Introductory course on theoretical neuroscience. Neuronal biophysics: ions, membranes, channels. Single neuron models: Hodgkin-Huxley, 2D reductions, phase plane analysis. Leaky integrate-and-fire model, response to stochastic inputs. Models of synapses and synaptic plasticity. Models of networks at various scales. Network dynamics: rate models, networks of spiking neurons. Coding and decoding by single neurons and populations of neurons. Unsupervised learning, supervised learning, reinforcement learning. Adequate for any graduate student in physics or other quantitative fields (mathematics, statistics, engineering, computer science) and undergraduate majors in such fields.