In order to further illustrate the potential of the present VO2 neuron in multisensory fusion, we have tested a total of 11 combinations of different pressures and temperatures (Figure S14, Supporting Information), and the measurements were repeated 10 times for each individual combination to form a small dataset. A multilayer perceptron (MLP) network was constructed as shown in Figure 6c, which is composed of 20 hidden neurons and 11 output neurons, and the total 110 data samples were used to train the network to identify 11 different combinations using backpropagation. According to the experimental data in Figure S14, it can be found that only the oscillation frequency also cannot classify all 11 situations. Fortunately, it has been revealed that the temperature elevation also leads to variations in the amplitude of oscillation (Figure 5e). As a result, the maximum and mean of the output voltage signal have also been extracted (Figure S14) and included as input variables together with the oscillation frequency (f). We have investigated three haptic-temperature input combinations, namely, Vmax – f, Vmean – f and Vmax – Vmean – f, and the training is performed for 200 epochs. Figure 6d shows that the classification accuracies of the network are 86.03% and 83.25% based on Vmax – f and Vmean – f inputs after 200 epochs, respectively. When Vmax – Vmean – f information is used as input, the classification accuracy can attain to 91.35% after 200 epochs, which has a better training performance. Figure 6e further shows a confusion matrix of the testing results for the 11 cases based on Vmax – Vmean – f inputs. As a measure on the classification accuracy, the confusion matrix in Figure 6e displays the classification result in each column while the expected (actual) result in each row, where the number of instances is depicted by the color bar, demonstrating that the test inputs are well classified after training.