def test_get_set(self):
m=5
n=Surface('unittest', **{'n_sets':m,
'sets':[my_slice(s, 10, m) for s in range(m)] })
s=[]
s.append(n.get_sets('set'))
s.append(n.get_sets('not_set'))
s.append(n.get_sets('all_set'))
self.assertEqual(len(s[0]),m)
self.assertEqual(len(s[1]),m)
self.assertEqual(len(s[1][0]),4)
for i, l in enumerate(s[1]):
self.assertTrue(not my_slice(i, 10, m) in l)
self.assertEqual(len(s[2]),m*m)
def __init__(self, name, **kwargs):
'''
Constructor
Arguments:
ids provide ids if nodes already created
model my_nest model type, can be a list
n number of model to create, can be a list
params common parameters for model to be set
mm_dt multimeter recording precision
sname file basename
spath Path to save file at
sd boolean, True if spikes should me recorded
mm boolean, True if mulitmeter should record
'''
model = kwargs.get('model', 'iaf_neuron')
n = kwargs.get('n', 1)
params = kwargs.get('params', {})
ids = kwargs.get('ids', my_nest.Create(model, n, params))
self._ids = slice(ids[0], ids[-1], 1)
self.local_ids = []
for _id in self.ids:
if my_nest.GetStatus([_id], 'local'):
self.local_ids.append(_id)
self.model = model
self.name = name
self.n = n
self.sets = kwargs.get('sets', [misc.my_slice(0, n, 1)])
def get_sets(self, rule):
if rule == 'all':
return [misc.my_slice(0, self.n)]
# Randomly connects presynaptic neurons of set i with postsynaptic
# neurons of set i. Constrained by k_source, mask_dist and the sets.
if rule == 'set':
return self.sets
# Randomly connects presynaptic neurons from source set i to all
# postsynaptic sets except to target set i.
if rule == 'not_set':
sets = []
if self.n_sets == 1:
return self.sets
for s in self.sets:
sets.append(list(set(self.sets).difference(set([s]))))
return sets
# Connect all sets to all other
if rule == 'all_set':
return self.sets * self.n_sets
def get_sets(self, rule):
if rule =='all':
return [misc.my_slice(0,self.n)]
# Randomly connects presynaptic neurons of set i with postsynaptic
# neurons of set i. Constrained by k_source, mask_dist and the sets.
if rule=='set':
return self.sets
# Randomly connects presynaptic neurons from source set i to all
# postsynaptic sets except to target set i.
if rule=='not_set':
sets=[]
if self.n_sets==1:
return self.sets
for s in self.sets:
sets.append(list(set(self.sets).difference(set([s]))))
return sets
# Connect all sets to all other
if rule=='all_set':
return self.sets*self.n_sets
def default_kwargs_inp(n):
sets=[misc.my_slice(i, n,1) for i in range(1)]
return {'n':n,
'model':'poisson_generator',
'sets':sets,
'rate':10.0}
def __init__(self, name, **kwargs ):
'''
Constructor
Arguments:
ids provide ids if nodes already created
model my_nest model type, can be a list
n number of model to create, can be a list
params common parameters for model to be set
mm_dt multimeter recording precision
sname file basename
spath Path to save file at
sd boolean, True if spikes should me recorded
mm boolean, True if mulitmeter should record
'''
model=kwargs.get('model', 'iaf_neuron')
n=kwargs.get('n', 1)
params=kwargs.get('params',{})
ids = kwargs.get('ids', my_nest.Create(model,n, params))
self._ids=slice(ids[0],ids[-1],1)
self.local_ids=[]
for _id in self.ids:
if my_nest.GetStatus([_id], 'local') :
self.local_ids.append(_id)
self.model=model
self.name=name
self.n=n
self.sets=kwargs.get('sets', [misc.my_slice(0, n, 1)])
def default_kwargs_net(n, n_sets):
d = {
'tau_w': 20., # I-V relation, spike frequency adaptation
'a_1': 3., # I-V relation
'a_2': 3., # I-V relation
'b': 200., # I-F relation
'C_m': 80., # t_m/R_in
'Delta_T': 1.8,
'g_L': 3.,
'E_L': -55.8, #
'I_e': 15.0,
'V_peak': 20., #
'V_reset': -65., # I-V relation
'V_th': -55.2, #
'V_a': -55.8, # I-V relation
#STN-SNr
'AMPA_1_Tau_decay': 12., # n.d.; set as for STN to GPE
'AMPA_1_E_rev': 0., # n.d. same as CTX to STN
# EXT-SNr
'AMPA_2_Tau_decay': 5.0,
'AMPA_2_E_rev': 0.,
# MSN D1-SNr
'GABAA_1_E_rev': -80., # (Connelly et al. 2010)
'GABAA_1_Tau_decay': 12., # (Connelly et al. 2010)
# GPe-SNr
'GABAA_2_E_rev': -72., # (Connelly et al. 2010)
'GABAA_2_Tau_decay': 5.,
}
path, sli_path = my_nest.get_default_module_paths(dr.HOME_MODULE)
my_nest.install_module(path, sli_path, model_to_exist='my_aeif_cond_exp')
sets = [misc.my_slice(i, n, n_sets) for i in range(n_sets)]
return {
'n': n,
