Update PyNEST examples to use NEST 3 features

pynest/examples/CampbellSiegert.py

@@ -106,8 +106,6 @@
     else:
         tau_syn = tau_syn_in
 
-    t_psp = np.arange(0., 10. * (tau_m * ms + tau_syn * ms), 0.0001)
-
     # We define the form of a single PSP, which allows us to match the
     # maximal value to or chosen weight.
 
@@ -204,11 +202,11 @@ def psp(x):
 # omitted so initial transients do not perturb our results. We then print the
 # results from theory and simulation.
 
-v_free = vm.get('events', 'V_m')[500:-1]
+v_free = vm.events['V_m']
+Nskip = 500
 print('mean membrane potential (actual / calculated): {0} / {1}'
-      .format(np.mean(v_free), mu * 1000))
+      .format(np.mean(v_free[Nskip:]), mu * 1000))
 print('variance (actual / calculated): {0} / {1}'
-      .format(np.var(v_free), sigma2 * 1e6))
+      .format(np.var(v_free[Nskip:]), sigma2 * 1e6))
 print('firing rate (actual / calculated): {0} / {1}'
-      .format(nest.GetStatus(sd, 'n_events')[0] /
-              (n_neurons * simtime * ms), r))
+      .format(sd.n_events / (n_neurons * simtime * ms), r))

pynest/examples/balancedneuron.py

@@ -84,7 +84,7 @@
 spikedetector = nest.Create("spike_detector")
 
 ###################################################################################
-# Fourth, the ``poisson_generator`` (`noise`) is configured using ``set``.
+# Fourth, the ``poisson_generator`` (`noise`) is configured.
 # Note that we need not set parameters for the neuron, the spike detector, and
 # the voltmeter, since they have satisfactory defaults.
 

pynest/examples/brunel_alpha_evolution_strategies.py

@@ -232,12 +232,11 @@ def ComputePSPnorm(tauMem, CMem, tauSyn):
     nest.SetKernelStatus({'rng_seeds': [parameters['seed']],
                           'resolution': parameters['dt']})
 
-    nest.SetDefaults('iaf_psc_alpha', neuron_parameters)
-    nest.SetDefaults('poisson_generator', {'rate': p_rate})
-
     nodes_ex = nest.Create('iaf_psc_alpha', NE)
     nodes_in = nest.Create('iaf_psc_alpha', NI)
-    noise = nest.Create('poisson_generator')
+    nodes_ex.set(neuron_parameters)
+    nodes_in.set(neuron_parameters)
+    noise = nest.Create('poisson_generator', params={'rate': p_rate})
     espikes = nest.Create('spike_detector', params={'label': 'brunel-py-ex'})
     ispikes = nest.Create('spike_detector', params={'label': 'brunel-py-in'})
 

pynest/examples/brunel_alpha_nest.py

@@ -25,9 +25,6 @@
 This script simulates an excitatory and an inhibitory population on
 the basis of the network used in [1]_.
 
-In contrast to ``brunel-alpha-numpy.py``, this variant uses NEST's builtin
-connection routines to draw the random connections instead of NumPy.
-
 When connecting the network customary synapse models are used, which
 allow for querying the number of created synapses. Using spike
 detectors the average firing rates of the neurons in the populations
@@ -171,25 +168,16 @@ def ComputePSPnorm(tauMem, CMem, tauSyn):
 
 print("Building network")
 
-###############################################################################
-# Configuration of the model ``iaf_psc_alpha`` and ``poisson_generator`` using
-# ``SetDefaults``. This function expects the model to be the inserted as a
-# string and the parameter to be specified in a dictionary. All instances of
-# theses models created after this point will have the properties specified
-# in the dictionary by default.
-
-nest.SetDefaults("iaf_psc_alpha", neuron_params)
-nest.SetDefaults("poisson_generator", {"rate": p_rate})
-
 ###############################################################################
 # Creation of the nodes using ``Create``. We store the returned handles in
 # variables for later reference. Here the excitatory and inhibitory, as well
 # as the poisson generator and two spike detectors. The spike detectors will
-# later be used to record excitatory and inhibitory spikes.
+# later be used to record excitatory and inhibitory spikes. Properties of the
+# nodes are specified via ``param``, which expects a dictionary.
 
