README.md

NeuronUnit: A SciUnit repository for neuroscience-related tests, capabilities and so on.

Concept:

https://github.com/rgerkin/papers/blob/master/neuronunit_frontiers/Paper.pdf

Presentations:

INCF Meeting (August, 2014) (Less code) https://github.com/scidash/assets/blob/master/presentations/SciUnit%20INCF%20Talk.pdf?raw=true

OpenWorm Journal Club (August, 2014) (More code) https://github.com/scidash/assets/blob/master/presentations/SciUnit%20OpenWorm%20Journal%20Club.pdf?raw=true

Examples:

(Example 1) Validating an ion channel model's IV curve against data from a published experiment

from channel_worm.ion_channel.models import GraphData
from neuronunit.tests.channel import IVCurvePeakTest
from neuronunit.models.channel import ChannelModel

# Instantiate the model
channel_model_name = 'EGL-19.channel' # Name of a NeuroML channel model
channel_id = 'ca_boyle'
channel_file_path = os.path.join('path', 'to', 'models', '%s.nml' % channel_model_name)
model = ChannelModel(channel_file_path, channel_index=0, name=channel_model_name)

# Get the experiment data from ChannelWorm and instantiate the test
doi = '10.1083/jcb.200203055'
fig = '2B'
sample_data = GraphData.objects.get(graph__experiment__reference__doi=doi, 
                                    graph__figure_ref_address=fig)
voltage, current_per_farad = sample_data.asunitedarray()
patch_capacitance = pq.Quantity(1e-13,'F') # Assume recorded patch had this capacitance; 
                                           # an arbitrary scaling factor.  
current = current_per_farad * patch_capacitance
observation = {'v':voltage, 
               'i':current}
test = IVCurvePeakTest(observation)

# Judge the model output against the experimental data
score = test.judge(model)
rd = score.related_data
score.plot(rd['v'],rd['i_obs'],color='k',label='Observed (data)')
score.plot(rd['v'],rd['i_pred'],same_fig=True,color='r',label='Predicted (model)')

score.summarize() 
""" OUTPUT:
Model EGL-19.channel (ChannelModel) achieved score Fail on test 'IV Curve Test (IVCurvePeakTest)'. ===
"""
score.describe()
""" OUTPUT:
The score was computed according to 'The sum-squared difference in the observed and predicted current values over the range of the tested holding potentials.' with raw value 3.151 pA^2
"""

(Example 2) Testing the membrane potential and action potential widths of several spiking neuron models against experimental data found at http://neuroelectro.org.

import sciunit
from neuronunit import neuroelectro,tests,capabilities
import neuronunit.neuroconstruct.models as nc_models
from pythonnC.utils.putils import OSB_MODELS

# We will test cerebellar granule cell models.  
brain_area = 'cerebellum'
neuron_type = 'cerebellar_granule_cell'
path = os.path.join(OSB_MODELS,brain_area,neuron_type)
neurolex_id = 'nifext_128' # Cerebellar Granule Cell

# Specify reference data for a test of resting potential for a granule cell.  
reference_data = neuroelectro.NeuroElectroSummary(
    neuron = {'nlex_id':neurolex_id}, # Neuron type.  
    ephysprop = {'name':'Resting Membrane Potential'}) # Electrophysiological property name. 
# Get and verify summary data for the combination above from neuroelectro.org. 
reference_data.get_values()
vm_test = tests.RestingPotentialTest(
                observation = {'mean':reference_data.mean,
                               'std':reference_data.std},
                name = 'Resting Potential')

# Specify reference data for a test of action potential width.  
reference_data = neuroelectro.NeuroElectroSummary(
    neuron = {'nlex_id':neurolex_id}, # Neuron type.  
    ephysprop = {'name':'Spike Half-Width'}) # Electrophysiological property name. 
# Get and verify summary data for the combination above from neuroelectro.org. 
reference_data.get_values()
spikewidth_test = tests.InjectedCurrentSpikeWidthTest(
                observation = {'mean':reference_data.mean,
                               'std':reference_data.std},
                name = 'Spike Width',
                params={'injected_current':{'ampl':0.0053}}) # 0.0053 nA (5.3 pA) of injected current.  

# Create a test suite from these two tests.  
suite = sciunit.TestSuite('Tests',(spikewidth_test,vm_test))

models = []
for model_name in model_names # Iterate through a list of models downloaded from http://opensourcebrain.org
    model_info = (brain_area,neuron_type,model_name)
    model = nc_models.OSBModel(*model_info)
    models.append(model) # Add to the list of models to be tested.  

score_matrix = suite.judge(models,stop_on_error=True) 
score_matrix.view()

Model	Spike Width	Resting Potential
	(InjectedCurrentSpikeWidthTest)	(RestingPotentialTest)
cereb_grc_mc (OSBModel)	Z = nan	Z = 4.92
GranuleCell (OSBModel)	Z = -3.56	Z = 0.88

import matplotlib.pyplot as plt
ax1 = plt.subplot2grid((1,3), (0,0), colspan=2)
ax2 = plt.subplot2grid((1,3), (0,2), colspan=1)
rd = score.related_data
score_matrix[0,0].plot(rd['t'],rd['v_pred'],ax=ax1[0])
score_matrix[0,1].plot(rd['t'],rd['v_pred'],ax=ax2[0])
ax2.set_xlim(283,284.7)

Tutorial: