From ba291f9e022664a4f09bdfec26f37e437f172ace Mon Sep 17 00:00:00 2001
From: Marius Goerner <mariusgoerner@gmail.com>
Date: Mon, 14 Jan 2019 13:15:21 +0100
Subject: [PATCH] v1
---
projects/project_face_selectivity/Makefile | 3 +
.../auto/face_selectivity.el | 15 +++
.../face_selectivity.tex | 127 ++++++++++++++++++
3 files changed, 145 insertions(+)
create mode 100644 projects/project_face_selectivity/Makefile
create mode 100644 projects/project_face_selectivity/auto/face_selectivity.el
create mode 100644 projects/project_face_selectivity/face_selectivity.tex
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}