Teaching

Lecture Notes

Linear algebra tutorial for neuroscientists.
Dynamical systems fixed point analysis in two pages
Linear control theory and the linear quadratic regulator
Kernel methods: RKHS, kernal PCA, kernel ridge regression (hand-written)
Convex optimization and support vector machines (SVM's) (hand-written)
Information theory
Demixed PCA
Models of Synaptic Plasticity (coming soon)
Mean-Field Analysis of Spiking Networks (coming soon)
Single neuron models (coming soon)
Latent variable models (coming soon)
Graphical models and approximate inference (coming soon)

For all the notes supposedly "coming soon", see here and here for a longer and not so proofread set of notes containing all the relevant material (and more!).

Python Tutorials

Each link downloads a zip file with code and Jupyter notebooks, along with a README doc with instructions to run them. All code is in Python 3.

Neural Coding, covering population vector decoding, optimal linear estimator, maximum likelihood decoding, Fisher Information, and noise correlations
Reinforcement Learning, covering the Rescorla-Wagner model, Temporal Difference learning, SARSA, and Q-learning
Deep Learning, covering stochastic gradient descent, feed-forward networks, and recurrent neural networks using PyTorch