Self-Evolutionary Neuron Model for Fast-Response Spiking Neural Networks

We propose two simple and effective spiking neuron models to improve the response time of conventional neural network. The proposed adaptively tune presynaptic input current depending on received from its presynapses subsequent firing events. analyze derive activity homeostatic convergence models. experimentally verify compare MNIST handwritten digits FashionMNIST classification tasks. show that significantly increase speed signal. Experiment codes are available at https://github.com/anvien/Evol-SNN .