neuronunit.tests package

Submodules

neuronunit.tests.base module

Base classes and attributes for many neuronunit tests.

No classes here meant for direct use in testing.

class neuronunit.tests.base.FakeTest(observation, name=None, **params)

Bases: sciunit.tests.Test

Fake test class.

Just computes agreement between an observation key and a model attribute. e.g. observation = {‘a’:[0.8,0.3], ‘b’:[0.5,0.1], ‘vr’:[-70*pq.mV,5*pq.mV]} fake_test_a = FakeTest(“test_a”,observation=observation) fake_test_b = FakeTest(“test_b”,observation=observation) fake_test_vr = FakeTest(“test_vr”,observation=observation)

compute_score(observation, prediction)

Generates a score given the observations provided in the constructor and the prediction generated by generate_prediction.

Must generate a score of score_type. No default implementation.

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

class neuronunit.tests.base.VmTest(observation={'mean': None, 'std': None}, name=None, **params)

Bases: sciunit.tests.ProtocolToFeaturesTest

Base class for tests involving the membrane potential of a model.

bind_score(score, model, observation, prediction)

For the user to bind additional features to the score.

compute_params()

Compute new params from existing self.params. Inserts those new params into self.params. Use this when some params depend upon the values of others. Example: self.params[‘c’] = self.params[‘a’] + self.params[‘b’]

condition_model(model)

Update the model in any way needed before generating the prediction.

This could include updating parameters such as simulation durations that do not define the model but do define experiments performed on the model. No default implementation.

default_params = {'amplitude': array(0.) * pA, 'delay': array(100.) * ms, 'dt': array(0.025) * ms, 'duration': array(300.) * ms, 'padding': array(200.) * ms}

ephysprop_name = ''

classmethod get_default_injected_square_current()

get_injected_square_current()

name = ''

classmethod neuroelectro_pooled_observation(neuron, cached=False, quiet=True)

classmethod neuroelectro_summary_observation(neuron, cached=False)

observation_schema = [('Mean, Standard Deviation, N', {'mean': {'units': True, 'required': True}, 'std': {'units': True, 'min': 0, 'required': True}, 'n': {'type': 'integer', 'min': 1}}), ('Mean, Standard Error, N', {'mean': {'units': True, 'required': True}, 'sem': {'units': True, 'min': 0, 'required': True}, 'n': {'type': 'integer', 'min': 1, 'required': True}})]

params_schema = {'amplitude': {'required': False, 'type': 'current'}, 'delay': {'min': 0, 'required': False, 'type': 'time'}, 'dt': {'min': 0, 'required': False, 'type': 'time'}, 'duration': {'min': 0, 'required': False, 'type': 'time'}, 'padding': {'min': 0, 'required': False, 'type': 'time'}, 'tmax': {'min': 0, 'required': False, 'type': 'time'}}

required_capabilities = (<class 'sciunit.capabilities.Runnable'>, <class 'neuronunit.capabilities.ProducesMembranePotential'>)

sanity_check(rheobase, model)

state

Get the frozen (pickled) model state.

units

alias of quantities.unitquantity.Dimensionless

validate_observation(observation)

Validate the observation provided to the constructor.

Raises an ObservationError if invalid.

neuronunit.tests.channel module

NeuronUnit Test classes for ion channel models

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

Bases: neuronunit.tests.channel._IVCurveTest

Test IV curves using steady-state curent

extract_features(model, results)

Implemented in the subclasses

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

Bases: neuronunit.tests.channel._IVCurveTest

Test IV curves using steady-state curent

extract_features(model, results)

Implemented in the subclasses

neuronunit.tests.druckmann2013 module

Tests of features described in Druckmann et. al. 2013 (https://academic.oup.com/cercor/article/23/12/2994/470476)

AP analysis details (from suplementary info): https://github.com/scidash/neuronunit/files/2295064/bhs290supp.pdf

Numbers in class names refer to the numbers in the publication table

class neuronunit.tests.druckmann2013.AP12AHPDepthPercentChangeTest(current_amplitude, **params)

Bases: neuronunit.tests.druckmann2013.Druckmann2013Test

16 Percent change in AP fast AHP depth, first to second spike (%)

Difference in depth of fast AHP between first and second AP divided by the first AP depth of fast AHP.

description = 'Difference in depth of fast AHP between first and second AP divided by the first\n AP depth of fast AHP.'

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Percent change in AP fast AHP depth, first to second spike'

units = Dimensionless('dimensionless', 1.0 * dimensionless)

class neuronunit.tests.druckmann2013.AP12AmplitudeChangePercentTest(current_amplitude, **params)

Bases: neuronunit.tests.druckmann2013.Druckmann2013Test

13. Percent change in AP amplitude, first to second spike (%)

Difference in AP amplitude between first and second AP divided by the first AP amplitude.

description = 'Difference in AP amplitude between first and second AP divided by the first AP\n amplitude.'

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Percent change in AP amplitude, first to second spike '

units = Dimensionless('dimensionless', 1.0 * dimensionless)

class neuronunit.tests.druckmann2013.AP12AmplitudeDropTest(current_amplitude, **params)

Bases: neuronunit.tests.druckmann2013.Druckmann2013Test

1. Drop in AP amplitude (amp.) from first to second spike (mV)

Difference in the voltage value between the amplitude of the first and second AP.

Negative values indicate 2nd AP amplitude > 1st

description = 'Difference in the voltage value between the amplitude of the first and second AP'

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Drop in AP amplitude from 1st to 2nd AP'

units = UnitQuantity('millivolt', 0.001 * V, 'mV')

class neuronunit.tests.druckmann2013.AP12HalfWidthChangePercentTest(current_amplitude, **params)

Bases: neuronunit.tests.druckmann2013.Druckmann2013Test

14. Percent change in AP width at half height, first to second spike (%)

Difference in AP width at half-height between first and second AP divided by the first AP width at half-height.

description = 'Difference in AP width at half-height between first and second AP divided by the\n first AP width at half-height.'

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Percent change in AP width at half height, first to second spike'

units = Dimensionless('dimensionless', 1.0 * dimensionless)

class neuronunit.tests.druckmann2013.AP12RateOfChangePeakToTroughPercentChangeTest(current_amplitude, **params)

Bases: neuronunit.tests.druckmann2013.Druckmann2013Test

15. Percent change in AP peak to trough rate of change, first to second spike (%)

Difference in peak to trough rate of change between first and second AP divided by the first AP peak to trough rate of change.

description = 'Difference in peak to trough rate of change between first and second AP divided\n by the first AP peak to trough rate of change.'

