spellcheck

generate_validation_results.ipynb

@@ -10,7 +10,7 @@
     "Gutzen, R., von Papen, M., Trensch, G., Quaglio, P., Grün, S., and Denker, M. (2018). \n",
     "*Reproducible neural network simulations: statistical methods for model validation on the level of network activity data*\n",
     "        \n",
-    "**Note:** because of the large number of neurons, the computations involving the calculation of correlation coefficients for all pairs of neurons are very expensive, i.e.very time consuming."
+    "**Note:** Because of the large number of neurons, the computations involving the calculation of correlation coefficients for all pairs of neurons are very expensive, i.e.very time consuming."
    ]
   },
   {
@@ -24,7 +24,7 @@
     "    - [Define model classes](#model1)\n",
     "    1. [Define test classes and how to perform a test](#test1)\n",
     "    1. [Visualization](#viz)\n",
-    "    1. [Artefact detection](#artfcts)\n",
+    "    1. [Artifact detection](#artfcts)\n",
     "1. [Iteration II](#it2)\n",
     "    - [Define model classes](#model2)\n",
     "    1. [Perform validation tests and average over network states](#test2)\n",
@@ -137,7 +137,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The local path of the repository is added to the python path so the module can be importet."
+    "The local path of the repository is added to the python path so the module can be imported."
    ]
   },
   {
@@ -169,7 +169,7 @@
    "metadata": {},
    "source": [
     "## Define the polychronization model class<a id='poly_model'></a>\n",
-    "Within the SciUnit framework the models are represented as class objects. These model classes inherit from a capability class (here `ProducesSpikeTrains`) and possibly other model classes. NetworkUnit provides an abstract model class `spiketrain_data` which already has the capability of `ProducesSpikeTrains` and several other utility functions for spiketrain data such as aligning the spiketrains to 0 ms.\n",
+    "Within the SciUnit framework the models are represented as class objects. These model classes inherit from a capability class (here `ProducesSpikeTrains`) and possibly other model classes. NetworkUnit provides an abstract model class `spiketrain_data` which already has the capability of `ProducesSpikeTrains` and several other utility functions for spike train data such as aligning the spiket rains to 0 ms.\n",
     "\n",
     "The essential property of the model class is to generate a simulation outcome, either by executing the simulation or by loading the data of a previously run simulation. Here we do the latter and thus equip the class with a load() function and the variable file_path."
    ]
@@ -286,7 +286,7 @@
    "metadata": {},
    "source": [
     "### Show rasterplots\n",
-    "This is equivalent to the spiking activity displayed in Fig. 5 in Gutzen et al."
+    "This is equivalent to the spiking activity displayed in Fig. 5 in the paper."
    ]
   },
   {
@@ -321,8 +321,8 @@
    "metadata": {},
    "source": [
     "### Define test classes<a id='test1'></a>\n",
-    "The abstract base classes for these tests are implemented in NetworkUnit so that here the tests can be easily defined by inherting from these test classes and setting the parameters and pair the test with a score class. To perfom a hypothesis test the `effect_size` score can be replaced for example with the `ks_distance` or the `mwu_statistic`.\n",
-    "The inhereted `TestM2M` class adapts the test such that the tests don't need to be initiliazed with experimental data. "
+    "The abstract base classes for these tests are implemented in NetworkUnit so that here the tests can be easily defined by inheriting from these test classes and setting the parameters and pair the test with a score class. To perform a hypothesis test the `effect_size` score can be replaced for example with the `ks_distance` or the `mwu_statistic`.\n",
+    "The inherited `TestM2M` class adapts the test such that the tests don't need to be initialized with experimental data. "
    ]
   },
   {
@@ -572,7 +572,7 @@
     "### Visualization<a id='viz'></a>\n",
     "The visual inspection of the samples of comparison measures is a very relevant part of the validation workflow. Thus, all tests have the inherent functionality to visualize their predictions by the function `visualize_samples()`.\n",
     "\n",
-    "These plots are equivalent to those displayed in Fig. 5 in Gutzen et al.."
+    "These plots are equivalent to those displayed in Fig. 5 in the paper."
    ]
   },
   {
@@ -630,8 +630,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Detection of artefacts<a id='artfcts'></a>\n",
-    "Because of the apparent mismatch between the two simualtion outcomes we designed more complex test to investiagte this further. The `cross_correlation_struct_test` computes the sum of the cross-correlation histogram within a window of +- 50 bins (100ms) for each neuron pair. In the following the resulting values are presented both in a histogram and in a matrix showing indication for a simulation artefact in the SpiNNaker architecture. The figures are equivalent to those displayed in Fig. 5 in Gutzen et al.."
