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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 11: Model Types
    • Tue, July 12: Model Fitting
    • Wed, July 13: Generalized Linear Models
    • Thu, July 14: Dimensionality Reduction
    • Fri, July 15: Deep Learning
  • Week 2

    • Mon, July 18: Modeling Practice & Half project day
    • Tue, July 19: Linear Systems
    • Wed, July 20: Biological Neuron Models
    • Thu, July 21: Dynamic Networks
    • Fri, July 22: Project Day (Abstract Writing Day)!
  • Week 3

    • Mon, July 25: Bayesian Decisions
    • Tue, July 26: Hidden Dynamics
    • Wed, July 27: Optimal Control
    • Thu, July 28: Reinforcement Learning
    • Fri, July 29: Network Causality

Daily coursework schedule

General schedule

All days (except W2D1, W2D5, and W3D5) will follow this schedule for course time:

Time (Hour) Lecture
-0:30-0:00* Intro video & text
0:00-0:15** Pod Discussion I
0:15-1:45 Tutorials I
1:45-2:45 Big break
2:45-4:15 Tutorials II
4:15-4:25 Pod Discussion II
4:25-4:30 Reflections & content checks

* : 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 0:00 with the first pod discussion!

Schedule of Specific Days

W2D1: Modeling Practice & Half project day (project proposals)

Time (Hour) Lecture
0:00-0:30* Intro video & text
0:30-2:30** Tutorials 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.

W2D5: Project Day (Abstract Writing Day)

Time (Hour) Lecture
0:00-2:20* Abstract workshop
2:20-2:50 Break
2:50-4:20 Individual abstract editing
4:20-5:05 Mentor meeting
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.

Last day (W3D5)

Time (Hour) Lecture
-1:30~-0:30* Project presentations (slots 1, 3, 4)
-0:30-0:00 Break (slots 1, 3, 4)
0:00-0:15 Pod Discussion I
0:15-1:45 Tutorials I
1:45-2:45 Big break
2:45-4:15 Tutorials II
4:15-4:30 Pod Discussion II
4:30-4:45 Break
4:45-5:10 Evaluation report
5:10-6:10 Project presentations (slots 3, 5)
6:10-6:25 Pod farewell
6:25-7:15 Closing ceremony*

* : For slots 1 & 2 this would occur at 6:55 AM EST, for slot 3 at 1:25 PM EST, and for slots 4 & 5 at 7:25 PM EST.


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.

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