Optimal Inhibitory-to-Excitatory Ratio Governs Slow and Fast Oscillations for Enhanced Neural Communication

Abstract

Neural oscillations at distinct frequency bands facilitate communication within and between neural populations. While single-frequency oscillations are well-characterized, the simultaneous emergence of slow (beta) and fast (gamma) oscillations within the same network remains unclear. Here, we demonstrate that multi-frequency oscillations naturally arise when the ratio of inhibitory-to-excitatory synaptic strength falls within a specific regime using a biologically plausible Izhikevich model. We show that this regime maximizes both information capacity and transmission efficiency, suggesting an optimal balance for neural communication. Deviations from this range lead to single-frequency oscillations and reduced communication efficiency, mirroring disruptions observed in neurological disorders. These findings provide mechanistic insight into how the brain leverages multiple oscillatory frequencies for efficient information processing and suggest a potential biomarker for impaired neural communication.