def run_test(self, pre, post):
# Make sure no objects are left over from previous tests
reset(raiseError=False)
# seed random generator using the name of this test
seed = "%s_%s" % (pre, post)
pre_cell = make_cell(pre)
post_cell = make_cell(post)
n_term = convergence.get(pre, {}).get(post, None)
if n_term is None:
n_term = 1
st = SynapseTest()
st.run(pre_cell.soma, post_cell.soma, n_term, seed=seed)
if self.audit:
st.show_result()
info = dict(
rel_events=st.release_events(),
rel_timings=st.release_timings(),
open_prob=st.open_probability(),
event_analysis=st.analyze_events(),
)
self.st = st
#import weakref
#global last_syn
#last_syn = weakref.ref(st.synapses[0].terminal.relsi)
return info
def test_max_open_probability():
reset(
raiseError=False
) # reset() fails as unable to remove all neuron objects, unless we ignore the error
sec = h.Section()
# Create AMPA and NMDA mechanisms
# AMPA uses mode=0; no rectification
apsd = h.AMPATRUSSELL(0.5, sec=sec)
# For NMDA we will hold the cell at +40 mV
npsd = h.NMDA_Kampa(0.5, sec=sec)
# And a presynaptic terminal to provide XMTR input
term = h.MultiSiteSynapse(0.5, sec=sec)
term.nZones = 1
term.setpointer(term._ref_XMTR[0], 'XMTR', apsd)
term.setpointer(term._ref_XMTR[0], 'XMTR', npsd)
h.celsius = 34.0
h.finitialize()
op = [[], []]
for i in range(100):
# force very high transmitter concentration for every timestep
term.XMTR[0] = 10000
sec.v = 40.0
h.fadvance()
op[0].append(apsd.Open)
op[1].append(npsd.Open)
assert np.allclose(max(op[0]), apsd.MaxOpen)
assert np.allclose(max(op[1]), npsd.MaxOpen)
def sgc_psd_test(cell_class, seed, plot=False, tstop=5.0, n_syn=20):
"""
Tests a multisite synapse from the SGC to a target cell.
The values returned from an actual set of runs of the synapse are compared
to the expected values in the synapses.py table. This is needed because
the maximal open probability of the receptor models is not 1, so the maximal
conductance per receptor needs to be adjusted empirically. If the measured current
does not match the expected current, then we print a message with the expected value,
and fail with an assert statment in the test.
The measurement itself is made in measure_gmax().
Parameters
----------
cell_class : an instance of the cell class
seed : int
random number seed for the call
plot : boolean (default False)
plot request, passed to measure_gmax
tstop : float (default 5.0 ms)
duration of run for measurement of gmax. Needs to be long enough to find the
maximum of the EPSC/IPSC.
n_syn : int (default 20)
number of synapses to instantiate for testing (to get an average value)
"""
celltyp = cell_class.__name__.lower()
random_seed.set_seed(seed)
reset(raiseError=False) # avoid failure because we cannot release NEURON objects completely.
tsc = cell_class.create(ttx=True)
(ampa_gmax, nmda_gmax, epsc_cv) = measure_gmax(tsc, n_syn=n_syn, tstop=tstop, plot=plot)
exp_ampa_gmax = data.get('sgc_synapse', species='mouse', post_type=celltyp, field='AMPA_gmax')[0]
exp_nmda_gmax = data.get('sgc_synapse', species='mouse', post_type=celltyp, field='NMDA_gmax')[0]
exp_epsc_cv = data.get('sgc_synapse', species='mouse', post_type=celltyp, field='EPSC_cv')
ampa_correct = np.allclose(exp_ampa_gmax, ampa_gmax)
if not ampa_correct:
AMPAR_gmax = data.get('sgc_synapse', species='mouse', post_type=celltyp, field='AMPAR_gmax')
ratio = exp_ampa_gmax/ampa_gmax
print('AMPA Receptor conductance in model should be %.16f (table is %.16f)'
% (AMPAR_gmax * ratio, AMPAR_gmax))
nmda_correct = np.allclose(exp_nmda_gmax, nmda_gmax)
if not nmda_correct:
NMDAR_gmax = data.get('sgc_synapse', species='mouse', post_type=celltyp, field='NMDAR_gmax')
ratio = exp_nmda_gmax/nmda_gmax
print('NMDA Receptor conductance in model should be %.16f (table is %.16f)'
% (NMDAR_gmax * ratio, NMDAR_gmax))
cv_correct = (abs(exp_epsc_cv / epsc_cv - 1.0) < 0.1)
print 'cv_correct: ', cv_correct
if not cv_correct:
ratio = exp_epsc_cv/epsc_cv
print('CV Receptor in synapses.py model should be %.6f (measured = %.6f; table = %.6f)'
% (epsc_cv * ratio, epsc_cv, exp_epsc_cv))
print ((abs(exp_epsc_cv / (epsc_cv * ratio) - 1.0) < 0.1))
assert cv_correct
assert ampa_correct and nmda_correct
def test_sgc_basal_middle():
reset(raiseError=False)
cell = cells.SGC.create(species='mouse', modelType='bm')
CellTester('SGC_rat_bm', cell)
def test_sgc_apical():
reset(raiseError=False)
cell = cells.SGC.create(species='mouse', modelType='a')
CellTester('SGC_rat_a', cell)
def test_tuberculoventral():
reset(raiseError=False)
cell = cells.Tuberculoventral.create(species='mouse', modelType='TVmouse')
CellTester('tuberculoventral_mouse_I', cell)
def test_cartwheel():
reset(raiseError=False)
cell = cells.Cartwheel.create(species='mouse', modelType='I')
CellTester('cartwheel_rat_I', cell)
def test_octopus():
reset(raiseError=False)
cell = cells.Octopus.create(species='guineapig', modelType='II-o')
CellTester('octopus_guineapig-typeII-o', cell)
def test_pyramidal():
reset(raiseError=False)
cell = cells.Pyramidal.create(species='rat', modelType='I')
CellTester('pyramidal_rat_I', cell)
def test_dstellate_mouse():
reset(raiseError=False)
cell = cells.DStellate.create(species='mouse', modelType='I-II')
CellTester('dstellate_mouse-typeI-II', cell)
def test_dstellate():
reset(raiseError=False)
cell = cells.DStellate.create(species='guineapig', modelType='I-II')
CellTester('dstellate_guineapig-typeI-II', cell)
def test_tstellatet():
reset(raiseError=False)
cell = cells.TStellate.create(species='guineapig', modelType='I-t')
CellTester('tstellate_guineapig-typeI-t', cell)
def test_bushy_mouse():
reset(raiseError=False)
cell = cells.Bushy.create(species='mouse', modelType='II')
CellTester('bushy-mouse-typeII', cell)
def test_bushy21():
reset(raiseError=False)
cell = cells.Bushy.create(species='guineapig', modelType='II-I')
CellTester('bushy_guineapig-typeII-I', cell)