-nodes_ex = nest.Create("iaf_psc_alpha", NE)
-nodes_in = nest.Create("iaf_psc_alpha", NI)
-noise = nest.Create("poisson_generator")
+nodes_ex = nest.Create("iaf_psc_alpha", NE, param=neuron_params)
+nodes_in = nest.Create("iaf_psc_alpha", NI, param=neuron_params)
+noise = nest.Create("poisson_generator", param={"rate": p_rate})
 espikes = nest.Create("spike_detector")
 ispikes = nest.Create("spike_detector")
 

pynest/examples/brunel_delta_nest.py

@@ -126,25 +126,16 @@
 
 print("Building network")
 
-###############################################################################
-# Configuration of the model ``iaf_psc_delta`` and ``poisson_generator`` using
-# ``SetDefaults``. This function expects the model to be the inserted as a
-# string and the parameter to be specified in a dictionary. All instances of
-# theses models created after this point will have the properties specified
-# in the dictionary by default.
-
-nest.SetDefaults("iaf_psc_delta", neuron_params)
-nest.SetDefaults("poisson_generator", {"rate": p_rate})
-
 ###############################################################################
 # Creation of the nodes using ``Create``. We store the returned handles in
 # variables for later reference. Here the excitatory and inhibitory, as well
 # as the poisson generator and two spike detectors. The spike detectors will
-# later be used to record excitatory and inhibitory spikes.
+# later be used to record excitatory and inhibitory spikes. Properties of the
+# nodes are specified via ``param``, which expects a dictionary.
 
-nodes_ex = nest.Create("iaf_psc_delta", NE)
-nodes_in = nest.Create("iaf_psc_delta", NI)
-noise = nest.Create("poisson_generator")
+nodes_ex = nest.Create("iaf_psc_delta", NE, param=neuron_params)
+nodes_in = nest.Create("iaf_psc_delta", NI, param=neuron_params)
+noise = nest.Create("poisson_generator", param={"rate": p_rate})
 espikes = nest.Create("spike_detector")
 ispikes = nest.Create("spike_detector")
 

pynest/examples/brunel_exp_multisynapse_nest.py

@@ -142,25 +142,16 @@
 
 print("Building network")
 
-##################################################################################
-# Configuration of the model ``iaf_psc_exp_multisynapse`` and
-# ``poisson_generator`` using ``SetDefaults``. This function expects the model to
-# be the inserted as a string and the parameter to be specified in a
-# dictionary. All instances of theses models created after this point will
-# have the properties specified in the dictionary by default.
-
-nest.SetDefaults("iaf_psc_exp_multisynapse", neuron_params)
-nest.SetDefaults("poisson_generator", {"rate": p_rate})
-
 ###############################################################################
 # Creation of the nodes using ``Create``. We store the returned handles in
 # variables for later reference. Here the excitatory and inhibitory, as well
 # as the poisson generator and two spike detectors. The spike detectors will
-# later be used to record excitatory and inhibitory spikes.
+# later be used to record excitatory and inhibitory spikes. Properties of the
+# nodes are specified via ``param``, which expects a dictionary.
 
-nodes_ex = nest.Create("iaf_psc_exp_multisynapse", NE)
-nodes_in = nest.Create("iaf_psc_exp_multisynapse", NI)
-noise = nest.Create("poisson_generator")
+nodes_ex = nest.Create("iaf_psc_exp_multisynapse", NE, param=neuron_params)
+nodes_in = nest.Create("iaf_psc_exp_multisynapse", NI, param=neuron_params)
+noise = nest.Create("poisson_generator", param={"rate": p_rate})
 espikes = nest.Create("spike_detector")
 ispikes = nest.Create("spike_detector")
 

pynest/examples/brunel_siegert_nest.py

@@ -123,26 +123,21 @@
 
 print("Building network")
 
-###############################################################################
-# Configuration of the model ``siegert_neuron`` using ``SetDefaults``.
-
-nest.SetDefaults("siegert_neuron", neuron_params)
-
 ###############################################################################
 # Creation of the nodes using ``Create``. One rate neuron represents the
 # excitatory population of LIF-neurons in the SLIFN and one the inhibitory
 # population assuming homogeneity of the populations.
 
