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projects/project_face_selectivity/auto/face_selectivity.el Normal file
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(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)

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projects/project_face_selectivity/face_selectivity.tex Normal file
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\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}

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projects/project_face_selectivity/gaze_following.tex
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\documentclass[a4paper,12pt,pdftex]{exam}
\newcommand{\ptitle}{Adaptation time-constant}
\input{../header.tex}
\firstpagefooter{Supervisor: Jan Grewe}{phone: 29 74588}%
{email: jan.grewe@uni-tuebingen.de}
\begin{document}
\input{../instructions.tex}
%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
\section{Estimating the adaptation time-constant}
Stimulating a neuron with a constant stimulus for an extended period of time
often leads to a strong initial response that relaxes over time. This
process is called adaptation. Your task here is to
estimate the time-constant of the firing-rate adaptation in P-unit
electroreceptors of the weakly electric fish \textit{Apteronotus
leptorhynchus}.
\begin{questions}
\question In the accompanying datasets you find the
\textit{spike\_times} of an P-unit electroreceptor to a stimulus of
a certain intensity, i.e. the \textit{contrast} which is also stored
in the file. The contrast of the stimulus is a measure relative to
the amplitude of fish's field, it has no unit. The data is sampled
with 20\,kHz sampling frequency and spike times are given in
milliseconds (not seconds!) relative to the stimulus onset.
\begin{parts}
\part Estimate for each stimulus intensity the PSTH. You will see
that there are three parts: (i) The first 200\,ms is the baseline
(no stimulus) activity. (ii) During the next 1000\,ms the stimulus
was switched on. (iii) After stimulus offset the neuronal activity
was recorded for further 825\,ms. Find an appropriate bin-width
for the PSTH.
\part Estimate the adaptation time-constant for both the stimulus
on- and offset. To do this fit an exponential function
$f_{A,\tau,y_0}(t)$ to appropriate regions of the data:
\begin{equation}
f_{A,\tau,y_0}(t) = A \cdot e^{-\frac{t}{\tau}} + y_0,
\end{equation}
where $t$ is time, $A$ the (positive or negative) amplitude of the
exponential decay, $\tau$ the adaptation time-constant, and $y_0$
an offset.
Before you do the fitting, familiarize yourself with the three
parameter of the exponential function. What is the value of
$f_{A,\tau,y_0}(t)$ at $t=0$? What is the value for large times? How does
$f_{A,\tau,y_0}(t)$ change if you change either of the parameter?
Which of the parameter could you directly estimate from the data
(without fitting)?
How could you get good estimates for the other parameter?
Do the fit and show the resulting exponential function together
with the data.
\part Do the estimated time-constants depend on stimulus intensity?
Use an appropriate statistical test to support your observation.
\end{parts}
\end{questions}
\end{document}