diff --git a/pointprocesses/lecture/countexamples.py b/pointprocesses/lecture/countexamples.py
index 7e0f98f..8c8a8f3 100644
--- a/pointprocesses/lecture/countexamples.py
+++ b/pointprocesses/lecture/countexamples.py
@@ -5,8 +5,8 @@ from plotstyle import *
 
 
 rate = 20.0
-trials = 10
-duration = 200.0
+trials = 20
+duration = 100.0
 
 
 def hompoisson(rate, trials, duration) :
@@ -28,7 +28,7 @@ def plot_count_hist(ax, spikes, win, pmax):
         counts.extend(c)
     cb = np.arange(0.0, 15.5, 1.0)
     h, b = np.histogram(counts, cb, density=True)
-    ax.bar(b[:-1], h, bar_fac, **fsA)
+    ax.bar(b[:-1], h, bar_fac, align='center', **fsA)
     ax.plot(cb, poisson.pmf(cb, rate*win), **lsBm)
     ax.plot(cb, poisson.pmf(cb, rate*win), **psBm)
     ax.text(0.9, 0.9, 'T=%.0fms' % (1000.0*win), transform=ax.transAxes, ha='right')
@@ -44,6 +44,8 @@ if __name__ == "__main__":
     fig, (ax1, ax2) = plt.subplots(1, 2)
     fig.subplots_adjust(**adjust_fs(fig, top=0.5, right=1.5))
     plot_count_hist(ax1, spikes, 0.02, 0.7)
-    plot_count_hist(ax2, spikes, 0.2, 0.25)
+    ax1.set_yticks(np.arange(0.0, 0.7, 0.2))
+    plot_count_hist(ax2, spikes, 0.2, 0.22)
+    ax2.set_yticks(np.arange(0.0, 0.25, 0.1))
     plt.savefig('countexamples.pdf')
     plt.close()
diff --git a/pointprocesses/lecture/fanoexamples.py b/pointprocesses/lecture/fanoexamples.py
index dc68c0e..228b473 100644
--- a/pointprocesses/lecture/fanoexamples.py
+++ b/pointprocesses/lecture/fanoexamples.py
@@ -76,23 +76,27 @@ def plot_count_fano(ax1, ax2, spikes):
             counts.extend(c)
         mean_counts[k] = np.mean(counts)
         var_counts[k] = np.var(counts)
-    ax1.plot(mean_counts, var_counts, **lsA)
+    ax1.plot(mean_counts, var_counts, zorder=100, **lsA)
     ax1.set_xlabel('Mean count')
     ax1.set_xlim(0.0, 20.0)
     ax1.set_ylim(0.0, 20.0)
+    ax1.set_xticks(np.arange(0.0, 21.0, 10.0))
+    ax1.set_yticks(np.arange(0.0, 21.0, 10.0))
     ax2.plot(1000.0*wins, var_counts/mean_counts, **lsB)
     ax2.set_xlabel('Window', 'ms')
     ax2.set_ylim(0.0, 1.2)
     ax2.set_xscale('log')
     ax2.set_xticks([10, 100, 1000])
     ax2.set_xticklabels(['10', '100', '1000'])
+    ax2.xaxis.set_minor_locator(mpt.NullLocator())
+    ax2.set_yticks(np.arange(0.0, 1.2, 0.5))
 
     
 if __name__ == "__main__":
     homspikes = hompoisson(rate, trials, duration)
     inhspikes = oupifspikes(rate, trials, duration, dt, 0.3, drate, tau)
     fig, axs = plt.subplots(2, 2)
-    fig.subplots_adjust(**adjust_fs(fig, top=0.5, right=1.5))
+    fig.subplots_adjust(**adjust_fs(fig, top=0.5, right=2.0))
     plot_count_fano(axs[0,0], axs[0,1], homspikes)
     axs[0,0].text(0.1, 0.95, 'Poisson', transform=axs[0,0].transAxes)
     axs[0,0].set_xlabel('')
@@ -100,8 +104,9 @@ if __name__ == "__main__":
     axs[0,0].xaxis.set_major_formatter(mpt.NullFormatter())
     axs[0,1].xaxis.set_major_formatter(mpt.NullFormatter())
     plot_count_fano(axs[1,0], axs[1,1], inhspikes)
+    axs[1,1].axhline(1.0, **lsGrid)
     axs[1,0].text(0.1, 0.95, 'OU noise', transform=axs[1,0].transAxes)
     fig.text(0.01, 0.58, 'Count variance', va='center', rotation='vertical')
-    fig.text(0.53, 0.58, 'Fano factor', va='center', rotation='vertical')
+    fig.text(0.51, 0.58, 'Fano factor', va='center', rotation='vertical')
     plt.savefig('fanoexamples.pdf')
     plt.close()
diff --git a/pointprocesses/lecture/pointprocesses.tex b/pointprocesses/lecture/pointprocesses.tex
index d13d0ab..65947c3 100644
--- a/pointprocesses/lecture/pointprocesses.tex
+++ b/pointprocesses/lecture/pointprocesses.tex
@@ -189,11 +189,11 @@ with itself and is always 1.
 
 \begin{figure}[t]
   \includegraphics{countexamples}
-  \titlecaption{\label{countstatsfig}Count statistics.}{The
-    distribution of the number of events $k$ (blue) counted within
-    windows of 20\,ms (left) or 200\,ms duration (right) of the
-    homogeneous Poisson spike train with a rate of 20\,Hz shown in
-    \figref{rasterexamplesfig}. For Poisson spike trains these
+  \titlecaption{\label{countstatsfig}Count statistics.}{Probability
+    distributions of counting $k$ events $k$ (blue) within windows of
+    20\,ms (left) or 200\,ms duration (right) of a homogeneous Poisson
+    spike train with a rate of 20\,Hz
+    (\figref{rasterexamplesfig}). For Poisson spike trains these
     distributions are given by Poisson distributions (red).}
 \end{figure}