Relationship between training database size and structural connectivity in artificial neural networks: Do specific local connections improve prediction accuracy?

Abstract Training of artificial neural networks is very expensive, as a large-size database is necessary. Moreover, it is usually difficult to find such large-size training databases. Hence, it will be interesting to design artificial neural networks that can be used for training with a small-size database, while maintaining a similar accuracy for prediction compared to fully connected neural networks. We studied neural networks with partial disconnections, additional bypass connections, and negative activation nodes, which are found in the neuronal systems of the human brain. By combining the fully connected neural network and the above three brain-like elements, we found that the modified neural network showed improved prediction accuracy of 13% compared to the fully connected one despite the small size of the training database. To analyze the improved neural network, the contribution of each node in the hidden layers affecting the total prediction accuracy of the neural networks was studied. We also found important local connections that improve the prediction accuracy, and discussed the design of a neural network with a small-size training database without reduction in prediction accuracy.