Drosophila 3rd instar larval aCC motoneuron

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.

Requirements:

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

Directories:

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: .

Credits:

• 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

Workflow/history of project:

• 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)