Fix NEST native Tsodyks synapse handling (issue #810)

pyNN/nest/connectors.py

@@ -116,7 +116,7 @@ def synapse_parameters(self, projection):
                 if (
                     name == "weight"
                     and projection.receptor_type == 'inhibitory'
-                    and self.post.conductance_based
+                    and projection.post.conductance_based
                 ):
                     # NEST wants negative values for inhibitory weights,
                     # even if these are conductances

pyNN/nest/projections.py

@@ -16,7 +16,7 @@
 from ..space import Space
 from ..parameters import simplify
 from . import simulator
-from .standardmodels.synapses import StaticSynapse
+from .standardmodels.synapses import StaticSynapse, TsodyksMarkramSynapse
 from .conversion import make_sli_compatible
 
 logger = logging.getLogger("PyNN")
@@ -123,16 +123,16 @@ def _connect(self, rule_params, syn_params):
         Create connections by calling nest. Connect on the presynaptic and postsynaptic population
         with the parameters provided by params.
         """
-        if 'tsodyks' in self.nest_synapse_model:
+        if isinstance(self.synapse_type, TsodyksMarkramSynapse):
             translations = self.post.local_cells[0].celltype.translations
             if self.receptor_type == 'inhibitory':
                 param_name = translations['tau_syn_I']['translated_name']
             elif self.receptor_type == 'excitatory':
                 param_name = translations['tau_syn_E']['translated_name']
             else:
                 raise NotImplementedError()
-            syn_params.update(
-                {'tau_psc': nest.GetStatus([self.nest_connections[0, 1]], param_name)})
+            tau_psc = nest.GetStatus(self.post.node_collection, param_name)
+            syn_params.update({'tau_psc': tau_psc[0] if len(set(tau_psc)) == 1 else list(tau_psc)})
 
         syn_params.update({'synapse_label': self.nest_synapse_label})
         nest.Connect(self.pre.node_collection,
@@ -272,9 +272,16 @@ def _convergent_connect(self, presynaptic_indices, postsynaptic_index,
                     syn_dict.update({'weight': weights, 'delay': delays})
 
                 if postsynaptic_cell.celltype.standard_receptor_type:
+<<<<<<< issue810
+                    # For the standard TsodyksMarkramSynapse, copy "tau_psc" from the
+                    # post-synaptic neuron's tau_syn so they stay consistent. For
+                    # native_synapse_type, the user-supplied tau_psc is left untouched.
+                    if isinstance(self.synapse_type, TsodyksMarkramSynapse):
+=======
                     # For Tsodyks-Markram synapses models we set the "tau_psc" parameter to match
                     # the relevant "tau_syn" parameter from the post-synaptic neuron.
                     if 'tsodyks' in self.nest_synapse_model and hasattr(postsynaptic_cell.celltype, 'translations'):
+>>>>>>> master
                         translations = postsynaptic_cell.celltype.translations
                         if self.receptor_type == 'inhibitory':
                             param_name = translations['tau_syn_I']['translated_name']

test/system/test_nest.py

@@ -191,6 +191,77 @@ def test_tsodyks_markram_synapse():
     assert_array_equal(tau_psc, np.arange(0.2, 0.7, 0.1))
 
 
+def test_native_tsodyks_synapse_convergent_connect():
+    """native_synapse_type tsodyks_synapse via _convergent_connect preserves user tau_psc (issue #810)."""
+    if not have_nest:
+        pytest.skip("nest not available")
+    import nest
+    sim = pyNN.nest
+    sim.setup()
+    spike_source = sim.Population(1, sim.SpikeSourceArray(spike_times=np.arange(10, 100, 10)))
+    neurons = sim.Population(5, sim.IF_cond_exp(e_rev_I=-75, tau_syn_I=np.arange(0.2, 0.7, 0.1)))
+    synapse_type = sim.native_synapse_type('tsodyks_synapse')(
+        U=0.04, tau_rec=100.0, tau_fac=1000.0, weight=10.0, delay=0.5, tau_psc=5.0)
+    connector = sim.AllToAllConnector()
+    prj = sim.Projection(spike_source, neurons, connector,
+                         receptor_type='inhibitory',
+                         synapse_type=synapse_type)
+    connections = nest.GetConnections(
+        nest.NodeCollection(list(prj._sources)),
+        synapse_model=prj.nest_synapse_model)
+    tau_psc = np.array(nest.GetStatus(connections, 'tau_psc'))
+    assert_array_equal(tau_psc, [5.0] * 5)
+    sim.end()
+
+
+def test_native_tsodyks_synapse_native_connect():
+    """native_synapse_type tsodyks_synapse via _connect (NativeRNG) preserves user tau_psc (issue #810)."""
+    if not have_nest:
+        pytest.skip("nest not available")
+    import nest
+    sim = pyNN.nest
+    sim.setup()
+    spike_source = sim.Population(1, sim.SpikeSourceArray(spike_times=np.arange(10, 100, 10)))
+    neurons = sim.Population(5, sim.IF_cond_exp(e_rev_I=-75, tau_syn_I=3.0))
+    synapse_type = sim.native_synapse_type('tsodyks_synapse')(
+        U=0.04, tau_rec=100.0, tau_fac=1000.0, weight=10.0, delay=0.5, tau_psc=5.0)
+    rng = sim.NativeRNG(seed=1234)
+    connector = sim.FixedProbabilityConnector(p_connect=1.0, rng=rng)
+    prj = sim.Projection(spike_source, neurons, connector,
+                         receptor_type='inhibitory',
+                         synapse_type=synapse_type)
+    connections = nest.GetConnections(
+        nest.NodeCollection(list(prj._sources)),
+        synapse_model=prj.nest_synapse_model)
+    tau_psc = np.array(nest.GetStatus(connections, 'tau_psc'))
+    assert_array_equal(tau_psc, [5.0] * 5)
+    sim.end()
+
+
+def test_native_tsodyks2_synapse():
+    """native_synapse_type tsodyks2_synapse creates projection without error (issue #810)."""
+    if not have_nest:
+        pytest.skip("nest not available")
+    import nest
+    sim = pyNN.nest
+    sim.setup()
+    spike_source = sim.Population(1, sim.SpikeSourceArray(spike_times=np.arange(10, 100, 10)))
+    neurons = sim.Population(5, sim.IF_cond_exp())
+    synapse_type = sim.native_synapse_type('tsodyks2_synapse')(
+        U=0.04, tau_rec=100.0, tau_fac=1000.0, weight=10.0, delay=0.5)
+    connector = sim.AllToAllConnector()
+    prj = sim.Projection(spike_source, neurons, connector,
+                         receptor_type='excitatory',
+                         synapse_type=synapse_type)
+    connections = nest.GetConnections(
+        nest.NodeCollection(list(prj._sources)),
+        synapse_model=prj.nest_synapse_model)
+    assert len(connections) == 5
+    U_vals = np.array(nest.GetStatus(connections, 'U'))
+    assert_array_equal(U_vals, [0.04] * 5)
+    sim.end()
+
+
 def test_native_electrode_types():
     """ Test of NativeElectrodeType class. (See issue #506)"""
     if not have_nest: