add nest based examples for PyNN

examples/nest/one_neuron_noise_PyNN.py

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+"""
+Conversion of NEST example script one_neuron_with_noise.py
+"""
+
+import pyNN.nest as sim 
+from pyNN.utility.plotting import Figure, Panel
+
+sim.setup()
+
+parameters = {
+ 'v_rest': -70,
+ 'cm': 0.25,
+ 'tau_m': 10.0,
+ 'tau_refrac': 2.0,
+ 'tau_syn_E': 2.0,
+ 'tau_syn_I': 2.0,
+ 'i_offset': 0.376,
+ 'v_reset': -70.0,
+ 'v_thresh': -55.0
+}
+
+
+neuron = sim.Population(1, sim.IF_curr_alpha(**parameters), initial_values={"v": -70})
+noise = sim.Population(2, sim.SpikeSourcePoisson(rate=[80000, 15000]))
+
+neuron.record("v")
+
+
+# weight = [0.0012, -0.001]  # nA
+weight = 0.0012
+delay = 1.0
+
+connections = sim.Projection(
+    neuron, noise,
+    sim.AllToAllConnector(), 
+    sim.StaticSynapse(weight=weight, delay=delay))
+
+sim.run(1000.0)
+
+data_1 = neuron.get_data().segments[0].analogsignals[0]
+assert data_1.name == "v"
+
+Figure(
+    Panel(
+        data_1,
+        xticks=True,
+        yticks=True
+    )
+).show()

examples/nest/oneneuron_pyNN.py

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+"""
+Conversion of NEST example script oneneuron.py
+"""
+
+import pyNN.nest as sim
+from pyNN.utility.plotting import Panel, Figure
+
+sim.setup()
+
+parameters = {
+ 'v_rest': -70,
+ 'cm': 0.25,
+ 'tau_m': 10.0,
+ 'tau_refrac': 2.0,
+ 'tau_syn_E': 2.0,
+ 'tau_syn_I': 2.0,
+ 'i_offset': 0.376,
+ 'v_reset': -70.0,
+ 'v_thresh': -55.0
+}
+
+
+neuron = sim.Population(1, sim.IF_curr_alpha(**parameters), initial_values={"v": -70})
+neuron.record("v")
+
+sim.run(200.0)
+
+
+data_1 = neuron.get_data().segments[0].analogsignals[0]
+
+assert data_1.name == "v"
+
+Figure(
+    Panel(
+        data_1,
+        xticks=True,
+        yticks=True
+    )
+).show()
+
+
+neuron.set(i_offset=0.450)
+
+
+sim.run(1000.0)
+
+data_2 = neuron.get_data().segments[0].analogsignals[0]
+
+assert data_2.name == "v"
+
+Figure(
+
+    Panel(
+        data_2,
+        xticks=True,
+        yticks=True
+    )
+).show()

examples/nest/twoneurons_pyNN.py

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+"""
+Conversion of NEST example script twoneurons.py
+"""
+
+import pyNN.nest as sim
+from pyNN.utility.plotting import Figure, Panel
+
+
+sim.setup()
+
+
+parameters_1 = {
+ 'v_rest': -70,
+ 'cm': 0.25,
+ 'tau_m': 10.0,
+ 'tau_refrac': 2.0,
+ 'tau_syn_E': 2.0,
+ 'tau_syn_I': 2.0,
+ 'i_offset': 0.376,
+ 'v_reset': -70.0,
+ 'v_thresh': -55.0
+}
+parameters_2 = parameters_1.copy()
+parameters_2["i_offset"] = 0.0
+
+neuron_1 = sim.Population(1, sim.IF_curr_alpha(**parameters_1), initial_values={"v": -70})
+neuron_2 = sim.Population(1, sim.IF_curr_alpha(**parameters_2), initial_values={"v": -70})
+
+neuron_1.record("v")
+neuron_2.record("v")
+
+weight = 0.02  # nA
+delay = 1.0
+
+connections = sim.Projection(
+    neuron_1, neuron_2, 
+    sim.AllToAllConnector(), 
+    sim.StaticSynapse(weight=weight, delay=delay))
+
+sim.run(1000.0)
+
+data_1 = neuron_1.get_data().segments[0].analogsignals[0]
+data_2 = neuron_2.get_data().segments[0].analogsignals[0]
+
+assert data_1.name == "v"
+
+Figure(
+    Panel(
+        data_1,
+        xticks=True,
+        yticks=True
+    ),
+    Panel(
+        data_2,
+        xticks=True,
+        yticks=True
+    )
+).show()
+