Self-Evolutionary Neuron Model for Fast-Response Spiking Neural Networks
- Anguo Zhang ,
- Yuzhen Niu ,
- Yueming Gao ,
- Ying Han ,
- Qing Chen ,
- Wei Zhu ,
- Ying Han ,
- Jing Hu ,
- Yueming Gao ,
- ZHIZHANG CHEN ,
- Kai Zhao
Abstract
We propose two simple and effective spiking neuron models to improve the
response time of the conventional spiking neural network. The proposed
neuron models adaptively tune the presynaptic input current depending on
the input received from its presynapses and subsequent neuron firing
events. We analyze and derive the firing activity homeostatic
convergence of the proposed models. We experimentally verify and compare
the models on MNIST handwritten digits and FashionMNIST classification
tasks. We show that the proposed neuron models significantly increase
the response speed to the input signal.Dec 2022Published in IEEE Transactions on Cognitive and Developmental Systems volume 14 issue 4 on pages 1766-1777. 10.1109/TCDS.2021.3139444