Large Scale Modelling and Large Scale Data Analysis 2020

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News and Updates:

13.05

The recordings, slides, reading material and assignment link for the third session are now available on Moodle.

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06.05

The recordings, slides and reading material for the second session are now available on the Moodle course page.

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29.04

The recordings and slides for the introductory session are now available on the Moodle course page. You can also see them here

Course outline:

The aim of the course is to teach you concepts and techniques for modelling and analysing neural systems. You will learn how to build mechanistic models of neural systems, analyse neural simulations and measurements with statistical encoding and decoding models, and how to process and interpret high-dimensional functional imaging measurements. On the way, we will aim to also improve your programming, version-control, data-visualization and reporting skills.

The course will be taught by Jakob Macke, Ruben Portugues and Joseph Donovan, with help from Jan Bölts, Michael Deistler, Jan-Matthis Lückmann, Poornima Ramesh, Auguste Schulz and Artur Speiser.

Module 0: Introduction and Logistics

Module 1: Mechanistic neural models of decision-making

Module 2: Statistical models for spiking activity

Module 3: High dimensional neural data

Timeline:

The course will begin on 29. April, where we will introduce the course, and the tools we will be using to conduct the course. There will be 14 lectures in total, one each week. The course will be divided into modules. Module 1 and 2 will be (roughly) three weeks each.

Tentative timeline

Course format:

Regular teaching hours for the course will be each Wednesday, 9:45 to 13:00. We will typically begin by live-streaming a short lecture from the course lecturers, and for the rest of the time-window we will have an open Q&A session -- during this time we will consult students in their project both via Zoom and also by answering queries over GitHub/chat.

Since we expect to have technical difficulties with live-streaming and (possibly) participants from different time zones, we are trying to set up a course that you can follow even if you are unable to attend the live-stream sessions. To that end, we will also provide plenty of reading material, slides and links to other video lectures, and will be relying on these resources to cover a lot of the course content. Please take these reading assignments and pointers to literature seriously; working through these materials will be an integral part of the course!

The course-work will be project-based. Each module will have one project that you will be expected to tackle in groups of 2 or 3. The groups will be self-assigned. For each project, you will be expected to submit code, plots and a short project report through GitHub. The details of the projects will also be made available via GitHub. We will also give you access to a compute cluster for running code, in case you need it.

Exams and Grading

We will ask you to hand in an assignment for each of the modules-- this assignment will include both your code, as well as a final report with figures which we expect you to make. The tentative plan is to have a short oral exam at the end of the course. In this oral exam, you might be asked questions both about your code and the project report. We will give you more information as the course progresses.

Communication:

Live-stream session:

We will have a live-streamed sessions via Zoom every Wednesday from 9:30 a.m. to 12:30 p.m. CEST. These sessions will be used to answer questions from the previous session, and introduce material for the next week.

Permanent link to the Zoom meeting room: https://tum-conf.zoom.us/j/99107124548?pwd=VXhjeXNJZHgrdUthb0FPOGZqY2dYZz09

Lecture materials and updates:

We will send you information and updates about the course, as well as lecture materials via the TUM Moodle course page

Project submission:

The project assignments will be uploaded to the course GitHub repository. Students will submit their work using GitHub classroom (which will be introduced in the first lecture). For each project, you will also be expected to submit a short project report as a pdf. If you are not familiar with GitHub, or do not know what forking or pull requests are, please do not worry. We will have an introduction to using GitHub in the introductory session on 29. April

Questions about course content and/or projects:

During the live-stream session within the 3-hour slot every week, we will set aside some time to answer questions from the previous session. For questions that you might have during the rest of the week, we ask that you submit issues on the GitHub repository. We have a strong preference for you to submit questions via Github-issues-- this has the benefit that everyone else can see the answer, so that we do not have to answer overlapping questions multiple times. Please refrain from emailing questions to the course instructors unless absolutely necessary.

Resources:

We will provide the following resources during the course.

• Lecture materials:

  Lecture materials will be uploaded to the Moodle course page

• GitHub repository:

  All project work will take place on the course repository. Please raise questions or problems by raising an issue on the repository.

• JupyterHub compute server:

  We will provide access to a compute server on which you can run jupyter notebooks. You will need a TUM account and the LRZ VPN client in order to access the server. Instructions for logging in to the server and using it will be made available after the introductory session on 29. April.