NbO2 Memristive Neurons for Burst‐Based Perceptron
| Autor: | Xinjun Liu, Juan Song, Peng Zhang, Yeheng Bo, Shuai Li, Ziqing Luo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
lcsh:Computer engineering. Computer hardware
Quantitative Biology::Neurons and Cognition Computer science business.industry lcsh:Control engineering systems. Automatic machinery (General) neurons Pattern recognition lcsh:TK7885-7895 Memristor Perceptron law.invention dynamic behavior Bursting lcsh:TJ212-225 Computer Science::Emerging Technologies memristors law Artificial intelligence bursting business General Economics Econometrics and Finance |
| Zdroj: | Advanced Intelligent Systems, Vol 2, Iss 8, Pp n/a-n/a (2020) |
| ISSN: | 2640-4567 |
| Popis: | Neuromorphic computing using spike‐based learning has broad prospects in reducing computing power. Memristive neurons composed with two locally active memristors have been used to mimic the dynamical behaviors of biological neurons. Herein, the dynamic operating conditions of NbO2‐based memristive neurons and their transformation boundaries between the spiking and the bursting are comprehensively investigated. Furthermore, the underlying mechanism of bursting is analyzed, and the controllability of the number of spikes during each burst period is demonstrated. Finally, pattern classification and information transmitting in a perceptron neural network by using the number of spikes per bursting period to encode information is proposed. The results show a promising approach for the practical implementation of neuristor in spiking neural networks. |
| Databáze: | OpenAIRE |
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