Building emergent representation of neural states using dynamical models

The brain is an intricate system, composed of myriad interacting components across a hierarchy of length and time scales. Understanding the dynamic collective state of these components is crucial for gaining insight into the emergent cognitive functions that ultimately control our actions and our perception of the world. However, fundamental principles that can provide a clear description of these dynamics remain elusive. Here, we develop a deep-learning framework that can reduce the complex dynamics across the different spatial and temporal scales to stereotyped brain states through fitting our data and associating points in time to differing basins of attraction. We demonstrate the potential of the approach using human electrocorticography (ECoG) data recorded from multiple patients over a period of several days. In addition to states that the brain revisits over time, using our pipeline some portion of the brain's activity can also be captured by repeated spatiotemporal signatures, which correspond to repeated sequences of brain states but also include information about the spatial signatures of activity that occur as one state transitions to another. These findings provide future avenues to not only decode large-scale interactions of complex brain dynamics, but also to associate these brain states to subtle alterations in behavior.