ACTIVITY STATES IN HIERARCHICAL NETWORKS
Relevant for Research Areas
Neuroanatomy, animal experiments and human lesion studies have converged in their view that brain networks are organized in multiple structural and functional hierarchies. Most strategies to interfere with brains by recording and stimulation operate at a mesoscopic scale of network organization. It is, therefore, important to improve our insight into the structural determinants of neuronal activity dynamics in healthy and dysfunctional brains at this scale. At the present time, computational methods – large-scale numerical simulations of neuronal networks, enhanced by analytical approaches – are essential to generate such an integrated understanding. Multi-scale network models matched to the specific research question will contribute to our understanding of neuronal network mechanisms of normal and pathological activity, help to optimize the yield of experiments in fundamental research, and support the design of novel methods of therapeutical intervention in the diseased brain.
Computational modeling approaches were employed to infer adaptive input-output relations in primary sensory networks of the neocortex. Stimulus processing by natural input channels to the brain serves as a blueprint for the design of interference protocols via artificial electrical or optical stimulation. Insight into adaptive input-output relations of brain networks is also inevitable to successfully modify sensory-motor loops, which are targeted in some classes of therapeutic interference.