Adjustable Leaky-Integrate-and-fire neurons based on memristor-coupled capacitors

Materials Today Advances

L. Wei¹  K. Pan²  Y. Yan²  B. Sun³  T. Guo³  Y.A. Wu³  Y.N. Zhou⁴ 

[1] School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials (Ministry of Education of China), Southwest Jiaotong University, Chengdu, Sichuan, 610031, China;Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada;Department of Mechanical and Mechatronics Engineering, Waterloo Institute of Nanotechnology, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada;School of Physics, Henan Key Laboratory of Photovoltaic Materials, Henan Normal University, Henan 453007, China;

关键词: Neuromorphic computing;    Artificial neuron;    Memristor;    Capacitor;    Genetic algorithm;   

DOI  :  

来源: DOAJ

【 摘 要 】

To address the von Neumann bottleneck, artificial neural networks (ANNs) are aroused to construct neuromorphic computing systems. The artificial neuron is one of the essential components that collect the weight updating information of artificial synapses. Leaky-Integrate-and-Fire (LIF) neuron mimicking the cell membrane of biological neurons is a promising neural model due to its simplicity. To adjust the performances of artificial neurons, multiple resistors with different resistive values need to be integrated into the circuit. Whereas more components mean higher manufacturing costs, more complex circuits, and more complicated control systems. In this work, the first adjustable LIF neuron was developed, which can further simplify the circuits. To achieve adjustable fashions, a memristor-coupled capacitor with binary intrinsic resistant states was employed to integrate input signals. The intrinsic tunable resistance can modify the charge leaking rate, which determines the neural spiking features. Another contribution of this work is to overcome the hinder of credible circuit design using novel memristor-coupled capacitors with entangled capacitive and memristive effects. The genetic algorithm (GA) was utilized to detach the entanglement of memristive and capacitive effects, which is crucial for circuit design. This method can be generalized to other entangled physical behaviors, facilitating the development of novel circuits. The results will not only strengthen neuromorphic computing capability but also provides a methodology to mathematically decode electronic devices with entangled physical behaviors for novel circuits.

【 授权许可】

Unknown