  The neural network is a powerful computing framework that has been exploited
by biological evolution and by humans for solving diverse problems. Although
the computational capabilities of neural networks are determined by their
structure, the current understanding of the relationships between a neural
network's architecture and function is still primitive. Here we reveal that
neural network's modular architecture plays a vital role in determining the
neural dynamics and memory performance of the network of threshold neurons. In
particular, we demonstrate that there exists an optimal modularity for memory
performance, where a balance between local cohesion and global connectivity is
established, allowing optimally modular networks to remember longer. Our
results suggest that insights from dynamical analysis of neural networks and
information spreading processes can be leveraged to better design neural
networks and may shed light on the brain's modular organization.