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Percent change in AP peak to trough rate of change, first to second spike'

units = Dimensionless('dimensionless', 1.0 * dimensionless)

class neuronunit.tests.druckmann2013.AP1AHPDepthTest(current_amplitude, **params)

Bases: neuronunit.tests.druckmann2013.Druckmann2013Test

7. AP 1 Fast AHP depth (mV)

Difference between the minimum of voltage at the trough and the voltage value at the beginning of the AP.

description = 'Difference between the minimum of voltage at the trough and the voltage value at\n the beginning of the AP.'

generate_prediction(model, ap_index=0)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'AP 1 Fast AHP depth'

units = UnitQuantity('millivolt', 0.001 * V, 'mV')

class neuronunit.tests.druckmann2013.AP1AmplitudeTest(current_amplitude, **params)

Bases: neuronunit.tests.druckmann2013.Druckmann2013Test

3. AP 1 amplitude (mV)

Amplitude of the first AP.

description = 'Amplitude of the first AP'

generate_prediction(model, ap_index=0)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'First AP amplitude'

units = UnitQuantity('millivolt', 0.001 * V, 'mV')

class neuronunit.tests.druckmann2013.AP1DelayMeanStrongStimTest(current_amplitude, repetitions=7, **params)

Bases: neuronunit.tests.druckmann2013.AP1DelayMeanTest

33 Average delay to AP 1, strong stimulus (ms)

Same as AP1DelayMeanTest but for strong stimulus

class neuronunit.tests.druckmann2013.AP1DelayMeanTest(current_amplitude, repetitions=7, **params)

Bases: neuronunit.tests.druckmann2013.Druckmann2013Test

18 Average delay to AP 1 (ms)

Mean of the delay to beginning of first AP over experimental repetitions of step currents.

description = 'Mean delay to the first AP'

generate_repetition_prediction(model, ap_index=0)

name = 'First AP delay mean'

units = UnitTime('millisecond', 0.001 * s, 'ms')

class neuronunit.tests.druckmann2013.AP1DelaySDStrongStimTest(current_amplitude, repetitions=7, **params)

Bases: neuronunit.tests.druckmann2013.AP1DelaySDTest

34 SD of delay to AP 1, strong stimulus (ms)

Same as AP1DelaySDTest but for strong stimulus

class neuronunit.tests.druckmann2013.AP1DelaySDTest(current_amplitude, repetitions=7, **params)

Bases: neuronunit.tests.druckmann2013.AP1DelayMeanTest

19 SD of delay to AP 1 (ms)

Standard deviation of the delay to beginning of first AP over experimental repetitions of step currents.

aggregate_repetitions(results)

description = 'Standard deviation of delay to the first AP'

name = 'First AP delay standard deviation'

units = UnitTime('millisecond', 0.001 * s, 'ms')

class neuronunit.tests.druckmann2013.AP1RateOfChangePeakToTroughTest(current_amplitude, **params)

Bases: neuronunit.tests.druckmann2013.Druckmann2013Test

6. AP 1 peak to trough rate of change (mV/ms)

Difference in voltage value between peak and trough divided by the amount of time in between the peak and trough.

description = 'Difference in voltage value between peak and trough over the amount of time in between the peak and trough.'

generate_prediction(model, ap_index=0)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'AP 1 peak to trough rate of change'

units = array(1.) * mV/ms

class neuronunit.tests.druckmann2013.AP1SSAmplitudeChangeTest(current_amplitude, **params)

Bases: neuronunit.tests.druckmann2013.Druckmann2013Test

2. AP amplitude change from first spike to steady-state (mV)

Steady state AP amplitude is calculated as the mean amplitude of the set of APs that occurred during the latter third of the current step.

description = 'Steady state AP amplitude is calculated as the mean amplitude of the set of APs\n that occurred during the latter third of the current step.'

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'AP amplitude change from 1st AP to steady-state'

units = UnitQuantity('millivolt', 0.001 * V, 'mV')

class neuronunit.tests.druckmann2013.AP1WidthHalfHeightTest(current_amplitude, **params)

Bases: neuronunit.tests.druckmann2013.Druckmann2013Test

4. AP 1 width at half height (ms)

description = 'Amount of time in between the first crossing (in the upwards direction) of the\n half-height voltage value and the second crossing (in the downwards direction) of\n this value, for the first AP. Half-height voltage is the voltage at the beginning of\n the AP plus half the AP amplitude.'

generate_prediction(model, ap_index=0)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'First AP width at its half height'

units = UnitTime('millisecond', 0.001 * s, 'ms')

class neuronunit.tests.druckmann2013.AP1WidthPeakToTroughTest(current_amplitude, **params)

Bases: neuronunit.tests.druckmann2013.Druckmann2013Test

5. AP 1 peak to trough time (ms)

Amount of time between the peak of the first AP and the trough, i.e., the minimum of the AHP.

description = 'Amount of time between the peak of the first AP and the trough, i.e., the minimum of the AHP'

generate_prediction(model, ap_index=0)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'AP 1 peak to trough time'

units = UnitTime('millisecond', 0.001 * s, 'ms')

class neuronunit.tests.druckmann2013.AP2AHPDepthTest(current_amplitude, **params)

Bases: neuronunit.tests.druckmann2013.AP1AHPDepthTest

12. AP 2 Fast AHP depth (mV)

Same as AP1AHPDepthTest but for second AP

description = 'Same as :any:`AP1AHPDepthTest` but for second AP'

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'AP 2 Fast AHP depth'

class neuronunit.tests.druckmann2013.AP2AmplitudeTest(current_amplitude, **params)

Bases: neuronunit.tests.druckmann2013.AP1AmplitudeTest

8. AP 2 amplitude (mV)

Same as AP1AmplitudeTest but for second AP

description = 'Same as :any:`AP1AmplitudeTest` but for second AP'

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'AP 2 amplitude'

class neuronunit.tests.druckmann2013.AP2DelayMeanStrongStimTest(current_amplitude, repetitions=7, **params)

Bases: neuronunit.tests.druckmann2013.AP2DelayMeanTest

35 Average delay to AP 2, strong stimulus (ms)

Same as AP2DelayMeanTest but for strong stimulus

class neuronunit.tests.druckmann2013.AP2DelayMeanTest(current_amplitude, repetitions=7, **params)

