diff --git a/plot.py b/plot.py
index c95fed2..a5f1b25 100644
--- a/plot.py
+++ b/plot.py
@@ -40,12 +40,12 @@ ax = SubplotZero(fig, 111)
 fig.add_subplot(ax)
 
 # Extend axes symmetrically around zero so that they fit data
-extent = max(
+axis_extent = max(
     abs(encoded_vectors.min()),
     abs(encoded_vectors.max())
 ) * 1.05
-ax.set_xlim(-extent, extent)
-ax.set_ylim(-extent, extent)
+ax.set_xlim(-axis_extent, axis_extent)
+ax.set_ylim(-axis_extent, axis_extent)
 
 # Hide borders
 for direction in ['left', 'bottom', 'right', 'top']:
@@ -65,6 +65,7 @@ ax.annotate(
 )
 
 ax.axis['xzero'].major_ticklabels.set_backgroundcolor('white')
+ax.axis['xzero'].set_zorder(9)
 ax.axis['xzero'].major_ticklabels.set_ha('center')
 ax.axis['xzero'].major_ticklabels.set_va('top')
 
@@ -76,6 +77,7 @@ ax.annotate(
 
 ax.axis['yzero'].major_ticklabels.set_rotation(-90)
 ax.axis['yzero'].major_ticklabels.set_backgroundcolor('white')
+ax.axis['yzero'].set_zorder(9)
 ax.axis['yzero'].major_ticklabels.set_ha('left')
 ax.axis['yzero'].major_ticklabels.set_va('center')
 
@@ -94,13 +96,15 @@ ax.grid()
 # Plot decision regions
 color_norm = matplotlib.colors.BoundaryNorm(range(order + 1), order)
 
-step = 0.001 * extent
-grid_range = torch.arange(-extent, extent, step)
+regions_extent = 2 * axis_extent
+step = 0.001 * regions_extent
+grid_range = torch.arange(-regions_extent, regions_extent, step)
 grid_y, grid_x = torch.meshgrid(grid_range, grid_range)
 grid_images = model.decoder(torch.stack((grid_x, grid_y), dim=2)).argmax(dim=2)
 
 ax.imshow(
-    grid_images, extent=(-extent, extent, -extent, extent),
+    grid_images,
+    extent=(-regions_extent, regions_extent, -regions_extent, regions_extent),
     aspect="auto",
     origin="lower",
     cmap=color_map,
@@ -125,4 +129,17 @@ for row in range(order):
         backgroundcolor='white', zorder=9
     )
 
+# Plot noise
+noisy_count = 1000
+noisy_vectors = model.channel(encoded_vectors.repeat(noisy_count, 1))
+
+ax.scatter(
+    *zip(*noisy_vectors.tolist()),
+    s=1,
+    c=list(range(len(encoded_vectors))) * noisy_count,
+    cmap=color_map,
+    norm=color_norm,
+    zorder=8
+)
+
 pyplot.show()