diff --git a/projects/disclaimer.tex b/projects/disclaimer.tex
index 4d63ac7..7d0d834 100644
--- a/projects/disclaimer.tex
+++ b/projects/disclaimer.tex
@@ -8,21 +8,21 @@
       \vspace{1ex}
 
       The {\bf code} and the {\bf presentation} should be uploaded to
-      ILIAS at latest on Wednesday, February 8th, 23:59h. We will
+      ILIAS at latest on Thursday, February 9th, 12:59h. We will
       store all presentations on one computer to allow fast
-      transitions between talks. The presentations start on
-      Thursday 9:00h. Please hand in your presentation as a pdf file. Bundle
-      everything (the pdf, the code, and the data) into a {\em
-      single} zip-file.
+      transitions between talks. The presentations start on Thursday,
+      February 9th at 1:00h c.t.. Please hand in your presentation as
+      a pdf file. Bundle everything (the pdf, the code, and the data)
+      into a {\em single} zip-file.
 
       \vspace{1ex}
 
-      The {\bf code} should be exectuable without any further
-      adjustments from our side.  A single {\em main} script should
+      The {\bf code} should be executable without any further
+      adjustments from our side.  A single \texttt{main.m} script should
       coordinate the analysis by calling functions and sub-scripts and
       should produce the {\em same} figures that you use in your
       slides. The code should be properly commented and comprehensible
-      by a third persons (use proper and consistent variable and
+      by a third person (use proper and consistent variable and
       function names).
 
       \vspace{1ex} 
@@ -34,11 +34,11 @@
       
       The {\bf presentation} should be {\em at most} 10min long and be
       held in English. In the presentation you should (i) briefly
-      describe the problem, (ii) explain how you solved it
-      algorithmically (don't show your entire code), and (iii) present
-      figures showing your results. All data-related figures you show
-      in the presentation should be produced by your program. It is
-      always a good idea to illustrate the problem with basic plots of
-      the raw-data.
-
+      describe the problem, (ii) present figures introducing, showing,
+      and discussing your results, and (iii) explain how you solved
+      the problem algorithmically (don't show your entire code). All
+      data-related figures you show in the presentation should be
+      produced by your program. It is always a good idea to illustrate
+      the problem with basic plots of the raw-data. Make sure the axis
+      labels are large enough!
     }}
diff --git a/projects/project_populationvector/populationvector.tex b/projects/project_populationvector/populationvector.tex
index 5cd87df..44c1b62 100644
--- a/projects/project_populationvector/populationvector.tex
+++ b/projects/project_populationvector/populationvector.tex
@@ -36,10 +36,10 @@
   respond to bars in dependence on their orientation.
 
   How is the orientation of a bar encoded by the activity of a
-  population of orientation sensisitive neurons?
+  population of orientation sensitive neurons?
 
   In an electrophysiological experiment, 6 neurons have been recorded
-  simultaneously. First, the tuning of these neurons was characteried
+  simultaneously. First, the tuning of these neurons was characterized
   by presenting them bars in a range of 12 orientation angles. Each
   orientation was presented 50 times. Each of the \texttt{unit*.mat}
   files contains the responses of one of the neurons. In there,
@@ -76,14 +76,24 @@
     gain factor that sets the maximum firing rate.
 
     \part How can the orientation angle of the presented bar be read
-    out from the population activity of the 6 neurons? One is the so
-    called ``population vector''. Think of another (simpler) method.
+    out from one trial of the population activity of the 6 neurons?
+    One is the so called ``population vector'' where unit vectors
+    pointing into the direction of the maximum response of each neuron
+    are weighted by their firing rate. The stimulus orientation is
+    then the direction of the averaged vectors.  
 
-    Load one of the \texttt{population*.mat} files, illustrate the data,
-    and estimate the orientation angle of the bar by two different methods.
+    Think of another (simpler) method how the orientation of the bar
+    may be approximately read out from the population.
+
+    Load one of the \texttt{population*.mat} files, illustrate the
+    data, and estimate the orientation angle of the bar by the two
+    different methods.
 
     \part Compare, illustrate and discuss the performance of your two
-    decoding methods.
+    decoding methods by using all of the recorded responses (all
+    \texttt{population*.mat} files). How exactly is the orientation of
+    the bar encoded? How robust is the estimate of the orientation
+    from trial to trial?
   \end{parts}
 \end{questions}