Bases: neuronunit.tests.druckmann2013.AP1DelayMeanTest

20 Average delay to AP 2 (ms)

Same as AP1DelayMeanTest but for 2nd AP

description = 'Mean of delay to the second AP'

generate_repetition_prediction(model, ap_index=0)

name = 'Second AP delay mean'

class neuronunit.tests.druckmann2013.AP2DelaySDStrongStimTest(current_amplitude, repetitions=7, **params)

Bases: neuronunit.tests.druckmann2013.AP2DelaySDTest

36 SD of delay to AP 2, strong stimulus (ms)

Same as AP2DelaySDTest but for strong stimulus

class neuronunit.tests.druckmann2013.AP2DelaySDTest(current_amplitude, repetitions=7, **params)

Bases: neuronunit.tests.druckmann2013.AP1DelaySDTest

21 SD of delay to AP 2 (ms)

Same as AP1DelaySDTest but for 2nd AP

Only stochastic models will have a non-zero value for this test

description = 'Standard deviation of delay to the second AP'

generate_repetition_prediction(model, ap_index=0)

name = 'Second AP delay standard deviation'

class neuronunit.tests.druckmann2013.AP2RateOfChangePeakToTroughTest(current_amplitude, **params)

Bases: neuronunit.tests.druckmann2013.AP1RateOfChangePeakToTroughTest

11. AP 2 peak to trough rate of change (mV/ms)

Same as AP1RateOfChangePeakToTroughTest but for second AP

description = 'Same as :any:`AP1RateOfChangePeakToTroughTest` but for second AP'

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'AP 2 peak to trough rate of change'

class neuronunit.tests.druckmann2013.AP2WidthHalfHeightTest(current_amplitude, **params)

Bases: neuronunit.tests.druckmann2013.AP1WidthHalfHeightTest

9. AP 2 width at half height (ms)

Same as AP1WidthHalfHeightTest but for second AP

description = 'Same as :any:`AP1WidthHalfHeightTest` but for second AP'

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'AP 2 width at half height'

class neuronunit.tests.druckmann2013.AP2WidthPeakToTroughTest(current_amplitude, **params)

Bases: neuronunit.tests.druckmann2013.AP1WidthPeakToTroughTest

10. AP 2 peak to trough time (ms)

Same as AP1WidthPeakToTroughTest but for second AP

description = 'Same as :any:`AP1WidthPeakToTroughTest` but for second AP'

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'AP 2 peak to trough time'

class neuronunit.tests.druckmann2013.AccommodationAtSSMeanTest(current_amplitude, **params)

Bases: neuronunit.tests.druckmann2013.Druckmann2013Test

27 Average accommodation at steady-state (%)

Accommodation analysis based on a fit of the ISIs to an exponential function: ISI = A+B*exp(-t/tau). This feature gives the relative size of the constant term (A) to the term before the exponent (B).

description = 'Mean of the ISI steady state accomodation'

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

get_final_result

name = 'ISI Steady state accomodation mean'

units = Dimensionless('dimensionless', 1.0 * dimensionless)

class neuronunit.tests.druckmann2013.AccommodationRateMeanAtSSTest(current_amplitude, **params)

Bases: neuronunit.tests.druckmann2013.AccommodationAtSSMeanTest

28 Average rate of accommodation during steady-state

Accommodation analysis based on a fit of the ISIs to an exponential function. This feature is the time constant (tau) of the exponent.

description = 'Time constant of the ISI accomodation'

get_final_result(A, B, tau)

name = 'ISI accomodation time constant'

units = UnitTime('millisecond', 0.001 * s, 'ms')

class neuronunit.tests.druckmann2013.AccommodationRateToSSTest(current_amplitude, **params)

Bases: neuronunit.tests.druckmann2013.Druckmann2013Test

26 Rate of accommodation to steady-state (percent/ms)

The percent difference between the spiking rate of the first fifth of the step current and final fifth of the step current divided by the time taken to first reach the rate of steady state accommodation.

Note: It’s not clear what is meant by “time taken to first reach the rate of steady state accommodation”. Here, it’s computed as smallest t of an ISI which is longer than 0.95 of the mean ISIs in the final fifth of the current step.

description = 'Rate of ISI Accomodation'

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'ISI Accomodation Rate'

units = UnitQuantity('per_ms', 1.0 * 1/ms, 'per_ms')

class neuronunit.tests.druckmann2013.Burst1ISIMeanStrongStimTest(current_amplitude, repetitions=7, **params)

Bases: neuronunit.tests.druckmann2013.Burst1ISIMeanTest

37 Average initial burst ISI, strong stimulus (ms)

Same as Burst1ISIMeanTest but for strong stimulus

class neuronunit.tests.druckmann2013.Burst1ISIMeanTest(current_amplitude, repetitions=7, **params)

Bases: neuronunit.tests.druckmann2013.Druckmann2013Test

22 Average initial burst interval (ms)

Initial burst interval is defined as the average of the first two ISIs, i.e., the average of the time differences between the first and second AP and the second and third AP. This feature is the average the initial burst interval across experimental repetitions.

description = 'Mean of the initial burst interval'

generate_repetition_prediction(model)

name = 'Initial burst interval mean'

units = UnitTime('millisecond', 0.001 * s, 'ms')

class neuronunit.tests.druckmann2013.Burst1ISISDStrongStimTest(current_amplitude, repetitions=7, **params)

Bases: neuronunit.tests.druckmann2013.Burst1ISISDTest

38 SD of average initial burst ISI, strong stimulus (ms)

Same as Burst1ISISDTest but for strong stimulus

class neuronunit.tests.druckmann2013.Burst1ISISDTest(current_amplitude, repetitions=7, **params)

Bases: neuronunit.tests.druckmann2013.Burst1ISIMeanTest

23 SD of average initial burst interval (ms)

The standard deviation of the initial burst interval across experimental repetitions.

aggregate_repetitions(results)

description = 'StDev of the initial burst interval'

name = 'Initial burst interval std'

class neuronunit.tests.druckmann2013.Druckmann2013AP(waveform, begin_time)

Bases: object

This is a helper class that computes/finds aspects of APs as defined in Druckmann 2013

get_amplitude()

The amplitude of a spike is given by the difference between the voltage at the beginning and peak of the spike.

Returns:the amplitude value

get_beginning()

The beginning of a spike was then determined by a crossing of a threshold on the derivative of the voltage (12mV/msec).

