def test_aa_goc(self):
# 5) GrC (aa) - GoC
# To check it, 20syn on basal dendrites, not near the soma.
# AMPA/NMDA syn with a burst of 5 spike at 100Hz. The response should be a burst
# composed by 3 spikes
config = JSONConfig(aa_goc_config)
scaffold = Scaffold(config)
scaffold.place_cell_types()
scaffold.compile_output()
grc_to_golgi = scaffold.configuration.connection_types[
"granule_to_golgi"]
grcs = scaffold.get_placement_set("granule_cell").identifiers
golgis = scaffold.get_placement_set("golgi_cell").identifiers
m_gol = scaffold.morphology_repository.get_morphology("GolgiCell")
m_grc = scaffold.morphology_repository.get_morphology("GranuleCell")
comps = m_gol.get_compartments(["basal_dendrites"])
conns = np.array([[grcs[0], golgis[0]]] * 20)
morpho_map = ["GranuleCell", "GolgiCell"]
morphologies = np.array([[0, 1]] * 20)
compartments = np.ones(
(20, 2)) * m_grc.get_compartments(["ascending_axon"])[0].id
compartments[:, 1] = np.random.choice([c.id for c in comps], size=20)
scaffold.connect_cells(
grc_to_golgi,
conns,
morphologies=morphologies,
compartments=compartments,
morpho_map=morpho_map,
)
scaffold.compile_output()
scaffold = from_hdf5(scaffold.output_formatter.file)
scaffold.run_simulation("test")
from glob import glob
from plotly import graph_objs as go
results = glob("results_test_*")[-1]
with h5py.File(results, "r") as f:
go.Figure([
go.Scatter(
x=f["recorders/soma_voltages/0"][:, 0],
y=f["recorders/soma_voltages/0"][:, 1],
),
go.Scatter(
x=f["recorders/soma_voltages/1"][:, 0],
y=f["recorders/soma_voltages/1"][:, 1],
),
]).show()
def test_grc_sc(self):
# 9) GrC - SC
# 3 random synapses on the dendrites. AMPA/NMDA, 10 spikes at 100Hz.
# It should do a burst of 5 spikes.
config = JSONConfig(grc_sc_config)
scaffold = Scaffold(config)
scaffold.place_cell_types()
scaffold.compile_output()
grc_to_golgi = scaffold.configuration.connection_types[
"granule_to_stellate"]
grcs = scaffold.get_placement_set("granule_cell").identifiers
golgis = scaffold.get_placement_set("stellate_cell").identifiers
m_grc = scaffold.morphology_repository.get_morphology("GranuleCell")
m_gol = scaffold.morphology_repository.get_morphology("StellateCell")
comps = m_gol.get_compartments(["dendrites"])
conns = np.array([[grcs[0], golgis[0]]] * 3)
morpho_map = ["GranuleCell", "StellateCell"]
morphologies = np.array([[0, 1]] * 3)
compartments = np.ones(
(3, 2)) * m_grc.get_compartments(["ascending_axon"])[0].id
compartments[:, 1] = np.random.choice([c.id for c in comps], size=3)
scaffold.connect_cells(
grc_to_golgi,
conns,
morphologies=morphologies,
compartments=compartments,
morpho_map=morpho_map,
)
scaffold.compile_output()
scaffold = from_hdf5(scaffold.output_formatter.file)
scaffold.run_simulation("test")
from glob import glob
from plotly import graph_objs as go
results = glob("results_test_*")[-1]
with h5py.File(results, "r") as f:
go.Figure([
go.Scatter(
x=f["recorders/soma_voltages/0"][:, 0],
y=f["recorders/soma_voltages/0"][:, 1],
),
go.Scatter(
x=f["recorders/soma_voltages/1"][:, 0],
y=f["recorders/soma_voltages/1"][:, 1],
),
]).show()
def test_pf_pc(self):
# 6) GrC (aa) - PC
# 100 random syn, on the apical dendrites. AMPA only, 10 spikes
# 500Hz. The response should be a burst composed by 3 spikes.
config = JSONConfig(aa_pc_config)
scaffold = Scaffold(config)
scaffold.place_cell_types()
scaffold.compile_output()
grc_to_golgi = scaffold.configuration.connection_types[
"granule_to_purkinje"]
grcs = scaffold.get_placement_set("granule_cell").identifiers
golgis = scaffold.get_placement_set("purkinje_cell").identifiers
m_gol = scaffold.morphology_repository.get_morphology("PurkinjeCell")
m_grc = scaffold.morphology_repository.get_morphology("GranuleCell")
comps = [c.id for c in m_gol.compartments if c.type == 3]
conns = np.array([[grcs[0], golgis[0]]] * 80)
morpho_map = ["GranuleCell", "PurkinjeCell"]
morphologies = np.array([[0, 1]] * 80)
compartments = np.ones(
(80, 2)) * m_grc.get_compartments(["parallel_fiber"])[0].id
compartments[:, 1] = np.random.choice(comps, size=80)
scaffold.connect_cells(
grc_to_golgi,
conns,
morphologies=morphologies,
compartments=compartments,
morpho_map=morpho_map,
)
scaffold.compile_output()
scaffold = from_hdf5(scaffold.output_formatter.file)
scaffold.run_simulation("test")
from glob import glob
from plotly import graph_objs as go
results = glob("results_test_*")[-1]
with h5py.File(results, "r") as f:
go.Figure([
go.Scatter(
x=f["recorders/soma_voltages/0"][:, 0],
y=f["recorders/soma_voltages/0"][:, 1],
),
go.Scatter(
x=f["recorders/soma_voltages/1"][:, 0],
y=f["recorders/soma_voltages/1"][:, 1],
),
]).show()
def test_pf_goc(self):
