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/fα 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.Author summary: The electrical activity of the brain contains both rhythmic oscillations and a broadband background signal that follows a characteristic 1/f pattern. Although this background activity is routinely measured with techniques such as EEG and local field potentials, its biological origin and functional significance remain debated. In particular, its shape changes systematically across sleep, wakefulness, anesthesia, and neurological disorders, but the mechanisms responsible for these changes are still unclear. Here, we use mathematical analyses and large-scale computer simulations of neural networks to show that many of these spectral changes can emerge naturally from the interaction between recurrent network dynamics and ongoing, noise-like fluctuations. Rather than requiring a specific cellular or molecular process, changes in broadband spectral features arise because fluctuations alter the effective timescales over which neural populations evolve. This shifts the characteristic “knee” of the power spectrum and changes its overall shape. Our findings provide a simple mechanistic explanation for how brain activity can reorganize across different functional states and suggest that commonly used spectral biomarkers are in fact dynamic and represent a generic feature of recurrent neural networks.