Computational neuroscience investigates the brain at many different levels, from single cell activity to small, local network computation to the dynamics of large neuronal populations. This course introduces modeling and quantitative techniques used to investigate neural activity at all these different levels. Topics covered include passive membrane properties, the Nernst potential, derivation of the Hodgkin-Huxley model, action potential generation, action potential propagation in cable and multi-compartmental models, reductions of the Hodgkin-Huxley model, phase plane analysis, linear stability and bifurcation analysis, synaptic currents, excitatory and inhibitory network dynamics.

No book for this course

For more information on this course, please visit the Department of Mathematics webpage

Class Format:

We are currently anticipating in-person instruction for the Fall 2021 term, however this may be subject to change due to the uncertainty of the pandemic and what shape Fall 2021 will take.