TY - GEN
T1 - Training Spiking Neural Networks with an Adaptive Leaky Integrate-and-Fire Neuron
AU - Sung, Mingyu
AU - Kim, Yongtae
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/1
Y1 - 2020/11/1
N2 - Spiking neural network (SNN) is more biologically plausible than traditional artificial neural networks. Since the spiking network uses binary values of spike to process, it can offer an excellent power and energy efficiency when implementing it in hardware. Therefore, it is widely utilized in various machine learning applications, such as pattern recognition. In this paper, we introduce an adaptive leaky integrate-and-fire (LIF) neuron model that improves the accuracy of the spiking network. The proposed method is employed in a spiking network that includes more than 1,500 neurons to classify the MNIST handwritten digits. The unsupervised spike timing-dependent plasticity (STDP) learning rule is used to train the network. The experimental results are shown that the accuracy performance of the network with the proposed method outperforms the baseline spiking network.
AB - Spiking neural network (SNN) is more biologically plausible than traditional artificial neural networks. Since the spiking network uses binary values of spike to process, it can offer an excellent power and energy efficiency when implementing it in hardware. Therefore, it is widely utilized in various machine learning applications, such as pattern recognition. In this paper, we introduce an adaptive leaky integrate-and-fire (LIF) neuron model that improves the accuracy of the spiking network. The proposed method is employed in a spiking network that includes more than 1,500 neurons to classify the MNIST handwritten digits. The unsupervised spike timing-dependent plasticity (STDP) learning rule is used to train the network. The experimental results are shown that the accuracy performance of the network with the proposed method outperforms the baseline spiking network.
KW - adaptive leakage
KW - adaptive leaky integrate-and-fire (LIF) neuron
KW - spiking neural network (SNN)
UR - http://www.scopus.com/inward/record.url?scp=85098860402&partnerID=8YFLogxK
U2 - 10.1109/ICCE-Asia49877.2020.9277455
DO - 10.1109/ICCE-Asia49877.2020.9277455
M3 - Conference contribution
AN - SCOPUS:85098860402
T3 - 2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020
BT - 2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020
Y2 - 1 November 2020 through 3 November 2020
ER -