'model': 'my_aeif_cond_exp',
'mm': {
'active': True,
'params': {
'interval': 1.0,
'to_memory': True,
'to_file': False,
'record_from': ['V_m', 'g_AMPA_1_', 'g_GABAA_1']
}
},
'params': d,
'sd': {
'active': True,
'params': {
'to_memory': True,
'to_file': False
}
},
'sets': sets,
'rate': 10.0
}
def test_get_set(self):
m = 5
n = Surface(
'unittest', **{
'n_sets': m,
'sets': [my_slice(s, 10, m) for s in range(m)]
})
s = []
s.append(n.get_sets('set'))
s.append(n.get_sets('not_set'))
s.append(n.get_sets('all_set'))
self.assertEqual(len(s[0]), m)
self.assertEqual(len(s[1]), m)
self.assertEqual(len(s[1][0]), 4)
for i, l in enumerate(s[1]):
self.assertTrue(not my_slice(i, 10, m) in l)
self.assertEqual(len(s[2]), m * m)
def __init__(self, name, **kwargs):
self.edge_wrap=kwargs.get('edge_wrap', True)
self.extent=kwargs.get('extend', [-0.5, 0.5])
self.fan_in_distribution=kwargs.get('fan_in_distribution', 'binomial')
self.input=None
self.model=kwargs.get('model', 'CO') # nest
self.name=name
self.n=kwargs.get('n', 1000)
self.n_sets=kwargs.get('n_sets',1)
self.sets=kwargs.get('sets',[my_slice(s, 1000, 1) for s in range(1)] )
assert self.n>=1.0, "Unit %s needs to have at least one node"%(name)
def default_kwargs_net(n, n_sets):
d={ 'tau_w': 20., # I-V relation, spike frequency adaptation
'a_1':3. , # I-V relation
'a_2':3., # I-V relation
'b':200., # I-F relation
'C_m': 80., # t_m/R_in
'Delta_T':1.8,
'g_L':3.,
'E_L':-55.8, #
'I_e':15.0 ,
'V_peak':20., #
'V_reset':-65., # I-V relation
'V_th':-55.2, #
'V_a':-55.8, # I-V relation
#STN-SNr
'AMPA_1_Tau_decay': 12., # n.d.; set as for STN to GPE
'AMPA_1_E_rev':0., # n.d. same as CTX to STN
# EXT-SNr
'AMPA_2_Tau_decay':5.0,
'AMPA_2_E_rev':0.,
# MSN D1-SNr
'GABAA_1_E_rev':-80., # (Connelly et al. 2010)
'GABAA_1_Tau_decay':12., # (Connelly et al. 2010)
# GPe-SNr
'GABAA_2_E_rev':-72., # (Connelly et al. 2010)
'GABAA_2_Tau_decay':5.,
}
path, sli_path=my_nest.get_default_module_paths(dr.HOME_MODULE)
my_nest.install_module(path, sli_path, model_to_exist='my_aeif_cond_exp' )
sets=[misc.my_slice(i, n,n_sets) for i in range(n_sets)]
return {'n':n,
'model':'my_aeif_cond_exp',
'mm':{'active':True,
'params':{'interval':1.0,
'to_memory':True,
'to_file':False,
'record_from':['V_m','g_AMPA_1_', 'g_GABAA_1']}},
'params':d,
'sd':{'active':True,
'params':{'to_memory':True,
'to_file':False}},
'sets':sets,
'rate':10.0}
def __init__(self, name, **kwargs):
self.edge_wrap = kwargs.get('edge_wrap', True)
self.extent = kwargs.get('extend', [-0.5, 0.5])
self.fan_in_distribution = kwargs.get('fan_in_distribution',
'binomial')
self.input = None
self.model = kwargs.get('model', 'CO') # nest
self.name = name
self.n = kwargs.get('n', 1000)
self.n_sets = kwargs.get('n_sets', 1)
self.sets = kwargs.get('sets',
[my_slice(s, 1000, 1) for s in range(1)])
assert self.n >= 1.0, "Unit %s needs to have at least one node" % (
name)
def setUp(self):
n_sets=3
sets=[my_slice(s, 100, n_sets) for s in range(n_sets)]
self.n_sets=len(sets)
nd=Surface_dic()
nd.add('i1', **{'n':100, 'n_sets':n_sets, 'sets':sets })
nd.add('i2', **{'n':200, 'n_sets':n_sets, 'sets':sets })
nd.add('i3', **{'n':50, 'n_sets':n_sets, 'sets':sets })
nd.add('n2', **{'n':100, 'n_sets':n_sets, 'sets':sets})
self.surfs=nd
self.source=nd['i1']
self.source2=nd['i2']
self.source3=nd['i3']
self.target=nd['n2']
self.path_base=HOME+'/results/unittest/structure/'
self.path_conn=HOME+'/results/unittest/structure/conn/'
self.path_learned=HOME+'/results/unittest/structure/learned.pkl'
def setUp(self):
n_sets = 3
sets = [my_slice(s, 100, n_sets) for s in range(n_sets)]
self.n_sets = len(sets)
nd = Surface_dic()
nd.add('i1', **{'n': 100, 'n_sets': n_sets, 'sets': sets})
nd.add('i2', **{'n': 200, 'n_sets': n_sets, 'sets': sets})
nd.add('i3', **{'n': 50, 'n_sets': n_sets, 'sets': sets})
nd.add('n2', **{'n': 100, 'n_sets': n_sets, 'sets': sets})
self.surfs = nd
self.source = nd['i1']
self.source2 = nd['i2']
self.source3 = nd['i3']
self.target = nd['n2']
self.path_base = HOME + '/results/unittest/structure/'
self.path_conn = HOME + '/results/unittest/structure/conn/'
self.path_learned = HOME + '/results/unittest/structure/learned.pkl'
def default_kwargs_inp(n):
sets = [misc.my_slice(i, n, 1) for i in range(1)]
return {'n': n, 'model': 'poisson_generator', 'sets': sets, 'rate': 10.0}