Returns:the voltage and time of the AP beginning

get_halfwidth()

Amount of time in between the first crossing (in the upwards direction) of the half-height voltage value and the second crossing (in the downwards direction) of this value, for the first AP. Half-height voltage is the voltage at the beginning of the AP plus half the AP amplitude.

Returns:

get_peak()

The peak point of the spike is the maximum in between the beginning and the end.

Returns:the voltage and time of the peak

get_trough()

class neuronunit.tests.druckmann2013.Druckmann2013Test(current_amplitude, **params)

Bases: neuronunit.tests.base.VmTest

All tests inheriting from this class assume that the subject model:

1. Is at steady state at time 0 (i.e. resume from SS)
2. Starting at t=0, will have a 2s step current injected into soma, at least once

aggregate_repetitions(results)

current_length()

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

generate_repetition_prediction(model)

get_APs(model)

Spikes were detected by a crossing of a voltage threshold (-20 mV).

Parameters:model – model which provides the waveform to analyse Returns:a list of Druckman2013APs

get_ISIs(model=None)

required_capabilities = (<class 'neuronunit.capabilities.ProducesActionPotentials'>,)

score_type

alias of sciunit.scores.complete.ZScore

class neuronunit.tests.druckmann2013.ISIBurstMeanChangeTest(current_amplitude, **params)

Bases: neuronunit.tests.druckmann2013.Druckmann2013Test

31 Average change in ISIs during a burst (%)

Difference between the first and second ISI divided by the value of the first ISI.

description = 'ISI Burst Mean Change'

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'ISI Burst Mean Change'

units = Dimensionless('dimensionless', 1.0 * dimensionless)

class neuronunit.tests.druckmann2013.ISICVTest(current_amplitude, **params)

Bases: neuronunit.tests.druckmann2013.Druckmann2013Test

29 Average inter-spike interval (ISI) coefficient of variation (CV) (unitless)

Coefficient of variation (mean divided by standard deviation) of the distribution of ISIs.

description = 'ISI Coefficient of Variation'

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'ISI CV'

units = Dimensionless('dimensionless', 1.0 * dimensionless)

class neuronunit.tests.druckmann2013.ISIMedianTest(current_amplitude, **params)

Bases: neuronunit.tests.druckmann2013.Druckmann2013Test

30 Median of the distribution of ISIs (ms)

Median of the distribution of ISIs.

description = 'ISI Median'

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'ISI Median'

units = UnitTime('millisecond', 0.001 * s, 'ms')

class neuronunit.tests.druckmann2013.InitialAccommodationMeanTest(current_amplitude, **params)

Bases: neuronunit.tests.druckmann2013.Druckmann2013Test

24 Average initial accommodation (%)

Initial accommodation is defined as the percent difference between the spiking rate of the first fifth of the step current and the third fifth of the step current.

description = 'Mean of the initial accomodation'

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Initial accomodation mean'

units = Dimensionless('dimensionless', 1.0 * dimensionless)

class neuronunit.tests.druckmann2013.InputResistanceTest(injection_currents=array([], dtype=float64) * nA, **params)

Bases: neuronunit.tests.druckmann2013.Druckmann2013Test

17 Input resistance for steady-state current (MOhm)

Input resistance calculated by injecting weak subthreshold hyperpolarizing and depolarizing step currents. Input resistance was taken as linear fit of current to voltage difference.

description = 'Input resistance calculated by injecting weak subthreshold hyperpolarizing and\n depolarizing step currents. Input resistance was taken as linear fit of current to\n voltage difference'

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Input resistance for steady-state current'

units = array(1) * megaohm

class neuronunit.tests.druckmann2013.SSAccommodationMeanTest(current_amplitude, **params)

Bases: neuronunit.tests.druckmann2013.Druckmann2013Test

25 Average steady-state accommodation (%)

Steady-state accommodation is defined as the percent difference between the spiking rate of the first fifth of the step current and the last fifth of the step current.

description = 'Mean of the steady state accomodation'

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Steady state accomodation mean'

units = Dimensionless('dimensionless', 1.0 * dimensionless)

class neuronunit.tests.druckmann2013.SpikeRateStrongStimTest(current_amplitude, **params)

Bases: neuronunit.tests.druckmann2013.Druckmann2013Test

32 Average rate, strong stimulus (Hz)

Firing rate of strong stimulus.

description = 'Strong Stimulus Firing Rate'

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Strong Stimulus Firing Rate'

units = UnitQuantity('hertz', 1.0 * 1/s, 'Hz')

neuronunit.tests.druckmann2013.get_diff

neuronunit.tests.druckmann2013.get_diff_spikes

neuronunit.tests.druckmann2013.isolate_code_block(threshold_crosses, start_time, dvdt_threshold_crosses, dvdt_zero_crosses, vm)

The introduction of this function is was not syntactically necissated. The reason for this functions existence is to support code modularity.

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)

Bases: neuronunit.tests.waveform.InjectedCurrentAPWidthTest

Test whether a model exhibits the observed burstiness

compute_score(observation, prediction)

Implement sciunit.Test.score_prediction.

cv_threshold = 1.0

description = 'A test of AP bursting at the provided current'

generate_prediction(model)

Implement 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)

Bases: neuronunit.tests.fi.RheobaseTest

Test whether a model exhibits the observed burstiness

compute_score(observation, prediction)

Implement sciunit.Test.score_prediction.

description = 'Spikes Per Second.'

generate_prediction(model=None)

Implements 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)

Bases: neuronunit.tests.base.VmTest

Test whether a model exhibits the observed burstiness

compute_score(observation, prediction)

Implement 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)

Generate 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)

Bases: neuronunit.tests.base.VmTest

Test whether a model exhibits the observed Inter Spike Intervals

compute_score(observation, prediction)

Implement 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)

Generate 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)

Bases: neuronunit.tests.base.VmTest

Tests whether a model exhibits the observed burstiness

compute_score(observation, prediction)

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)

Generate 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)

Bases: neuronunit.tests.fi.RheobaseTest

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

compute_score(observation, prediction)

Implement sciunit.Test.score_prediction.

description = 'A test of the instantaneous firing rate dynamics, i.e. type 1 or type 2'

generate_prediction(model)

Implement sciunit.Test.generate_prediction.

name = 'Firing Rate Type test'

score_type

alias of sciunit.scores.complete.BooleanScore

validate_observation(observation)

Validate the observation provided to the constructor.

