Noise-like fluctuations drive shifts in neuronal timescales and 1/f power spectra across brain states

The aperiodic, broadband components of many aggregated electrophysiological recordings such as LFP, EEG and ECoG exhibit characteristic features, notably 1 / f α power-law scaling and a spectral knee. These features of the power spectral density (PSD) vary with behavioral state, arousal, pharmacological interventions, and are linked to transitions between distinct brain states. We investigate the origins of this variability using large-scale recurrent neural networks with sparse, balanced, and random connectivity, driven by state-dependent fluctuations. By integrating recent advances in random matrix theory, we develop an analytical framework that characterizes how such fluctuations shape key features of the PSD, accounting for both nonlinear and stochastic contributions. Our results show that the variability of broadband spectral features can arise as a generic property of nonlinear recurrent networks driven by noise-like fluctuations. In particular, the emergence and modulation of the spectral knee reflect shifts in effective neuronal timescales, linking noise-like fluctuations to state-dependent temporal organization in large-scale networks. Together, these findings provide a mechanistic account of how broadband spectral features can emerge from intrinsic network dynamics, and suggest that changes in spectral features may reflect noise-driven, nonlinear transitions in recurrent neural systems, rather than the action of a single underlying biophysical mechanism.