Linear Network Theory and Sloppy Models

Taught by: Mark Goldman, UC Davis (November 21, 2016)

Description: This tutorial describes how to apply linear network theory to the analysis and interpretation of neural data. It introduces the concept of “sloppy models” that capture a common problem in model-fitting, in which individual model parameters are poorly constrained by available data (i.e. have “poorly/sloppily constrained parameter values”). Simple methods are illustrated for describing which combination of parameters most affect a particular model fit. This material is relevant to problems in neuroscience involving the interpretation of multidimensional data from recurrently connected systems.


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Course Info

As Taught In
Fall 2023
Learning Resource Types
Lecture Videos
Tutorial Videos