diff --git a/bindsnet/learning/learning.py b/bindsnet/learning/learning.py
index f602254b..9c582f78 100644
--- a/bindsnet/learning/learning.py
+++ b/bindsnet/learning/learning.py
@@ -13,7 +13,6 @@
 )
 from ..utils import im2col_indices
 
-
 class LearningRule(ABC):
     # language=rst
     """
@@ -56,9 +55,12 @@ def __init__(
 
         # Parameter update reduction across minibatch dimension.
         if reduction is None:
-            reduction = torch.mean
-
-        self.reduction = reduction
+            if self.source.batch_size == 1:
+                self.reduction = torch.squeeze
+            else:
+                self.reduction = torch.sum
+        else:
+            self.reduction = reduction
 
         # Weight decay.
         self.weight_decay = weight_decay
diff --git a/bindsnet/network/nodes.py b/bindsnet/network/nodes.py
index fba2cb70..0ffbf829 100644
--- a/bindsnet/network/nodes.py
+++ b/bindsnet/network/nodes.py
@@ -375,12 +375,10 @@ def forward(self, x: torch.Tensor) -> None:
         :param x: Inputs to the layer.
         """
         # Integrate input voltages.
-        self.v += (self.refrac_count == 0).float() * x
+        self.v += (self.refrac_count <= 0).float() * x
 
         # Decrement refractory counters.
-        self.refrac_count = (self.refrac_count > 0).float() * (
-            self.refrac_count - self.dt
-        )
+        self.refrac_count -= self.dt
 
         # Check for spiking neurons.
         self.s = self.v >= self.thresh
@@ -509,16 +507,16 @@ def forward(self, x: torch.Tensor) -> None:
         self.v = self.decay * (self.v - self.rest) + self.rest
 
         # Integrate inputs.
-        self.v += (self.refrac_count == 0).float() * x
-
+        x.masked_fill_(self.refrac_count > 0, 0.0) # OPTIM 2
         # Decrement refractory counters.
-        self.refrac_count = (self.refrac_count > 0).float() * (
-            self.refrac_count - self.dt
-        )
+        self.refrac_count -= self.dt  # OPTIM 1
+
+        self.v += x # interlaced
 
         # Check for spiking neurons.
         self.s = self.v >= self.thresh
 
+
         # Refractoriness and voltage reset.
         self.refrac_count.masked_fill_(self.s, self.refrac)
         self.v.masked_fill_(self.s, self.reset)
@@ -653,13 +651,11 @@ def forward(self, x: torch.Tensor) -> None:
         self.i *= self.i_decay
 
         # Decrement refractory counters.
-        self.refrac_count = (self.refrac_count > 0).float() * (
-            self.refrac_count - self.dt
-        )
+        self.refrac_count -= self.dt
 
         # Integrate inputs.
         self.i += x
-        self.v += (self.refrac_count == 0).float() * self.i
+        self.v += (self.refrac_count <= 0).float() * self.i
 
         # Check for spiking neurons.
         self.s = self.v >= self.thresh
@@ -776,7 +772,7 @@ def __init__(
             "tc_decay", torch.tensor(tc_decay)
         )  # Time constant of neuron voltage decay.
         self.register_buffer(
-            "decay", torch.empty_like(self.tc_decay)
+            "decay", torch.empty_like(self.tc_decay, dtype=torch.float32)
         )  # Set in compute_decays.
         self.register_buffer(
             "theta_plus", torch.tensor(theta_plus)
@@ -808,12 +804,10 @@ def forward(self, x: torch.Tensor) -> None:
             self.theta *= self.theta_decay
 
         # Integrate inputs.
-        self.v += (self.refrac_count == 0).float() * x
+        self.v += (self.refrac_count <= 0).float() * x
 
         # Decrement refractory counters.
-        self.refrac_count = (self.refrac_count > 0).float() * (
-            self.refrac_count - self.dt
-        )
+        self.refrac_count -= self.dt
 
         # Check for spiking neurons.
         self.s = self.v >= self.thresh + self.theta
@@ -965,12 +959,10 @@ def forward(self, x: torch.Tensor) -> None:
             self.theta *= self.theta_decay
 
         # Integrate inputs.
-        self.v += (self.refrac_count == 0).float() * x
+        self.v += (self.refrac_count <= 0).float() * x
 
         # Decrement refractory counters.
-        self.refrac_count = (self.refrac_count > 0).float() * (
-            self.refrac_count - self.dt
-        )
+        self.refrac_count -= self.dt
 
         # Check for spiking neurons.
         self.s = self.v >= self.thresh + self.theta
@@ -1298,7 +1290,7 @@ def forward(self, x: torch.Tensor) -> None:
         self.v = self.decay * (self.v - self.rest) + self.rest
 
         # Integrate inputs.
-        self.v += (self.refrac_count == 0).float() * self.eps_0 * x
+        self.v += (self.refrac_count <= 0).float() * self.eps_0 * x
 
         # Compute (instantaneous) probabilities of spiking, clamp between 0 and 1 using exponentials.
         # Also known as 'escape noise', this simulates nearby neurons.
@@ -1306,9 +1298,7 @@ def forward(self, x: torch.Tensor) -> None:
         self.s_prob = 1.0 - torch.exp(-self.rho * self.dt)
 
         # Decrement refractory counters.
-        self.refrac_count = (self.refrac_count > 0).float() * (
-            self.refrac_count - self.dt
-        )
+        self.refrac_count -= self.dt
 
         # Check for spiking neurons (spike when probability > some random number).
         self.s = torch.rand_like(self.s_prob) < self.s_prob