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zarin_et_al_multilayer_2019
Copyright (C) 2019 Brandon Mark and Ashok Litwin-Kumar

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.

SUMMARY:
This repository contains scripts and data used in:
A. A. Zarin, B. Mark, A. Cardona, A. Litwin-Kumar & C. Q. Doe (2019). A multilayer circuit architecture for the generation of distinct motor behaviors in Drosophila. eLife 8, e51781.

There are four parts: 
(1) Image_Processing:  This contains the scripts necessary for dealing with the raw imaging data.  To run, use Muscle_GCamp and follow the prompts.  The outputs are .mat files which can be used for analysis.

(2) GCamp_Analysis: These scrips do the bulk of the analysis for figures 2 and 3.  To run these, use muscle_gcamp_compiler once for each direction (fwd and bwd).  

(3) EM_analysis: This uses EM data to look at the distributions of motor neuron synapses for figure 5.  The input is a data structure containing anatomical and connectivity information for the motor neurons.  The MN and PMN EM data has been included. 

(4) RNN: Implementation of a connectome-constrained recurrent network model of motor and premotor neurons. Uses Python (python.org) and TensorFlow (tensorflow.org). Tested on Python 3.7.3, TensorFlow 1.14.

CONTACT:
[email protected]
lk.zi.columbia.edu
[email protected]