From 9c8d15ca8f9516d6c4482455743eb035cf6a86bc Mon Sep 17 00:00:00 2001 From: Marius Goerner Date: Mon, 14 Jan 2019 13:06:40 +0100 Subject: [PATCH] v1 --- .../auto/face_selectivity.el | 15 +++ .../face_selectivity.tex | 127 ++++++++++++++++++ .../gaze_following.tex | 67 --------- 3 files changed, 142 insertions(+), 67 deletions(-) create mode 100644 projects/project_face_selectivity/auto/face_selectivity.el create mode 100644 projects/project_face_selectivity/face_selectivity.tex delete mode 100644 projects/project_face_selectivity/gaze_following.tex 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} diff --git a/projects/project_face_selectivity/gaze_following.tex b/projects/project_face_selectivity/gaze_following.tex deleted file mode 100644 index 24d4355..0000000 --- a/projects/project_face_selectivity/gaze_following.tex +++ /dev/null @@ -1,67 +0,0 @@ -\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}