experiment.

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Section: Introductionmentioning

confidence: 90%

Features of Group Online Learning Behaviours Based on Data Mining

Zhu¹,

Wang²,

Wang³

2022

Int. J. Emerg. Technol. Learn.

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show abstract

“…Memristors are considered artificial synapses for neuromorphic circuits [13], as their conductance characterizes the synaptic weights and remains preserved after power-down. Furthermore, in terms of efficiency, memristors have the advantage of consuming less power and space for less cost [14,15]. Therefore, the appropriate neuron model, memristors, and connections with STDP learning rules, can be used to construct the most basic SNN models.…”

Section: Introductionmentioning

confidence: 99%

Python-Based Circuit Design for Fundamental Building Blocks of Spiking Neural Network

Qin

et al. 2023

Electronics

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Spiking neural networks (SNNs) are considered a crucial research direction to address the “storage wall” and “power wall” challenges faced by traditional artificial intelligence computing. However, developing SNN chips based on CMOS (complementary metal oxide semiconductor) circuits remains a challenge. Although memristor process technology is the best alternative to synapses, it is still undergoing refinement. In this study, a novel approach is proposed that employs tools to automatically generate HDL (hardware description language) code for constructing neuron and memristor circuits after using Python to describe the neuron and memristor models. Based on this approach, HR (Hindmash–Rose), LIF (leaky integrate-and-fire), and IZ (Izhikevich) neuron circuits, as well as HP, EG (enhanced generalized), and TB (the behavioral threshold bipolar) memristor circuits are designed to construct the most basic connection of a SNN: the neuron–memristor–neuron circuit that satisfies the STDP (spike-timing-dependent-plasticity) learning rule. Through simulation experiments and FPGA (field programmable gate array) prototype verification, it is confirmed that the IZ and LIF circuits are suitable as neurons in SNNs, while the X variables of the EG memristor model serve as characteristic synaptic weights. The EG memristor circuits best satisfy the STDP learning rule and are suitable as synapses in SNNs. In comparison to previous works on hardware spiking neurons, the proposed method needed fewer area resources for creating spiking neurons models on FPGA. The proposed SNN basic components design method, and the resulting circuits, are beneficial for architectural exploration and hardware–software co-design of SNN chips.

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“…The memristor bridge synapse used to build the multilayer neural network can solve the nonvolatile weight storage (Adhikari et al, 2012 ). Memristors are used as synapses to link the pre-neurons and post-neurons in the spiking networks (Hajiabadi and Shalchian, 2020 ). A memristive (Ag/SiO2/Au) integration-and-fire neuron can implement the primary functions of neurons (Hajiabadi and Shalchian, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Transient Response and Firing Behaviors of Memristive Neuron Circuit

et al. 2022

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The signal transmission mechanism of the Resistor-Capacitor (RC) circuit is similar to the intracellular and extracellular signal propagating mechanism of the neuron. Thus, the RC circuit can be utilized as the circuit model of the neuron cell membrane. However, resistors are electronic components with the fixed-resistance and have no memory properties. A memristor is a promising neuro-morphological electronic device with nonvolatile, switching, and nonlinear characteristics. First of all, we consider replacing the resistor in the RC neuron circuit with a memristor, which is named the Memristor-Capacitor (MC) circuit, then the MC neuron model is constructed. We compare the charging and discharging processes between the RC and MC neuron circuits. Secondly, two models are compared under the different external stimuli. Finally, the synchronous and asynchronous activities of the RC and MC neuron circuits are performed. Extensive experimental results suggest that the charging and discharging speed of the MC neuron circuit is faster than that of the RC neuron circuit. Given sufficient time and proper external stimuli, the RC and MC neuron circuits can produce the action potentials. The synchronous and asynchronous phenomena in the two neuron circuits reproduce nonlinear dynamic behaviors of the biological neurons.

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Behavioral Modeling and STDP Learning Characteristics of a Memristive Synapse

Cited by 4 publications

References 14 publications

Features of Group Online Learning Behaviours Based on Data Mining

Features of Group Online Learning Behaviours Based on Data Mining

Python-Based Circuit Design for Fundamental Building Blocks of Spiking Neural Network

Transient Response and Firing Behaviors of Memristive Neuron Circuit

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