  The evolution of convolutional neural networks (CNNs) can be largely
attributed to the design of its architecture, i.e., the network wiring pattern.
Neural architecture search (NAS) advances this by automating the search for the
optimal network architecture, but the resulting network instance may not
generalize well in different tasks. To overcome this, exploring network design
principles that are generalizable across tasks is a more practical solution. In
this study, We explore a novel brain-inspired design principle based on the
core-periphery property of the human brain network to guide the design of CNNs.
Our work draws inspiration from recent studies suggesting that artificial and
biological neural networks may have common principles in optimizing network
architecture. We implement the core-periphery principle in the design of
network wiring patterns and the sparsification of the convolution operation.
The resulting core-periphery principle guided CNNs (CP-CNNs) are evaluated on
three different datasets. The experiments demonstrate the effectiveness and
superiority compared to CNNs and ViT-based methods. Overall, our work
contributes to the growing field of brain-inspired AI by incorporating insights
from the human brain into the design of neural networks.
