Individual-Level Large-Scale Circuit Modelling of Human Cortex
Individuality is a defining feature of human existence. Yet the neural mechanisms underlying such individual variation are not fully understood. Prior research has suggested that variation in the balance between excitation and inhibition within the brain may contribute to these differences. To investigate this hypothesis, this thesis employed a large-scale, biophysically-based model of the human cortex. This model is governed by a small set of biologically interpretable parameters, allowing for a focused analysis of how shifts in excitation and inhibition shape individual brain function. This work highlights the potential of computational neuroscience to bridge the gap between neural mechanisms and individual variation. By leveraging biophysically realistic models of cortical function, this thesis provides new insights into how fundamental neural processes may give rise to the wide spectrum of human individualization.
Thesis committee: John Murray (thesis advisor), Christopher Lynn, Damon Clark, and Youngsun Cho