Alterations in brain oscillations are linked to numerous neurological and psychiatric disorders, including epilepsy, schizophrenia, and neurodegenerative diseases. One prominent idea about the emergence of these issues posits that the equilibrium between Excitation and Inhibition (E-I) within the brain's circuits is disrupted. Understanding how E-I interactions change oscillatory activity could help us learn more about how diseases work and how to treat them better. This study utilizes a computational neural mass framework to examine the impact of excitation-inhibition dynamics on oscillatory brain activity. The goal is to find the processes that cause oscillatory problems that patients have. A Wilson-Cowan neural mass model was utilized to simulate population-level brain activity. We conducted a comprehensive analysis of the effects of many parameters, such as excitatory input, inhibitory coupling strength, inhibitory timing, synaptic delay, random fluctuations, and homeostatic inhibitory adaptation, on the start and control of rhythmic dynamics. 

It shows stable oscillatory states across a wide range of parameters. When an external excitatory drive and inhibitory feedback come together, oscillations start. Alterations in lower-frequency oscillations are associated with prolonged synapse delays and extended inhibitory durations. This suggests mechanistic parallels with the slowing of brain activity observed in pathological states. 

The oscillatory dynamics remain unchanged, even in the presence of minor random fluctuations or alterations in the initial conditions. A homeostatic inhibitory adaptation mechanism also keeps oscillatory activity steady even when parameters change. This indicates a possible regulatory mechanism that could be impaired in disease. 

The results corroborate the idea that abnormal oscillatory patterns in clinical populations result from an imbalance between excitation and inhibition, coupled with modifications in synaptic timing. This model illustrates the connection between circuit-level problems and critical brain rhythms for patients. It could also help with future research on biomarkers and drugs that modify how the brain works.

Rajkumar Daniel completed his bachelor’s in electrical engineering from the National Institute of Technology Manipur. He is currently pursuing a master’s degree in biomedical engineering from the Indian Institute of Technology Kharagpur.