def test_con_proba5(self):
error = True
try:
NormalConnectionProba(threshold=0.5, mean=-2, std=0.5)
error = False
except ValueError:
self.assertTrue(error)
def test_con_proba8(self):
error = True
try:
NormalConnectionProba(threshold=0, mean=0, std=0)
error = False
except ValueError:
self.assertFalse(error)
def test_con_proba1(self):
error = True
try:
NormalConnectionProba(threshold=1, mean=10, std=2)
error = False
except ValueError:
self.assertTrue(error)
def test_normal_proba_connection_between_cells(self):
"""
Compare NormalConnectionProba (which is truncated normal dist) with a regular
truncated normal dist of the same size as the maximal all-to-all connections.
The test will pass if difference between potential conn number is less than 0.95
"""
std = 0.2
mean = 0.1
threshold = 0.4 # if Truncated Normal Dist pass this threshold - creates connection
Dist.set_seed(13)
cell_conn_proba = NormalConnectionProba(threshold=threshold,
mean=mean,
std=std)
conn = self.pop2.connect(rule="all",
cell_connection_proba=cell_conn_proba,
seg_dist="uniform")
conn.set_source([c.filter_secs("soma")(0.5) for c in self.pop1.cells])
conn.set_target(self.pop2.cells)
conn.add_synapse("Exp2Syn").add_netcon()
conn.build()
# create Truncated Normal comparative distribution of the same size as maximal
# all-to-all connections
norm = np.abs(np.random.normal(loc=mean, scale=std, size=200 * 100))
non_zero_norm = np.count_nonzero(norm[norm > threshold])
non_zero_cell = [len(c.syns) for c in self.pop2.cells]
non_zero_cell_sum = sum(non_zero_cell)
# difference of connection number is less then 0.95
if non_zero_cell_sum > non_zero_norm:
diff = non_zero_norm / non_zero_cell_sum
else:
diff = non_zero_cell_sum / non_zero_norm
self.assertGreater(diff, 0.95)