Neural mass modeling of cortical and subcortical circuits
Abstract: In this presentation I will discuss feasibility and usefulness of modeling the brain by the interactions beween neural populations, described by their mean behavior, rather than single neurons. I will show that such mean field (or neural mass) models enable a compact and simple, yet biologically plausible, description of brain dynamics and neuronal information processing. First, I will focus on the reproduction of fundamental dynamic phenomena, such as oscillations, bursting, and bistability. Then, I will demonstrate how mean field dynamics can give rise to mechanistic buildings blocks of cognitition, such as gating, short-term memory, and priming, followed by a brief outlook of how such primitives might be combined to realize more complex cognitive operations, such as language processing. In the latter context, I will finally present work describing the mean behavior of long-range fiber bundles, with focus on the roles of myelination and ephaptic coupling between nerve fibers.
Prof. Dr. Thomas R. Knösche
Max Planck Institute for Human Cognitive and Brain Sciences
Research Interests: Mathematical modeling of neuronal networks, with focus on mean field models and application to generation of EEG/MEG, brain stimulation, and cognitive functions; Biophysical modeling of EEG and MEG, with focus on source reconstruction and connectivity estimation; Reconstruction of fiber connections and identification of microstructural properties in the brain using diffusion MRI; Neurocognition of music, language and memory.
Recording available on kcnhub youtube channel