From 2cef7e0a3ea1c2abc14fb83f90c8a3846f8eb0c8 Mon Sep 17 00:00:00 2001
From: "Doorn, Nina (UT-TNW)" <n.doorn-1@utwente.nl>
Date: Mon, 17 Mar 2025 15:58:06 +0100
Subject: [PATCH] fixed indent

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index c7e1883..4966266 100644
--- a/README.md
+++ b/README.md
@@ -6,6 +6,7 @@ https://www.biorxiv.org/content/10.1101/2024.05.23.595522v1
 All code uses the mackelab sbi package (https://github.com/sbi-dev/sbi) in a python 3.9 environment. 
 
 This directory contains:
+
     - The parameters (Simulations_modelparameters.csv) and resulting MEA features (Simulations_MEAfeatures.csv) of the 300,000 simulations used to train the NDE for the paper. Every row represents one simulation. In "Simulations_modelparameters", the columns in ascending order represent the parameters: 'noise', '$g_{Na}$', '$g_{K}$', '$g_{AHP}$', '$g_{AMPA}$', '$g_{NMDA}$', 'Conn%', r'$\tau_{D}$', 'U (STD)', 'U asyn'. In "Simulations_MEAfeatures", the columns in ascending order represent the MEA features: 'MFR', 'NBR', 'NBD', 'PSIB', '#FBs', 'CVIBI', 'mean CC', 'sd CC', 'mean ISI CC', 'sd ISI CC', 'ISI dist', 'mean ISI', 'sd ISI temp', 'sd isi elec', 'MAC'
 
     - The "TrainedNDE": the neural density estimator trained using the 300,000 simulations in the mackelab sbi package. This can be loaded in python to evaluate and obtain the posterior distribution as described in 'FindPosterior.py".
-- 
GitLab