Spiros chv patch 1 (#105)

* Update _config.yml * fixes * fixes * fixes * fixes * fixes * Process tutorial notebooks --------- Co-authored-by: GitHub Action <action@github.com>
NeuromatchAcademy · Jul 3, 2023 · 7cab91d · 7cab91d
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diff --git a/book/_config.yml b/book/_config.yml
@@ -30,6 +30,7 @@ parse:
     - amsmath
     - dollarmath
     - html_image
+    - substitution
 sphinx:
   config:
     html_show_copyright: false

diff --git a/tutorials/W0D2_PythonWorkshop2/W0D2_Tutorial1.ipynb b/tutorials/W0D2_PythonWorkshop2/W0D2_Tutorial1.ipynb
@@ -47,9 +47,10 @@
    "source": [
     "---\n",
     "## Tutorial objectives\n",
+    "\n",
     "We learned basic Python and NumPy concepts in the previous tutorial. These new and efficient coding techniques can be applied repeatedly in tutorials from the NMA course, and elsewhere.\n",
     "\n",
-    "In this tutorial, we'll introduce spikes in our LIF neuron and evaluate the refractory period's effect in spiking dynamics!\n"
+    "In this tutorial, we'll introduce spikes in our LIF neuron and evaluate the refractory period's effect in spiking dynamics!"
    ]
   },
   {
@@ -296,7 +297,7 @@
     "\n",
     "<br>\n",
     "\n",
-    "Another important statistic is the sample [histogram](https://en.wikipedia.org/wiki/Histogram). For our LIF neuron it provides an approximate representation of the distribution of membrane potential $V_m(t)$ at time $t=t_k\\in[0,t_{max}]$. For $N$ realizations $V\\left(t_k\\right)$ and $J$ bins is given by:\n",
+    "Another important statistic is the sample [histogram](https://en.wikipedia.org/wiki/Histogram). For our LIF neuron, it provides an approximate representation of the distribution of membrane potential $V_m(t)$ at time $t=t_k\\in[0,t_{max}]$. For $N$ realizations $V\\left(t_k\\right)$ and $J$ bins is given by:\n",
     "\n",
     "<br>\n",
     "\\begin{equation}\n",
@@ -609,7 +610,7 @@
     "execution": {}
    },
    "source": [
-    "A spike takes place whenever $V(t)$ crosses $V_{th}$. In that case, a spike is recorded and $V(t)$ resets to $V_{reset}$ value. This is summarized in the *reset condition*:\n",
+    "A spike occures whenever $V(t)$ crosses $V_{th}$. In that case, a spike is recorded, and $V(t)$ resets to $V_{reset}$ value. This is summarized in the *reset condition*:\n",
     "\n",
     "\\begin{equation}\n",
     "V(t) = V_{reset}\\quad \\text{ if } V(t)\\geq V_{th}\n",
@@ -771,7 +772,7 @@
     "for step, t in enumerate(t_range):\n",
     "\n",
     "  # Skip first iteration\n",
-    "  if step==0:\n",
+    "  if step == 0:\n",
     "    continue\n",
     "\n",
     "  # Compute v_n\n",
@@ -852,7 +853,7 @@
     "for step, t in enumerate(t_range):\n",
     "\n",
     "  # Skip first iteration\n",
-    "  if step==0:\n",
+    "  if step == 0:\n",
     "    continue\n",
     "\n",
     "  # Compute v_n\n",
@@ -1004,7 +1005,7 @@
     "execution": {}
    },
    "source": [
-    "Numpy arrays can be indexed by boolean arrays to select a subset of elements (also works with lists of booleans).\n",
+    "Boolean arrays can index NumPy arrays to select a subset of elements (also works with lists of booleans).\n",
     "\n",
     "The boolean array itself can be initiated by a condition, as shown in the example below.\n",
     "\n",
@@ -1189,7 +1190,7 @@
    },
    "outputs": [],
    "source": [
-    "# to_remove solutions\n",
+    "# to_remove solution\n",
     "\n",
     "# Set random number generator\n",
     "np.random.seed(2020)\n",
@@ -1549,9 +1550,8 @@
    },
    "source": [
     "## Coding Exercise 5: Investigating refactory periods\n",
-    "Investigate the effect of (absolute) refractory period $t_{ref}$ on the evolution of output rate $\\lambda(t)$. Add refractory period $t_{ref}=10$ ms after each spike, during which $V(t)$ is clamped to $V_{reset}$.\n",
     "\n",
-    "\n"
+    "Investigate the effect of (absolute) refractory period $t_{ref}$ on the evolution of output rate $\\lambda(t)$. Add refractory period $t_{ref}=10$ ms after each spike, during which $V(t)$ is clamped to $V_{reset}$."
