fix spelling errors

Author: rcpeene

docs/higher-order/GLM_pynapple_nemos.ipynb

@@ -836,7 +836,7 @@
   },
   {
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-   "execution_count": 2035,
+   "execution_count": null,
    "id": "07eab1fd",
    "metadata": {},
    "outputs": [],
@@ -845,14 +845,14 @@
     "window_size = (-.250, .500) \n",
     "\n",
     "# Re-center timestamps for white stimuli\n",
-    "# +50 because we substracted 500 ms at beginning of stimulus presentation\n",
+    "# +50 because we subtracted 500 ms at beginning of stimulus presentation\n",
     "peri_white = nap.compute_perievent(timestamps = units,\n",
     "                                        tref = nap.Ts(extended_flashes_white.start +.50), \n",
     "                                        minmax = window_size\n",
     ")\n",
     "\n",
     "# Re-center timestamps for black stimuli\n",
-    "# +50 because we substracted 500 ms at beginning of stimulus presentation\n",
+    "# +50 because we subtracted 500 ms at beginning of stimulus presentation\n",
     "peri_black = nap.compute_perievent(timestamps = units,\n",
     "                                        tref = nap.Ts(extended_flashes_black.start +.50), \n",
     "                                        minmax = window_size\n",
@@ -1178,7 +1178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2040,
+   "execution_count": null,
    "id": "d12020f6",
    "metadata": {
     "tags": [
@@ -1197,7 +1197,7 @@
     "\n",
     "    pre_presentation_avg :\n",
     "        Average firing rate prior (250 ms) to the presentation of stimulus\n",
-    "        accross all instances prior of stimulus. \n",
+    "        across all instances prior of stimulus. \n",
     "\n",
     "    responsiveness : \n",
     "        abs((post_presentation_avg - pre_presentation_avg) / (post_presentation_avg + pre_presentation_avg))\n",
@@ -1230,11 +1230,11 @@
     "        # Get the firing rate\n",
     "        peri_rate = peri_counts/bin_size\n",
     "\n",
-    "        # Compute average firing rate for each milisecond in the\n",
+    "        # Compute average firing rate for each millisecond in the\n",
     "        # the 250 ms before stimulus presentation\n",
     "        pre_presentation = np.mean(peri_rate,1).restrict(nap.IntervalSet([-.25,0]))\n",
     "\n",
-    "        # Compute average firing rate for each milisecond in the\n",
+    "        # Compute average firing rate for each millisecond in the\n",
     "        # the 250 ms after stimulus presentation\n",
     "        post_presentation = np.mean(peri_rate,1).restrict(nap.IntervalSet([0,.25]))\n",
     "\n",
@@ -1254,7 +1254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2041,
+   "execution_count": null,
    "id": "cb82cfab",
    "metadata": {},
    "outputs": [
@@ -1287,7 +1287,7 @@
     "responsiveness_white,_ = get_responsiveness(peri_white, bin_size)\n",
     "responsiveness_black,_ = get_responsiveness(peri_black, bin_size)\n",
     "\n",
-    "# Add resposiveness as metadata for units\n",
+    "# Add responsiveness as metadata for units\n",
     "units.set_info(responsiveness_white=responsiveness_white)\n",
     "units.set_info(responsiveness_black=responsiveness_black)\n",
     "\n",
@@ -1646,7 +1646,7 @@
    "metadata": {},
    "source": [
     "### Preparing the data for **NeMoS**\n",
-    "Now that we have a good understanding of our data, and that we have splitted our dataset in the corresponding test and train subsets, we are almost ready to run our model. However, before we can construct it, we need to get our data in the right format.\n",
+    "Now that we have a good understanding of our data, and that we have split our dataset in the corresponding test and train subsets, we are almost ready to run our model. However, before we can construct it, we need to get our data in the right format.\n",
     "\n",
     "When fitting a single neuron, **NeMoS** requires that the predictors and spike counts it operates on have the following properties:\n",
     "\n",
@@ -2311,7 +2311,7 @@
     ":::{admonition} What is the ```predictor_causality``` parameter doing in the initialization of the ```RaisedCosineLinearConv``` basis?\n",
     ":class: info\n",
     ":class: dropdown\n",
-    "This manages the causality of the predictor: ```\"causal\"``` is the default setting, and it means that the convolution will occurr with respect to the input. Conversely ```\"acausal\"```, the one we are using now for the raised cosine linear basis, applies the convolution to both sides of the stimulus equally. For more information, please refer to [**NeMoS** notebook on causal, anti-causal and acausal filters](https://nemos.readthedocs.io/en/latest/background/plot_03_1D_convolution.html#causal-anti-causal-and-acausal-filters)\n",
+    "This manages the causality of the predictor: ```\"causal\"``` is the default setting, and it means that the convolution will occur with respect to the input. Conversely ```\"acausal\"```, the one we are using now for the raised cosine linear basis, applies the convolution to both sides of the stimulus equally. For more information, please refer to [**NeMoS** notebook on causal, anti-causal and acausal filters](https://nemos.readthedocs.io/en/latest/background/plot_03_1D_convolution.html#causal-anti-causal-and-acausal-filters)\n",
     ":::"
    ]
   },
@@ -2975,7 +2975,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2075,
+   "execution_count": null,
    "id": "51db7e73",
    "metadata": {},
    "outputs": [
@@ -3006,14 +3006,14 @@
    ],
    "source": [
     "# Re-center timestamps around white stimuli\n",
-    "# +50 because we substracted .50 at beginning of stimulus presentation\n",
+    "# +50 because we subtracted .50 at beginning of stimulus presentation\n",
     "peri_white_pred_unit = nap.compute_perievent_continuous(\n",
     "    timeseries = pred_unit, \n",
     "    tref = nap.Ts(flashes_test_white.start+.50),\n",
     "    minmax=window_size\n",
     ")  \n",
     "# Re-center timestamps for black stimuli\n",
-    "# +50 because we substracted .50 at beginning of stimulus presentation\n",
+    "# +50 because we subtracted .50 at beginning of stimulus presentation\n",
     "peri_black_pred_unit = nap.compute_perievent_continuous(\n",
     "    timeseries = pred_unit, \n",
     "    tref = nap.Ts(flashes_test_black.start+.50), \n",
@@ -3485,7 +3485,7 @@
     "We can try extending the model in order to improve its performance. There are many ways one can do this: the iterative refinement and improvement of your model is an important part of the scientific process! In this tutorial, we’ll discuss one such extension, but you’re encouraged to try others.\n",
     "\n",
     "Now, we'll extend the model by adding coupling terms—that is, including the activity of other neurons as predictors—to account for shared variability within the network. It's been shown by  {cite:t}`pillowSpatiotemporalCorrelationsVisual2008` \n",
-    "   <span id=\"cite1b\"></span><a href=\"#ref1\">[1b]</a> that spike times can be predicted more accurately when taking into acount the spiking of neighbouring units. "
+    "   <span id=\"cite1b\"></span><a href=\"#ref1\">[1b]</a> that spike times can be predicted more accurately when taking into account the spiking of neighbouring units. "
    ]
   },
   {
@@ -4546,7 +4546,7 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "databook",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -4560,7 +4560,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.13"
+   "version": "3.10.11"
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