diff --git a/projects/project_lif/lif.tex b/projects/project_lif/lif.tex
index dc24b05..b42042d 100644
--- a/projects/project_lif/lif.tex
+++ b/projects/project_lif/lif.tex
@@ -2,43 +2,47 @@
 
 \newcommand{\ptitle}{Integrate-and-fire neuron}
 \input{../header.tex}
-\firstpagefooter{Supervisor: Jan Benda}{phone: 29 74573}%
+\firstpagefooter{Supervisor: Jan Benda}{}%
 {email: jan.benda@uni-tuebingen.de}
 
 \begin{document}
 
 \input{../instructions.tex}
 
-
-%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
-
-\begin{questions}
-  \question The temporal evolution of the membrane voltage $V(t)$ of a
-  passive neuron is described by the membrane equation
-  \begin{equation}
-    \label{passivemembrane}
-    \tau \frac{dV}{dt} = -V + E
-  \end{equation}
-  where $\tau=10$\,ms is the membrane time constant and $E(t)$ is the
-  reversal potential that also depends on time $t$.
-
-  Such a differential equation can be numerically solved with the Euler method.
-  For this the time is discretized by a time step $\Delta t=0.1$\,ms.
-  The $i$-th time point is then at time $t_i = i \cdot \Delta t$.
-  In matlab we get the time points $t_i$ simply by
-  \begin{lstlisting}
+The leaky integrate-and-fire model is a simple but powerful model of
+spiking neurons. It adds to the dynamics of a passive membrane a
+voltage threshold to simulate the generation of action potentials. In
+this project you learn how to simulate such a model and explore some
+of its stimulus encoding properties.
+
+The temporal evolution of the membrane voltage $V(t)$ of a passive
+neuron is described by the membrane equation
+\begin{equation}
+  \label{passivemembrane}
+  \tau \frac{dV}{dt} = -V + E
+\end{equation}
+where $\tau=10$\,ms is the membrane time constant and $E(t)$ is the
+reversal potential that also depends on time $t$.
+
+Such a differential equation can be numerically solved with the Euler
+method.  For this the time is discretized by a time step $\Delta
+t=0.1$\,ms.  The $i$-th time point is then at time $t_i = i \cdot
+\Delta t$.  In matlab we get the time points $t_i$ simply by
+\begin{lstlisting}
 dt = 0.1;
 tmax = 100.0;
 time = [0.0:dt:tmax]; % t_i
-  \end{lstlisting}
-  When the membrane potential at time $t_0 = 0$ is $V_0$, the so
-  called ``initial condition'', then we can iteratively compute the 
-  membrane potentials $V_i$ for successive time points $t_i$ according to
-  \begin{equation}
-    \label{euler}
-    V_{i+1} = V_i + (-V_i + E_i) \frac{\Delta t}{\tau}
-  \end{equation}
+\end{lstlisting}
+When the membrane potential at time $t_0 = 0$ is $V_0$, the so called
+``initial condition'', then we can iteratively compute the membrane
+potentials $V_i$ for successive time points $t_i$ according to
+\begin{equation}
+  \label{euler}
+  V_{i+1} = V_i + (-V_i + E_i) \frac{\Delta t}{\tau}
+\end{equation}
 
+\begin{questions}
+  \question Passive membrane
   \begin{parts}
     \part Write a function that computes the time course of the
     membrane potential of the passive membrane. The function gets as
@@ -80,19 +84,22 @@ time = [0.0:dt:tmax]; % t_i
     neuron.
 
     How does the filter function depend on the membrane time constant?
+  \end{parts}
 
-    \part Leaky integrate-and-fire neuron.
+  \question Leaky integrate-and-fire neuron
 
-    The passive neuron can be turned into a spiking neuron by
-    introducing a fixed voltage threshold. Whenever the computed
-    membrane potential of the passive neuron crosses the voltage
-    threshold a spike is generated and the membrane voltage is set to
-    the reset potential $V_R$ that we here set to zero. ``Generating a
-    spike'' only means that we note down the time of the threshold
-    crossing as a time where an action potential occurred. The
-    waveform of the action potential is not modeled. Here we use a
-    voltage threshold of 1\,mV.
+  The passive neuron can be turned into a spiking neuron by
+  introducing a fixed voltage threshold. Whenever the computed
+  membrane potential of the passive neuron crosses the voltage
+  threshold a spike is generated and the membrane voltage is set to
+  the reset potential $V_R$ that we here set to zero. ``Generating a
+  spike'' only means that we note down the time of the threshold
+  crossing as a time where an action potential occurred. The waveform
+  of the action potential is not modeled. Here we use a voltage
+  threshold of 1\,mV.
 
