v1
This commit is contained in:
Marius Goerner
parent
9c8d15ca8f
commit
07be3f83fb
@ -1,3 +0,0 @@
|
|||||||
ZIPFILES=
|
|
||||||
|
|
||||||
include ../project.mk
|
|
||||||
@ -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)
|
|
||||||
|
|
||||||
@ -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. 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}
|
|
||||||
Reference in New Issue
Block a user