In this tutorial we will investigate statistical models for neural dynamics. The focus lies on generalized linear models (GLMs). You will learn how to calculate the maximum likelihood estimator (MLE) of neural firing rates and construct LNP models including a spike-history dependet LNP model before moving on to fit an LNP model with MLE.
Go through the tutorial. Part 1 will give you some practice with the maths. There is no need to include the whole derivation in the results you hand in, but just the final results. (Note that this will most likely be different in the final reports - so if you want to get practice with Markdown/ LaTeX already now, feel free to include everything.) In part 2 you will construct two different LNP models and will try out different approaches to estimate the convolution filter of a simulated neuron.
Link to the assignment: https://classroom.github.com/a/jz8elvYf
In this tutorial you will fit an NLP model. First based on the Maximum Likelihooed Estimator (MLE) and then with Maximum Aposteriori (MAP). The deadline is Wednesday June 10, 2020. This notebook will help you work on the mini project (Week 6) which we encourage you to start simulataneously.
Link to the assignment: https://classroom.github.com/g/hIqY85Vz
In this tutorial/ mini porject you will deepen your understanding of GLMs while working with a real neural dataset: electrophysiology in monkeys while they perform a reaching task. The deadline for this tutorial is Wednesday June 17, 2020.
Link to the assignment: https://classroom.github.com/g/9g8t2TSK
We summarised all the logistics you need to take care of in logistics.md.