This repository contains my notes for the course NeuroBio 316QC: Probabilistic models for neural data: from single neurons to population dynamics at Harvard Medical School:
Probabilistic models are a powerful approach for gaining an understanding of what drives the activity of individual neurons and neural populations. ? This course will dissect their modular, plug-and-play structure, from single-neuron models over generalized linear models to state space models for population dynamics. Students will learn their basic building blocks, and how to flexibly assemble them to suit their own data analysis needs.
Upon completion of the course, students should be able to (i) identify the model structure and associated assumptions of common models in the literature; (ii) apply existing probabilistic models to neural datasets; and (iii) flexibly design new models by re-using existing model components.
The topics for the sessions of the course are outlined below:
Create the conda environment for this repo. I used mamba because it is generally faster than conda.
conda create -n neuro316 -c conda-forge python=3.9 mamba
mamba env update --name neuro316 --file environment.yamlConvert a Markdown "write-up" into a PDF for submission.
cd exercises/
pandoc --defaults writeup-defaults.yaml -o 02_exercise-1-writeup.pdf 02_exercise-1-writeup.md