## 3 Experimental Verification and Analysis  
The experimental environment of this article is: Ubuntu19 operating system, 32 G memory, Inter(r) Core TM i5-5200U CPU 2.20 Hz, 2 GTX 1080 Ti graphics cards. Use Python2.7.5 as the environment to build the SNN model for Lateral Suppression of STDP with Adaptive Threshold, and conduct verification experiments on the MNIST data set. The network parameters used in the experiment are shown in Table 3. Take the training data of the MNIST data set, and iterate it in the SNN model 1000 times to obtain the classification of numbers 0-9, test the classification of the test data of the MNIST data set, and use the correct rate of the classification of the test data as the performance evaluation index of the model. At the same time, record the accuracy of the test data set for reference. All synapses connected to neurons in the output layer, if scaled to appropriate values and rearranged in the form of images, will describe the pattern that the neuron has learned and how to distinguish the pattern. After training the network with the MNIST data set, zoom all the weights of the neurons connected to the output layer, enlarge the weights to form a 28 \(\times\) 28 image, and obtain a gray-scale neuron, which can more intuitively see the algorithm classification effect. Figure 7 is a comparison diagram of pulse thresholds, and Figure 8 is a corresponding reconstruction diagram of weights. The red line in Figure 7 is the threshold, the blue line is the number of pulses, and Figure 7a,b is the case of a fixed threshold. In Figure 7a, the threshold is too high, resulting in no pulses reaching the threshold, and the result becomes noise as shown in Figure 8a; in Figure 7b, the threshold is too low, causing the number of pulses to be too high, and there are too many pulses reaching the threshold. The result will be as shown in Figure 8b, the contours of the displayed digits are recognized, but the specific digits are not recognized; Figure 7c-e uses a variable threshold, and different thresholds are used for different pictures to avoid the occurrence of excessively high or low pulse numbers such as Figure 7a,b. Figure 7d does not add the lateral suppression algorithm. It is seen that digital ghosting appears in Figure 8d. The lateral suppression algorithm is used in Figure 7e. The number of pulses that exceed the threshold is further reduced, and the ghosting is also reduced a lot, which is much clearer than Figure 8d. Since the traditional SNN model has a fixed threshold, the final result is prone to noise or ghosting problems. The threshold is changed into an adaptive threshold, and the variable threshold is adapted to different pictures, avoiding too many or too few pulses that exceed the threshold. At the same time, adding a lateral suppression algorithm can greatly reduce the ghost phenomenon. Table 4 shows the results of six comparative experiments. The results show that compared with the traditional SNN model based on the fully connected structure, the recognition effect of the SNN model with adaptive threshold can be improved to a certain extent. At the same time, the SNN model fused with the lateral suppression algorithm has achieved accuracy that competes with the traditional SNN network model in the EEG recognition task. In this paper, the scheduling method based on the discretized time domain greatly simplifies the pulse conversion and weight learning process: On the one hand, the pulse mode is limited to \(T\) = 200 through TTFS encoding. Compared to the sort encoding, the compression rate has reached 200/256 \(\times\) 100% = 78.125%. On the other hand, discretizing the pulse sequence interval, which optimize the calculation process of weight update, reduces the complexity from O(n\({}^{2}\)) to O(1) compared with the classic continuous STDP algorithm.