def test_6_prepare_recording_NEURON_voltageclamp(self):
os.chdir(rootwd) # move up to load the model
# pick the model
sm.prepare_model_NEURON(parameters=self.sec_parameters,
chosenmodel=self.chosenmodel)
# self.chosenmodel.regions ->
# {"soma": ["v", "i_cap"], "axon": ["v"],
# "channels": {"soma": {"hh": ["il", "el"], "pas": ["i"]}, "axon": {"pas": ["i"]}}}
vstimuli = sm.stimulate_model_NEURON(
stimparameters=self.sec_stimparam,
modelsite=self.chosenmodel.cell.soma)
rec_t, recs, rec_v_stim = rm.prepare_recording_NEURON(
self.chosenmodel,
stimuli=vstimuli,
stimtype=self.sec_stimparam["type"])
sm.engage_NEURON()
total_iterations = len(
range(
-1,
int(self.sec_parameters["tstop"] / self.sec_parameters["dt"])))
self.assertEqual([
len(rec_t) + len(recs["soma"][0]), rec_v_stim.amp1,
rec_v_stim.dur1, rec_v_stim.amp2, rec_v_stim.dur2
], [
2 * total_iterations, self.sec_stimparam["stimlist"][0]["amp1"],
self.sec_stimparam["stimlist"][0]["dur1"],
self.sec_stimparam["stimlist"][1]["amp2"],
self.sec_stimparam["stimlist"][1]["dur2"]
])
#self.assertEqual( len(rec_t) + len(rec_v["soma"]),#len(rec_v[self.regionslist_str[0]]),
# 2*total_iterations )
os.chdir(pwd) # reset to the location of this managerRecordTest.py
def test_9_postrun_recording_NEURON_voltageclamp(self):
os.chdir(rootwd) # move up to load the model
# pick the model
sm.prepare_model_NEURON(parameters=self.sec_parameters,
chosenmodel=self.chosenmodel)
# self.chosenmodel.regions ->
# {"soma": ["v", "i_cap"], "axon": ["v"],
# "channels": {"soma": {"hh": ["il", "el"], "pas": ["i"]}, "axon": {"pas": ["i"]}}}
vstimuli = sm.stimulate_model_NEURON(
stimparameters=self.sec_stimparam,
modelsite=self.chosenmodel.cell.soma)
rec_t, recs, rec_v_stim = rm.prepare_recording_NEURON(
self.chosenmodel,
stimuli=vstimuli,
stimtype=self.sec_stimparam["type"])
sm.engage_NEURON()
rec_v_stim = rm.postrun_record_NEURON(
injectedstimuli=rec_v_stim, stimtype=self.sec_stimparam["type"])
total_iterations = len(
range(
-1,
int(self.sec_parameters["tstop"] / self.sec_parameters["dt"])))
self.assertEqual(
len(rec_t) + len(recs["soma"][0]) + len(rec_v_stim),
3 * total_iterations)
os.chdir(pwd) # reset to the location of this managerRecordTest.py
def test_10_stimulate_model_NEURON_current(self):
os.chdir(rootwd) # move up to load the model
# pick the model
modelmodule = importlib.import_module("models.cells.modelDummyTest")
pickedmodel = getattr(modelmodule,
uu.classesinmodule(modelmodule)[0].__name__)
chosenmodel = pickedmodel()
#
parameters = {"dt": 0.01, "celsius": 30, "tstop": 100, "v_init": 65}
currparameters = {
"type": ["current", "IClamp"],
"stimlist": [{
'amp': 0.5,
'dur': 100.0,
'delay': 10.0
}, {
'amp': 1.0,
'dur': 50.0,
'delay': 10.0 + 100.0
}]
}
sm.prepare_model_NEURON(parameters=parameters, chosenmodel=chosenmodel)
self.assertEqual(
len(
sm.stimulate_model_NEURON(stimparameters=currparameters,
modelsite=chosenmodel.cell.soma)),
len(currparameters["stimlist"]))
os.chdir(pwd) # return to the location of this test file
def test_6_stimulus_overall_voltage_NEURON(self):
#os.chdir("..") # this moves you up to ~/managers
#os.chdir("..") # you are now in parent /cerebmodels
os.chdir(rootwd)
parameters = {"dt": 0.1, "celsius": 20, "tstop": 10, "v_init": 65}
voltparameters = {
"type": ["voltage", "SEClamp"],
"stimlist": [{
'amp1': 0.0,
'dur1': 5.0
