Spinncer weight scale

examples/extra_models_examples/stdp_example_izk_cond.py

@@ -172,7 +172,7 @@
     weight_dependence=sim.AdditiveWeightDependence(w_min=0, w_max=0.1))
 
 plastic_projection = sim.Projection(
-    pre_pop, post_pop, sim.FixedProbabilityConnector(p_connect=1.0),  # 0.5
+    pre_pop, post_pop, sim.FixedProbabilityConnector(p_connect=0.5),  # 0.5
     synapse_type=stdp_model)
 
 # Record neurons' potentials

examples/extra_models_examples/vogel_2011/vogels_2011_live.py

@@ -69,7 +69,7 @@
 # Build inhibitory plasticity  model
 stdp_model = sim.STDPMechanism(
     timing_dependence=sim.extra_models.Vogels2011Rule(alpha=0.12, tau=20.0,
-                                                      A_plus=0.05),
+                                                      A_plus=0.0005),
     weight_dependence=sim.AdditiveWeightDependence(w_min=0.0, w_max=1.0))
 
 # Make inhibitory->excitatory projections

examples/stdp_curve_cond.py

@@ -26,7 +26,7 @@
 # ------------------------------------------------------------------
 time_between_pairs = 1000
 num_pairs = 60
-start_w = 0.005
+start_w = 0.05
 delta_t = [-100, -60, -40, -30, -20, -10, -1, 1, 10, 20, 30, 40, 60, 100]
 start_time = 200
 mad = True
@@ -89,7 +89,7 @@
     post_stim = sim.Population(
         1, sim.SpikeSourceArray(spike_times=[post_times]))
 
-    weight = 0.035
+    weight = 0.075
 
     # Connections between spike sources and neuron populations
     ee_connector = sim.OneToOneConnector()
@@ -104,9 +104,9 @@
     stdp_model = sim.STDPMechanism(
         timing_dependence=sim.SpikePairRule(
             tau_plus=16.7, tau_minus=33.7,
-            A_plus=0.0000875, A_minus=0.0000875),
+            A_plus=0.00075, A_minus=0.00075),
         weight_dependence=sim.AdditiveWeightDependence(
-            w_min=0.0, w_max=0.0175), weight=start_w)
+            w_min=0.0, w_max=1.0), weight=start_w)
 
     projections.append(sim.Projection(
         pre_pop, post_pop, sim.OneToOneConnector(),

examples/stdp_example_cond.py

@@ -142,20 +142,20 @@
 # Noise projections
 sim.Projection(
     INoisePre, pre_pop, ee_connector, receptor_type='excitatory',
-    synapse_type=sim.StaticSynapse(weight=JEE * 0.001))
+    synapse_type=sim.StaticSynapse(weight=JEE * 0.0012))
 sim.Projection(
     INoisePost, post_pop, ee_connector, receptor_type='excitatory',
-    synapse_type=sim.StaticSynapse(weight=JEE * 0.001))
+    synapse_type=sim.StaticSynapse(weight=JEE * 0.0012))
 
 # Additional Inputs projections
 for i in range(len(IAddPre)):
     sim.Projection(
         IAddPre[i], pre_pop, ee_connector, receptor_type='excitatory',
-        synapse_type=sim.StaticSynapse(weight=JEE * 0.001))
+        synapse_type=sim.StaticSynapse(weight=JEE * 0.0012))
 for i in range(len(IAddPost)):
     sim.Projection(
         IAddPost[i], post_pop, ee_connector, receptor_type='excitatory',
-        synapse_type=sim.StaticSynapse(weight=JEE * 0.001))
+        synapse_type=sim.StaticSynapse(weight=JEE * 0.0012))
 
 # Plastic Connections between pre_pop and post_pop
 stdp_model = sim.STDPMechanism(
@@ -178,7 +178,7 @@
 # Run simulation
 sim.run(simtime)
 
-print("Weights:{}".format(plastic_projection.get('weight', 'list')))
+# print("Weights:{}".format(plastic_projection.get('weight', 'list')))
 
 pre_spikes = pre_pop.get_data('spikes')
 post_spikes = post_pop.get_data('spikes')
@@ -188,7 +188,7 @@
     Panel(pre_spikes.segments[0].spiketrains,
           yticks=True, markersize=0.5, xlim=(0, simtime)),
     Panel(post_spikes.segments[0].spiketrains,
-          yticks=True, markersize=0.5, xlim=(0, simtime)),
+          yticks=True, xticks=True, markersize=0.5, xlim=(0, simtime)),
     title="stdp example cond",
     annotations="Simulated with {}".format(sim.name()))
 plt.show()