+  \begin{parts}
+    \part 
     Write a function that implements this leaky integrate-and-fire
     neuron by expanding the function for the passive neuron
     appropriately. The function returns a vector of spike times.
diff --git a/projects/project_noiseficurves/noiseficurves.tex b/projects/project_noiseficurves/noiseficurves.tex
index a2df3df..279a0fc 100644
--- a/projects/project_noiseficurves/noiseficurves.tex
+++ b/projects/project_noiseficurves/noiseficurves.tex
@@ -2,7 +2,7 @@
 
 \newcommand{\ptitle}{Neural tuning and noise}
 \input{../header.tex}
-\firstpagefooter{Supervisor: Jan Benda}{phone: 29 74573}%
+\firstpagefooter{Supervisor: Jan Benda}{}%
 {email: jan.benda@uni-tuebingen.de}
 
 \begin{document}
@@ -15,7 +15,7 @@ $I$ (think of that, for example, as a current $I$ injected via a
 patch-electrode into the neuron).
 
 We first characterize the neurons by their tuning curves (also called
-intensity-response curve).  That is, what is the mean firing rate of
+intensity-response curves).  That is, what is the mean firing rate of
 the neuron's response as a function of the constant input current $I$?
 
 In the second part we demonstrate how intrinsic noise can be useful
@@ -83,9 +83,9 @@ from different neurons with different noise properties by setting the
   
   \question Subthreshold stochastic resonance
   
-  Let's now use as an input to the neuron a 1\,s long sine wave $I(t)
-  = I_0 + A \sin(2\pi f t)$ with offset current $I_0$, amplitude $A$,
-  and frequency $f$. Set $I_0=5$, $A=4$, and $f=5$\,Hz.
+  Let's now use a 1\,s long sine wave $I(t) = I_0 + A \sin(2\pi f t)$
+  with offset current $I_0$, amplitude $A$, and frequency $f$. Set
+  $I_0=5$, $A=4$, and $f=5$\,Hz as an input to the neuron.
 
   \begin{parts}
     \part  Do you get a response of the noiseless ($D_{noise}=0$) neuron?
diff --git a/projects/project_populationvector/populationvector.tex b/projects/project_populationvector/populationvector.tex
index 4f98614..15c3398 100644
--- a/projects/project_populationvector/populationvector.tex
+++ b/projects/project_populationvector/populationvector.tex
@@ -2,22 +2,22 @@
 
 \newcommand{\ptitle}{Orientation tuning}
 \input{../header.tex}
-\firstpagefooter{Supervisor: Jan Benda}{phone: 29 74573}%
+\firstpagefooter{Supervisor: Jan Benda}{}%
 {email: jan.benda@uni-tuebingen.de}
 
 \begin{document}
 
 \input{../instructions.tex}
 
-%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
-\begin{questions}
+In the visual cortex V1 orientation sensitive neurons respond to bars
+in dependence on their orientation. In this project we explore the
+question:
 
-  \question In the visual cortex V1 orientation sensitive neurons
-  respond to bars in dependence on their orientation. In this project
-  we explore the question:
+How is the orientation of a bar encoded by the activity of a
+population of orientation sensitive neurons?
 
-  How is the orientation of a bar encoded by the activity of a
-  population of orientation sensitive neurons?
+\begin{questions}
+  \question Orientation tuning of the neurons
 
   In an electrophysiological experiment, 6 neurons have been recorded
   simultaneously. First, the tuning of these neurons was characterized
@@ -30,13 +30,6 @@
   times are given in seconds. The stimulation with the bar starts a
   time $t_0=0$ and ends at time $t_1=200$\,ms.
 
-  Then the population activity of the 6 neurons was measured in
-  response to arbitrarily oriented bars. The responses of the 6
-  neurons to 50 presentation of a bar are stored in the
-  \texttt{spikes} variables of the \texttt{population*.mat} files.
-  The \texttt{angle} variable holds the angle of the presented bar.
-
-\continue
   \begin{parts}
     \part Illustrate the spiking activity of the V1 cells in response
     to different orientation angles of the bars by means of spike
@@ -56,7 +49,17 @@
     modulation depth of the firing rate, and $a$ is an offset.
 
     Why is there a factor two in the argument of the cosine?
+  \end{parts}
 
+  \question Inferring stimulus orientation from neural activity  
+
+  In the second part of the experiment the population activity of the
+  6 neurons was measured in response to arbitrarily oriented bars. The
+  responses of the 6 neurons to 50 presentation of a bar are stored in
+  the \texttt{spikes} variables of the \texttt{population*.mat} files.
+  The \texttt{angle} variable holds the angle of the presented bar.
+
+  \begin{parts}
     \part How can the orientation angle of the presented bar be read
     out from one trial of the population activity of the 6 neurons?
     One possible method is the so called ``population vector'' where