def init_connection(pop1, pop2, target_name, weight, p, delay=None, allow_self_conn=True):
"""
Initialize a connection between two populations
pop1 = population sending projections
pop2 = populations receiving projections
target_name = name of synapse type to project to
weight = weight of connection
p = probability of connection between any two neurons
delay = delay
allow_self_conn = allow neuron to project to itself
"""
if delay is not None:
conn = DelayConnection(pop1, pop2, target_name, sparseness=p, weight=weight, delay=delay)
else:
conn = Connection(pop1, pop2, sparseness=p, weight=weight)
# Remove self-connections
if not allow_self_conn and len(pop1) == len(pop2):
for j in xrange(len(pop1)):
conn[j, j] = 0.0
conn[j, j] = 0.0
if delay is not None:
conn.delay[j, j] = 0.0
conn.delay[j, j] = 0.0
return conn
def init_connection(pop1, pop2, target_name, weight, p, delay=None, allow_self_conn=True):
"""
Initialize a connection between two populations
pop1 = population sending projections
pop2 = populations receiving projections
target_name = name of synapse type to project to
weight = weight of connection
p = probability of connection between any two neurons
delay = delay
allow_self_conn = allow neuron to project to itself
"""
if delay is not None:
conn=DelayConnection(pop1, pop2, target_name, sparseness=p, weight=weight, delay=delay)
else:
conn=Connection(pop1, pop2, sparseness=p, weight=weight)
# Remove self-connections
if not allow_self_conn and len(pop1)==len(pop2):
for j in xrange(len(pop1)):
conn[j,j]=0.0
conn[j,j]=0.0
if delay is not None:
conn.delay[j,j]=0.0
conn.delay[j,j]=0.0
return conn
def init_rand_weight_connection(pop1, pop2, target_name, min_weight, max_weight, p, delay, allow_self_conn=True):
"""
Initialize a connection between two populations
pop1 = population sending projections
pop2 = populations receiving projections
target_name = name of synapse type to project to
min_weight = min weight of connection
max_weight = max weight of connection
p = probability of connection between any two neurons
delay = delay
allow_self_conn = allow neuron to project to itself
"""
W = min_weight + np.random.rand(len(pop1), len(pop2)) * (max_weight - min_weight)
conn = DelayConnection(pop1, pop2, target_name, sparseness=p, W=W, delay=delay)
# Remove self-connections
if not allow_self_conn and len(pop1) == len(pop2):
for j in xrange(len(pop1)):
conn[j, j] = 0.0
conn.delay[j, j] = 0.0
conn[j, j] = 0.0
conn.delay[j, j] = 0.0
return conn
def init_rand_weight_connection(pop1, pop2, target_name, min_weight, max_weight, p, delay, allow_self_conn=True):
"""
Initialize a connection between two populations
pop1 = population sending projections
pop2 = populations receiving projections
target_name = name of synapse type to project to
min_weight = min weight of connection
max_weight = max weight of connection
p = probability of connection between any two neurons
delay = delay
allow_self_conn = allow neuron to project to itself
"""
W=min_weight+np.random.rand(len(pop1),len(pop2))*(max_weight-min_weight)
conn=DelayConnection(pop1, pop2, target_name, sparseness=p, W=W, delay=delay)
# Remove self-connections
if not allow_self_conn and len(pop1)==len(pop2):
for j in xrange(len(pop1)):
conn[j,j]=0.0
conn.delay[j,j]=0.0
conn[j,j]=0.0
conn.delay[j,j]=0.0
return conn
