Book Description
Understanding the inner workings of neural networks is a paramount scientific challenge. The challenge is rooted in our pursuit to unravel the human mind and reproduce its intelligence in our own machines. This task transcends any single discipline and borrows knowledge and expertise from the brain sciences, physics, mathematics, network science, machine learning, and complex systems. Neural networks, both biological and artificial, are built from the same underlying principles. They are a system of non-linear elements connected together via a network through which they communicate to perform complex computations beyond the capacity of any single element. Neural networks display fantastically rich dynamical properties and computational abilities. It is crucial to understand how the structural organization of the network affects its dynamics and functional capabilities. In this thesis, I explore modularity, a prominent topological feature found in many brain networks. It holds a crucial key for unraveling the mystery of neural systems.