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face selectivity update

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maalaria 2019-01-14 21:27:52 +01:00
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projects/project_face_selectivity/face_selectivity.tex
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@ -11,7 +11,7 @@
%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
\section{Estimating the face-selectivity index (FSI) of neurons}
\section{Estimating the selectivity index (SI) of neurons}
In the temporal lobe of primates you can find neurons that respond
@ -26,7 +26,7 @@ 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{face selectivity index} ($FSI$)
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
@ -50,14 +50,14 @@ Institute).
\begin{parts}
\part
Illustrate the spiking activity of all neurons, sorted by object
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 combined. Add also
a marker where the stimulus starts.
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
@ -65,13 +65,14 @@ Institute).
(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 best to represent
the spiking activity seen in the raster plots?
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 figures that show for each neuron the firing rates
Generate a figure that shows for 20 neurons the firing rates
belonging to each object category. Don't forget to add an
appropriate legend.
@ -80,46 +81,46 @@ Institute).
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 neurons that do not
repond to the visual stimulation or exhibit inhibitory responses?
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 $FSI$ gives an estimate of how strongly a neuron is tuned to
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 the one category compared to the other
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}
$FSI$ can take values between -1 and 1 which indicates tuning to
$SI$ 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 $FSI$. Therefore, you should limit the estimate
underestimate the $SI$. Therefore, you should limit the estimate
to periods of significant modulations. Use the periods determined
in (d). Store all obtained $FSI$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.
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 $FSI$ values and describe it
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 $FSI$ that reaches significance when choosing $\alpha =
0.05$? Is it an all or nothing selectivity?
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