This paper introduces seven brain-inspired rules that are deeply rooted in the understanding of the brain to improve multi-layer spiking neural networks(SNNs). The dynamics of neurons, synapses, and plasticity models are considered to be major characteristics of information processing in brain neural networks.Hence, incorporating these models and rules to traditional SNNs is expected to improve their efficiency. The proposed SNN model can mainly be divided into three parts: the spike generation layer, the hidden layers, and the output layer. In the spike generation layer, non-temporary signals such as static images are converted into spikes by both local and global feature-converting methods. In the hidden layers, the rules of dynamic neurons,synapses, the proportion of different kinds of neurons, and various spike timing dependent plasticity(STDP)models are incorporated. In the output layer, the function of classification for excitatory neurons and winner take all(WTA) for inhibitory neurons are realized. MNIST dataset is used to validate the classification accuracy of the proposed neural network model. Experimental results show that higher accuracy will be achieved when more brain-inspired rules(with careful selection) are integrated into the learning procedure. This paper introduces seven brain-inspired rules that are deeply rooted in the understanding of the brain to improve multi-layer spiking neural networks (SNNs). The dynamics of neurons, synapses, and plasticity models are considered to be major characteristics of information processing in brain neural networks.ence, incorporating these models and rules to traditional SNNs is expected to improve their efficiency. The proposed SNN model can mainly be divided into three parts: the spike generation layer, the hidden layers, and the output layer. In the spike generation layer, non-temporary signals such as static images are converted into spikes by both local and global feature-converting methods. In the hidden layers, the rules of dynamic neurons, synapses, the proportion of different kinds of neurons, and various spike timing dependent plasticity (STDP) models are incorporated. In the output layer, the function of classification for excitatory neurons and winner take all (WTA) for inhibitory neurons are realized. MNIST dataset is used to validate the classification accuracy of the proposed neural network model. Experimental results show that higher accuracy will be achieved when more brain-inspired rules (with careful selection) are integrated into the learning procedure.