Date of Award
Doctor of Philosophy (PhD)
Chair and Committee
The role of computation in science is continually growing and neuroscience is no exception. Despite this, a severe lack of scientific software infrastructure persists, slowing progress in many domains. In this thesis, we will see how the combination of neuroscience and software engineering can build infrastructure that enables discovery. The first chapter discusses the Turtle Electrophysiology Project, or TEP, an experiment-automation and data-management system. This system has allowed us to automate away some of the most tedious tasks involved in conducting experiments. As a result, we can collect more data in less time, and with fewer errors related to the loss of metadata: information about how the data were collected). Also, since all of the metadata is automatically digitized during the experiment we can now completely automate our analyses. Chapters two and three are examples of research conducted using the ever-evolving TEP system. In the first instance, we used TEP to deliver visual stimuli and handle data-management. In chapter three, the experiments involved delivering electrical stimuli instead of visual stimuli, and much more rigorous analysis. And even though TEP was not specifically designed to handle collecting data this way, the flexible tags system enabled us to do so. Finally, chapter four details the construction of a robust analysis tool called Spikepy. Whereas TEP is specially designed for the turtle preparation we have, Spikepy is a general-purpose spike-sorting application and framework. Spikepy takes flexibility to the extreme by being a plugin-based framework, yet maintaining a very easy to use interface.
Morton, David, "The Automation of Electrophysiological Experiments and Data Analysis" (2012). All Theses and Dissertations (ETDs). 720.
Permanent URL: http://dx.doi.org/10.7936/K7W0941T