}, {
'amp2': -70.0,
'dur2': 10.0
}],
"tstop": parameters["tstop"]
}
sm.prepare_model_NEURON(parameters=parameters,
chosenmodel=self.chosenmodel)
stimuli = sm.stimulate_model_NEURON(
stimparameters=voltparameters,
modelsite=self.chosenmodel.cell.soma)
sm.engage_NEURON()
rec_v = rc.stimulus_overall_voltage_NEURON(stimuli)
# check the length of the rec_v = 0:dt:tstop
self.assertEqual(
len(rec_v),
len(range(-1, int(parameters["tstop"] / parameters["dt"]))))
os.chdir(pwd) # reset to the location of this recorderTest.py
def test_11_stimulate_model_NEURON_voltage(self):
os.chdir(rootwd) # move up to load the model
# pick the model
modelmodule = importlib.import_module("models.cells.modelDummyTest")
pickedmodel = getattr(modelmodule,
uu.classesinmodule(modelmodule)[0].__name__)
chosenmodel = pickedmodel()
#
parameters = {"dt": 0.01, "celsius": 30, "tstop": 100, "v_init": 65}
voltparameters = {
"type": ["voltage", "SEClamp"],
"stimlist": [{
'amp1': 0.,
'dur1': 100.0
}, {
'amp2': 20.,
'dur2': 150.0
}]
}
sm.prepare_model_NEURON(parameters=parameters, chosenmodel=chosenmodel)
stimuli = sm.stimulate_model_NEURON(stimparameters=voltparameters,
modelsite=chosenmodel.cell.soma)
self.assertEqual([stimuli.amp1, stimuli.amp2, stimuli.dur2], [
voltparameters["stimlist"][0]["amp1"],
voltparameters["stimlist"][1]["amp2"],
voltparameters["stimlist"][1]["dur2"]
])
os.chdir(pwd) # return to the location of this test file
def __init__(self):
### ===================== Descriptive Attributes ======================
self.modelscale = "cells"
self.modelname = "PC2011Brown"
# ------specify cell-regions from with response are recorded-------
self.regions = {
"soma": ["v"],
"dend_root": ["v"],
"dend_sm": ["v"],
"dend_sp": ["v"],
"spine_head": ["v"],
"spine_neck": ["v"]
}
self.recordingunits = {"v": "mV"}
# -----------attributed inheritance from sciunit.Model--------------
self.name = "Brown et al. 2011 model of PurkinjeCell"
self.description = "Brown 2011 model of PurkinjeCell (PC) and published in 10.1007/s10827-011-0317-0 This is a multi-compartment (38) model. It is a PPR (preserved path reduction) model. This model is the SciUnit wrapped version of the NEURON model in modelDB accession # 126637."
#
### =================== Instantiate cell template ====================
sm.lock_and_load_model_libraries(modelscale=self.modelscale,
modelname=self.modelname)
os.chdir(path_to_files)
self.cell = Purkinje()
os.chdir(pwd)
### ===============================================================
self.fullfilename = "nil"
self.prediction = "nil"
def __init__(self):
### ===================== Descriptive Attributes ======================
self.modelscale = "cells"
self.modelname = "PC2018Zang"
# ------specify cell-regions from with response are recorded-------
self.regions = {
"soma": ["v"],
"ais": ["v"],
"dend_root": ["v"],
"dend_sm": ["v"],
"dend_sp": ["v"]
}
self.recordingunits = {"v": "mV"}
# -----------attributed inheritance from sciunit.Model--------------
self.name = "Zang et al. 2018 model of PurkinjeCell"
self.description = "Zang et al. 2018 model of PurkinjeCell (PC) and published in 10.1016/j.celrep.2018.07.011 This is a multi-compartment modified version of the Purkinje cell from Mike Hausser (cell 2, 19.2.97). This model is the SciUnit wrapped version of the NEURON model in modelDB accession # 243446."
