My favorite course of all time is one that I had the chance to TA. It was based on a Princeton course originally organized by Ned Wingreen and David Botstein (see this paper for their teaching philosophy), and brought to BU by my advisor Pankaj Mehta.
The class was intended for upper level undergrads and graduate students from a variety of backgrounds including biology, physics, engineering, etc. In order to establish a common vocabulary and shared knowledge base, each week we read and discussed foundational papers in quantitative biology. The papers were a mix of theoretical papers and experimental papers that contributed key concepts (we did not read overly mathematical theory paper or experimentally detailed protocol papers). By the end of the course, everyone had a shared set of fundamental concepts that both theorists and experimentalists could understand.
Since I’m relatively new to computational neuroscience, I’m trying to startup a journal club. However, computational neuroscience is a grab-bag of topics that only have the brain as a unifier. Additionally, journal clubs usually cover the latest breaking research, which in computational neuroscience would lead to papers from week to week that may have minimal in common.
So inspired by the Wingreen and Botstein course, we will be using an approach that I’m calling “journal club for credit”. We are going to try and blend the best ideas from a course based on fundamental papers with a journal club that covers the latest research. We are organizing around units that will last 2-4 weeks. Each unit will be a self-contained introduction to a topic. The first weeks cover the essential papers needed to understand the background, while the final week will discuss current research.
Since I suggested this organization, I’m starting us off with a unit on Deep Learning. My intent is to blog about each unit and topic. In order to encourage others to actually read the paper, my blog posts will be deliberately vague. My plan is to provide the needed background to get you interested (the WHY you should care), start you towards understanding (define WHAT the topic is), but avoiding explaining the topic in enough detail that you do not feel compelled to read the papers (I want you to discover the detailed HOW and WHY of the topic on your own). I will outline a set of fundamental questions that everyone should understand as well as additional questions that go further into supplementary points or advanced topics.
I’m not exactly sure how many units will get covered (highly dependent on the rest of the journal club!), but my dream is that by the end of my postdoc, we have covered enough topics in computational neuroscience that we have a “course” in the similar philosophy to Wingreen and Botstein.
For the details of the papers, check out Journal Club for Computational Neuroscience