-siegert_ex = nest.Create("siegert_neuron", 1)
-siegert_in = nest.Create("siegert_neuron", 1)
+siegert_ex = nest.Create("siegert_neuron", params=neuron_params)
+siegert_in = nest.Create("siegert_neuron", params=neuron_params)
 
 ###############################################################################
 # The Poisson drive in the SLIFN is replaced by a driving rate neuron,
 # which does not receive input from other neurons. The activity of the rate
 # neuron is controlled by setting ``mean`` to the rate of the corresponding
 # poisson generator in the SLIFN.
 
-siegert_drive = nest.Create('siegert_neuron', 1, params={'mean': p_rate})
+siegert_drive = nest.Create('siegert_neuron', params={'mean': p_rate})
 
 ###############################################################################
 # To record from the rate neurons a multimeter is created and the parameter

pynest/examples/clopath_synapse_small_network.py

@@ -116,7 +116,7 @@
 
 # Create exc->exc connections
 nest.CopyModel('clopath_synapse', 'clopath_exc_to_exc',
-               {'Wmax': 0.75, 'weight_recorder': wr[0]})
+               {'Wmax': 0.75, 'weight_recorder': wr})
 syn_dict_exc_to_exc = {'synapse_model': 'clopath_exc_to_exc', 'weight': 0.25,
                        'delay': delay}
 conn_dict_exc_to_exc = {'rule': 'all_to_all', 'allow_autapses': False}
@@ -157,7 +157,7 @@
 ##############################################################################
 # Plot results
 
-fig1, axA = plt.subplots(1, sharex=False)
+fig, ax = plt.subplots(1, sharex=False)
 
 # Plot synapse weights of the synapses within the excitatory population
 # Sort weights according to sender and reshape
@@ -186,13 +186,13 @@
 init_w_matrix = np.ones((10, 10))*0.25
 init_w_matrix -= np.identity(10)*0.25
 
-caxA = axA.imshow(weight_matrix - init_w_matrix)
-cbarB = fig1.colorbar(caxA, ax=axA)
-axA.set_xticks([0, 2, 4, 6, 8])
-axA.set_xticklabels(['1', '3', '5', '7', '9'])
-axA.set_yticks([0, 2, 4, 6, 8])
-axA.set_xticklabels(['1', '3', '5', '7', '9'])
-axA.set_xlabel("to neuron")
-axA.set_ylabel("from neuron")
-axA.set_title("Change of syn weights before and after simulation")
+cax = ax.imshow(weight_matrix - init_w_matrix)
+cbarB = fig.colorbar(cax, ax=ax)
+ax.set_xticks([0, 2, 4, 6, 8])
+ax.set_xticklabels(['1', '3', '5', '7', '9'])
+ax.set_yticks([0, 2, 4, 6, 8])
+ax.set_xticklabels(['1', '3', '5', '7', '9'])
+ax.set_xlabel("to neuron")
+ax.set_ylabel("from neuron")
+ax.set_title("Change of syn weights before and after simulation")
 plt.show()

pynest/examples/clopath_synapse_spike_pairing.py

@@ -103,7 +103,7 @@
 ##############################################################################
 # Loop over pairs of spike trains
 
-for (s_t_pre, s_t_post) in zip(spike_times_pre, spike_times_post):
+for s_t_pre, s_t_post in zip(spike_times_pre, spike_times_post):
     nest.ResetKernel()
     nest.SetKernelStatus({"resolution": resolution})
 
