The tutorial will cover linear network theory and how to apply it to neural data.  It also will introduce the concept of “sloppy models,” which refers to a common problem in model-fitting:  what to do when individual model parameters are poorly constrained by available data (i.e. have “poorly/sloppily constrained parameter values”).  Simple methods will be illustrated for describing which combination of parameters most affect your model fit.  This tutorial should be interesting to many in the BCS community who are considering different ways of interpreting multi-dimensional data from recurrently connected systems.  

This is part of the BCS Computational Tutorial Series.  Tutorial materials will be posted here: https://stellar.mit.edu/S/project/bcs-comp-tut/materials.html

Videos of tutorials are posted on the CBMM website: https://cbmm.mit.edu/videos?field_video_grouping_tid%5B%5D=781

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