Raises an ObservationError if invalid.

neuronunit.tests.fi module

F/I neuronunit tests.

For example, investigating firing rates and patterns as a function of input current.

class neuronunit.tests.fi.RheobaseTest(observation={'mean': None, 'std': None}, name=None, **params)

Bases: neuronunit.tests.base.VmTest

Serial implementation of a binary search to test the rheobase.

Strengths: this algorithm is faster than the parallel class, present in this file under important and limited circumstances: this serial algorithm is faster than parallel for model backends that are able to implement numba jit optimization.

Weaknesses this serial class is significantly slower, for many backend implementations including raw NEURON, NEURON via PyNN, and possibly GLIF.

bind_score(score, model, observation, prediction)

Bind additional attributes to the test score.

compute_score(observation, prediction)

Implement sciunit.Test.score_prediction.

condition_model(model)

Update the model in any way needed before generating the prediction.

This could include updating parameters such as simulation durations that do not define the model but do define experiments performed on the model. No default implementation.

default_params = {'amplitude': array(100.) * pA, 'delay': array(100.) * ms, 'dt': array(0.025) * ms, 'duration': array(1000.) * ms, 'padding': array(200.) * ms, 'tolerance': array(1.) * pA}

description = 'A test of the rheobase, i.e. the minimum injected current needed to evoke at least one spike.'

ephysprop_name = 'Rheobase'

generate_prediction(model)

Implement sciunit.Test.generate_prediction.

name = 'Rheobase test'

params_schema = {'amplitude': {'required': False, 'type': 'current'}, 'delay': {'min': 0, 'required': False, 'type': 'time'}, 'dt': {'min': 0, 'required': False, 'type': 'time'}, 'duration': {'min': 0, 'required': False, 'type': 'time'}, 'padding': {'min': 0, 'required': False, 'type': 'time'}, 'tmax': {'min': 0, 'required': False, 'type': 'time'}, 'tolerance': {'min': 1, 'required': False, 'type': 'current'}}

required_capabilities = (<class 'neuronunit.capabilities.ReceivesSquareCurrent'>, <class 'neuronunit.capabilities.ProducesSpikes'>)

score_type

alias of sciunit.scores.complete.RatioScore

threshold_FI(model, units, guess=None)

Use binary search to generate an FI curve including rheobase.

units = UnitCurrent('picoampere', 0.001 * nA, 'pA')

class neuronunit.tests.fi.RheobaseTestP(observation={'mean': None, 'std': None}, name=None, **params)

Bases: neuronunit.tests.fi.RheobaseTest

Parallel implementation of a binary search to test the rheobase.

Strengths: this algorithm is faster than the serial class, present in this file for model backends that are not able to implement numba jit optimization, which actually happens to be typical of a signifcant number of backends.

condition_model(model)

Update the model in any way needed before generating the prediction.

This could include updating parameters such as simulation durations that do not define the model but do define experiments performed on the model. No default implementation.

description = 'A test of the rheobase, i.e. the minimum injected current needed to evoke at least one spike.'

ephysprop_name = 'Rheobase'

generate_prediction(model)

Generate the test prediction.

get_rheobase_vm = True

name = 'Rheobase test'

score_type

alias of sciunit.scores.complete.RatioScore

units = UnitCurrent('picoampere', 0.001 * nA, 'pA')

neuronunit.tests.fi.check_current(dtc)

Check the response to the proposed current and count spikes.

Inputs are an amplitude to test and a virtual model output is an virtual model with an updated dictionary.

neuronunit.tests.fi.check_fix_range(dtc)

Check for the rheobase value.

Inputs: lookup, A dictionary of previous current injection values used to search rheobase Outputs: A boolean to indicate if the correct rheobase current was found and a dictionary containing the range of values used. If rheobase was actually found then rather returning a boolean and a dictionary, instead logical True, and the rheobase current is returned. given a dictionary of rheobase search values, use that dictionary as input for a subsequent search.

neuronunit.tests.fi.find_rheobase(self, dtc)

neuronunit.tests.fi.get_sub_supra(lookup)

Get subthreshold and suprathreshold current values.

neuronunit.tests.fi.init_dtc(dtc)

Exploit memory of last model in genes.

neuronunit.tests.morphology module

NeuronUnit Test classes for cell models with morphology

class neuronunit.tests.morphology.ApicalDendriteAverageBifurcationAngleLocalTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.AverageBifurcationAngleLocalTest

name = 'Apical Dendrite Average Bifurcation Angle Local Test'

specificity = 'Type==4'

class neuronunit.tests.morphology.ApicalDendriteAverageBifurcationAngleRemoteTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.AverageBifurcationAngleRemoteTest

name = 'Apical Dendrite Average Bifurcation Angle Remote Test'

specificity = 'Type==4'

class neuronunit.tests.morphology.ApicalDendriteAverageContractionTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.AverageContractionTest

name = 'Apical Dendrite Average Contraction Test'

specificity = 'Type==4'

class neuronunit.tests.morphology.ApicalDendriteAverageDiameterTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.AverageDiameterTest

name = 'Apical Dendrite Average Diameter Test'

specificity = 'Type==4'

class neuronunit.tests.morphology.ApicalDendriteAverageRallsRatioTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.AverageRallsRatioTest

name = 'Apical Dendrite Average Ralls Ratio Test'

specificity = 'Type==4'

class neuronunit.tests.morphology.ApicalDendriteFractalDimensionTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.FractalDimensionTest

name = 'Apical Dendrite Fractal Dimension Test'

specificity = 'Type==4'

class neuronunit.tests.morphology.ApicalDendriteMaxBranchOrderTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.MaxBranchOrderTest

name = 'Apical Dendrite Max Branch Order Test'

specificity = 'Type==4'

class neuronunit.tests.morphology.ApicalDendriteMaxEuclideanDistanceTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.MaxEuclideanDistanceTest

name = 'Apical Dendrite Max Euclidean Distance Test'

specificity = 'Type==4'

class neuronunit.tests.morphology.ApicalDendriteMaxPathDistanceTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.MaxPathDistanceTest

name = 'Apical Dendrite Max Path Distance Test'

specificity = 'Type==4'

class neuronunit.tests.morphology.ApicalDendriteNumberofBifurcationsTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.NumberofBifurcationsTest