@@ -115,17 +115,14 @@
     prrt_nrn = nest.Create("parrot_neuron", 1)
 
     # Create and connect spike generators
-    spike_gen_pre = nest.Create("spike_generator", 1, {
-                                "spike_times": s_t_pre})
+    spike_gen_pre = nest.Create("spike_generator", {"spike_times": s_t_pre})
 
     nest.Connect(spike_gen_pre, prrt_nrn,
                  syn_spec={"delay": resolution})
 
-    spike_gen_post = nest.Create("spike_generator", 1, {
-                                 "spike_times": s_t_post})
+    spike_gen_post = nest.Create("spike_generator", {"spike_times": s_t_post})
 
-    nest.Connect(spike_gen_post, nrn, syn_spec={
-                 "delay": resolution, "weight": 80.0})
+    nest.Connect(spike_gen_post, nrn, syn_spec={"delay": resolution, "weight": 80.0})
 
     # Create weight recorder
     wr = nest.Create('weight_recorder', 1)
@@ -150,14 +147,14 @@
 syn_weights = 100.0*15.0*(syn_weights - init_w)/init_w + 100.0
 
 # Plot results
-fig1, axA = plt.subplots(1, sharex=False)
-axA.plot([10., 20., 30., 40., 50.], syn_weights[5:], color='b', lw=2.5, ls='-',
-         label="pre-post pairing")
-axA.plot([10., 20., 30., 40., 50.], syn_weights[:5], color='g', lw=2.5, ls='-',
-         label="post-pre pairing")
-axA.set_ylabel("normalized weight change")
-axA.set_xlabel("rho (Hz)")
-axA.legend()
-axA.set_title("synaptic weight")
+fig, ax = plt.subplots(1, sharex=False)
+ax.plot([10., 20., 30., 40., 50.], syn_weights[5:], color='b', lw=2.5, ls='-',
+        label="pre-post pairing")
+ax.plot([10., 20., 30., 40., 50.], syn_weights[:5], color='g', lw=2.5, ls='-',
+        label="post-pre pairing")
+ax.set_ylabel("normalized weight change")
+ax.set_xlabel("rho (Hz)")
+ax.legend()
+ax.set_title("synaptic weight")
 
 plt.show()

pynest/examples/correlospinmatrix_detector_two_neuron.py

@@ -47,12 +47,11 @@
 csd = nest.Create("correlospinmatrix_detector")
 csd.set(N_channels=2, tau_max=tau_max, Tstart=tau_max, delta_tau=h)
 
-nest.SetDefaults('ginzburg_neuron', {'theta': 0.0, 'tau_m': tau_m,
-                                     'c_1': 0.0, 'c_2': 2. * m_x, 'c_3': 1.0})
 n1 = nest.Create("ginzburg_neuron")
+n1.set(theta=0.0, tau_m=tau_m, c_1=0.0, c_2=2. * m_x, c_3=1.0)
 
-nest.SetDefaults("mcculloch_pitts_neuron", {'theta': 0.5, 'tau_m': tau_m})
 n2 = nest.Create("mcculloch_pitts_neuron")
+n2.set(theta=0.5, tau_m=tau_m)
 
 nest.Connect(n1, n2, syn_spec={"weight": 1.0})
 
@@ -61,27 +60,27 @@
 
 nest.Simulate(T)
 
-c = csd.get("count_covariance")
+count_covariance = csd.count_covariance
 
-m = np.zeros(2, dtype=float)
+mean_activities = np.zeros(2, dtype=float)
 for i in range(2):
-    m[i] = c[i][i][int(tau_max / h)] * (h / T)
+    mean_activities[i] = count_covariance[i][i][int(tau_max / h)] * (h / T)
 
-print('mean activities =', m)
+print('mean activities =', mean_activities)
 
