diff --git a/pointprocesses/exercises/pointprocesses-1.tex b/pointprocesses/exercises/pointprocesses-1.tex
index a27704c..7b28e6a 100644
--- a/pointprocesses/exercises/pointprocesses-1.tex
+++ b/pointprocesses/exercises/pointprocesses-1.tex
@@ -158,20 +158,25 @@
   \question \qt{Statistics of spike counts}
   Now let's have a look at the statistics of the spike counts.
   \begin{parts}
-    \part Write a function that counts and returns a vector with the
-    number of spikes in windows of a given width $W$.
+    \part \label{counts} Write a function that counts with the number
+    of spikes in windows of a given width $W$. The spikes are passed
+    to the function as a cell array containing vectors of spike times.
+    The function returns a single vector with all the spike counts.
+    \begin{solution}
+      \lstinputlisting{counts.m}
+    \end{solution}
 
-    Use this function to generate a properly normalized histogram of
-    spike counts for the data of the three types of neurons. Use
-    100\,ms for the window width.
+    \part Generate a properly normalized histogram of spike counts for
+    the data of the three types of neurons. Use 100\,ms for the window
+    width and the function from (\ref{counts}) for computing the spike
+    counts.
 
-    Compare the distributions with the Poisson distribution expected
-    for a Poisson spike train:
+    In addition, compare the distributions with the Poisson
+    distribution expected for a Poisson spike train:
     \[ P(k) = \frac{(\lambda W)^ke^{\lambda W}}{k!} \; , \] where
     $\lambda$ is the rate of the spike train that you should estimate
     from the data.
     \begin{solution}
-      \lstinputlisting{counts.m}
       \newsolutionpage
       \lstinputlisting{spikecountshists.m}
       \colorbox{white}{\includegraphics[width=1\textwidth]{spikecountshists}}