\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 selectivity index (SI) 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
about the famous grandmother neuron which is supposed to respond
exclusively when the subject sees a particular person, i.e. the
grandmother. 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 stimulus types. One of the most important and first visual
stimulus the newborn typically sees 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 (data courtesy of the Sensorymotor-Lab, Hertie
Institute).


\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 10 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 as 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. For the 10
    neurons that you plotted above plot the firing rates and their
    standard deviations on top of the raster plots. Which of the
    methods appears best to represent the spiking activity seen in the
    raster plots?

  \part
    Generate a figure that shows for 20 neurons 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?
    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 neurons that do not respond to the
    visual stimulation or exhibit inhibitory responses?
    \par
    

  \part
    The $SI$ gives an estimate of how strongly a neuron is tuned to
    the chosen object categories. It is given by the neuron's response
    during the presentation of one category compared to another
    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 $SI$. Therefore, you should limit the estimate
    to periods of significant modulations. Use the periods determined
    in (d). Why may using the value of the peak activity be inappropriate?
    Store all obtained $SI$s within a single variable. We are mainly
    interested in identifying 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 modulations 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?
    
  \part 
    Take a look at the time resolved firing rates of the identified
    face-selective neurons and examine their response properties. What
    are their response-latencies (choose an appropriate
    visualisation), are their responses phasic or tonic.

  \end{parts}
\end{questions}



\end{document}
