Our field is at a uniquely wonderful stage. We are in a period of intense discovery about brain circuitry, but many fundamental questions about how these circuits produce the brain's dynamics and the elements of its computation remain wide open. As a result, even students who are just learning the basics of programming can rapidly explore the unknown! In our experience this gives a window into what doing science feels like that can be, well, pure fun -- a powerful reason to want more.
Below are some materials for sharing this experience with students, mostly through 1-2 week workshops in college "bridge" programs with incoming freshmen. In each, you'll find overview information that gives the motivation and "syllabus" for the workshop and other suggestions, as well as code, suggested readings, and other materials.
This is the oppositve of a comprehensive course, see the fantastic online Neuromatch program. And for the delight of delving into the latest neural data, see the superb materials from the Allen Institute and UW's Summer Workshp on the Dynamic Brain.
OK, now for a very first and old-school taste:
Signal propagation in neural networks: By Alex Cayco Gajic, Eric Shea-Brown, and colleagues. How can the brain send a signal through its layers of neurons without losing information? Learn MATLAB programming and the basics of neuroscience from scratch, and put your new skills to work.
Persistent activity in neural populations: By Nile Graddis. Our sense of the world comes from assembling information that arrives at different points in time: consider intepreting a spoken sentence. How does the brain link signals over time? A leading idea is that it contains neurons whose activity carries a long lasting trace of past inputs. This is the concept of persistent activity. In this mini-workshop we'll explore how groups of persistently active neurons can represent a specific type information: information about elapsed time. First, we'll learn elements of PYTHON, a wonderful open-source language, through Nile's punchy and crisp tutorial. Next, head over to the workshop on persistent activity, which is another self-guided ipython notebook. This references the linked neurosciene article (Nature Neuroscience, 2010): this, and the more mathematical 2016 article, give rich views on persistent neurons in action.
For another classical route into neuronal dynamics, see our classical answer to SIAM's timeless question "whydomath?" Hodgkin, Huxley, and the mathematics of the spike. (with Brent Doiron)
For a touch of background, please see our overview articles in Scholarpedia: Stability (with Phil Holmes); Limit cycle (with Jeff Moehlis and Kresimir Josic); Isochron (with Kresimir Josic and Jeff Moehlis)