Fig 1. Architecture of the MSTNN.

The architecture consists of one input layer, three convolutional layers, two max-pooling layers, and one fully-connected output layer. Convolutional layers apply convolution operations with kernels to previous layers (black solid lines). Max-pooling layers select maximum values within local windows from previous convolutional layers (black dotted lines). Each layer has a set of parameters: dimensions of layer (feature map column size×feature map row size×number of feature maps), kernel size, and max-pooling size. Only the convolutional layers have an additional time constant parameter τ (red solid arrow), which plays a key role in this model. The higher convolutional layer has a larger time constant than the lower convolutional layer. Layer 6 is the softmax activation function used for classification (N is the number of classes).