Topic: power-law distribution

Ongoing neural activity unfolds over intrinsic timescales that vary widely across the brain. The increase in timescales along the sensory-transmodal cortical axis parallels the cortical hierarchy, while diversity of timescales within each area may reflect functional specialization of individual neurons. However, mechanisms that give rise to the diversity of timescales within and across brain areas remain largely unknown. We leveraged a brain-wide dataset of single-neuron activity to characterize the intrinsic timescales across the whole mouse brain. Timescales varied widely across brain region

Since the brain operates as a scale-free network, the frequency-power plot of fMRI BOLD signals follows a power-law distribution. However, conventional deep neural network models that fail to account for this characteristic tend to underperform, lacking a proper model structure to capture the brain signal as a complex system adequately. In this paper, we propose DivfreqBERT, an end-to-end timeseries data model that leverages the features of scale-free networks to encode biological characteristics better. We employed Lorentzian and multi-fractal functions to partition the whole-brain dynamics i