#
### =================== Instantiate cell template ====================
sm.lock_and_load_model_libraries(modelscale=self.modelscale,
modelname=self.modelname)
os.chdir(path_to_files)
self.cell = Purkinje()
os.chdir(pwd)
### ===============================================================
self.fullfilename = "nil"
self.prediction = "nil"
def __init__(self):
### ===================== Descriptive Attributes ======================
self.modelscale = "cells"
self.modelname = "PC2015Masoli"
# ------specify cell-regions from with response are recorded-------
self.regions = {
"soma": ["v"],
"dend": ["v"],
"axonAIS": ["v"],
"axonNOR": ["v"],
"axonNOR2": ["v"],
"axonNOR3": ["v"]
}
self.recordingunits = {"v": "mV"}
# -----------attributed inheritance from sciunit.Model--------------
# pc.name defaults to class name, i.e, PurkinjeCell
self.name = "Masoli et al. 2015 model of PurkinjeCell"
self.description = "Masoli et al. 2015 model of PurkinjeCell (PC) and published in 10.3389/fncel.2015.00047 This is general PC model unlike special Z+ or Z- models. The model is based on adult (P90 or 3 months) Guinea pig. PC in younger ones are not mature and they grow until P90. This model is the SciUnit wrapped version of the NEURON model in modelDB accession # 229585."
#
### =================== Instantiate cell template ====================
sm.lock_and_load_model_libraries(modelscale=self.modelscale,
modelname=self.modelname)
os.chdir(path_to_files)
self.cell = Purkinje()
os.chdir(pwd)
### ===============================================================
self.fullfilename = "nil"
self.prediction = "nil"
def test_5_stimulus_overall_currents_NEURON(self):
#os.chdir("..") # this moves you up to ~/managers
#os.chdir("..") # you are now in parent /cerebmodels
os.chdir(rootwd)
parameters = {"dt": 0.1, "celsius": 20, "tstop": 10, "v_init": 65}
currparameters = {
"type": ["current", "IClamp"],
"stimlist": [{
'amp': 0.5,
'dur': 10.0,
'delay': 5.0
}, {
'amp': 1.0,
'dur': 20.0,
'delay': 5.0 + 10.0
}],
"tstop":
parameters["tstop"]
}
sm.prepare_model_NEURON(parameters=parameters,
chosenmodel=self.chosenmodel)
stimuli_list = sm.stimulate_model_NEURON(
stimparameters=currparameters,
modelsite=self.chosenmodel.cell.soma)
rec_i_indivs = rc.stimulus_individual_currents_NEURON(stimuli_list)
sm.engage_NEURON()
rec_i = rc.stimulus_overall_current_NEURON(rec_i_indivs)
# check the length of the rec_v = 0:dt:tstop
self.assertEqual(
len(rec_i),
len(range(-1, int(parameters["tstop"] / parameters["dt"]))))
os.chdir(pwd) # reset to the location of this recorderTest.py
def test_8_stimulate_model_NEURON_parameter_None(self):
os.chdir(rootwd) # move up to load the model
# pick the model
modelmodule = importlib.import_module("models.cells.modelDummyTest")
pickedmodel = getattr(modelmodule,
uu.classesinmodule(modelmodule)[0].__name__)
chosenmodel = pickedmodel()
#
parameters = {"dt": 0.01, "celsius": 30, "tstop": 100, "v_init": 65}
sm.prepare_model_NEURON(parameters=parameters, chosenmodel=chosenmodel)
self.assertEqual(sm.stimulate_model_NEURON(),
"Model is not stimulated")
os.chdir(pwd) # return to the location of this test file
def test_7_trigger_NEURON_raw(self):
os.chdir(rootwd) # move up to load the model
# pick the model
modelmodule = importlib.import_module("models.cells.modelDummyTest")
pickedmodel = getattr(modelmodule,
uu.classesinmodule(modelmodule)[0].__name__)
chosenmodel = pickedmodel()
#
parameters = {"dt": 0.01, "celsius": 30, "tstop": 100, "v_init": 65}
sm.prepare_model_NEURON(parameters=parameters, chosenmodel=chosenmodel)
self.assertEqual(sm.trigger_NEURON(chosenmodel),
"model was successfully triggered via NEURON")
os.chdir(pwd) # return to the location of this test file
def test_7_postrun_recording_NEURON_without_stimulating(self):
os.chdir(rootwd) # move up to load the model
# pick the model
sm.prepare_model_NEURON(parameters=self.no_parameters,