# 7) GrC (pf) - GoC
# The same as 5) except on 80 apical dendrites.
config = JSONConfig(aa_goc_config)
scaffold = Scaffold(config)
scaffold.place_cell_types()
scaffold.compile_output()
grc_to_golgi = scaffold.configuration.connection_types[
"granule_to_golgi"]
grcs = scaffold.get_placement_set("granule_cell").identifiers
golgis = scaffold.get_placement_set("golgi_cell").identifiers
m_gol = scaffold.morphology_repository.get_morphology("GolgiCell")
m_grc = scaffold.morphology_repository.get_morphology("GranuleCell")
comps = m_gol.get_compartments(["apical_dendrites"])
conns = np.array([[grcs[0], golgis[0]]] * 80)
morpho_map = ["GranuleCell", "GolgiCell"]
morphologies = np.array([[0, 1]] * 80)
compartments = np.ones(
(80, 2)) * m_grc.get_compartments(["ascending_axon"])[0].id
compartments[:, 1] = np.random.choice([c.id for c in comps], size=80)
scaffold.connect_cells(
grc_to_golgi,
conns,
morphologies=morphologies,
compartments=compartments,
morpho_map=morpho_map,
)
scaffold.compile_output()
scaffold = from_hdf5(scaffold.output_formatter.file)
scaffold.run_simulation("test")
from glob import glob
from plotly import graph_objs as go
results = glob("results_test_*")[-1]
with h5py.File(results, "r") as f:
go.Figure([
go.Scatter(
x=f["recorders/soma_voltages/0"][:, 0],
y=f["recorders/soma_voltages/0"][:, 1],
),
go.Scatter(
x=f["recorders/soma_voltages/1"][:, 0],
y=f["recorders/soma_voltages/1"][:, 1],
),
]).show()
def test_mf_granule(self):
config = JSONConfig(mf_grc_config)
scaffold = Scaffold(config)
scaffold.place_cell_types()
scaffold.compile_output()
mf_to_glom = scaffold.configuration.connection_types[
"mossy_to_glomerulus"]
glom_to_grc = scaffold.configuration.connection_types[
"glomerulus_to_granule"]
mfs = scaffold.get_placement_set("mossy_fibers").identifiers
gloms = scaffold.get_placement_set("glomerulus").identifiers
grcs = scaffold.get_placement_set("granule_cell").identifiers
scaffold.connect_cells(
mf_to_glom, np.array([[mfs[0], gloms[0]], [mfs[1], gloms[1]]]))
scaffold.connect_cells(
mf_to_glom, np.array([[mfs[0], gloms[0]], [mfs[1], gloms[1]]]))
conns = np.array([[gloms[0], grcs[0]], [gloms[1], grcs[0]]])
morpho_map = ["GranuleCell"]
morphologies = np.array([[0, 0], [0, 0]])
compartments = np.array([[0, 9], [0, 18]])
scaffold.connect_cells(
glom_to_grc,
conns,
morphologies=morphologies,
compartments=compartments,
morpho_map=morpho_map,
)
scaffold.compile_output()
scaffold = from_hdf5(scaffold.output_formatter.file)
scaffold.run_simulation("test")
from glob import glob
from plotly import graph_objs as go
results = glob("results_test_*")[-1]
with h5py.File(results, "r") as f:
go.Figure(
go.Scatter(
x=f["recorders/soma_voltages/0"][:, 0],
y=f["recorders/soma_voltages/0"][:, 1],
)).show()
def test_glom_golgi_granule(self):
config = JSONConfig(mf_gol_config)
scaffold = Scaffold(config)
scaffold.place_cell_types()
scaffold.compile_output()
mf_to_glom = scaffold.configuration.connection_types[
"mossy_to_glomerulus"]
glom_to_gc = scaffold.configuration.connection_types[
"glomerulus_to_golgi"]
gc_to_grc = scaffold.configuration.connection_types["golgi_to_granule"]
mfs = scaffold.get_placement_set("mossy_fibers").identifiers
gloms = scaffold.get_placement_set("glomerulus").identifiers
golgis = scaffold.get_placement_set("golgi_cell").identifiers
granules = scaffold.get_placement_set("granule_cell").identifiers
scaffold.connect_cells(mf_to_glom, np.array([[mfs[0], gloms[0]]]))
conns = np.array([[gloms[0], golgis[0]]] * 20)
m = scaffold.morphology_repository.get_morphology("GolgiCell")
morpho_map = ["GolgiCell"]
morphologies = np.zeros((20, 2))
compartments = np.zeros((20, 2))
compartments[:, 1] = np.random.choice(
[c.id for c in m.compartments if c.type == 302], size=20)
scaffold.connect_cells(
glom_to_gc,
conns,
morphologies=morphologies,
compartments=compartments,
morpho_map=morpho_map,
)
conns_grc = np.array([[golgis[0], granules[0]]] * 4)
morpho_map_grc = ["GranuleCell", "GolgiCell"]
