Figure 2.

Schematic of the multi-layer perceptron (MLP). For each neuron, the interspike intervals of M spike trains underwent principal-component analysis (PCA) to extract relevant features. For each spike train, the N components derived from the PCA were fed into the input layer of the MLP sequentially. The hidden layer of the network contained 100 nodes and the output layer contained 2 nodes. The network was a fully connected feedforward network. A winner-take-all approach applied to the output node: thus, the node with the highest activation determined the MLP's answer.