Recurrent amplification of grid-cell activity (D'Albis and Kempter 2020)

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Accession:262356

Reference:
1 . D'Albis T, Kempter R (2020) Recurrent amplification of grid-cell activity Hippocampus (in press)
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Abstract rate-based neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: Python;
Model Concept(s): Grid cell; Attractor Neural Network;
Implementer(s):
_________________________________________________________________________________
## Source code for the artice 
# Recurrent amplification of grid-cell activity
## by Tiziano D'Albis and Richard Kempter

___________________________________________________________________________________




REQUIREMENTS
============

All source code is in Python language and was tested in the following environmnent:

- Python 2.7.15
- Matplotlib 2.2.3 
- NumPy 1.15.1
- Scipy 1.2.1
- Pandas 0.24.2
- Psutils 5.6.3
- Pytables 3.5.2

The easiest way to run the code is to create an Anaconda environment using the file `conda_env.yml` provided in the root folder of this package.
This can be done with the command:

conda env create -f conda_env.yml

This will create a new conda environment named `grid_amp`. Note that this environment may contain more packages then actually needed (i.e., it provides more than the minimal set of required dependencies).


USAGE
=====

0. Before starting make you have to create a file called `config.json` in the project root.
The file contains the paths on your local machines where the simulation results   (RESULTS_PATH) and the figures (FIGURES_PATH) are going to be saved. Here is an example:
```
{
  "RESULTS_PATH": "/home/tiziano/grid_amp/results",
  "FIGURES_PATH": "/home/tiziano/grid_amp/figures"
}
```
   
2D MODEL
--------

1. Run the script `amp_paper_2d_main.py`. This generates and saves to disk all the required simulation data to be plotted.  Note: if you need only a subset of the result you can easily filter out simulations in the main section of the program.

2. Run the snippet of code that generates the required figure. 
   The script `amp_paper_2d_fig_main.py` contains the code to generate Figure 1
   The script `amp_paper_2d_fig_temporal.py` contains the code to generate Figures 2 and 3
   The script `amp_paper_2d_fig_grid_index.py` contains the code to generate Figure 8
   The script `amp_paper_2d_fig_noise.py` contains the code to generate all the remaining figures of the 2D model

3. Find the generated figures saved as SVG and PNG in the selected target folder (see `FIGURES_PATH` in `config.json`)


1D MODEL
--------

1. Run the script `amp_paper_1d_plots.py` to generate all figures related to the 1D model
   
2. Find the generated figures saved as SVG and PNG in the selected target folder (see `FIGURES_PATH` in `config.json`)

TECHNICAL NOTE
==============

This project imports an external git repository (grid_utils) using  ``git subtree''.
For a tutorial see: http://atlassianblog.wpengine.com/2013/05/alternatives-to-git-submodule-git-subtree/

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