I’m currently a postdoctoral researcher in systems neuroscience with Laura Busse in the Vision Circuits Lab at LMU München in Germany. I graduated with a PhD in Neuroscience from UBC in Nick Swindale’s lab in Vancouver, Canada, and before that with a BSc in Engineering Physics from the University of Alberta in Edmonton, Canada.
My original interests lie in systems neuroscience and computational neuroscience, specifically the activity of neural assemblies in the brain during naturalistic stimulation and varying brain states, and at millisecond timescales. I have a notion that what we learn about the rules of neural interactions governing perception, memory, decision making and ultimately behaviour, can be generalized to other complex systems of interacting agents – like, perhaps, human beings making up a society. This might be a naïve notion, but I hope not.
My ORCiD page lists all of my publications, and my Linkedin page has some more details about my academic and work history.
Here is my PhD thesis (June, 2015): Characterizing patches of primary visual cortex with minimal bias
However, I’ve also long been interested in renewable energy, the electrification of transport, and decarbonization in general. Perhaps if we humans were better at group decision making, we would act more rationally overall and find ourselves in fewer crises.
Over the years, I’ve also realized how much I enjoy the broad methods that were required during my BSc, PhD, and postdoc, including data acquisition, instrumentation, programming, and analysis. As an example, rpimv is a recent Raspberry Pi project I put together that serves 9 degree of freedom IMU data and GPS data over TCP/IP. It’s impressive what can be accomplished these days with only a couple hundred lines of source code.
My GitHub account page shows some of the public software projects I’ve created or contributed to, though others remain in private repos.
I have three major public projects here: dimstim, spyke, and neuropy. The first generates multidimensional visual stimuli with high temporal precision. The second takes raw extracellular neural waveforms and sorts them into spikes from distinct neurons. The third analyzes spike trains in as many ways as possible.
In the Busse lab, among other projects, I work on an internal project based on datajoint for organizing and analyzing data in a relational database.