def testInputArrayToStdpSynapse(self):
params = ['Wmax', 'alpha', 'lambda', 'mu_minus', 'mu_plus', 'tau_plus']
syn_params = {'synapse_model': 'stdp_synapse'}
values = [np.arange(self.N1, dtype=float) for i in range(6)]
for i, param in enumerate(params):
syn_params[param] = values[i]
self.setUpNetwork(self.conn_dict, syn_params)
for i, param in enumerate(params):
a = connect_test_base.get_weighted_connectivity_matrix(
self.pop1, self.pop2, param)
connect_test_base.mpi_assert(np.diag(a), values[i], self)
def testInputArrayRPort(self):
syn_params = {}
neuron_model = 'iaf_psc_exp_multisynapse'
neuron_dict = {'tau_syn': [0.1 + i for i in range(self.N1)]}
self.pop1 = nest.Create(neuron_model, self.N1, neuron_dict)
self.pop2 = nest.Create(neuron_model, self.N1, neuron_dict)
self.param_array = np.arange(1, self.N1 + 1, dtype=int)
syn_params['receptor_type'] = self.param_array
nest.Connect(self.pop1, self.pop2, self.conn_dict, syn_params)
M = connect_test_base.get_weighted_connectivity_matrix(
self.pop1, self.pop2, 'receptor')
connect_test_base.mpi_assert(M, np.diag(self.param_array), self)
def testInputArrayWithoutAutapses(self):
self.conn_dict['allow_autapses'] = False
for label in ['weight', 'delay']:
syn_params = {}
if label == 'weight':
self.param_array = np.arange(self.N1 * self.N1,
dtype=float).reshape(
self.N1, self.N1)
elif label == 'delay':
self.param_array = np.arange(1, self.N1 * self.N1 + 1).reshape(
self.N1, self.N1) * 0.1
syn_params[label] = self.param_array
self.setUpNetworkOnePop(self.conn_dict, syn_params)
M_nest = connect_test_base.get_weighted_connectivity_matrix(
self.pop, self.pop, label)
np.fill_diagonal(self.param_array, 0)
connect_test_base.mpi_assert(M_nest, self.param_array, self)
def testInputArray(self):
syn_params = {}
for label in ['weight', 'delay']:
if label == 'weight':
self.param_array = np.arange(self.N_array, dtype=float)
elif label == 'delay':
self.param_array = np.arange(1, self.N_array + 1) * 0.1
syn_params[label] = self.param_array
nest.ResetKernel()
self.setUpNetwork(self.conn_dict,
syn_params,
N1=self.N_array,
N2=self.N_array)
M_nest = connect_test_base.get_weighted_connectivity_matrix(
self.pop1, self.pop2, label)
connect_test_base.mpi_assert(M_nest, np.diag(self.param_array),
self)
def testRPortDistribution(self):
n_rport = 10
nr_neurons = 100
nest.ResetKernel() # To reset local_num_threads
neuron_model = 'iaf_psc_exp_multisynapse'
neuron_dict = {'tau_syn': [0.1 + i for i in range(n_rport)]}
self.pop1 = nest.Create(neuron_model, nr_neurons, neuron_dict)
self.pop2 = nest.Create(neuron_model, nr_neurons, neuron_dict)
syn_params = {'synapse_model': 'static_synapse'}
syn_params['receptor_type'] = 1 + nest.random.uniform_int(n_rport)
nest.Connect(self.pop1, self.pop2, self.conn_dict, syn_params)
M = connect_test_base.get_weighted_connectivity_matrix(
self.pop1, self.pop2, 'receptor')
M = connect_test_base.gather_data(M)
if M is not None:
M = M.flatten()
frequencies = scipy.stats.itemfreq(M)
self.assertTrue(
np.array_equal(frequencies[:, 0], np.arange(1, n_rport + 1)),
'Missing or invalid rports')
chi, p = scipy.stats.chisquare(frequencies[:, 1])
self.assertGreater(p, self.pval)