A SciUnit library for validation testing of spiking networks.
pip install networkunitThe NetworkUnit module builds upon the formalized validation scheme of the SciUnit package, which enables the validation of models against experimental data (or other models) via tests. A test is matched to the model by capabilities and quantitatively evaluated by a score. The following figure illustrates a typical test design within NetworkUnit. The blue boxes indicate the components of the implementation of the validation test, i.e., classes, class instances, data sets, and parameters. The relation between the boxes are indicated by annotated arrows.The basic functionality is shown by green arrows. The difference in the test design for comparing against experimental data (validation) and another simulation (substantiation) is indicated by yellow and red arrows, respectively. The relevant functionality of some components for the computation of test score is indicated by pseudo-code. The capability class ProducesProperty contains the function calc_property(). The test XYTest has a function generate_prediction() which makes use of this capability, inherited by the model class, to generate a model prediction. The initialized test instance XYTest_paramZ makes use of its judge() function to evaluate this model prediction and compute the score TestScore. The XYTest can inherit from multiple abstract test classes (BaseTest), which is for example used with the M2MTest to add the functionality of evaluating multiple model classes. To make the test executable it has to be linked to a ScoreType and all free parameters need to be set (by a Params dict) to ensure a reproducible result.
Showcase examples on how to use NetworkUnit can be found in this repository and interactive reveal.js slides are accessible via the launch-binder button at the top.
| Class name | Parent class | Prediction measure |
|---|---|---|
| two_sample_test | - | - |
| correlation_test | two_sample_test | - |
| correlation_dist_test | correlation_test | correlation coefficients |
| correlation_matrix_test | correlation_test | correlation coefficient matrix |
| generalized_correlation_matrix_test | correlation_matrix_test | matrix of derived cross-correlation measures |
| eigenvalue_test | correlation_test | eigenvalues of the correlation coefficient matrix |
| covariance_test | two_sample_test | covariances |
| firing_rate_test | two_sample_test | firing rates |
| isi_variation_test | two_sample_test | inter-spike-intervals, their CV, or LV |
| graph_centrality_helperclass | sciunit.Test | graph centrality measures of given adjacency matrix |
Inheritance order in case of multiple inheritance for derived test classes:
class new_test(sciunit.TestM2M, graph_centrality_helperclass, <base_test_class>)| Class name | Test name | Comparison measure |
|---|---|---|
| students_t | Student't test | sample mean |
| ks_distance | Kolmogorov-Smirnov test | sample distribution |
| kl_divergence | Kullback-Leibler divergence | sample entropy |
| mwu_statistic | Mann-Whitney U test | rank sum |
| levene_score | Levene's test | sample variance |
| effect_size | Effect size | standardized mean |
| best_effect_size | Bayesian estimation effect size | standardized mean |
| Model name | Capability | Parent class | Purpose |
|---|---|---|---|
| loaded_data | - | sciunit.Model | loading simulated data |
| spiketrain_data | ProducesSpikeTrains | simulation_data | loading simulated spiking data |
| stochastic_activity | ProducesSpikeTrains | sciunit.Model | generating stochastic spiking data |
This open source software code was developed in part or in whole in the Human Brain Project, funded from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreements No. 720270, No. 785907 and No. 945539 (Human Brain Project SGA1, SGA2 and SGA3).