Optimizing Information Capacity in Modular Neural Networks through Excitatory and Inhibitory Connectivity
Mozhgan Khanjanianpak, Alireza Valizadeh · 2025
Modularity is a fundamental organizational principle in complex networks, including the brain, where it supports scalability, flexibility, and robustness. This study examines the influence of modularity on information capacity in neural networks, with a specific focus on the interplay between excitatory and inhibitory connectivity in balanced networks. Using a computational model of neuronal networks, we evaluate the information capacity of different modular architectures composed of excitatory and inhibitory neurons, with varying probabilities of connections type both within and between modules while maintaining a global balance between excitation and inhibition. By analyzing the networks' dynamical states with different levels of external inputs, we explore how different connectivity patterns shape the network's information capacity. Our findings indicate that global long-range excitation drives the system into periodic states, resulting in minimal information content. Conversely, a combination of inter-module excitatory and inhibitory interconnections generates correlated activity among modules, limiting the scaling of global information with the number of modules. In contrast, exclusive inhibitory interconnections foster uncorrelated, intermittent activity within modules, maximizing information capacity both locally and across the entire network. This study highlights the importance of differential connectivity patterns in excitatory and inhibitory synapses, as well as modular organization, in optimizing the brain's information processing capabilities. Our findings provide valuable insights into the mechanisms underlying neural dynamics and their role in efficient information processing.