Stationary covariance spectra of discrete-time non-normal random recurrent dynamics
Why brain-inspired networks behave differently than math predicts
Brain-inspired artificial networks don't follow the simple mathematical rules that scientists expected. A new mathematical approach reveals how noise ripples through these networks in unexpected ways, showing that the randomness in how neurons connect matters far more than previous theory suggested.
Neuroscientists use these artificial networks to understand how real brains process information by comparing network behavior to actual brain recordings. Without knowing how these networks truly distribute activity across their components, researchers can't tell whether their models actually capture real brain dynamics or just produce superficially similar results. This work provides the missing mathematical foundation to make those comparisons meaningful.