UC San Diego

Computational modeling is critical in forming hypotheses about the true biological functioning of the human brain. Understanding the limitations and compromises inherent to modeling, however, is an incredibly important and often under-looked aspect of creating and using these models. While every model will have its shortcomings, these can be compensated for with a combination of different modeling techniques across different scales. In this dissertation, we here provide a detailed example of how modeling across scales can provide necessary compensation of compromises in the context of understanding the effects of electrical stimulation on the development of epilepsy following traumatic brain injury. As we will see, this is a highly complex problem which requires a variety of techniques to make substantive progress forward in our understanding of the neural dynamics at play. We first employ network models with realistic ion channel dynamics to investigate the changes in oscillatory and network activity that occurs following Traumatic Brain Injury. We then develop a whole-brain model capable of replicating the states of wake and biophysically realistic slow wave sleep, dynamically transitioning from local to global slow waves as is seen in-vivo. This type of model capable of simulating complex, mixed dynamics across the entire cortex is exactly what will be needed for full models of seizure genesis and propagation following traumatic brain injury. Finally, we use a highly detailed single neuron model based on morphological reconstructions of real cells to determine the effects of electrical stimulation across cell type, cell layer, and species - this knowledge will be needed to determine accurate effects of stimulation following traumatic brain injury. With these 3 models combined, we are now poised to gain insight into the entire system, from single cell responses to complex network interactions.