diff --git a/bindsnet/evaluation/evaluation.py b/bindsnet/evaluation/evaluation.py
index cd4fe2a6..f2f44dfc 100644
--- a/bindsnet/evaluation/evaluation.py
+++ b/bindsnet/evaluation/evaluation.py
@@ -27,6 +27,7 @@ def assign_labels(
     :return: Tuple of class assignments, per-class spike proportions, and per-class
         firing rates.
     """
+
     n_neurons = spikes.size(2)
 
     if rates is None:
@@ -34,29 +35,28 @@ def assign_labels(
 
     # Sum over time dimension (spike ordering doesn't matter).
     spikes = spikes.sum(1)
-
+    
     for i in range(n_labels):
+        # Create mask (faster and allows future steps to stay on GPU).
+        mask = (labels == i)
         # Count the number of samples with this label.
-        n_labeled = torch.sum(labels == i).float()
+        n_labeled = mask.sum().float()
 
         if n_labeled > 0:
-            # Get indices of samples with this label.
-            indices = torch.nonzero(labels == i).view(-1)
-
-            # Compute average firing rates for this label.
-            selected_spikes = torch.index_select(
-                spikes, dim=0, index=torch.tensor(indices)
-            )
-            rates[:, i] = alpha * rates[:, i] + (
-                torch.sum(selected_spikes, 0) / n_labeled
-            )
+            # Get indices of samples with this label (masking is faster and stays on the GPU).
+            label_sum = spikes[mask].sum(0)
+            # Update rates.
+            rates[:, i] = alpha * rates[:, i] + (label_sum / n_labeled)
 
-    # Compute proportions of spike activity per class.
-    proportions = rates / rates.sum(1, keepdim=True)
-    proportions[proportions != proportions] = 0  # Set NaNs to 0
+    # Compute proportions (and use 'torch.where' to avoid NaN bug).
+    total_activity = rates.sum(1, keepdim=True)
+    proportions = torch.where(total_activity > 0, rates / total_activity, torch.zeros_like(rates))
 
     # Neuron assignments are the labels they fire most for.
-    assignments = torch.max(proportions, 1)[1]
+    max_vals, assignments = torch.max(proportions, 1)
+    
+    # Set unassigned (silent) neurons to -1 instead of defaulting to 0.
+    assignments[max_vals == 0] = -1 
 
     return assignments, proportions, rates