Merge pull request #530 from BindsNET/hananel

Changing execution order in Izhikevich  and inject Vmem in network forward

bindsnet/network/network.py

@@ -371,6 +371,14 @@ def run(
                     # Get input to this layer (one-step mode).
                     current_inputs.update(self._get_inputs(layers=[l]))
 
+                # Inject voltage to neurons.
+                inject_v = injects_v.get(l, None)
+                if inject_v is not None:
+                    if inject_v.ndimension() == 1:
+                        self.layers[l].v += inject_v
+                    else:
+                        self.layers[l].v += inject_v[t]
+
                 if l in current_inputs:
                     self.layers[l].forward(x=current_inputs[l])
                 else:
@@ -392,14 +400,6 @@ def run(
                     else:
                         self.layers[l].s[:, unclamp[t]] = 0
 
-                # Inject voltage to neurons.
-                inject_v = injects_v.get(l, None)
-                if inject_v is not None:
-                    if inject_v.ndimension() == 1:
-                        self.layers[l].v += inject_v
-                    else:
-                        self.layers[l].v += inject_v[t]
-
             # Run synapse updates.
             for c in self.connections:
                 self.connections[c].update(

bindsnet/network/nodes.py

@@ -1269,8 +1269,6 @@ def forward(self, x: torch.Tensor) -> None:
 
         :param x: Inputs to the layer.
         """
-        # Check for spiking neurons.
-        self.s = self.v >= self.thresh
 
         # Voltage and recovery reset.
         self.v = torch.where(self.s, self.c, self.v)
@@ -1292,6 +1290,9 @@ def forward(self, x: torch.Tensor) -> None:
         if self.lbound is not None:
             self.v.masked_fill_(self.v < self.lbound, self.lbound)
 
+        # Check for spiking neurons.
+        self.s = self.v >= self.thresh
+
         super().forward(x)
 
     def reset_state_variables(self) -> None: