Joint Ph.D. Program in Neural Computation & Machine Learning
Machine Learning Department, Carnegie Mellon University
Neural Dynamics andInteractions in the Human Ventral Visual Pathway
The ventral visual pathway in the brain plays central role in visual object recognition. The classical model of the ventral visual pathway, which poses it as a hierarchical, distributed and feed-forward network, does not match the actual structure of the pathway, which is highly interconnected with reciprocal and non-hierarchical projections. Here we address three major consequences of this non-classical structure with regard to neural dynamics and interactions: (i) the model does not consider any extended information processing dynamics; (ii) the model does not allow for adaptive and recurrent interactions between areas; (iii) the model only characterizes evoked-response with no state-dependence from the neural context. To begin to address these gaps in the classical model, we focus on the categorical-selective regions in the ventral pathway and study the neural dynamics and interactions using intracranial electroencephalography (iEEG), which overcomes the limitations of spatiotemporal resolution in current non-invasive human neuroimaging techniques.
Thesis Committee: Avniel Singh Ghuman (Co-Chair/Pitt Neurosurgery) Max G. G'Sell (Co-Chair) Robert E. Kass Christopher I. Baker (NIMH)