Test the agreement between number of apical dendrite bifurcations observed in the model and experiments

name = 'Number of Apical Dendrite Bifurcations Test'

specificity = 'Type==4'

class neuronunit.tests.morphology.ApicalDendriteNumberofBranchesTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.NumberofBranchesTest

name = 'Apical Dendrite Number of Branches Test'

specificity = 'Type==4'

class neuronunit.tests.morphology.ApicalDendriteOverallDepthTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.OverallDepthTest

name = 'Apical Dendrite Overall Depth Test'

specificity = 'Type==4'

class neuronunit.tests.morphology.ApicalDendriteOverallHeightTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.OverallHeightTest

name = 'Apical Dendrite Overall Height Test'

specificity = 'Type==4'

class neuronunit.tests.morphology.ApicalDendriteOverallWidthTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.OverallWidthTest

name = 'Apical Dendrite Overall Width Test'

specificity = 'Type==4'

class neuronunit.tests.morphology.ApicalDendritePartitionAsymmetryTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.PartitionAsymmetryTest

name = 'Apical Dendrite Partition Asymmetry Test'

specificity = 'Type==4'

class neuronunit.tests.morphology.ApicalDendriteTotalLengthTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.TotalLengthTest

name = 'Apical Dendrite Total Length Test'

specificity = 'Type==4'

class neuronunit.tests.morphology.ApicalDendriteTotalSurfaceTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.TotalSurfaceTest

name = 'Apical Dendrite Total Surface Test'

specificity = 'Type==4'

class neuronunit.tests.morphology.ApicalDendriteTotalVolumeTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.TotalVolumeTest

name = 'Apical Dendrite Total Volume Test'

specificity = 'Type==4'

class neuronunit.tests.morphology.AverageBifurcationAngleLocalTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.MorphologyTest

Test the agreement between average bifurcation angle local observed in the model and experiments

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Average Bifurcation Angle Local Test'

units = Dimensionless('dimensionless', 1.0 * dimensionless)

class neuronunit.tests.morphology.AverageBifurcationAngleRemoteTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.MorphologyTest

Test the agreement between average bifurcation angle remote observed in the model and experiments

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Average Bifurcation Angle Remote Test'

units = Dimensionless('dimensionless', 1.0 * dimensionless)

class neuronunit.tests.morphology.AverageContractionTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.MorphologyTest

Test the agreement between average contraction observed in the model and experiments

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Average Contraction Test'

units = Dimensionless('dimensionless', 1.0 * dimensionless)

class neuronunit.tests.morphology.AverageDiameterTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.MorphologyTest

Test the agreement between average diameter observed in the model and experiments

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Average Diameter Test'

pca = True

units = UnitLength('micrometer', 0.001 * mm, 'um')

class neuronunit.tests.morphology.AverageRallsRatioTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.MorphologyTest

Test the agreement between average Rall’s ratio observed in the model and experiments

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Average Ralls Ratio Test'

units = Dimensionless('dimensionless', 1.0 * dimensionless)

class neuronunit.tests.morphology.BasalDendriteAverageBifurcationAngleLocalTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.AverageBifurcationAngleLocalTest

name = 'Basal Dendrite Average Bifurcation Angle Local Test'

specificity = 'Type==3'

class neuronunit.tests.morphology.BasalDendriteAverageBifurcationAngleRemoteTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.AverageBifurcationAngleRemoteTest

name = 'Basal Dendrite Average Bifurcation Angle Remote Test'

specificity = 'Type==3'

class neuronunit.tests.morphology.BasalDendriteAverageContractionTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.AverageContractionTest

name = 'Basal Dendrite Average Contraction Test'

specificity = 'Type==3'

class neuronunit.tests.morphology.BasalDendriteAverageDiameterTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.AverageDiameterTest

name = 'Basal Dendrite Average Diameter Test'

specificity = 'Type==3'

class neuronunit.tests.morphology.BasalDendriteAverageRallsRatioTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.AverageRallsRatioTest

name = 'Basal Dendrite Average Ralls Ratio Test'

specificity = 'Type==3'

class neuronunit.tests.morphology.BasalDendriteFractalDimensionTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.FractalDimensionTest

name = 'Basal Dendrite Fractal Dimension Test'

specificity = 'Type==3'

class neuronunit.tests.morphology.BasalDendriteMaxBranchOrderTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.MaxBranchOrderTest

name = 'Basal Dendrite Max Branch Order Test'

specificity = 'Type==3'

class neuronunit.tests.morphology.BasalDendriteMaxEuclideanDistanceTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.MaxEuclideanDistanceTest

name = 'Basal Dendrite Max Euclidean Distance Test'

specificity = 'Type==3'

class neuronunit.tests.morphology.BasalDendriteMaxPathDistanceTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.MaxPathDistanceTest

name = 'Basal Dendrite Max Path Distance Test'

specificity = 'Type==3'

class neuronunit.tests.morphology.BasalDendriteNumberofBifurcationsTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.NumberofBifurcationsTest

Test the agreement between number of basal dendrite bifurcations observed in the model and experiments

name = 'Number of Basal Dendrite Bifurcations Test'

specificity = 'Type==3'

class neuronunit.tests.morphology.BasalDendriteNumberofBranchesTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.NumberofBranchesTest

name = 'Basal Dendrite Number of Branches Test'

specificity = 'Type==3'

class neuronunit.tests.morphology.BasalDendriteOverallDepthTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.OverallDepthTest

name = 'Basal Dendrite Overall Depth Test'

specificity = 'Type==3'

class neuronunit.tests.morphology.BasalDendriteOverallHeightTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.OverallHeightTest

name = 'Basal Dendrite Overall Height Test'

specificity = 'Type==3'

class neuronunit.tests.morphology.BasalDendriteOverallWidthTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.OverallWidthTest

name = 'Basal Dendrite Overall Width Test'

specificity = 'Type==3'

class neuronunit.tests.morphology.BasalDendritePartitionAsymmetryTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.PartitionAsymmetryTest

name = 'Basal Dendrite Partition Asymmetry Test'

specificity = 'Type==3'

class neuronunit.tests.morphology.BasalDendriteTotalLengthTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.TotalLengthTest

name = 'Basal Dendrite Total Length Test'

specificity = 'Type==3'

class neuronunit.tests.morphology.BasalDendriteTotalSurfaceTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.TotalSurfaceTest

name = 'Basal Dendrite Total Surface Test'

specificity = 'Type==3'

class neuronunit.tests.morphology.BasalDendriteTotalVolumeTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.TotalVolumeTest

name = 'Basal Dendrite Total Volume Test'

specificity = 'Type==3'

class neuronunit.tests.morphology.FractalDimensionTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.MorphologyTest

