Model for the Drosophila brain

Activate and silence neurons in a computational model based on the fruit fly connectome

Paper

This repository accompanies the paper A leaky integrate-and-fire computational model based on the connectome of the entire adult Drosophila brain reveals insights into sensorimotor processing. It includes all code necessary to reproduce the data presented there. figures.ipynb contains the code used to perform the calculations. However, the raw output of the model is several GB and therefore not posted on github. Instead, it can be found in this online archive.

Usage

With this computational model, one can manipulate the neural activity of a set of Drosophila neurons, which can be addressed via their Flywire ID. The output of the model is the spike times and rates of all affected neurons.

Two types of manipulations are currently implemented:

• Activation: Neurons can be activated at a fixed frequency to model optogenetic activation. This triggers Poisson spiking in the target neurons. Two sets of neurons with distinct frequencies can be defined.
• Silencing: In addition to activation, a different set of neurons can be silenced to model optogenetic silencing. This sets all synaptic connections to and from those neurons to zero.

See example.ipynb for a tutorial and a more detailed explanation The actual model resides in model.py. Some helper functions are provided by utils.py.

Installation

The Drosophila brain model can be run on Mac, Windows or Unix, and installation should take 10 minutes.

Quick Start

To begin using the model without a local install, click on the Open In Colab badge at the top of this README. Note that simulations can take substantially longer to run on Google Colab than on a local installation, depending on the number of CPU cores you have access to.

via conda

Install in a Anaconda environment:

conda env create -f environment.yml

Run using jupyter notebooks as shown in example.ipynb and figures.ipynb.

Note that this may install Brian 2 with limited performance (see below).

Brian 2 performance

The model is written in python built using the Brian 2 simulator. See the official Brian 2 documentation for detailed installation instructions for you system. Specifically, follow the instructions for C++ code generation to install Brian 2 with increased performance.

dependencies

See environment_full.yml for specific package versions used in the original work.

Version 783

The code is currently set up for the Flywire version 630, which the paper used. To use public version 783, change the config dictionary:

config = {
'path_res'  : './results/new',                 # directory to store results
'path_comp' : './Completeness_783.csv',        # csv of the complete list of Flywire neurons
'path_con'  : './Connectivity_783.parquet',    # connectivity data
'n_proc'    : -1,                              # number of CPU cores (-1: use all)
}