import numpy as np
from ..strategy import ConnectionStrategy
from ...reporting import report, warn
[docs]class ConnectomeDcnGranule(ConnectionStrategy):
"""
Implementation for the connections between mossy fibers and glomeruli.
The connectivity is somatotopic and
"""
[docs] def validate(self):
pass
def connect(self):
# Source and target neurons are extracted
dcn_cell_type = self.from_cell_types[0]
granule_cell_type = self.to_cell_types[0]
dcn_cells = self.from_cells[dcn_cell_type.name]
granule_cells = self.to_cells[granule_cell_type.name]
divergence = int(self.divergence)
# NC fibers reach only superficially the granule cell layer: only 50 um
y_min = self.scaffold.configuration.layers["dcn_layer"].dimensions[
1
] # this value represents the beginning of GCL
thickness_y = 50 # only the superficial part of GCL
granule_selected = granule_cells[
np.where(granule_cells[:, 3] <= y_min + thickness_y)[0], :
]
N_MF = len(dcn_cells[:, 0]) # Each NC DCN cell forms 1 MF
# Position of phantom glom are taken from the ConnectomeDcnGolgi
connettoma_DCN_golgi = self.scaffold.configuration.connection_types[
"NC_dcn_glut_large_to_golgi"
]
x_points = connettoma_DCN_golgi.x_points
y_points = connettoma_DCN_golgi.y_points
z_points = connettoma_DCN_golgi.z_points
pre_post = np.zeros((divergence * N_MF, 2))
k = 0
for i in range(N_MF):
distance_array = np.zeros(len(granule_selected))
for j in range(len(granule_selected)):
distance_array[j] = np.sqrt(
(x_points[i] - granule_selected[j, 2]) ** 2
+ (y_points[i] - granule_selected[j, 3]) ** 2
+ (z_points[i] - granule_selected[j, 4]) ** 2
)
sorted_distance_indexes = np.argsort(
distance_array
) # sorted so that the first values correspond to the closest granule cells
best_granule = granule_selected[sorted_distance_indexes[0:divergence], 0]
pre_post[divergence * k : divergence * (k + 1), 0] = dcn_cells[i, 0]
pre_post[divergence * k : divergence * (k + 1), 1] = best_granule
k += 1
self.scaffold.connect_cells(self, pre_post)
