Merge pull request #251 from neurolib-dev/docs-fixes

Minor Docs Fixes

.github/workflows/documentation.yml

@@ -29,7 +29,7 @@ jobs:
       - name: Install dependencies 🛠
         run: |
           python -m pip install --upgrade pip
-          pip install mkdocs mkdocs-material mkdocstrings mkdocstrings-python mknotebooks Pygments mkdocs-include-markdown-plugin livereload
+          pip install mkdocs mkdocs-material mkdocstrings mkdocstrings-python mknotebooks Pygments livereload
           if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
           pip install .
       - name: Build documentation 👷‍♀️

examples/example-0.3-fhn-minimal.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# The Fitz-Hugh Nagumo oscillator\n",
     "\n",
-    "In this notebook, the basic use of the implementation of the Fitz-Hugh Nagumo (`fhn`) model is presented. Usually, the `fhn` model is used to represent a single neuron (for example in `Cakan et al. (2014)`, \"Heterogeneous delays in neural networks\"). This is due to the difference in timescales of the two equations that define the FHN model: The first equation is often referred to as the \"fast variable\" whereas the second one is the \"slow variable\". This makes it possible to create a model with a very fast spiking mechanism but with a slow refractory period. \n",
+    "In this notebook, the basic use of the implementation of the FitzHugh-Nagumo (`fhn`) model is presented. Usually, the `fhn` model is used to represent a single neuron (for example in `Cakan et al. (2014)`, \"Heterogeneous delays in neural networks\"). This is due to the difference in timescales of the two equations that define the FHN model: The first equation is often referred to as the \"fast variable\" whereas the second one is the \"slow variable\". This makes it possible to create a model with a very fast spiking mechanism but with a slow refractory period. \n",
     "\n",
     "In our case, we are using a parameterization of the `fhn` model that is not quite as usual. Inspired by the paper by `Kostova et al. (2004)` \"FitzHugh–Nagumo revisited: Types of bifurcations, periodical forcing and stability regions by a Lyapunov functional.\", the implementation in `neurolib` produces a slowly oscillating dynamics and has the advantage to incorporate an external input term that causes a Hopf bifurcation. This means, that the model roughly approximates the behaviour of the `aln` model: For low input values, there is a low-activity fixed point, for intermediate inputs, there is an oscillatory region, and for high input values, the system is in a high-activity fixed point. Thus, it offers a simple way of exploring the dynamics of a neural mass model with these properties, such as the `aln` model.\n",
     "\n",

examples/example-0.5-kuramoto.ipynb

@@ -63,7 +63,7 @@
    "source": [
     "# Single node simulation \n",
     "\n",
-    "Here we will simulate a signal node with no noise. We then cap the phase values to be between 0 and 2*pi. We also willo plot the phase values over time."
+    "Here we will simulate a signal node with no noise. We then cap the phase values to be between 0 and 2*pi. We also will plot the phase values over time."
    ]
   },
   {
@@ -117,7 +117,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here we simulate networks of oscillators. We will simulate a network of 8 oscillators with a global coupling strength 0.3. Here we initialize a connectivity matrix with all-to-all connectivity. We then simulate the network for 30 miliseconds assuming dt is in ms. We will also plot the phase values over time."
+    "Here we simulate networks of oscillators. We will simulate a network of 8 oscillators with a global coupling strength 0.3. Here we initialize a connectivity matrix with all-to-all connectivity. We then simulate the network for 30 milliseconds assuming dt is in ms. We will also plot the phase values over time."
    ]
   },
   {
@@ -222,13 +222,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now the syncrhonization happens after 7 ms which is faster compared to the previous simulation."
+    "Now the synchronization happens after 7 ms which is faster compared to the previous simulation."
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "kuramoto",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -242,10 +242,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  },
-  "orig_nbformat": 4
+   "version": "3.8.13"
+  }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }