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This is the repository for code and data to generate results and analysis for the following paper:

"The rat frontal orienting field dynamically encodes value for economic decisions under risk". Bao, C.†, Zhu, X.†, Moller-Mara, J., Li, J., Dubroqua, S., & Erlich, J. C. (2023). https://www.nature.com/articles/s41593-023-01461-x

† These authors contributed equally to the work.

Repository contents

The repository comprises:

  1. Behavioral data (including muscimol and optogenetic perturbation data) in the CSV files (in CSV folder).
  2. Electrophysiological data in Matlab ".mat" files (in matlab folder).
  3. R code for generating the plots and statistics for the behavior and the three-agent model (in R folder)
  4. Matlab (R2022a) code and analysis for electrophysiological recording (in matlab folder).
  5. Julia code to generate the plots for the dynamical model, alternative dynamical models and pseudopopulation decoding of lottery magnitude from FOF electrophysiological data (in julia folder).

Instructions for replicating plots and results

Clone this reposistory

cd 
git clone https://github.com/erlichlab/risk-fof-ppc-2023

Generating the behavior and the three-agent model related analysis and plots

Download the model fits from the following link: https://rdr.ucl.ac.uk/ndownloader/files/42774595

Move the brms_model_fits.RData file to the risk-fof-ppc-2023 root directory

Create the R enviroment using conda:

First install Miniconda and mamba. (Note: mamba isn't required but it is faster than using the base conda setup.)

then in a terminal run:

cd  ~/risk-fof-ppc-2023
mamba install -f environment.yml

In your R enviroment, set your working directory: setwd('~/risk-fof-ppc-2023').

Load all the library and source code for generating the plots and analysis: source('R/init.R')

Then, generate all the R plots for the paper: source('R/main.R')

Generate the electrophysiology analysis and plots

Install elutils and add to the path

bash> git clone  https://github.com/erlichlab/elutils.git
matlab> addpath ~/elutils
matlab> cd risk-fof-pcc-2023/matlab
matlab> main_ephys_plots
matlab> lottery_sound_control

Generate the analysis for pseudopopulation decoding of lottery magnitude from FOF ephys data

If you don't have julia, then first install julia (recommended way is to use juliaup).

Go into the julia folder of this project and run julia like this:

julia --project=. -tauto

import Pkg
Pkg.instantiate() # This gets the dependcies
using Pluto
Pluto.run(notebook="fof_lottery_decode.jl")

Note: each Pluto notebook is its own environment. The first time you run the notebook, it will download and precompile a lot of dependecies, so it can take quite a while.

Generate the dynamic model related plots

Follow the instructions above but run Pluto.run(notebook="julia/dynamical.jl")