NetworkUnit

A SciUnit library for validation testing of spiking networks.

Installation

pip install networkunit

Concept

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.

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	-	-
firing_rate_test	two_sample_test	firing rates
isi_variation_test	two_sample_test	inter-spike-intervals, their CV, CV2, LV, or LVR
covariance_test	two_sample_test	covariances
correlation_test	two_sample_test	-
correlation_dist_test	correlation_test	correlation coefficients
correlation_matrix_test	correlation_test	correlation coefficient matrix
avg_std_correlation_test	correlation_test	avg or std of correlation coefficients
generalized_correlation_matrix_test	correlation_matrix_test	matrix of derived cross-correlation measures
eigenvalue_test	correlation_test	eigenvalues of the correlation coefficient matrix
power_spectrum_test	two_sample_test	power spectral density values
freqband_power_test	power_spectrum_test	power spectral density values in frequency band
timescale_test	two_sample_test	spike train timescale
graph_centrality_helperclass	sciunit.Test	graph centrality measures of given adjacency matrix
joint_test	two_sample_test	joining other test classes for multivariate evaluation

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
wasserstein_distance	Wasserstein Distance	multivariate score distance
eigenangle	Eigenangle Test	eigenangle similarity

Overview of model classes

Model name	Capability	Parent class	Purpose
loaded_spiketrains	ProducesSpikeTrains	sciunit.RunnableModel	loading simulated spiking data
stochastic_activity	ProducesSpikeTrains	sciunit.RunnableModel	generating stochastic spiking data
nest_simulation	ProducesSpikeTrains	sciunit.RunnableModel	template for NEST models

Other validation test repositories

• NeuronUnit
• HippoUnit
• BasalUnit
• MorphoUnit
• CerebellumUnit

Acknowledgments

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).