chosenmodel=self.chosenmodel)
# self.chosenmodel.regions ->
# {"soma": ["v", "i_cap"], "axon": ["v"],
# "channels": {"soma": {"hh": ["il", "el"], "pas": ["i"]}, "axon": {"pas": ["i"]}}}
stimuli_list = sm.stimulate_model_NEURON()
rec_t, recs, rec_i_indivs = rm.prepare_recording_NEURON(
self.chosenmodel, stimuli=stimuli_list)
sm.engage_NEURON()
rec_i = rm.postrun_record_NEURON(injectedstimuli=rec_i_indivs)
self.assertEqual(rm.postrun_record_NEURON(), "Model is not stimulated")
os.chdir(pwd) # reset to the location of this managerRecordTest.py
def test_1_transform_signal_None(self):
os.chdir("..") # move up to load the model
# pick the model
parameters = {"dt": 0.1, "celsius": 30, "tstop": 10, "v_init": 65}
sm.prepare_model_NEURON(parameters=parameters,
chosenmodel=self.chosenmodel)
rec_t, rec_v, x = rm.prepare_recording_NEURON(self.chosenmodel)
sm.engage_NEURON()
total_iterations = len(
range(-1, int(parameters["tstop"] / parameters["dt"])))
self.assertRaises(ValueError,
sp.transform_signal,
chosenmodel=self.chosenmodel,
recordings=rec_v[self.regionslist_str[0]])
os.chdir(pwd) # reset to the location of this managerRecordTest.py
def test_1_time_NEURON_without_engaging(self):
#os.chdir("..") # this moves you up to ~/managers
#os.chdir("..") # you are now in parent /cerebmodels
os.chdir(rootwd)
sm.prepare_model_NEURON(parameters=self.parameters,
chosenmodel=self.chosenmodel)
rec_t = rc.time_NEURON()
# the length of the rec_time != 0:dt:tstop since recording was not done
self.assertNotEqual(
len(rec_t),
len(range(0, int(
self.parameters["tstop"] / self.parameters["dt"]))) +
1 # for the additional dt step
)
os.chdir(pwd) # reset to the location of this recorderTest.py
def test_2_recordings_of_cellular_componenets_NEURON(self):
os.chdir(rootwd) # move up to load the model
# pick the model
sm.prepare_model_NEURON(parameters=self.no_parameters,
chosenmodel=self.chosenmodel)
# self.chosenmodel.regions ->
# {"soma": ["v", "i_cap"], "axon": ["v"],
# "channels": {"soma": {"hh": ["il", "el"], "pas": ["i"]}, "axon": {"pas": ["i"]}}}
recs = rm.recordings_of_cellular_components_NEURON(self.chosenmodel)
sm.engage_NEURON()
a = (Counter(list(recs["channels"]["soma"]["hh"][0])) != Counter(
list(recs["channels"]["soma"]["hh"][1])))
b = (Counter(list(recs["channels"]["soma"]["pas"][0])) != Counter(
list(recs["channels"]["axon"]["pas"][0])))
self.assertEqual([a, b], [True, True])
os.chdir(pwd) # reset to the location of this managerRecordTest.py
def test_1_recordings_of_cellular_regionbodies_NEURON(self):
os.chdir(rootwd) # move up to load the model
# pick the model
sm.prepare_model_NEURON(parameters=self.no_parameters,
chosenmodel=self.chosenmodel)
# self.chosenmodel.regions ->
# {"soma": ["v", "i_cap"], "axon": ["v"],
# "channels": {"soma": {"hh": ["il", "el"], "pas": ["i"]}, "axon": {"pas": ["i"]}}}
recs = rm.recordings_of_cellular_regionbodies_NEURON(self.chosenmodel)
sm.engage_NEURON()
total_iterations = len(
range(-1,
int(self.no_parameters["tstop"] / self.no_parameters["dt"])))
a = (Counter(list(recs["soma"][0])) != Counter(list(recs["axon"][0])))
self.assertEqual([len(recs["soma"][0]), a], [total_iterations, True])
os.chdir(pwd) # reset to the location of this managerRecordTest.py
def test_3_voltage_to_spiketrain_without_stimulation(self):
os.chdir("..") # this moves you up to ~/managers
os.chdir("..") # you are now in parent /cerebmodels
parameters = {"dt": 0.1, "celsius": 20, "tstop": 10, "v_init": 65}
sm.prepare_model_NEURON(parameters=parameters, chosenmodel=self.chosenmodel)
self.rec["time"], self.rec["response"], self.rec["stimulus"] = \
rm.prepare_recording_NEURON(self.chosenmodel)
sm.engage_NEURON()
spikes = co.voltage_to_spiketrain(self.chosenmodel, self.rec)
ans = \
len( range(
int(spikes[self.regionslist_str[0]].t_start.magnitude),
int(spikes[self.regionslist_str[0]].t_stop.magnitude/parameters["dt"])
