Mazzucato, LucaIstrate, Nicolae2022-10-042022-10-042022-10-04https://hdl.handle.net/1794/27559The temporal activity of many biological systems, including neural circuits, exhibits fluctuations simultaneously varying over a large range of timescales. The mechanisms leading to this temporal heterogeneity are yet unknown. Here we show that random neural networks endowed with a distribution of self-couplings, representing functional neural clusters of different sizes, generate multiple timescales of activity spanning several orders of magnitude. When driven by a time-dependent broadband input, slow and fast neural clusters preferentially entrain slow and fast spectral components of the input, respectively, suggesting a potential mechanism for spectral demixing in cortical circuits.en-USAll Rights Reserved.ClustersHeterogeneousNetworksRNNTimescalesElectronic Thesis or Dissertation