diff --git a/pyNN/nest/projections.py b/pyNN/nest/projections.py
index 76a1aa96c..db0c6bd78 100644
--- a/pyNN/nest/projections.py
+++ b/pyNN/nest/projections.py
@@ -113,15 +113,15 @@ def _connect(self, rule_params, syn_params):
             else:
                 raise NotImplementedError()
             syn_params.update({'tau_psc': nest.GetStatus([self.nest_connections[0,1]], param_name)})
-           
-                
+
+
         syn_params.update({'synapse_label': self.nest_synapse_label})
         nest.Connect(self.pre.all_cells.astype(int).tolist(),
                      self.post.all_cells.astype(int).tolist(),
                      rule_params, syn_params)
         self._sources = [cid[0] for cid in nest.GetConnections(synapse_model=self.nest_synapse_model,
                                                                synapse_label=self.nest_synapse_label)]
-    
+
     def _convergent_connect(self, presynaptic_indices, postsynaptic_index,
                             **connection_parameters):
         """
@@ -137,10 +137,20 @@ def _convergent_connect(self, presynaptic_indices, postsynaptic_index,
         postsynaptic_cell = self.post[postsynaptic_index]
         assert presynaptic_cells.size == presynaptic_indices.size
         assert len(presynaptic_cells) > 0, presynaptic_cells
+        syn_dict = {
+            'model': self.nest_synapse_model,
+            'synapse_label': self.nest_synapse_label,
+        }
 
         weights = connection_parameters.pop('weight')
         if self.receptor_type == 'inhibitory' and self.post.conductance_based:
             weights *= -1  # NEST wants negative values for inhibitory weights, even if these are conductances
+            if "stdp" in self.nest_synapse_model:
+                syn_dict["Wmax"] = -1.2345e6  # just some very large negative value to avoid
+                                              # NEST complaining about weight and Wmax having different signs
+                                              # (see https://github.com/NeuralEnsemble/PyNN/issues/636)
+                                              # Will be overwritten below.
+                connection_parameters["Wmax"] *= -1
         if hasattr(self.post, "celltype") and hasattr(self.post.celltype, "receptor_scale"):  # this is a bit of a hack
             weights *= self.post.celltype.receptor_scale                                      # needed for the Izhikevich model
         delays = connection_parameters.pop('delay')
@@ -153,10 +163,7 @@ def _convergent_connect(self, presynaptic_indices, postsynaptic_index,
                     weights = numpy.array([weights])
                 if not numpy.isscalar(delays):
                     delays = numpy.array([delays])
-                syn_dict = {'model': self.nest_synapse_model,
-                            'weight': weights, 'delay': delays,
-                            'synapse_label': self.nest_synapse_label,
-                           }
+                syn_dict.update({'weight': weights, 'delay': delays})
 
                 if 'tsodyks' in self.nest_synapse_model:
                     if self.receptor_type == 'inhibitory':
@@ -165,7 +172,7 @@ def _convergent_connect(self, presynaptic_indices, postsynaptic_index,
                         param_name = self.post.local_cells[0].celltype.translations['tau_syn_E']['translated_name']
                     else:
                         raise NotImplementedError()
-                    syn_dict.update({'tau_psc': numpy.array([[nest.GetStatus([postsynaptic_cell], param_name)[0]] * len(presynaptic_cells.astype(int).tolist())])})             
+                    syn_dict.update({'tau_psc': numpy.array([[nest.GetStatus([postsynaptic_cell], param_name)[0]] * len(presynaptic_cells.astype(int).tolist())])})
 
                 nest.Connect(presynaptic_cells.astype(int).tolist(),
                              [int(postsynaptic_cell)],
@@ -183,13 +190,9 @@ def _convergent_connect(self, presynaptic_indices, postsynaptic_index,
             if numpy.isscalar(delays):
                 delays = repeat(delays)
             for pre, w, d in zip(presynaptic_cells, weights, delays):
-
-                syn_dict = {'weight': w, 'delay': d, 'receptor_type': receptor_type,
-                              'model': self.nest_synapse_model,
-                              'synapse_label': self.nest_synapse_label}
-                
-                if 'tsodyks' in self.nest_synapse_model:    
-                   syn_dict.update({'tau_psc': numpy.array([[nest.GetStatus([postsynaptic_cell], param_name)[0]] * len(presynaptic_cells.astype(int).tolist())])}) 
+                syn_dict.update({'weight': w, 'delay': d, 'receptor_type': receptor_type})
+                if 'tsodyks' in self.nest_synapse_model:
+                   syn_dict.update({'tau_psc': numpy.array([[nest.GetStatus([postsynaptic_cell], param_name)[0]] * len(presynaptic_cells.astype(int).tolist())])})
 
                 nest.Connect([pre], [postsynaptic_cell], 'one_to_one', syn_dict)