Problem Statement: Deep neural networks have emerged as leading models for predicting neural data from a variety of brain areas and species. Here we would like to explore whether this modeling framework can be used to predict how neural representations of visual stimuli change over development.
Core research idea:  We will approach the modeling in two ways, one is to build models that represent the initial conditions of  the visual cortex prior to the onset of visual experience and the second is to use  the training phase of models that differ in their architectures and training rules as models of human brain development.  Critically, to test these models, we will acquire rich, high temporal resolution data sets from developing human infants using high-density EEG recordings.