A SciUnit library for validation testing of neural network models.

pip install networkunit==0.1.2



A SciUnit library for validation testing of spiking networks.

Binder Link


pip install networkunit


The 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.

NetworkUnit Flowchart

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.

Overview of tests

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>)

Overview of scores

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

Overview of model classes

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

Other validation test repositories


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).