Note: For previous versions of the models and information about changes, see Releases
This is the modeling package to accompany the paper:
Cengiz Günay, Fred Sieling, Logesh Dharmar, Wei-Hsiang Lin, Verena Wolfram, Richard Marley, Richard A. Baines, and Astrid A. Prinz. Distal Spike Initiation Zone Location Estimation by Morphological Simulation of Ionic Current Filtering Demonstrated in a Novel Model of an Identified Drosophila Motoneuron, PLoS Comput Biol 2015, 11(5): e1004189
OpenSourceBrain page: http://www.opensourcebrain.org/projects/drosophila-acc-l3-motoneuron-gunay-et-al-2014
ModelDB accession number is: 152028 https://senselab.med.yale.edu/modeldb/ShowModel.asp?model=152028
Download from:
https://github.com/cengique/drosophila-aCC-L3-motoneuron-model/archive/master.zip OR http://www.biology.emory.edu/research/Prinz/Cengiz/Gunay_etal_2014.zip
Single compartmental, ball-and-stick models implemented in XPP and full morphological model in Neuron. Paper correlates anatomical properties with electrophysiological recordings from these hard-to-access neurons. For instance we make predictions about location of the spike initiation zone, channel distributions, and synaptic input parameters.
XPPAUT 5.99 - http://www.math.pitt.edu/~bard/xpp/xpp.html
Neuron 7.1 - http://www.neuron.yale.edu/neuron/
jNeuroML - https://github.com/NeuroML/jNeuroML
xpp-models/ Isopotential and ball-and-stick models using the XPPAUT simulator.
neuron-model/ Multicompartmental model using the Neuron simulator. Follow the tutorial in the tutorial-replicate-paper-figure/ subdirectory README to get started with the model and replicate paper figures. You can also use Python to work with the model in tutorial-python-neuron.
NeuroML2/ Ports of all models to NeuroML2 and LEMS. Includes OMV tests to check for consistency with the original models: 
- 2010-2015: Models constructed by Cengiz Gunay in the lab of Astrid Prinz with help from Logesh Dharmar and Fred Sieling.
- 2015-2016: NeuroML2 and LEMS translation by Johannes Rieke (see thesis project that analyzes this model).
- Jan-June 2017: Neuron tutorial by Musa Drammeh.
- Dec 2017: Python with Neuron tutorial by Reuben Massaquoi
- June 2018: Updates to Python tutorial by Alex McWhorter
- Feb 2019: Linux instructions for Python tutorial by Don Charles Sugatapala
- Channel data were fit with the Neurofit tool and then re-adjusted with param-fitter in Matlab.
- XPP models were built with these channels and hand-tuned to fit observed f-I, v-I, and delay.
- Neuron model used these channels and some properties of the ball-and-stick model.
- Morphological reconstruction passive parameters were tuned to recorded capacitance responses.
- Channel distribution hand tuned to mimick observed current responses.
Please report problems/suggestions/comments to:
Cengiz Gunay (cengique AT users.sf.net)