+    "#### Detection of artifacts<a id='artfcts'></a>\n",
+    "Because of the apparent mismatch between the two simulation outcomes we designed more complex test to investigate this further. The `cross_correlation_struct_test` computes the sum of the cross-correlation histogram within a window of +- 50 bins (100ms) for each neuron pair. In the following the resulting values are presented both in a histogram and in a matrix showing indication for a simulation artifact in the SpiNNaker architecture. The figures are equivalent to those displayed in Fig. 5 in the paper."
    ]
   },
   {
@@ -711,7 +711,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The dominant lines in the matrix represenation of the SpiNNaker cross-correlation measures indicate that a couple of neurons significantly deviate from the 'normal' activity and cause the substantial mismatch between the simulations. In the following we identifying these overactive neurons by counting for each neuron the number of highly correlated interaction (cross-correlation measure > 1.5) it has with other neurons."
+    "The dominant lines in the matrix representation of the SpiNNaker cross-correlation measures indicate that a couple of neurons significantly deviate from the 'normal' activity and cause the substantial mismatch between the simulations. In the following we identifying these overactive neurons by counting for each neuron the number of highly correlated interaction (cross-correlation measure > 1.5) it has with other neurons."
    ]
   },
   {
@@ -890,7 +890,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The validation tests of these three measures of the network activty provide a assessment of the similarity (or discrepancy) between the simulations. Most notably the effect size for the LV comparison is relatively large. This is also clearly visibile in visualization of the samples for both simulations (network state after 5h). This figure is equivalent to Fig. 6 in Gutzen et al.. "
+    "The validation tests of these three measures of the network activity provide a assessment of the similarity (or discrepancy) between the simulations. Most notably the effect size for the LV comparison is relatively large. This is also clearly visible in visualization of the samples for both simulations (network state after 5h). This figure is equivalent to Fig. 6 in the paper. "
    ]
   },
   {
@@ -1022,7 +1022,7 @@
    "metadata": {},
    "source": [
     "### Define additional tests<a id='test3'></a>\n",
-    "We add additional validation tests in order to get a more comprehensive assesment of the network dynamics, quantify the overall similarity/difference more accurately, and ideally increase the confidence we have in ther (qualitative) equivalence."
+    "We add additional validation tests in order to get a more comprehensive assessment of the network dynamics, quantify the overall similarity/difference more accurately, and ideally increase the confidence we have in their (qualitative) equivalence."
    ]
   },
   {
@@ -1057,7 +1057,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### perform tests and average over the 5 network states<a id='test3_avg'></a>"
+    "### Perform tests and average over the 5 network states<a id='test3_avg'></a>"
    ]
   },
   {
@@ -1114,7 +1114,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Again, we also visualize the corresponding distributions. This is of additional relevance when using the effect size a score, because only quantifies differences between the means of the distribution and can not account for mismatches in their shape. The figure is equivalent to Fig. 7 in Gutzen et al.."
+    "Again, we also visualize the corresponding distributions. This is of additional relevance when using the effect size a score, because only quantifies differences between the means of the distribution and can not account for mismatches in their shape. The figure is equivalent to Fig. 7 in the paper."
    ]
   },
   {
@@ -1305,7 +1305,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Further testing of the more specific properties of the network activity invloves the analysis of spatiotemporal pattern. For this we use the SPADE method, availale in the Elephant package. The full SPADE analysis and results can be found in the folder spade_analysis. The main insights from this analysis are that the SpiNNaker simuluation shows significantly more pattern and that the spikes within pattern (for both simulations) have a reoccuring temporal structure. Here, we calcualte the power spectrum of the simluated activity to assess whether the temporal structure of the pattern reflect the oscilliatory behavior of the population activity."
+    "Further testing of the more specific properties of the network activity invloves the analysis of spatiotemporal pattern. For this we use the SPADE method, availale in the Elephant package. The full SPADE analysis and results can be found in the folder [spade_analysis](https://web.gin.g-node.org/INM-6/network_validation/src/master/spade_analysis). The main insights from this analysis are that the SpiNNaker simuluation shows significantly more pattern and that the spikes within pattern (for both simulations) have a reoccuring temporal structure. Here, we calcualte the power spectrum of the simluated activity to assess whether the temporal structure of the pattern reflect the oscilliatory behavior of the population activity."
    ]
   },
   {
@@ -1367,7 +1367,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This figure is equivalent to panel C in Fig. 9 in Gutzen et al.. Comparing it to the SPADE results, the temporal structure of the pattern (most lags around ~27ms) can be explained by the population activity which has a dominant peak at around 35Hz."
+    "This figure is equivalent to panel C in Fig. 9 in the paper. Comparing it to the SPADE results, the temporal structure of the pattern (most lags around ~27ms) can be explained by the population activity which has a dominant peak at around 35Hz."
    ]
   },
   {