def distance_connection_handler(source, target, d_weight_min, d_weight_max,
d_max, nsyn_min, nsyn_max, center):
# Avoid self-connections.
if (source['id'] == target['id']):
return None
# first create weights by euclidean distance between cells
# DO NOT use PERIODIC boundary conditions in x and y!
dw = nutil.distance_weight(
np.array(source['position'][:2]) - np.array(target['position'][:2]),
d_weight_min, d_weight_max, d_max)
# drop the connection if the weight is too low
if dw <= 0:
return None
# filter out nodes by treating the weight as a probability of connection
if random.random() > dw:
return None
# Add the number of synapses for every connection.
tmp_nsyn = random.randint(nsyn_min, nsyn_max)
return {'nsyn': tmp_nsyn}
def distance_tuning_connection_handler(source, target, d_weight_min,
d_weight_max, d_max, t_weight_min,
t_weight_max, nsyn_min, nsyn_max,
center):
# Avoid self-connections.
if (source['id'] == target['id']):
return None
# first create weights by euclidean distance between cells
# DO NOT use PERIODIC boundary conditions in x and y!
dw = nutil.distance_weight(
np.array(source['position'][:2]) - np.array(target['position'][:2]),
d_weight_min, d_weight_max, d_max)
# drop the connection if the weight is too low
if dw <= 0:
return None
# next create weights by orientation tuning [ aligned, misaligned ] --> [ 1, 0 ]
# Check that the orientation tuning property exists for both cells; otherwise,
# ignore the orientation tuning.
if (('orientation_tuning' in source.keys())
and ('orientation_tuning' in target.keys())):
tw = dw * nutil.orientation_tuning_weight(source['orientation_tuning'],
target['orientation_tuning'],
t_weight_min, t_weight_max)
else:
tw = dw
# drop the connection if the weight is too low
if tw <= 0:
return None
# filter out nodes by treating the weight as a probability of connection
if random.random() > tw:
return None
# Add the number of synapses for every connection.
tmp_nsyn = random.randint(nsyn_min, nsyn_max)
return {'nsyn': tmp_nsyn}