TY - GEN
T1 - An optimized Hebbian Learning Rule for Spiking Neural Networks on the Classification Problems with Informative Data Features
AU - Chen, Tingyu
AU - Hu, Xin
AU - Zhou, Yiren
AU - Zou, Zhuo
AU - Liang, Longfei
AU - Yang, Wen Chi
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - We proposed a new Hebbian learning rule that Neglects Historical data and only Compares Voltages (referred to NHCV in the paper). Unlike the traditional Hebbian learning rules that rely on comparing the spike timing, NHCV is designed to adjust the weight of the synapse based on the voltage of the neuron as soon as it fires. NHCV is computationally efficient and have advantages in processing informative features. Compared to traditional STDP learning rules, it accelerated training process (0.5 to 2 seconds improvement on each sample) and achieved better accuracy on Wine dataset (5.7% absolute improvement) and Diabetes dataset (12% absolute improvement). We reveal that the information amount inside the features of a dataset considerably affects the performance of SNNs.
AB - We proposed a new Hebbian learning rule that Neglects Historical data and only Compares Voltages (referred to NHCV in the paper). Unlike the traditional Hebbian learning rules that rely on comparing the spike timing, NHCV is designed to adjust the weight of the synapse based on the voltage of the neuron as soon as it fires. NHCV is computationally efficient and have advantages in processing informative features. Compared to traditional STDP learning rules, it accelerated training process (0.5 to 2 seconds improvement on each sample) and achieved better accuracy on Wine dataset (5.7% absolute improvement) and Diabetes dataset (12% absolute improvement). We reveal that the information amount inside the features of a dataset considerably affects the performance of SNNs.
KW - Neural Network Theory and Architectures
KW - Performance analysis of Machine Learning Algorithms
KW - Spiking Neural Network
KW - Unsupervised and Supervised Learning
UR - http://www.scopus.com/inward/record.url?scp=85143435835&partnerID=8YFLogxK
U2 - 10.1109/ARACE56528.2022.00012
DO - 10.1109/ARACE56528.2022.00012
M3 - Conference Proceeding
AN - SCOPUS:85143435835
T3 - Proceedings - 2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering, ARACE 2022
SP - 18
EP - 23
BT - Proceedings - 2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering, ARACE 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering, ARACE 2022
Y2 - 26 August 2022 through 28 August 2022
ER -