-cmat = np.zeros((2, 2, int(2 * tau_max / h) + 1), dtype=float)
+covariance_matrix = np.zeros((2, 2, int(2 * tau_max / h) + 1), dtype=float)
 for i in range(2):
     for j in range(2):
-        cmat[i, j] = c[i][j] * (h / T) - m[i] * m[j]
+        covariance_matrix[i, j] = count_covariance[i][j] * (h / T) - mean_activities[i] * mean_activities[j]
 
 ts = np.arange(-tau_max, tau_max + h, h)
 
 plt.title("auto- and cross covariance functions")
 
-plt.plot(ts, cmat[0, 1], 'r', label=r"$c_{12}$")
-plt.plot(ts, cmat[1, 0], 'b', label=r"$c_{21}$")
-plt.plot(ts, cmat[0, 0], 'g', label=r"$c_{11}$")
-plt.plot(ts, cmat[1, 1], 'y', label=r"$c_{22}$")
+plt.plot(ts, covariance_matrix[0, 1], 'r', label=r"$c_{12}$")
+plt.plot(ts, covariance_matrix[1, 0], 'b', label=r"$c_{21}$")
+plt.plot(ts, covariance_matrix[0, 0], 'g', label=r"$c_{11}$")
+plt.plot(ts, covariance_matrix[1, 1], 'y', label=r"$c_{22}$")
 plt.xlabel(r"time $t \; \mathrm{ms}$")
 plt.ylabel(r"$c$")
 plt.legend()

pynest/examples/cross_check_mip_corrdet.py

@@ -69,7 +69,8 @@ def corr_spikes_sorted(spike1, spike2, tbin, tau_max, h):
 h = 0.1             # Computation step size in ms
 T = 100000.0        # Total duration
 delta_tau = 10.0
-tau_max = 100.0
+tau_max = 100.0  # ms correlation window
+t_bin = 10.0  # ms bin size
 pc = 0.5
 nu = 100.0
 
@@ -102,16 +103,10 @@ def corr_spikes_sorted(spike1, spike2, tbin, tau_max, h):
 
 nest.Simulate(T)
 
-n_events = cd.get('n_events')
-n1 = n_events[0]
-n2 = n_events[1]
-
-lmbd1 = (n1 / (T - tau_max)) * 1000.0
-lmbd2 = (n2 / (T - tau_max)) * 1000.0
+n_events_1, n_events_2 = cd.n_events
 
-h = 0.1
-tau_max = 100.0  # ms correlation window
-t_bin = 10.0  # ms bin size
+lmbd1 = (n_events_1 / (T - tau_max)) * 1000.0
+lmbd2 = (n_events_2 / (T - tau_max)) * 1000.0
 
 spikes = sd.get('events', 'senders')
 

pynest/examples/evaluate_quantal_stp_synapse.py

@@ -110,13 +110,12 @@
 
 nest.SetDefaults("tsodyks2_synapse", t1_params)
 nest.SetDefaults("quantal_stp_synapse", t2_params)
-nest.SetDefaults("iaf_psc_exp", {"tau_syn_ex": 3.})
 
 ###############################################################################
 # We create three different neurons.
 # Neuron one is the sender, the two other neurons receive the synapses.
 
-neuron = nest.Create("iaf_psc_exp", 3)
+neuron = nest.Create("iaf_psc_exp", 3, params={"tau_syn_ex": 3.})
 
 ###############################################################################
 # The connection from neuron 1 to neuron 2 is a deterministic synapse.

pynest/examples/evaluate_tsodyks2_synapse.py

@@ -92,12 +92,11 @@
 
 nest.SetDefaults("tsodyks2_synapse", t1_params)
 nest.SetDefaults("tsodyks_synapse", t2_params)
-nest.SetDefaults("iaf_psc_exp", {"tau_syn_ex": 3.})
 
 ###############################################################################
 # Create three neurons.
 
-neuron = nest.Create("iaf_psc_exp", 3)
+neuron = nest.Create("iaf_psc_exp", 3, params={"tau_syn_ex": 3.})
 
 ###############################################################################
 # Neuron one produces spikes. Neurons 2 and 3 receive the spikes via the two