) ) + 1 # for the additional dt step
self.assertEqual( ans, len(self.rec["time"]) )
os.chdir(pwd) # reset to the location of this converterTest.py
def test_2_lock_and_load_model_libraries(self):
os.chdir(rootwd) # move up to ~/cerebmodels
self.assertEqual(
sm.lock_and_load_model_libraries(modelscale="cells",
modelname="DummyTest"),
"Model libraries area loaded")
os.chdir(pwd)
def test_4_response_body_NEURON_tworesponses(self):
#os.chdir("..") # this moves you up to ~/managers
#os.chdir("..") # you are now in parent /cerebmodels
os.chdir(rootwd)
sm.prepare_model_NEURON(parameters=self.parameters,
chosenmodel=self.chosenmodel)
# self.chosenmodel.regions ->
# {'axon': ['v'], 'soma': ['v', 'i_cap'], 'channels': {'axon': {'pas': ['i']},
# 'soma': {'hh': ['il', 'el'], 'pas': ['i']}}}
rec_v = rc.response_body_NEURON(self.chosenmodel.cell.soma, "v")
rec_i = rc.response_body_NEURON(self.chosenmodel.cell.soma, "i_cap")
sm.engage_NEURON()
# check the length of the rec_v = 0:dt:tstop
a = all(boolean == True
for boolean in np.array(rec_v) != np.array(rec_i))
self.assertEqual(a, True)
os.chdir(pwd) # reset to the location of this recorderTest.py
def test_9_stimulate_model_NEURON_parameter_error(self):
os.chdir(rootwd) # move up to load the model
# pick the model
modelmodule = importlib.import_module("models.cells.modelDummyTest")
pickedmodel = getattr(modelmodule,
uu.classesinmodule(modelmodule)[0].__name__)
chosenmodel = pickedmodel()
#
parameters = {"dt": 0.01, "celsius": 30, "tstop": 100, "v_init": 65}
#currparameters = {"type": ["current", "IClamp"]} # default
currparameters = {"type": "current"} # alternative
sm.prepare_model_NEURON(parameters=parameters, chosenmodel=chosenmodel)
self.assertRaises(ValueError,
sm.stimulate_model_NEURON,
stimparameters=currparameters,
modelsite=chosenmodel.cell.soma)
os.chdir(pwd) # return to the location of this test file
def __init__(self):
### ===================== Descriptive Attributes ======================
self.modelscale = "synapses"
self.modelname = "CFPC2019AitOuares"
# ------specify cell-regions from with response are recorded-------
self.regions = {"dend": 0.0}
# -----------attributed inheritance from sciunit.Model--------------
self.name = "Ait Ouares et al. 2019 model of Climbing fiber to Purkinje cell dendrite"
self.description = "Ait Ouares 2019 model of ClimbingFiber (CF) to PurkinjeCell (PC) and published in 10.1523/JNEUROSCI.2155-18.2018 This is the single compartment (dendrite) model. This model is the SciUnit wrapped version of the NEURON model in modelDB accession # 244679."
#
### =================== Instantiate cell template ====================
sm.lock_and_load_model_libraries(modelscale=self.modelscale,
modelname=self.modelname)
os.chdir(path_to_files)
self.cell = ClimbingPurkinje()
os.chdir(pwd)
### ===============================================================
self.fullfilename = "nil"
self.prediction = "nil"
def __init__(self):
### ===================== Descriptive Attributes ======================
self.modelscale = "synapses"
self.modelname = "PCDCNnMFDCN2015bSudhakar"
# ------specify cell-regions from with response are recorded-------
self.regions = {"soma": 0.0}
# -----------attributed inheritance from sciunit.Model--------------
self.name = "Sudhakar et al. 2015 model of PC and MF to Deep Cerebellar neuron"
self.description = "Sudhakar 2015 model of Purkinje cell (PC) and MossyFiber (MF) to Deep Cerebellar Neuron (DCN) and published in 10.1371/journal.pcbi.1004641 There are 200 incoming inhibitory connection from PC's, 100 incoming excitatory connection from MF's into a DCN. This is the 'm2' model mentioned in the paper characterized by fast rebound burst and a pause but very long prolonged rebound spiking activity. This model is the SciUnit wrapped version of the NEURON model in modelDB accession # 185513."