    ]
   },
   {
@@ -1697,13 +1697,13 @@
    },
    "source": [
     "## Interactive Demo 1: Random refractory period\n",
+    "\n",
     "In the following interactive demo, we will investigate the effect of random refractory periods. We will use random refactory periods $t_{ref}$ with\n",
     "$t_{ref} = \\mu + \\sigma\\,\\mathcal{N}$, where $\\mathcal{N}$ is the [normal distribution](https://en.wikipedia.org/wiki/Normal_distribution), $\\mu=0.01$ and $\\sigma=0.007$.\n",
     "\n",
-    "Refractory period samples `t_ref` of size `n` is initialized with `np.random.normal`. We clip negative values to `0` with boolean indexes. (Why?) You can double click the cell to see the hidden code.\n",
+    "Refractory period samples `t_ref` of size `n` is initialized with `np.random.normal`. We clip negative values to `0` with boolean indexes. (Why?) You can double-click the cell to see the hidden code.\n",
     "\n",
-    "You can play with the parameters mu and sigma and visualize the resulting simulation.\n",
-    "What is the effect of different $\\sigma$ values?\n"
+    "You can play with the parameters mu and sigma and visualize the resulting simulation. What is the effect of different $\\sigma$ values?"
    ]
   },
   {
@@ -1934,6 +1934,7 @@
    },
    "source": [
     "## Coding Exercise 6: Rewriting code with functions\n",
+    "\n",
     "We will now re-organize parts of the code from the previous exercise with functions. You need to complete the function `spike_clamp()` to update $V(t)$ and deal with spiking and refractoriness"
    ]
   },
@@ -1953,7 +1954,7 @@
     "    v (numpy array of floats)\n",
     "      membrane potential at previous time step of shape (neurons)\n",
     "\n",
-    "    v (numpy array of floats)\n",
+    "    i (numpy array of floats)\n",
     "      synaptic input at current time step of shape (neurons)\n",
     "\n",
     "    dt (float)\n",
@@ -1967,6 +1968,7 @@
     "\n",
     "  return v\n",
     "\n",
+    "\n",
     "def spike_clamp(v, delta_spike):\n",
     "  \"\"\"\n",
     "  Resets membrane potential of neurons if v>= vth\n",
@@ -2051,6 +2053,8 @@
    },
    "outputs": [],
    "source": [
+    "# to_remove solution\n",
+    "\n",
     "def ode_step(v, i, dt):\n",
     "  \"\"\"\n",
     "  Evolves membrane potential by one step of discrete time integration\n",
@@ -2059,7 +2063,7 @@
     "    v (numpy array of floats)\n",
     "      membrane potential at previous time step of shape (neurons)\n",
     "\n",
-    "    v (numpy array of floats)\n",
+    "    i (numpy array of floats)\n",
     "      synaptic input at current time step of shape (neurons)\n",
     "\n",
     "    dt (float)\n",
@@ -2073,7 +2077,7 @@
     "\n",
     "  return v\n",
     "\n",
-    "# to_remove solution\n",
+    "\n",
     "def spike_clamp(v, delta_spike):\n",
     "  \"\"\"\n",
     "  Resets membrane potential of neurons if v>= vth\n",
@@ -2244,7 +2248,7 @@
     "execution": {}
    },
    "source": [
-    "Using classes helps with code reuse and reliability. Well-designed classes are like black boxes in that they receive inputs and provide expected outputs. The details of how the class processes inputs and produces outputs are unimportant.\n",
+    "Using classes helps with code reuse and reliability. Well-designed classes are like black boxes, receiving inputs and providing expected outputs. The details of how the class processes inputs and produces outputs are unimportant.\n",
     "\n",
     "See additional details here: [A Beginner's Python Tutorial/Classes](https://en.wikibooks.org/wiki/A_Beginner%27s_Python_Tutorial/Classes)\n",
     "\n",

diff --git a/tutorials/W0D2_PythonWorkshop2/instructor/W0D2_Tutorial1.ipynb b/tutorials/W0D2_PythonWorkshop2/instructor/W0D2_Tutorial1.ipynb
@@ -47,9 +47,10 @@
    "source": [
     "---\n",
     "## Tutorial objectives\n",
+    "\n",
     "We learned basic Python and NumPy concepts in the previous tutorial. These new and efficient coding techniques can be applied repeatedly in tutorials from the NMA course, and elsewhere.\n",
     "\n",
-    "In this tutorial, we'll introduce spikes in our LIF neuron and evaluate the refractory period's effect in spiking dynamics!\n"
+    "In this tutorial, we'll introduce spikes in our LIF neuron and evaluate the refractory period's effect in spiking dynamics!"
