Neuroscience focuses largely on how the brain mediates perception and cognition. However, this leaves open the basic organization and hierarchies of the brain’s neural activity by itself prior to and independent of its role in cognition. A recent model characterizes the brain’s intrinsic features in terms of temporo- spatial dynamical (rather than cognitive) terms – the brain’s spatiotemporal hierarchies shape what is called ‘brain’s intrinsicality’. The brain’s intrinsicality may provide potential applications in designing artificial intelligence (AI). In this dissertation, I explore ‘intrinsic neural timescales’ and their spatial topography as one main building block of the brain’s intrinsicality. First, I present empirical investigation of temporal hierarchy and information flux as two basic facets of brain’s intrinsicality using magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) data. That is complemented by introducing the notion of intrinsicality through intrinsic neural timescales and how they shape input processing in the brain. Then, I propose a model for input processing through intrinsic neural timescales and provide some notes on how that model can be implemented in an artificial agent. I conclude that the spatiotemporal dynamics of the brain’s intrinsicality provides potential key insights for Artificial General Intelligence (AGI).