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The thesis explores the role machine learning methods play in creatingintuitive computational models of neural processing. Combined withinterpretability techniques, machine learning could replace human modeler andshift the focus of human effort to extracting the knowledge from the ready-mademodels and articulating that knowledge into intuitive descroptions of reality.This perspective makes the case in favor of the larger role that exploratoryand data-driven approach to computational neuroscience could play whilecoexisting alongside the traditional hypothesis-driven approach.  We exemplify the proposed approach in the context of the knowledgerepresentation taxonomy with three research projects that employinterpretability techniques on top of machine learning methods at threedifferent levels of neural organization. The first study (Chapter 3) exploresfeature importance analysis of a random forest decoder trained on intracerebralrecordings from 100 human subjects to identify spectrotemporal signatures thatcharacterize local neural activity during the task of visual categorization.The second study (Chapter 4) employs representation similarity analysis tocompare the neural responses of the areas along the ventral stream with theactivations of the layers of a deep convolutional neural network. The thirdstudy (Chapter 5) proposes a method that allows test subjects to visuallyexplore the state representation of their neural signal in real time. This isachieved by using a topology-preserving dimensionality reduction technique thatallows to transform the neural data from the multidimensional representationused by the computer into a two-dimensional representation a human can grasp.  The approach, the taxonomy, and the examples, present a strong case for theapplicability of machine learning methods to automatic knowledge discovery inneuroscience.

Understanding Information Processing in Human Brain by Interpreting Machine Learning Models