# Fork Escape Response

This repository is a fork of the 2019 eNeuron paper model:

"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 toward reproducing the paper using Brian2.

We installed `neuron=7.5.0` because it was released around the time of the original paper and was the earliest version that worked on WSL during 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 Figure 3 of the article by calling the module `gfpn.py`.

Questions about how to use the original model should be directed to:
asaph.zylbertal at mail.huji.ac.il

## Synopsis

The Giant Fiber System (GFS) is a multi-component neuronal pathway that mediates rapid escape responses in adult fruit fly Drosophila melanogaster, usually in response to a threatening visual stimulus. Two branches of the circuit promote the response by stimulating an escape jump followed by flight initiation.

Prior work showed an age-associated decline in the speed of signal propagation through the circuit, likely due to a diminishing number of gap junctions between circuit components in aging flies. This model reproduces those experimental results and identifies several critical anatomical and physiological components that influence response latency.

Overall, the model suggests that anatomical properties of GFS neurons have a stronger effect on transmission speed than changes in neuronal membrane conductance densities. The model also provides testable predictions for improving circuit performance in aging animals through experimental intervention.

## Example protocol

This protocol plots 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, then compile the channels in the `channels` directory.

- On Linux/Unix, run `nrnivmodl`
- On Windows or macOS, run `mknrndll`

For more help with compiling NEURON channel mechanisms:
http://senselab.med.yale.edu/ModelDB/NEURON_DwnldGuide.html

After compiling the channels, run:

`gfs_param_scan_conductances.py`

After some time, the script will generate the latency maps.

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## Contributors

Each project member should create their own file in the `contributors` folder instead of editing one shared contributor file. This helps reduce merge conflicts.

### Contributor steps

1. Pull the latest version of the repo
2. Create a new file in the `contributors` folder using the template
3. Name the file with your own name
4. Fill out your information
5. Commit and push your changes

### Contributor file format

Name:
Major:
Role:
Hobbies:

### Suggested file name

`contributors/your-name.md`

Example:
`contributors/sebastian-davalos.md`

### Example workflow

Pull the latest changes:

`git pull origin master`

Create your file in the `contributors` folder and fill it out using the template.

Then run:

`git add contributors/your-name.md`
`git commit -m "Add contributor profile for Your Name"`
`git push origin master`

If your push is rejected, pull the latest changes and try again:

`git pull origin master`
`git push origin master`