williamqzy/course-content

Content for NMA Computational Neuroscience course

NeuroMatch Academy (NMA) Computational Neuroscience syllabus

⚠️ This repository is under construction for the 2021 edition of the course ⚠️
See the NMA website for links to archival materials from the 2020 edition

Objectives: Introduce traditional and emerging computational neuroscience tools, their complementarity, and what they can tell us about the brain. A main focus is on modeling choices, model creation, model evaluation and understanding how they relate to biological questions.

Day structure: Opening keynote, 3h15min lecture/live Q&A/tutorial modules, 40min group discussion, 55min interpretation lecture (Outro) + several live Q&As (what did we learn today, what does it mean, underlying philosophy). There will also be many networking activities (professional development sessions, yoga, social hangouts etc)! Details below.

Note for visitors from China: This repository contains many links to YouTube and Google Colab. We have a version of the repository with those same videos posted on bilibili, and the Google Colab links replaced with links to Aliyun. Please visit the China Accessible Neuromatch Course-Content

Prerequisites: See here

Course materials

• Welcome Video
• Tutorials: videos, notebooks, and slides
• Projects: videos, notebooks, and slides

Group projects are offered for the interactive track only and will be running during all 3 weeks of NMA!

Course outline

• Week 0 (Optional)

  • Asynchronous: Python Workshop Part 1
  • Asynchronous: Python Workshop Part 2
  • Wed, June 30th: Linear Algebra
  • Thus, July 1st:Calculus
  • Fri, July 2nd: Probability & Statistics

• Week 1

  • Mon, July 5: Model Types
  • Tue, July 6: Modeling Practice
  • Wed, July 7: Model Fitting
  • Thu, July 8: Generalized Linear Models
  • Fri, July 9: Dimensionality Reduction

• Week 2

  • Mon, July 12: Deep Learning
  • Tue, July 13: Linear Systems
  • Wed, July 14: Biological Neuron Models
  • Thu, July 15: Dynamic Networks
  • Fri, July 16: Project day!

• Week 3

  • Mon, July 19: Bayesian Decisions
  • Tue, July 20: Hidden Dynamics
  • Wed, July 21: Optimal Control
  • Thu, July 22: Reinforcement Learning
  • Fri, July 23: Network Causality

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Licensing

The contents of this repository are shared under under a Creative Commons Attribution 4.0 International License.

Software elements are additionally licensed under the BSD (3-Clause) License.

Derivative works may use the license that is more appropriate to the relevant context.