diff --git a/projects/project_face_selectivity/Makefile b/projects/project_face_selectivity/Makefile
deleted file mode 100644
index a7b3726..0000000
--- a/projects/project_face_selectivity/Makefile
+++ /dev/null
@@ -1,3 +0,0 @@
-ZIPFILES=
-
-include ../project.mk
diff --git a/projects/project_face_selectivity/auto/face_selectivity.el b/projects/project_face_selectivity/auto/face_selectivity.el
deleted file mode 100644
index 0e5be37..0000000
--- a/projects/project_face_selectivity/auto/face_selectivity.el
+++ /dev/null
@@ -1,15 +0,0 @@
-(TeX-add-style-hook
- "face_selectivity"
- (lambda ()
-   (TeX-add-to-alist 'LaTeX-provided-class-options
-                     '(("exam" "a4paper" "12pt" "pdftex")))
-   (TeX-run-style-hooks
-    "latex2e"
-    "../header"
-    "../instructions"
-    "exam"
-    "exam12")
-   (TeX-add-symbols
-    "ptitle"))
- :latex)
-
diff --git a/projects/project_face_selectivity/face_selectivity.tex b/projects/project_face_selectivity/face_selectivity.tex
deleted file mode 100644
index 029eae7..0000000
--- a/projects/project_face_selectivity/face_selectivity.tex
+++ /dev/null
@@ -1,127 +0,0 @@
-\documentclass[a4paper,12pt,pdftex]{exam}
-
-\newcommand{\ptitle}{Face-selectivity index}
-\input{../header.tex}
-\firstpagefooter{Supervisor: Marius G\"orner}{}%
-{email: marius.goerner@uni-tuebingen.de}
-
-\begin{document}
-
-\input{../instructions.tex}
-
-
-%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
-\section{Estimating the face-selectivity index (FSI) of neurons}
-
-
-In the temporal lobe of primates you can find neurons that respond
-selectively to a certain type of object category. You may have heard
-stories about the famous grandmother neurons which are supposed to
-respond exclusively when the subject perceives a particular
-person. Even though the existence of a grandmother neuron in the
-strict sense is implausible, the concept exemplifies the observation
-that sensory neurons within the ventral visual stream are tuned to
-certain stimuli types. One of the most important and first visual
-stimulus the newborn typically perceives is the mother's face. It is
-believed that the early ubiquity of faces and their importance for
-social interactions triggers the development of the so called
-face-patch system within the temporal lobe of primates.\par
-Your task here will be to estimate the \textit{selectivity index}
-($SI$) of neurons that were recorded in the superior temporal sulcus
-of a rhesus monkey during the visual presentation of objects of different
-categories.
-
-
-\begin{questions}
-\question
-  In the accompanying datasets you find the
-  \texttt{spiketimes} of 184 neurons that were recorded during the visual
-  presentation of non-face like stimuli (tools, fruits, hands and
-  bodies) and averted and directed faces of humans and rhesus
-  monkeys. Each \texttt{.mat}-file contains the data of one neuron
-  which was recorded during multiple trials. Spike times are given in
-  ms relative to trial onset. The field \texttt{spiketimes} contains 
-  cells that contain the the data of the trails. Each trial consists of 
-  400 ms of baseline recording (presentation of white noise) followed by 400 ms
-  of stimulus presentation. Each trial belongs to one object category,
-  trial identities can be found in the \texttt{*\_trials}-fields
-  (9 fields).
-
-  \begin{parts}
-  \part
-    Illustrate the spiking activity of all neurons, sorted by object
-    category, in a raster plot. As a result you should get one plot
-    for each neuron subdevided in subplots for the different
-    categories. Mind that there are four categories that contain faces
-    (\texttt{averted\_human}, \texttt{face} (straight human face),
-    \texttt{monkey} (straight monkey face) and \texttt{gaze\_monkey}),
-    you may want to analyze them separately as well combined. Add also
-    a marker where the stimulus starts.
-
-  \part
-    Estimate the time-resolved firing rate of each neuron for each
-    object category. Use at least two different methods
-    (e.g. instantaneous firing rate based on interspike intervals,
-    spike counting within bins (PSTH), kernel density estimation). Do
-    this individually for each trial and average afterwards in order
-    to obtain the standard deviation of the firing rates. Plot the
-    firing rates and their standard deviations on top of the raster
-    plots. Which of the methods appears to be a better representation
-    of the spike rasters?
-
-  \part
-    Generate figures that show for each neuron the firing rates
-    belonging to each object category. Don't forget to add an
-    appropriate legend.
-
-  \part
-    Next step is to examine the obtained firing rates for significant
-    modulations.
-    % First, normalize each response to baseline activity
-    % (first 400 ms). Why is the normalization useful?
-    % \par
-    Now, determine the periods within which the neurons activity
-    deviates from the baseline activity at least by $2*\sigma$. Do
-    this for each object category and mark the periods in the plots in
-    an appropriate way. Are there also inhibitory responses? \par
-    Describe qualitatively the response properties (phasic, tonic, are
-    there differences between neurons and/or stimulus categories?).
-
-  \part
-    The $SI$ gives an estimate of how strong a neuron is tuned to the
-    chosen object categories. It is given by the neurons response
-    during the presentation of the one category compared to the other
-    category.
-    \begin{equation}
-      SI = \frac{ \mu_{\text{Response to category A}} - \mu_{ \text{Response
-            to category B}} } { \mu_{\text{Response to category A}} + \mu_{ \text{Response
-            to category B} } }
-    \end{equation}
-    $SI$ can take values between -1 and 1 which indicates tuning to
-    the one or to the other category. There are different
-    possibilities of how it can be estimated. The easiest way would be
-    to average the spike count during the whole time of stimulus
-    presentation. However, if responses are phasic you will
-    underestimate the $SI$. Therefor, you should limit the estimate to
-    periods of significant modulations. Use the periods determined in
-    (d). Store all obtained $SI$s within one variable. We are mainly
-    interested to identify face-selective neurons but feel free to test
-    the neurons for selectivity to other categories, as well.
-    
-  \part
-    Plot the distribution of $SI$ values and describe it
-    qualitatively. Does it indicate a continuum or a distinct
-    population of face-selective neurons. \par
-    Think about a statistical test that tells you whether a given
-    neuron is significantly modulated by one or the other category
-    (try different combinations of categories). List cells that show
-    significant modulation to faces and non-faces. Which is the
-    minimum SI that reaches significance when choosing
-    $\alpha = 0.05$? Is it an all or nothing selectivity?
-    
-  \end{parts}
-\end{questions}
-
-
-
-\end{document}