From fce434bcb967430ac061766e36caf6ac85bc87a4 Mon Sep 17 00:00:00 2001
From: Sacha van Albada <sacha.vanalbada@gmail.com>
Date: Thu, 31 Oct 2019 13:33:54 +0100
Subject: [PATCH] fixed some typos in README.md
---
README.md | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/README.md b/README.md
index e24f1e7..4684275 100644
--- a/README.md
+++ b/README.md
@@ -69,13 +69,13 @@ technical parameters such as the number of parallel MPI processes and
threads. The simulation uses the network simulator NEST
(https://www.nest-simulator.org). For the simulations in [2, 3], we
used NEST version 2.8.0. The code in this repository runs with a
-later release of NEST, version 2.14.0 .
+later release of NEST, version 2.14.0.
`Theory`
This class can be initialized by `MultiAreaModel` or as standalone and
takes simulation parameters as input. It provides two main features:
-- predict the stable fixed point of the system using mean-field theory and characterize them (for instance by computing the gain matrix).
+- predict the stable fixed points of the system using mean-field theory and characterize them (for instance by computing the gain matrix).
- via the script `stabilize.py`, one can execute the stabilization method described in [2] on a network instance. Please see `figures/SchueckerSchmidt2017/stabilization.py` for an example of running the stabilization.
`Analysis`
@@ -168,7 +168,7 @@ The multi-area model can be run in different modes.
This can be achieved by setting the parameters `N_scaling` and `K_scaling` in `network_params`
to values smaller than 1. In general, this will affect the dynamics of the network.
To approximately preserve the population-averaged spike rates, one can specify a set of target rates
- that is used to scale synaptic weights and apply an additional external DC current.
+ that is used to scale synaptic weights and apply an additional external DC input.
3. Subset of the network
--
GitLab