diff --git a/src/spikeanalysis/spike_plotter.py b/src/spikeanalysis/spike_plotter.py
index 2f8f837..54db335 100644
--- a/src/spikeanalysis/spike_plotter.py
+++ b/src/spikeanalysis/spike_plotter.py
@@ -759,6 +759,7 @@ def plot_response_trace(
         by_trial: bool = False,
         ebar: bool = False,
         color="black",
+        show_stim: bool = True
     ):
         """
         Function for plotting response traces for either z scored or raw firing rates
@@ -787,48 +788,70 @@ def plot_response_trace(
             data = self.data.mean_firing_rate
             bins = self.data.fr_bins
 
+        stim_lengths = self._get_event_lengths()
+
         for stimulus, response in data.items():
+            current_length = stim_lengths[stimulus]
             current_bins = bins[stimulus]
+            bin_size = current_bins[1]-current_bins[0]
+            start_pt = np.where((current_bins>-bin_size) & (current_bins< bin_size))[0][0]
+            end_pt = np.where((current_bins > current_length-bin_size) & (current_bins < current_length + bin_size))[0][0]
+            stim_lines = [start_pt, end_pt]
             if by_trial and by_neuron:
                 for neuron in range(np.shape(response)[0]):
                     for trial in range(np.shape(response)[1]):
                         self._plot_one_trace(
-                            current_bins, response[neuron, trial, :], ebars=None, color=color, stim=stimulus
+                            current_bins, response[neuron, trial, :], ebars=None, color=color, stim=stimulus, show_stim=show_stim, stim_lines=stim_lines
                         )
             elif by_neuron:
                 for neuron in range(np.shape(response)[0]):
                     avg_response = np.mean(response[neuron], axis=0)
                     ebars = np.std(response[neuron], axis=0)
                     if ebar:
-                        self._plot_one_trace(current_bins, avg_response, ebars=ebars, color=color, stim=stimulus)
+                        self._plot_one_trace(current_bins, avg_response, ebars=ebars, color=color, stim=stimulus,show_stim=show_stim, stim_lines=stim_lines)
                     else:
-                        self._plot_one_trace(current_bins, avg_response, ebars=None, color=color, stim=stimulus)
+                        self._plot_one_trace(current_bins, avg_response, ebars=None, color=color, stim=stimulus,show_stim=show_stim, stim_lines=stim_lines)
             elif by_trial:
                 for trial in range(np.shape(response)[1]):
                     avg_response = np.mean(response[:, trial, :], axis=0)
                     ebars = np.std(response[:, trial, :], axis=0)
                     if ebar:
-                        self._plot_one_trace(current_bins, avg_response, ebars=ebars, color=color, stim=stimulus)
+                        self._plot_one_trace(current_bins, avg_response, ebars=ebars, color=color, stim=stimulus,show_stim=show_stim, stim_lines=stim_lines)
                     else:
-                        self._plot_one_trace(current_bins, avg_response, ebars=None, color=color, stim=stimulus)
+                        self._plot_one_trace(current_bins, avg_response, ebars=None, color=color, stim=stimulus,show_stim=show_stim, stim_lines=stim_lines)
             else:
                 avg_response = np.mean(np.mean(response, axis=1), axis=0)
                 if ebar:
-                    self._plot_one_trace(current_bins, avg_response, ebars=ebars, color=color, stim=stimulus)
+                    self._plot_one_trace(current_bins, avg_response, ebars=ebars, color=color, stim=stimulus,show_stim=show_stim, stim_lines=stim_lines)
                 else:
-                    self._plot_one_trace(current_bins, avg_response, ebars=None, color=color, stim=stimulus)
+                    self._plot_one_trace(current_bins, avg_response, ebars=None, color=color, stim=stimulus,show_stim=show_stim, stim_lines=stim_lines)
 
-    def _plot_one_trace(self, bins, trace, ebars=None, color="black", stim=""):
+    def _plot_one_trace(self, bins, trace, ebars=None, color="black", stim="", show_stim: bool= True, stim_lines: list = [0,0]):
         """
         Function for plotting one response trace in 2D. I'm going to try
         to let it autoscale
         """
         fig, ax = plt.subplots(figsize=self.figsize)
-        ax.plot(bins, trace, color=color)
+        ax.plot(bins, trace, color=color, linewdith=0.75)
+        max_pt = np.max(trace)
         if ebars is not None:
-            ax.plot(bins, trace + ebars, color=color)
-            ax.plot(bins, trace - ebars, color=color)
-            ax.fill_between(bins, trace - ebars, trace + ebars, color=color, alpha=0.02)
+            ax.plot(bins, trace + ebars, color=color, linewidth=0.25)
+            ax.plot(bins, trace - ebars, color=color, linewidth=0.25)
+            ax.fill_between(bins, trace - ebars, trace + ebars, color=color, alpha=0.4)
+            max_pt = np.max(trace + ebars)
+        if show_stim:
+            ax.axvline(stim_lines[0],
+                        0,
+                        max_pt,
+                        color="black",
+                        linestyle=":",
+                        linewidth=0.5,)
+            ax.axvline(stim_lines[1],
+                        0,
+                        max_pt,
+                        color="black",
+                        linestyle=":",
+                        linewidth=0.5,)
 
         ax.set_xlabel("Time (s)")
         ax.set_ylabel(self.y_axis)