diff --git a/projects/project_face_selectivity/Makefile b/projects/project_face_selectivity/Makefile
new file mode 100644
index 0000000..a7b3726
--- /dev/null
+++ b/projects/project_face_selectivity/Makefile
@@ -0,0 +1,3 @@
+ZIPFILES=
+
+include ../project.mk
diff --git a/projects/project_face_selectivity/auto/face_selectivity.el b/projects/project_face_selectivity/auto/face_selectivity.el
new file mode 100644
index 0000000..0e5be37
--- /dev/null
+++ b/projects/project_face_selectivity/auto/face_selectivity.el
@@ -0,0 +1,15 @@
+(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
new file mode 100644
index 0000000..b6ff677
--- /dev/null
+++ b/projects/project_face_selectivity/face_selectivity.tex
@@ -0,0 +1,127 @@
+\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. 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}