morphologies_grc = np.zeros((4, 2))
morphologies_grc[:, 0] = [1] * 4
compartments_grc = np.zeros((4, 2))
compartments_grc[:, 0] = [c.id for c in m.compartments
if c.type == 2][0:4]
compartments_grc[:, 1] = [9 * (i + 1) for i in range(4)]
scaffold.connect_cells(
gc_to_grc,
conns_grc,
morphologies=morphologies_grc,
compartments=compartments_grc,
morpho_map=morpho_map_grc,
)
scaffold.compile_output()
scaffold = from_hdf5(scaffold.output_formatter.file)
scaffold.run_simulation("test")
from glob import glob
from plotly import graph_objs as go
results = glob("results_test_*")[-1]
with h5py.File(results, "r") as f:
g = f["recorders/soma_voltages"]
a = f["recorders/axons"]
def L(g, s):
h = g[s]
return {"x": h[:, 0], "y": h[:, 1], "name": h.attrs["label"]}
go.Figure([
*(go.Scatter(**L(g, i)) for i in g),
*(go.Scatter(**L(a, i)) for i in a),
]).show()
class TestFiberIntersection(unittest.TestCase):
@classmethod
def setUpClass(self):
super(TestFiberIntersection, self).setUpClass()
# Make sure the MR exists
test_setup.prep_morphologies()
# The scaffold has only the Granular layer (100x100x150) with 20 GrCs
# and 1 GoC placed, as specified in the config file
self.config = JSONConfig(file=fiber_transform_config)
# Defining quivers field to include also voxels outside the scaffold
# volume
self.quivers_field = np.zeros(
shape=(3, 80, 80, 80)) # Each voxel in the volume has vol_res=25um
# Fake quiver, oriented as original fibers
basic_quiver = np.array([0, 1.0, 0.0])
self.quivers_field[0, :] = basic_quiver[0]
self.quivers_field[1, :] = basic_quiver[1]
self.quivers_field[2, :] = basic_quiver[2]
self.config.connection_types[
"parallel_fiber_to_golgi_bended"].transformation.quivers = self.quivers_field
self.config.connection_types[
"parallel_fiber_to_golgi_bended"].transformation.vol_start = [
-500.0, -500.0, -500.0
]
self.scaffold = Scaffold(self.config)
self.scaffold.morphology_repository = MorphologyRepository(morpho_file)
self.scaffold.compile_network()
def test_fiber_connections(self):
pre_type = "granule_cell"
pre_neu = self.scaffold.get_placement_set(pre_type)
conn_type = "parallel_fiber_to_golgi"
cs = self.scaffold.get_connectivity_set(conn_type)
num_conn = len(cs.connections)
# Check that no more connections are formed than the number of
# presynaptic neurons - how could happen otherwise?
self.assertTrue(num_conn <= len(pre_neu.identifiers))
# Check that increasing resolution in FiberIntersection does not change
# connection number if there are no transformations (and thus the fibers
# are parallel to main axes)
conn_type_HR = "parallel_fiber_to_golgi_HR"
cs_HR = self.scaffold.get_connectivity_set(conn_type_HR)
self.assertEqual(num_conn, len(cs_HR.connections))
# Check that half (+- 5) connections are obtained with half the affinity
conn_type_affinity = "parallel_fiber_to_golgi_affinity"
cs_affinity = self.scaffold.get_connectivity_set(conn_type_affinity)
self.assertAlmostEqual(num_conn / 2,
len(cs_affinity.connections),
delta=5)
# Check that same number of connections are obtained when a fake quiver
# transformation is applied
conn_type_transform = "parallel_fiber_to_golgi_bended"
cs_fake_transform = self.scaffold.get_connectivity_set(
conn_type_transform)
self.assertEqual(len(cs_fake_transform.connections), num_conn)
# Check that less connections are obtained when the PC surface is
# oriented according to orientation vector of 45° rotation in yz plane,
# for how the Golgi cell is placed and the parallel fibers are rotated
basic_quiver = np.array([0, 0.7, 0.7])
self.quivers_field[0, :] = basic_quiver[0]
self.quivers_field[1, :] = basic_quiver[1]
self.quivers_field[2, :] = basic_quiver[2]
self.config.connection_types[
"parallel_fiber_to_golgi_bended"].transformation.quivers = self.quivers_field
self.scaffold = Scaffold(self.config)
self.scaffold.compile_network()
cs_transform = self.scaffold.get_connectivity_set(conn_type_transform)
self.assertTrue(len(cs_transform.connections) <= num_conn)