All class meetings are in Powell-Focht Bioengineering Hall, Fung Auditorium
||Orientation and computational laboratory introduction
||Biophysical foundations. Channel transport and membrane dynamics; Nernst equilibrium and Goldman-Hodgkin-Katz equation.
||Full and reduced single-compartment neural models. Hodgkin-Huxley, FitzHugh-Nagumo and Moris-Lecar equations.
||Nonlinear dynamics of neural firing. Null clines, stationary points, stability, bifurcation, limit cycles.
||Conductance based models of synaptic coupling between neurons. Fast and slow acting chemical synapses, gap junctions.
||Spike timing-dependent synaptic plasticity. Hebbian learning and principal component analysis. Mean-rate STDP model, correlation learning and homeostasis.
||Computational modeling and efficient emulation of large
networks. Conductance-based integrate-and-fire models;
time-domain and event-based emulators; neuromorphic systems.
||Neurophysiology foundations, and brain connectivity. Making sense of the connectome data: causal dynamics from structural networks.
||Nonlinear dynamical system identification in neuroscience.
||Data assimilation and deep(est) learning.
||Project final presentations. All are welcome!
||Fall 2019 Projects