#
### =================== Instantiate cell template ====================
sm.lock_and_load_model_libraries(modelscale=self.modelscale,
modelname=self.modelname)
os.chdir(path_to_files)
self.cell = PurkinjeAndMossyToDeepCerebellar()
os.chdir(pwd)
### ===============================================================
self.fullfilename = "nil"
self.prediction = "nil"
def __init__(self):
### ===================== Descriptive Attributes ======================
self.modelscale = "synapses"
self.modelname = "CFPCnPFPC2018Zang"
# ------specify cell-regions from with response are recorded-------
self.regions = {"soma": 0.0}
# -----------attributed inheritance from sciunit.Model--------------
self.name = "Zang et al. 2018 model of CF and PF to PurkinjeCell"
self.description = "Zang et al. 2018 model of ClimbingFiber (CF) and ParallelFIber (PF) to PurkinjeCell (PC) and published in 10.1016/j.celrep.2018.07.011 This is a multi-compartment modified version of the Purkinje cell from Mike Hausser (cell 2, 19.2.97). This model is the SciUnit wrapped version of the NEURON model in modelDB accession # 243446."
#
### =================== Instantiate cell template ====================
sm.lock_and_load_model_libraries(modelscale=self.modelscale,
modelname=self.modelname)
os.chdir(path_to_files)
self.cell = CFPFtoPurkinje()
os.chdir(pwd)
### ===============================================================
self.fullfilename = "nil"
self.prediction = "nil"
def __init__(self):
### ===================== Descriptive Attributes ======================
self.modelscale = "cells"
self.modelname = "GrC2001DAngelo"
# ------specify cell-regions from with response are recorded-------
self.regions = {"soma": ["v"]}
self.recordingunits = {"v": "mV"}
# -----------attributed inheritance from sciunit.Model--------------
self.name = "D'Angelo et al. 2001 model of GranuleCell"
self.description = "D'Angelo et al. 2001 model of GranuleCell (GrC) and published in 10.1523/JNEUROSCI.21-03-00759.2001 This is the single compartment model. It models the rat granule cells because the model was derived from slices taken from 20 +/- 2 days old rats. This model is the SciUnit wrapped version of the NEURON model in modelDB accession # 46839."
#
### =================== Instantiate cell template ====================
sm.lock_and_load_model_libraries(modelscale=self.modelscale,
modelname=self.modelname)
os.chdir(path_to_files)
self.cell = Granule()
os.chdir(pwd)
### ===============================================================
self.fullfilename = "nil"
self.prediction = "nil"
def __init__(self):
### ===================== Descriptive Attributes ======================
self.modelscale = "cells"
self.modelname = "PC2015bForrest"
# ------specify cell-regions from with response are recorded-------
self.regions = {"soma": ["v"], "dend": ["v"]}
self.recordingunits = {"v": "mV"}
# -----------attributed inheritance from sciunit.Model--------------
self.name = "Forrest 2015 model of PurkinjeCell"
self.description = "Forrest 2015 model of PurkinjeCell (PC) and published in 10.1186/s12868-015-0162-6 This is th two compartment model, reduced from the 1088 compartment PC2015aForrest. This model is the SciUnit wrapped version of the NEURON model in modelDB accession # 180789."
#
### =================== Instantiate cell template ====================
sm.lock_and_load_model_libraries(modelscale=self.modelscale,
modelname=self.modelname)
os.chdir(path_to_files)
self.cell = Purkinje()
os.chdir(pwd)
### ===============================================================
self.fullfilename = "nil"
self.prediction = "nil"
def __init__(self):
### ===================== Descriptive Attributes ======================
self.modelscale = "cells"
self.modelname = "GrC1994Gabbiani"
# ------specify cell-regions from with response are recorded-------
self.regions = {"soma": ["v"]}
self.recordingunits = {"v": "mV"}
# -----------attributed inheritance from sciunit.Model--------------
self.name = "Gabbiani et al. 1994 model of GranuleCell"
self.description = "Gabbiani 1994 model of GranuleCell (GrC) and published in 10.1152/jn.1994.72.2.999 This is the single compartment model. It models a turtle cerebellar granule cell consisting of 13 compartments that represent the soma and 4 dendrites. This model is the SciUnit wrapped version of the NEURON model in modelDB accession # 19591."
