  We have reported nanometer-scale three-dimensional studies of brain networks
of schizophrenia cases and found that their neurites are thin and tortuous
compared to healthy controls. This suggests that connections between distal
neurons are suppressed in microcircuits of schizophrenia cases. In this study,
we applied these biological findings to the design of schizophrenia-mimicking
artificial neural network to simulate the observed connection alteration in the
disorder. Neural networks having a "schizophrenia connection layer" in place of
a fully connected layer were subjected to image classification tasks using the
MNIST and CIFAR-10 datasets. The results revealed that the schizophrenia
connection layer is tolerant to overfitting and outperforms a fully connected
layer. The outperformance was observed only for networks using band matrices as
weight windows, indicating that the shape of the weight matrix is relevant to
the network performance. A schizophrenia convolution layer was also tested
using the VGG configuration, showing that 60% of the kernel weights of the last
three convolution layers can be eliminated without loss of accuracy. The
schizophrenia layers can be used instead of conventional layers without any
change in the network configuration and training procedures; hence, neural
networks can easily take advantage of these layers. The results of this study
suggest that the connection alteration found in schizophrenia is not a burden
to the brain, but has functional roles in brain performance.
