  The thesis explores the role machine learning methods play in creating
intuitive computational models of neural processing. Combined with
interpretability techniques, machine learning could replace human modeler and
shift the focus of human effort to extracting the knowledge from the ready-made
models and articulating that knowledge into intuitive descroptions of reality.
This perspective makes the case in favor of the larger role that exploratory
and data-driven approach to computational neuroscience could play while
coexisting alongside the traditional hypothesis-driven approach.
  We exemplify the proposed approach in the context of the knowledge
representation taxonomy with three research projects that employ
interpretability techniques on top of machine learning methods at three
different levels of neural organization. The first study (Chapter 3) explores
feature importance analysis of a random forest decoder trained on intracerebral
recordings from 100 human subjects to identify spectrotemporal signatures that
characterize local neural activity during the task of visual categorization.
The second study (Chapter 4) employs representation similarity analysis to
compare the neural responses of the areas along the ventral stream with the
activations of the layers of a deep convolutional neural network. The third
study (Chapter 5) proposes a method that allows test subjects to visually
explore the state representation of their neural signal in real time. This is
achieved by using a topology-preserving dimensionality reduction technique that
allows to transform the neural data from the multidimensional representation
used by the computer into a two-dimensional representation a human can grasp.
  The approach, the taxonomy, and the examples, present a strong case for the
applicability of machine learning methods to automatic knowledge discovery in
neuroscience.
