This is a forked repo of the 2019 eNeuron paper " A Computational Model of the Escape Response Latency in the Giant Fiber System of Drosophila melanogaster" by Augustin, H, Zylbertal, A and Partridge L

The purpose of this fork is to create a shared starting point for the Spring 2026 Pena Lab Project Team to work towards reproducing the paper using Brian2

We installed neuron=7.5.0 because it came out around the time of the original paper and it was the earliest version that worked on WSL at the time of testing

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Model files from the manuscript:

Augustin, H, Zylbertal, A and Partridge L, "A computational model of the escape latency in the Giant Fiber System of D. melanogaster" (preprint)
The file gfs_param_scan_conductances.py reproduces the protocol used
in Fig. 3 of the article by calling the module gfpn.py.

Questions on how to use this model should be directed to
asaph.zylbertal at mail.huji.ac.il

Synopsis:

The Giant Fiber System (GFS) is a multi-component neuronal pathway mediating rapid escape
response in adult fruit-fly Drosophila melanogaster, usually in the face of a threatening visual
stimulus. Two branches of the circuit promote the response by stimulating an escape jump
followed by flight initiation. Our recent work demonstrated an age-associated decline in the
speed of signal propagation through the circuit, likely due to the diminishing number of gap
junctions between its components in ageing flies. In this work, we generated a realistic
conductance-based computational model of the GFS that recapitulates our experimental
results and identifies some of the critical anatomical and physiological components governing
the response latency of the circuit. Overall, anatomical properties of the GFS neurons have a
stronger impact on the transmission speed compared to the effect of changes in neuronal
membrane conductance densities. Our model and provides testable predictions for improving
the circuit’s performance in ageing animals by means of experimental interventions.

This example protocol plots the GFS latency as a function of gap junction conductance and:
1) Transient voltage gated sodium conductance
2) Voltage gated potassium conductance
3) Leak conductance

Example use:

Extract the archive, run nrnivmodl in the channels directory
(linux/unix) or mknrndll (mswin or mac os x) (see
http://senselab.med.yale.edu/ModelDB/NEURON_DwnldGuide.html
for more help) to compile the channels, and run the file
gfs_param_scan_conductances.py. After a while, it will plot
the latency maps.