neuronunit.tests package

Submodules

neuronunit.tests.analysis module

neuronunit.tests.channel module

NeuronUnit Test classes for ion channel models

class neuronunit.tests.channel.IVCurvePeakTest(observation, name='IV Curve Test', scale=False, **params)[source]

Bases: neuronunit.tests.channel._IVCurveTest

Test IV curves using steady-state curent

generate_prediction(model)[source]

Generates a prediction from a model using the required capabilities. No default implementation.

class neuronunit.tests.channel.IVCurveSSTest(observation, name='IV Curve Test', scale=False, **params)[source]

Bases: neuronunit.tests.channel._IVCurveTest

Test IV curves using steady-state curent

generate_prediction(model)[source]

Generates a prediction from a model using the required capabilities. No default implementation.

neuronunit.tests.dynamics module

Dynamic neuronunit tests, e.g. investigating dynamical systems properties

class neuronunit.tests.dynamics.BurstinessTest(observation={'mean': None, 'std': None}, name=None, **params)[source]

Bases: neuronunit.tests.waveform.InjectedCurrentAPWidthTest

Tests whether a model exhibits the observed burstiness

compute_score(observation, prediction)[source]

Implementation of sciunit.Test.score_prediction.

cv_threshold = 1.0
description = 'A test of AP bursting at the provided current'
generate_prediction(model)[source]

Implementation of sciunit.Test.generate_prediction.

name = 'Burstiness test'
nonunited_observation_keys = ['cv']
score_type

alias of sciunit.scores.complete.RatioScore

units = Dimensionless('dimensionless', 1.0 * dimensionless)
class neuronunit.tests.dynamics.FiringRateTest(*args, **kwargs)[source]

Bases: neuronunit.tests.fi.RheobaseTest

Tests whether a model exhibits the observed burstiness

compute_score(observation, prediction)[source]

Implementation of sciunit.Test.score_prediction.

description = 'Spikes Per Second.'
generate_prediction(model=None)[source]

Implementation of sciunit.Test.generate_prediction.

name = 'Firing Rate Test'
score_type

alias of sciunit.scores.complete.RatioScore

units = Dimensionless('dimensionless', 1.0 * dimensionless)
class neuronunit.tests.dynamics.ISICVTest(observation={'mean': None, 'std': None}, name=None, **params)[source]

Bases: neuronunit.tests.base.VmTest

Tests whether a model exhibits the observed burstiness

compute_score(observation, prediction)[source]

Implementation of sciunit.Test.score_prediction.

description = 'For neurons and muscle cells check the Coefficient of Variation on a list of Interval Between Spikes given a spike train recording.'
generate_prediction(model=None)[source]

Generates a prediction from a model using the required capabilities. No default implementation.

name = 'ISI Coefficient of Variation Test'
nonunited_observation_keys = ['cv']
score_type

alias of sciunit.scores.complete.RatioScore

united_observation_keys = []
units = Dimensionless('dimensionless', 1.0 * dimensionless)
class neuronunit.tests.dynamics.ISITest(*args, **kwargs)[source]

Bases: neuronunit.tests.base.VmTest

Tests whether a model exhibits the observed Inter Spike Intervals

compute_score(observation, prediction)[source]

Implementation of sciunit.Test.score_prediction.

description = 'For neurons and muscle cells check the mean Interval Between Spikes given a spike train recording.'
generate_prediction(model=None)[source]

Generates a prediction from a model using the required capabilities. No default implementation.

name = 'Inter Spike Interval Tests'
score_type

alias of sciunit.scores.complete.ZScore

units = UnitTime('millisecond', 0.001 * s, 'ms')
class neuronunit.tests.dynamics.LocalVariationTest(*args, **kwargs)[source]

Bases: neuronunit.tests.base.VmTest

Tests whether a model exhibits the observed burstiness

compute_score(observation, prediction)[source]

Implementation of sciunit.Test.score_prediction.

description = 'For neurons and muscle cells with slower non firing dynamics like CElegans neurons check to see how much variation is in the continuous membrane potential.'
generate_prediction(model=None)[source]

Generates a prediction from a model using the required capabilities. No default implementation.

local_variation = 0.0
name = 'Local Variation test'
required_capabilities = (<class 'neuronunit.capabilities.ReceivesSquareCurrent'>, <class 'neuronunit.capabilities.ProducesSpikes'>)
score_type

alias of sciunit.scores.complete.RatioScore

units = Dimensionless('dimensionless', 1.0 * dimensionless)
class neuronunit.tests.dynamics.TFRTypeTest(*args, **kwargs)[source]

Bases: neuronunit.tests.fi.RheobaseTest

Tests whether a model has particular threshold firing rate dynamics, i.e. type 1 or type 2.

compute_score(observation, prediction)[source]

Implementation of sciunit.Test.score_prediction.

description = 'A test of the instantaneous firing rate dynamics, i.e. type 1 or type 2'
generate_prediction(model)[source]

Implementation of sciunit.Test.generate_prediction.

name = 'Firing Rate Type test'
score_type

alias of sciunit.scores.complete.BooleanScore

validate_observation(observation)[source]

(Optional) Implement to validate the observation provided to the constructor. Raises an ObservationError if invalid.

neuronunit.tests.exhaustive_search module

neuronunit.tests.gbevaluator module

neuronunit.tests.get_neab module

neuronunit.tests.model_parameters module

neuronunit.tests.nsga module

neuronunit.tests.rheobase_old module

neuronunit.tests.rheobase_old2 module

neuronunit.tests.rheobase_old3 module

neuronunit.tests.rheobase_only module

neuronunit.tests.stdputil module

neuronunit.tests.test_all module

Module contents

NeuronUnit Test classes.