Modeling Practice

Related collection

Neuromatch Academy 2020

Modeling Practice

By
Neuromatch Academy
Category
Computational neuroscience
Level
Beginner

Neuromatch Academy aims to introduce traditional and emerging tools of computational neuroscience to trainees. It is appropriate for student population ranging from undergraduates to faculty in academic settings and also includes industry professionals. In addition to teaching the technical details of computational methods, Neuromatch Academy also provide a curriculum centered on modern neuroscience concepts taught by leading professors along with explicit instruction on how and why to apply models. 

 

This course focuses on how to get from a scientific question to a model using concrete examples, presents a 10-step practical guide on how to succeed in modeling.

Course Features
Lectures
Interactive Tutorials
Suggested reading
Recordings of question and answer sessions
Discussion Forum on Neurostars.org
Lessons of this Course
1
1
Modeling Practice I (Intro Lecture)
Duration:
29:52
Speaker:
Megan Peters

This lecture is part of the Neuromatch Academy (NMA), a massive, interactive online summer school held in 2020 that provided participants with experiences spanning from hands-on modeling experience to meta-science interpretation skills across just about everything that could reasonably be included in the label "computational neuroscience". 

 

This lecture focuses on how to get from a scientific question to a model using concrete examples. We will present a 10-step practical guide on how to succeed in modeling. This lecture contains links to 2 tutorials, lecture/tutorial slides, suggested reading list, and 3 recorded question and answer sessions.

2
4
Modeling Practice II (Outro Lecture)
Duration:
22:51
Speaker:
Gunnar Blohm

This lecture is part of the Neuromatch Academy (NMA), a massive, interactive online summer school held in 2020 that provided participants with experiences spanning from hands-on modeling experience to meta-science interpretation skills across just about everything that could reasonably be included in the label "computational neuroscience". 

 

This lecture formalizes modeling as a decision process that is constrained by a precise problem statement and specific model goals. We provide real-life examples on how model building is usually less linear than presented in Modeling Practice I. 

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