Criticality, Connectivity, and Neural Disorder: A Multifaceted Approach to Neural Computation - Heiney, Kristine; Huse Ramstad, Ola; Fiskum, Vegard; Christiansen, Nicholas; Sandvig, Axel; Nichele, Stefano; Sandvig, Ioanna (Frontiers in Computational Neuroscience, 2021)
It has been theorized that the brain optimizes its capacity for computation by self-organizing into the critical state. This dynamical state, also termed the ‘edge of chaos’, is achieved by striking a balance such that activity can effectively spread through the network without overwhelming it and is commonly identified in neuronal networks by observing the behavior of cascades of network activity termed ‘neuronal avalanches’. The dynamic activity that occurs in neuronal networks is closely intertwined with how the elements of the network are connected and how they influence one another’s activity. In this review, we argue that taking a more holistic approach to studying criticality would provide a greater understanding of the mechanisms that drive networks to criticality and how their disruption may manifest in different disorders. First, integrating graph theory into experimental studies on criticality, as is becoming more common in theoretical and modeling studies, would provide insight into the kinds of network structures that support criticality. Furthermore, plasticity mechanisms play a crucial role in shaping these structures, both in terms of homeostatic maintenance and learning. Finally, information theoretical approaches can tie in more concrete evidence of a network’s computational capabilities. Approaching neural dynamics with all these facets in mind has the potential to provide a greater understanding of what goes wrong during the onset of neural disease and disorder. Criticality analysis therefore holds potential to identify disruptions to healthy dynamics, granted that robust methods and approaches are considered.