    ]
   },
   {
@@ -296,7 +297,7 @@
     "\n",
     "<br>\n",
     "\n",
-    "Another important statistic is the sample [histogram](https://en.wikipedia.org/wiki/Histogram). For our LIF neuron it provides an approximate representation of the distribution of membrane potential $V_m(t)$ at time $t=t_k\\in[0,t_{max}]$. For $N$ realizations $V\\left(t_k\\right)$ and $J$ bins is given by:\n",
+    "Another important statistic is the sample [histogram](https://en.wikipedia.org/wiki/Histogram). For our LIF neuron, it provides an approximate representation of the distribution of membrane potential $V_m(t)$ at time $t=t_k\\in[0,t_{max}]$. For $N$ realizations $V\\left(t_k\\right)$ and $J$ bins is given by:\n",
     "\n",
     "<br>\n",
     "\\begin{equation}\n",
@@ -611,7 +612,7 @@
     "execution": {}
    },
    "source": [
-    "A spike takes place whenever $V(t)$ crosses $V_{th}$. In that case, a spike is recorded and $V(t)$ resets to $V_{reset}$ value. This is summarized in the *reset condition*:\n",
+    "A spike occures whenever $V(t)$ crosses $V_{th}$. In that case, a spike is recorded, and $V(t)$ resets to $V_{reset}$ value. This is summarized in the *reset condition*:\n",
     "\n",
     "\\begin{equation}\n",
     "V(t) = V_{reset}\\quad \\text{ if } V(t)\\geq V_{th}\n",
@@ -773,7 +774,7 @@
     "for step, t in enumerate(t_range):\n",
     "\n",
     "  # Skip first iteration\n",
-    "  if step==0:\n",
+    "  if step == 0:\n",
     "    continue\n",
     "\n",
     "  # Compute v_n\n",
@@ -856,7 +857,7 @@
     "for step, t in enumerate(t_range):\n",
     "\n",
     "  # Skip first iteration\n",
-    "  if step==0:\n",
+    "  if step == 0:\n",
     "    continue\n",
     "\n",
     "  # Compute v_n\n",
@@ -1008,7 +1009,7 @@
     "execution": {}
    },
    "source": [
-    "Numpy arrays can be indexed by boolean arrays to select a subset of elements (also works with lists of booleans).\n",
+    "Boolean arrays can index NumPy arrays to select a subset of elements (also works with lists of booleans).\n",
     "\n",
     "The boolean array itself can be initiated by a condition, as shown in the example below.\n",
     "\n",
@@ -1195,7 +1196,7 @@
    },
    "outputs": [],
    "source": [
-    "# to_remove solutions\n",
+    "# to_remove solution\n",
     "\n",
     "# Set random number generator\n",
     "np.random.seed(2020)\n",
@@ -1557,9 +1558,8 @@
    },
    "source": [
     "## Coding Exercise 5: Investigating refactory periods\n",
-    "Investigate the effect of (absolute) refractory period $t_{ref}$ on the evolution of output rate $\\lambda(t)$. Add refractory period $t_{ref}=10$ ms after each spike, during which $V(t)$ is clamped to $V_{reset}$.\n",
     "\n",
-    "\n"
+    "Investigate the effect of (absolute) refractory period $t_{ref}$ on the evolution of output rate $\\lambda(t)$. Add refractory period $t_{ref}=10$ ms after each spike, during which $V(t)$ is clamped to $V_{reset}$."