#
### =================== Instantiate cell template ====================
sm.lock_and_load_model_libraries(modelscale=self.modelscale,
modelname=self.modelname)
os.chdir(path_to_files)
self.cell = Granule()
os.chdir(pwd)
### ===============================================================
self.fullfilename = "nil"
self.prediction = "nil"
def __init__(self):
### ===================== Descriptive Attributes ======================
self.modelscale = "cells"
self.modelname = "PC2010Genet"
# ------specify cell-regions from with response are recorded-------
self.regions = {"soma": ["v"], "dend_sm": ["v"], "dend_sp": ["v"]}
self.recordingunits = {"v": "mV"}
# -----------attributed inheritance from sciunit.Model--------------
self.name = "Genet et al. 2010 model of PurkinjeCell"
self.description = "Genet 2010 model of PurkinjeCell (PC) and published in 10.1016/j.bpj.2010.04.056 This is a multi-compartment (1088) model. It is a resonstruction of Shelton's 1985 model which has been rescaled for dimensions of a guinea pig cell. This model is the SciUnit wrapped version of the NEURON model in modelDB accession # 147218."
#
### =================== Instantiate cell template ====================
sm.lock_and_load_model_libraries(modelscale=self.modelscale,
modelname=self.modelname)
os.chdir(path_to_files)
self.cell = Purkinje()
os.chdir(pwd)
### ===============================================================
self.fullfilename = "nil"
self.prediction = "nil"
def __init__(self):
### ===================== Descriptive Attributes ======================
self.modelscale = "cells"
self.modelname = "GrC2011Souza"
# ------specify cell-regions from with response are recorded-------
self.regions = {"soma": ["v"], }
self.recordingunits = {"v": "mV"}
# -----------attributed inheritance from sciunit.Model--------------
self.name = "Souza and Schutter 2011 model of GranuleCell"
self.description = "Souza & Schutter 2011 model of GranuleCell (GrC) and published in 10.1186/2042-1001-1-7 This is a single compartment model; it is a modification of Diwakar et al. 2009 multicompartment model. It was used as the component for GrC in the network the authors constructed. This model is the SciUnit wrapped version of the NEURON model in modelDB accession # 139656."
#
### =================== Instantiate cell template ====================
sm.lock_and_load_model_libraries(modelscale=self.modelscale,
modelname=self.modelname)
os.chdir(path_to_files)
self.cell = Granule()
os.chdir(pwd)
### ===============================================================
self.fullfilename = "nil"
self.prediction = "nil"
def __init__(self):
### ===================== Descriptive Attributes ======================
self.modelscale = "cells"
self.modelname = "DCN2011Luthman"
# ------specify cell-regions from with response are recorded-------
self.regions = {"soma": ["v"]}
self.recordingunits = {"v": "mV"}
# -----------attributed inheritance from sciunit.Model--------------
self.name = "Luthman et al. 2011 model of DeepCerebellarNucleiCell"
self.description = "Luthman 2011 model of a neuron in Deep Cerebellar Nuclei (DCN) and published in 10.1007/s12311-011-0295-9 This is a multi-compartment (517) model. This model is the SciUnit wrapped version of the NEURON model in modelDB accession # 144523."
#
### =================== Instantiate cell template ====================
sm.lock_and_load_model_libraries(modelscale=self.modelscale,
modelname=self.modelname)
os.chdir(path_to_files)
self.cell = ExcitatoryDCNneuron()
os.chdir(pwd)
### ===============================================================
self.fullfilename = "nil"
self.prediction = "nil"
def __init__(self):
### ===================== Descriptive Attributes ======================
self.modelscale = "cells"
self.modelname = "PC2001Miyasho"
# ------specify cell-regions from with response are recorded-------
self.regions = {"soma": ["v"], "dend_sm": ["v"], "dend_sp": ["v"]}
self.recordingunits = {"v": "mV"}
# -----------attributed inheritance from sciunit.Model--------------
self.name = "Miyasho et al. 2001 model of PurkinjeCell"
self.description = "Miyasho 2001 model of PurkinjeCell (PC) and published in 10.1016/S0006-8993(00)03206-6 This is a multi-compartment (1088) model. This model is the SciUnit wrapped version of the NEURON model in modelDB accession # 17664."
#
### =================== Instantiate cell template ====================
sm.lock_and_load_model_libraries(modelscale=self.modelscale,
modelname=self.modelname)
os.chdir(path_to_files)
self.cell = Purkinje()
os.chdir(pwd)
### ===============================================================
self.fullfilename = "nil"
self.prediction = "nil"