Test the agreement between fractal dimension observed in the model and experiments

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Fractal Dimension Test'

units = Dimensionless('dimensionless', 1.0 * dimensionless)

class neuronunit.tests.morphology.MaxBranchOrderTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.MorphologyTest

Test the agreement between max branch order observed in the model and experiments

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Max Branch Order Test'

units = Dimensionless('dimensionless', 1.0 * dimensionless)

class neuronunit.tests.morphology.MaxEuclideanDistanceTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.MorphologyTest

Test the agreement between max euclidean distance observed in the model and experiments

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Max Euclidean Distance Test'

units = UnitLength('micrometer', 0.001 * mm, 'um')

class neuronunit.tests.morphology.MaxPathDistanceTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.MorphologyTest

Test the agreement between max path distance observed in the model and experiments

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Max Path Distance Test'

units = UnitLength('micrometer', 0.001 * mm, 'um')

class neuronunit.tests.morphology.MorphologyTest(observation, name=None, **params)

Bases: sciunit.tests.Test

An abstract class to hold common elements among the morphology tests below

get_lmeasure(model_swc, measure, stat)

Computes the specified L-Measure measure and selects one of the statistics

Parameters:

• model_swc – A model that has ProducesSWC capability
• measure – One of the functions from the list: http://cng.gmu.edu:8080/Lm/help/index.htm
• stat – One of: Average, Maximum, Minimum, StdDev, TotalSum

Returns:

The computed measure statistic

pca = False

required_capabilities = (<class 'neuronunit.capabilities.morphology.ProducesSWC'>,)

score_type

alias of sciunit.scores.complete.ZScore

specificity = 'Type > 1'

class neuronunit.tests.morphology.NumberofBifurcationsTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.MorphologyTest

Test the agreement between number of bifurcations observed in the model and experiments

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Number of Bifurcations Test'

units = Dimensionless('dimensionless', 1.0 * dimensionless)

class neuronunit.tests.morphology.NumberofBranchesTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.MorphologyTest

Test the agreement between number of branches observed in the model and experiments

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Number of Branches Test'

units = Dimensionless('dimensionless', 1.0 * dimensionless)

class neuronunit.tests.morphology.NumberofStemsTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.MorphologyTest

Test the agreement between number of stems observed in the model and experiments

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Number of Stems Test'

units = Dimensionless('dimensionless', 1.0 * dimensionless)

class neuronunit.tests.morphology.OverallDepthTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.MorphologyTest

Test the agreement between overall depth observed in the model and experiments

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Overall Depth Test'

pca = True

units = UnitLength('micrometer', 0.001 * mm, 'um')

class neuronunit.tests.morphology.OverallHeightTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.MorphologyTest

Test the agreement between overall height observed in the model and experiments

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Overall Height Test'

pca = True

units = UnitLength('micrometer', 0.001 * mm, 'um')

class neuronunit.tests.morphology.OverallWidthTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.MorphologyTest

Test the agreement between overall width observed in the model and experiments

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Overall Width Test'

pca = True

units = UnitLength('micrometer', 0.001 * mm, 'um')

class neuronunit.tests.morphology.PartitionAsymmetryTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.MorphologyTest

Test the agreement between partition asymmetry observed in the model and experiments

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Partition Asymmetry Test'

units = Dimensionless('dimensionless', 1.0 * dimensionless)

class neuronunit.tests.morphology.SomaSurfaceAreaTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.MorphologyTest

Test the agreement between soma surface area observed in the model and experiments

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Soma Surface Area Test'

units = array(1.) * um**2

class neuronunit.tests.morphology.TotalLengthTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.MorphologyTest

Test the agreement between total length observed in the model and experiments

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Total Length Test'

units = UnitLength('micrometer', 0.001 * mm, 'um')

class neuronunit.tests.morphology.TotalSurfaceTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.MorphologyTest

Test the agreement between total surface observed in the model and experiments

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Total Surface Test'

units = array(1.) * um**2

class neuronunit.tests.morphology.TotalVolumeTest(observation, name=None, **params)

Bases: neuronunit.tests.morphology.MorphologyTest

Test the agreement between total volume (excl. soma) observed in the model and experiments

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

name = 'Total Volume Test'

units = array(1.) * um**3

neuronunit.tests.passive module

Passive neuronunit tests, requiring no active conductances or spiking.

class neuronunit.tests.passive.CapacitanceTest(*args, **kwargs)

Bases: neuronunit.tests.passive.TestPulseTest

Tests the input resistance of a cell.

compute_score(observation, prediction)

Implement sciunit.Test.score_prediction.

description = 'A test of the membrane capacitance of a cell.'

ephysprop_name = 'Cell Capacitance'

extract_features(model, result)

name = 'Capacitance test'

units = UnitQuantity('picofarad', 0.001 * nF, 'pF')

class neuronunit.tests.passive.InputResistanceTest(*args, **kwargs)

Bases: neuronunit.tests.passive.TestPulseTest

Test the input resistance of a cell.

description = 'A test of the input resistance of a cell.'

ephysprop_name = 'Input Resistance'

extract_features(model, result)

name = 'Input resistance test'

units = UnitQuantity('megaohm', 1000000.0 * ohm, 'Mohm')

class neuronunit.tests.passive.RestingPotentialTest(*args, **kwargs)

Bases: neuronunit.tests.passive.TestPulseTest

Tests the resting potential under zero current injection.

compute_score(observation, prediction)

Implement sciunit.Test.score_prediction.

default_params = {'amplitude': array(0.) * pA, 'delay': array(100.) * ms, 'dt': array(0.025) * ms, 'duration': array(300.) * ms, 'padding': array(200.) * ms}

description = 'A test of the resting potential of a cell where injected current is set to zero.'

ephysprop_name = 'Resting membrane potential'

extract_features(model, result)

name = 'Resting potential test'

score_type

alias of sciunit.scores.complete.ZScore

units = UnitQuantity('millivolt', 0.001 * V, 'mV')

class neuronunit.tests.passive.TestPulseTest(*args, **kwargs)

Bases: neuronunit.tests.base.VmTest

A base class for tests that use a square test pulse.

compute_params()

Compute new params from existing self.params. Inserts those new params into self.params. Use this when some params depend upon the values of others. Example: self.params[‘c’] = self.params[‘a’] + self.params[‘b’]

compute_score(observation, prediction)

Implement sciunit.Test.score_prediction.

condition_model(model)

Update the model in any way needed before generating the prediction.