    ]
   },
   {
@@ -1707,13 +1707,13 @@
    },
    "source": [
     "## Interactive Demo 1: Random refractory period\n",
+    "\n",
     "In the following interactive demo, we will investigate the effect of random refractory periods. We will use random refactory periods $t_{ref}$ with\n",
     "$t_{ref} = \\mu + \\sigma\\,\\mathcal{N}$, where $\\mathcal{N}$ is the [normal distribution](https://en.wikipedia.org/wiki/Normal_distribution), $\\mu=0.01$ and $\\sigma=0.007$.\n",
     "\n",
-    "Refractory period samples `t_ref` of size `n` is initialized with `np.random.normal`. We clip negative values to `0` with boolean indexes. (Why?) You can double click the cell to see the hidden code.\n",
+    "Refractory period samples `t_ref` of size `n` is initialized with `np.random.normal`. We clip negative values to `0` with boolean indexes. (Why?) You can double-click the cell to see the hidden code.\n",
     "\n",
-    "You can play with the parameters mu and sigma and visualize the resulting simulation.\n",
-    "What is the effect of different $\\sigma$ values?\n"
+    "You can play with the parameters mu and sigma and visualize the resulting simulation. What is the effect of different $\\sigma$ values?"
    ]
   },
   {
@@ -1944,17 +1944,18 @@
    },
    "source": [
     "## Coding Exercise 6: Rewriting code with functions\n",
+    "\n",
     "We will now re-organize parts of the code from the previous exercise with functions. You need to complete the function `spike_clamp()` to update $V(t)$ and deal with spiking and refractoriness"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {
+    "colab_type": "text",
     "execution": {}
    },
-   "outputs": [],
    "source": [
+    "```python\n",
     "def ode_step(v, i, dt):\n",
     "  \"\"\"\n",
     "  Evolves membrane potential by one step of discrete time integration\n",
@@ -1963,7 +1964,7 @@
     "    v (numpy array of floats)\n",
     "      membrane potential at previous time step of shape (neurons)\n",
     "\n",
-    "    v (numpy array of floats)\n",
+    "    i (numpy array of floats)\n",
     "      synaptic input at current time step of shape (neurons)\n",
     "\n",
     "    dt (float)\n",
@@ -1977,6 +1978,7 @@
     "\n",
     "  return v\n",
     "\n",
+    "\n",
     "def spike_clamp(v, delta_spike):\n",
     "  \"\"\"\n",
     "  Resets membrane potential of neurons if v>= vth\n",
@@ -2050,7 +2052,9 @@
     "  last_spike[spiked] = t\n",
     "\n",
     "# Plot multiple realizations of Vm, spikes and mean spike rate\n",
-    "plot_all(t_range, v_n, raster)"
+    "plot_all(t_range, v_n, raster)\n",
+    "\n",
+    "```"
    ]
   },
   {
@@ -2061,6 +2065,8 @@
    },
    "outputs": [],
    "source": [
+    "# to_remove solution\n",
+    "\n",
     "def ode_step(v, i, dt):\n",
     "  \"\"\"\n",
     "  Evolves membrane potential by one step of discrete time integration\n",
@@ -2069,7 +2075,7 @@
     "    v (numpy array of floats)\n",
     "      membrane potential at previous time step of shape (neurons)\n",
     "\n",
-    "    v (numpy array of floats)\n",
+    "    i (numpy array of floats)\n",
     "      synaptic input at current time step of shape (neurons)\n",
     "\n",
     "    dt (float)\n",
@@ -2083,7 +2089,7 @@
     "\n",
     "  return v\n",
     "\n",
-    "# to_remove solution\n",
+    "\n",
     "def spike_clamp(v, delta_spike):\n",
     "  \"\"\"\n",
     "  Resets membrane potential of neurons if v>= vth\n",
@@ -2254,7 +2260,7 @@
     "execution": {}
    },
    "source": [
-    "Using classes helps with code reuse and reliability. Well-designed classes are like black boxes in that they receive inputs and provide expected outputs. The details of how the class processes inputs and produces outputs are unimportant.\n",
+    "Using classes helps with code reuse and reliability. Well-designed classes are like black boxes, receiving inputs and providing expected outputs. The details of how the class processes inputs and produces outputs are unimportant.\n",
     "\n",
     "See additional details here: [A Beginner's Python Tutorial/Classes](https://en.wikibooks.org/wiki/A_Beginner%27s_Python_Tutorial/Classes)\n",
     "\n",

diff --git a/...tions/W0D2_Tutorial1_Solution_9aaee1d8.py → ...tions/W0D2_Tutorial1_Solution_950b2963.py b/...tions/W0D2_Tutorial1_Solution_9aaee1d8.py → ...tions/W0D2_Tutorial1_Solution_950b2963.py
@@ -17,7 +17,7 @@
 for step, t in enumerate(t_range):
 
   # Skip first iteration
-  if step==0:
+  if step == 0:
     continue
 
   # Compute v_n