Giter Site home page Giter Site logo

madrigaljose / course-content Goto Github PK

View Code? Open in Web Editor NEW

This project forked from neuromatchacademy/course-content

0.0 0.0 0.0 1.97 GB

NMA Computational Neuroscience course

Home Page: https://compneuro.neuromatch.io

License: Creative Commons Attribution 4.0 International

Jupyter Notebook 99.17% Python 0.83%

course-content's Introduction

NeuroMatch Academy (NMA) Computational Neuroscience syllabus

The content should primarily be accessed from our new ebook: https://compneuro.neuromatch.io/

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.

Prerequisites: See here

Course materials

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 for students + Mandatory TA training for ALL TAS
    • Asynchronous: Python Workshop Part 2 for students + Mandatory TA training for ALL TAS
    • Wed, June 30th: Linear Algebra (Mandatory for all Tutorial TAs). Project TAs have separate training.
    • Thus, July 1st:Calculus (Mandatory for all Tutorial TAs). Project TAs have separate training.
    • Fri, July 2nd: Probability & Statistics (Mandatory for all Tutorial TAs). Project TAs have separate training.
  • 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

Daily schedule

All days (except W1D2, W2D5, and W3D5) will follow this schedule for course time:

Time (Hour) Lecture
0:00-0:30* Intro video & text
0:30-0:45** Pod discussion I
0:45-2:15 Tutorials + nano-lectures I
2:15-3:15 Big break
3:15-4:45 Tutorials + nano-lectures II
4:45-4:55 Pod dicussion II
4:55-5:00 Reflections & content checks
5:05-5:35* Outro

* The intro and outro will be watched asynchronously, which means that you can watch this lecture before and after the start of the synchronous session

** Note that the synchronous session starts at 0:30 with the first pod discussion!

On W2D1, W2D4, and W3D4:

Time (Hour) Lecture
5:40-6:40 Live Q&A

On W1D2 (project launch day):

Time (Hour) Lecture
0:00-0:30* Intro video & text
0:30-2:30** Tutorials + nano-lectures I
2:30-2:45 Outro
2:45-3:45 Big break
3:45-5:30 Literature review
5:30-5:45 Break
5:45-8:30*** Project proposal

* The intro and outro will be watched asynchronously, which means that you can watch this lecture before and after the start of the synchronous session

** Note that the synchronous session starts at 0:30 with the first pod discussion!

*** Note that this includes the next available project time, which may be on the next day.

On W2D5 (abstract writing day):

Time (Hour) Lecture
0:00-2:00* Abstract workshop
2:00-2:50 Big Break
2:50-4:20 Individual abstract editing
4:20-5:05 Mentor meeting (flexible time)
5:05-5:25 Break
5:25-6:25 Pod abstract swap
6:25-8:00 Finalize abstract
  • This day is completely asynchronous, so you should combine tutorial and project time for a total of 8 hours.

On W3D5 (final day!), we will have an extra celebration and pod wrap-ups after the material:

Time (Hour) Lecture
0:00-0:30* Intro video & text
0:30-0:45** Pod discussion I
0:45-2:15 Tutorials + nano-lectures I
2:15-3:15 Big break
3:15-4:45 Tutorials + nano-lectures II
4:45-4:55 Pod dicussion II
4:55-5:00 Reflections & content checks
5:05-5:35* Outro
5:35-5:45 Break
5:45-6:10 Evaluation report
6:10-7:10 Project presentations
7:10-7:25 Pod farewell
7:25-8:15 Closing ceremony

* The intro and outro will be watched asynchronously, which means that you can watch this lecture before and after the start of the synchronous session

** Note that the synchronous session starts at 0:30 with the first pod discussion!

Licensing

CC BY 4.0

CC BY 4.0 BSD-3

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.

course-content's People

Contributors

actions-user avatar athenaakrami avatar bgalbraith avatar carsen-stringer avatar dougollerenshaw avatar ebatty avatar echeng6 avatar eegdude avatar eejd avatar gunnarblohm avatar jamenendez11 avatar jesparent avatar jesselivezey avatar kshitijd20 avatar marius10p avatar mk-mccann avatar mmyros avatar mpbrigham avatar mrkrause avatar msarvestani avatar mwaskom avatar patrickmineault avatar rdgao avatar siddsuresh97 avatar spiroschv avatar ssnio avatar taravanviegen avatar titipata avatar vincentvalton avatar yifr avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.