UC San Diego

Neuroscience is a discipline rich in data. In neuroscience, information about the nervous system is derived using a wide-range of experimental methodologies. While each of these methodologies can provide unique insight into nervous system function, each of them are also fundamentally tied to the subjects they measure. Because of this, no single experimental methodology can provide a universal perspective on nervous system function. It becomes sensible, therefore, to explore how we might gain greater insight into the nervous system from unifying the data derived from different experimental methodologies.

In this dissertation, I apply techniques from computer science to assemble the rich sets of data in neuroscience into data structures that allow otherwise impractical or impossible questions to be answered. Because of the nature of the nervous system, these data structures must be capable of dealing with data describing parts of the nervous system that vary in size across eight orders of magnitude, otherwise known as "multi-scale" data. These parts include molecules, parts of cells, cells and their circuits, brain regions and features of gross anatomy.

In order to address this challenge, I distinguish between information about the nervous system that expresses facts or semantic relationships (the "what"), from information that expresses images or spatial information (the "where"). I apply a data structure known as an ontology to describe parts of cells relevant to neuroscience, i.e. subcellular anatomy. This ontology is applied to enable automated question answering, i.e. inference, from data sets displaying parts of cells that are derived from electron microscopy. The challenges of building these data structures as a neuroscience community are reviewed, and in response I present an online software system, NeuroLex.org, that provides a first step towards addressing them. Additional examples of automated question answering in neuroanatomy are shown using the NeuroLex knowledge base, which now spans ~17,000 concepts specific to neuroscience.

To address the challenge of the "where" of neuroscience, I present an online software system, the Whole Brain Catalog, that enables two-dimensional, three-dimensional, and four-dimensional (i.e. time-varying) multi-scale data sets to be spatially integrated within a common coordinate system. These data are then navigable by a user in three dimensions in real time and across spatial scales, and can be integrated to answer scientific questions. The dissertation concludes with a look towards the future of the continued convergence between computer science and neuroscience.