Understanding neuronal dynamics, and how they are affected in neurological disorders, is one of the key problems in neuroscience today. Neuronal dynamics are not only altered in various diseases but are also instrumental in the manifestation of behavior, learning, decision-making, and our perception of the world. This talk will describe advances in multiscale theoretical and biophysically grounded tools to understand neuronal mechanisms, with models ranging in granularity from a single neuron to the entire brain.

Firstly, this talk will outline a computational framework based on the electrophysiological activity of a single pain-sensing neuron, which we employed to identify mechanisms of pain sensation mutations and neuropathic pain. Secondly, it will demonstrate a graph-based mathematical model that captures the spectral and spatial features of the brain’s functional activity. This modeling approach revealed biophysical alterations in Alzheimer’s disease, different stages of sleep, and spontaneous fluctuations in electrophysiological functional activity. Together, these results aim to highlight the importance of such modeling techniques in identifying the underlying biophysical mechanisms of neuronal dynamics, which can be intractable to infer using neuroimaging data alone.