diff --git a/neuralpredictors/layers/encoders/firing_rate.py b/neuralpredictors/layers/encoders/firing_rate.py
index 4a27d5e8..59dc0699 100644
--- a/neuralpredictors/layers/encoders/firing_rate.py
+++ b/neuralpredictors/layers/encoders/firing_rate.py
@@ -39,7 +39,9 @@ def __init__(
         self.offset = elu_offset
 
         if nonlinearity_type != "elu" and not np.isclose(elu_offset, 0.0):
-            warnings.warn("If `nonlinearity_type` is not 'elu', `elu_offset` will be ignored")
+            warnings.warn(
+                "If `nonlinearity_type` is not 'elu', `elu_offset` will be ignored"
+            )
         if nonlinearity_type == "elu":
             self.nonlinearity_fn = nn.ELU()
         elif nonlinearity_type == "identity":
@@ -80,7 +82,7 @@ def forward(
             x = self.modulator[data_key](x, behavior=behavior)
 
         if self.nonlinearity_type == "elu":
-            return self.nonlinearity_fn(x + self.offset) + 1
+            return self.nonlinearity_fn(x) + 1 + self.offset
         else:
             return self.nonlinearity_fn(x)
 
diff --git a/neuralpredictors/measures/zero_inflated_losses.py b/neuralpredictors/measures/zero_inflated_losses.py
index 918d7e5e..9069332a 100644
--- a/neuralpredictors/measures/zero_inflated_losses.py
+++ b/neuralpredictors/measures/zero_inflated_losses.py
@@ -17,8 +17,8 @@ def forward(self, target, output, **kwargs):
         if loc.requires_grad:
             self.multi_clamp(loc, [0.0] * neurons_n, target.max(dim=0)[0])
 
-        zero_mask = target < loc
-        nonzero_mask = target >= loc
+        zero_mask = target <= loc
+        nonzero_mask = target > loc
 
         # spike loss
         spike_logl = torch.log(1 - q) - torch.log(loc)