This could include updating parameters such as simulation durations that do not define the model but do define experiments performed on the model. No default implementation.

default_params = {'amplitude': array(-10.) * pA, 'delay': array(100.) * ms, 'dt': array(0.025) * ms, 'duration': array(300.) * ms, 'padding': array(200.) * ms}

classmethod exponential_fit(segment, offset)

extract_features(model, vm)

get_result(model)

classmethod get_rin(vm, i)

classmethod get_segment(vm, start, finish)

classmethod get_tau(vm, i)

name = ''

required_capabilities = (<class 'neuronunit.capabilities.ReceivesSquareCurrent'>,)

score_type

alias of sciunit.scores.complete.ZScore

setup_protocol(model)

Implement sciunit.tests.ProtocolToFeatureTest.setup_protocol.

class neuronunit.tests.passive.TimeConstantTest(*args, **kwargs)

Bases: neuronunit.tests.passive.TestPulseTest

Test the input resistance of a cell.

compute_score(observation, prediction)

Implement sciunit.Test.score_prediction.

description = 'A test of membrane time constant of a cell.'

ephysprop_name = 'Membrane Time Constant'

extract_features(model, result)

name = 'Time constant test'

units = UnitTime('millisecond', 0.001 * s, 'ms')

neuronunit.tests.waveform module

Waveform neuronunit tests, e.g. testing AP waveform properties

class neuronunit.tests.waveform.APAmplitudeTest(observation={'mean': None, 'std': None}, name=None, **params)

Bases: neuronunit.tests.base.VmTest

Test the heights (peak amplitude) of action potentials.

compute_score(observation, prediction)

Implementat sciunit.Test.score_prediction.

description = 'A test of the amplitude (peak minus threshold) of action potentials.'

ephysprop_name = 'Spike Amplitude'

generate_prediction(model)

Implement sciunit.Test.generate_prediction.

name = 'AP amplitude test'

required_capabilities = (<class 'neuronunit.capabilities.ProducesActionPotentials'>,)

score_type

alias of sciunit.scores.complete.ZScore

units = UnitQuantity('millivolt', 0.001 * V, 'mV')

class neuronunit.tests.waveform.APThresholdTest(observation={'mean': None, 'std': None}, name=None, **params)

Bases: neuronunit.tests.base.VmTest

Test the full widths of action potentials at their half-maximum.

compute_score(observation, prediction)

Implement sciunit.Test.score_prediction.

description = 'A test of the membrane potential threshold at which action potentials are produced.'

ephysprop_name = 'Spike Threshold'

generate_prediction(model)

Implement sciunit.Test.generate_prediction.

name = 'AP threshold test'

required_capabilities = (<class 'neuronunit.capabilities.ProducesActionPotentials'>,)

score_type

alias of sciunit.scores.complete.ZScore

units = UnitQuantity('millivolt', 0.001 * V, 'mV')

class neuronunit.tests.waveform.APWidthTest(observation={'mean': None, 'std': None}, name=None, **params)

Bases: neuronunit.tests.base.VmTest

Test the full widths of action potentials at their half-maximum.

compute_score(observation, prediction)

Implement sciunit.Test.score_prediction.

description = 'A test of the widths of action potentials at half of their maximum height.'

ephysprop_name = 'Spike Half-Width'

generate_prediction(model)

Implement sciunit.Test.generate_prediction.

name = 'AP width test'

required_capabilities = (<class 'neuronunit.capabilities.ProducesActionPotentials'>,)

score_type

alias of sciunit.scores.complete.RatioScore

units = UnitTime('millisecond', 0.001 * s, 'ms')

class neuronunit.tests.waveform.InjectedCurrent

Bases: object

Metaclass to mixin with InjectedCurrent tests.

compute_params()

default_params = {'amplitude': array(100.) * pA, 'delay': array(100.) * ms, 'dt': array(0.025) * ms, 'duration': array(300.) * ms, 'padding': array(200.) * ms}

required_capabilities = (<class 'neuronunit.capabilities.ReceivesSquareCurrent'>,)

class neuronunit.tests.waveform.InjectedCurrentAPAmplitudeTest(observation={'mean': None, 'std': None}, name=None, **params)

Bases: neuronunit.tests.waveform.InjectedCurrent, neuronunit.tests.waveform.APAmplitudeTest

Test the heights (peak amplitude) of action potentials.

Uses current injection.

description = 'A test of the heights (peak amplitudes) of action potentials when current is injected into cell.'

generate_prediction(model)

Implement sciunit.Test.generate_prediction.

name = 'Injected current AP amplitude test'

class neuronunit.tests.waveform.InjectedCurrentAPThresholdTest(observation={'mean': None, 'std': None}, name=None, **params)

Bases: neuronunit.tests.waveform.InjectedCurrent, neuronunit.tests.waveform.APThresholdTest

Test the thresholds of action potentials under current injection.

description = 'A test of the membrane potential threshold at which action potentials are produced under current injection.'

generate_prediction(model)

Implement sciunit.Test.generate_prediction.

name = 'Injected current AP threshold test'

class neuronunit.tests.waveform.InjectedCurrentAPWidthTest(observation={'mean': None, 'std': None}, name=None, **params)

Bases: neuronunit.tests.waveform.InjectedCurrent, neuronunit.tests.waveform.APWidthTest

Tests the full widths of APs at their half-maximum under current injection.

description = 'A test of the widths of action potentials at half of their maximum height when current is injected into cell.'

generate_prediction(model)

Implement sciunit.Test.generate_prediction.

name = 'Injected current AP width test'

score_type

alias of sciunit.scores.complete.ZScore

units = UnitTime('millisecond', 0.001 * s, 'ms')

Module contents

NeuronUnit Test classes.

class neuronunit.tests.FakeTest(observation, name=None, **params)

Bases: sciunit.tests.Test

compute_score(observation, prediction)

Generates a score given the observations provided in the constructor and the prediction generated by generate_prediction.

Must generate a score of score_type. No default implementation.

generate_prediction(model)

Generate a prediction from a model using the required capabilities.

No default implementation.

score_type

alias of sciunit.scores.complete.ZScore