bgNetwork

This repository contains code for implementing the spiking cortico-basal ganglia-thalamus (CBGT) network and drift-diffusion model (DDM) fits described in the manuscript Reward-driven changes in striatal pathway competition shape evidence evaluation in decision-making.

The code requires several dependencies to be installed (see below for instructions). After completing the installation procedure below, the demo notebook can be downloaded and opened inside Jupyter.

Requirements

• OSX or Linux

• Anaconda with Python 3.* (for OSX, Linux)

• gcc (if Linux) or gcc-8 (if OSX, see here)

Installation Instructions

# create a new conda environment with python 3.6
# and hit 'y' to verify the install 
conda create -n py36 python=3.6 anaconda

# activate 'py36' environment
source activate py36

# use conda (not pip) to install pymc
conda install pymc=2.3.6 --no-deps

# install hddm and kabuki
pip install --upgrade kabuki hddm

# Alternative Install: (not recommended) 
# conda install -c pymc hddm

# finally install numpy version 1.11.3
# (avoids hddm incompatibility with later numpy)
pip install numpy==1.11.3

• After installing everything, run the line below to open up Jupyter in your browser, then drag/drop the demo notebook (CBGT_PLOSCompBio2019_Demo.ipynb) into the Jupyter browser window (see this tutorial if you're new to Jupyter)

# make sure you've activated the py36 env first
jupyter notebook

HDDM Resources:

• For those interested in working more with HDDM, see the methods paper here as well as the introductory demo and how-to sections