This repository has been archived on 2021-05-17. You can view files and clone it, but cannot push or open issues or pull requests.
scientificComputing/projects/project_populationvector/populationvector.tex

93 lines
3.6 KiB
TeX

\documentclass[a4paper,12pt,pdftex]{exam}
\newcommand{\ptitle}{Orientation tuning}
\input{../header.tex}
\firstpagefooter{Supervisor: Jan Benda}{phone: 29 74573}%
{email: jan.benda@uni-tuebingen.de}
\begin{document}
\input{../instructions.tex}
%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
\begin{questions}
\question In the visual cortex V1 orientation sensitive neurons
respond to bars in dependence on their orientation.
How is the orientation of a bar encoded by the activity of a
population of orientation sensitive neurons?
In an electrophysiological experiment, 6 neurons have been recorded
simultaneously. First, the tuning of these neurons was characterized
by presenting them bars in a range of 12 orientation angles. Each
orientation was presented 50 times. Each of the \texttt{unit*.mat}
files contains the responses of one of the neurons. In there,
\texttt{angles} is a vector with the orientation angles of the bars
in degrees. \texttt{spikes} is a cell array that contains the
vectors of spike times for each angle and presentation. The spike
times are given in seconds. The stimulation with the bar starts a
time $t_0=0$ and ends at time $t_1=200$\,ms.
Then the population activity of the 6 neurons was measured in
response to arbitrarily oriented bars. The responses of the 6
neurons to 50 presentation of a bar are stored in the
\texttt{spikes} variables of the \texttt{population*.mat} files.
The \texttt{angle} variable holds the angle of the presented bar.
\continue
\begin{parts}
\part Illustrate the spiking activity of the V1 cells in response
to different orientation angles of the bars by means of spike
raster plots (of a single unit).
\part Plot the firing rate of each of the 6 neurons as a function
of the orientation angle of the bar. As the firing rate compute
the number of spikes in the time interval $0<t<200$\,ms divided by
200\,ms. The resulting curves are the tuning curves $r(\varphi)$
of the neurons.
\part Fit the function \[ r(\varphi) = g \cdot
(1+\cos(2(\varphi-\varphi_0)))/2 + a \] to the measured tuning
curves in order to estimated the orientation angle at which the
neurons respond strongest. In this function $\varphi_0$ is the
position of the peak, $g$ is a gain factor that sets the
modulation depth of the firing rate, and $a$ is an offset.
\part How can the orientation angle of the presented bar be read
out from one trial of the population activity of the 6 neurons?
One possible method is the so called ``population vector'' where
unit vectors pointing into the direction of the maximum response
of each neuron are weighted by their firing rate. The stimulus
orientation is then the direction of the averaged vectors.
%Think of another (simpler) method how the orientation of the bar
%may be approximately read out from the population.
An alternative read out is maximum likelihood (see script).
Load one of the \texttt{population*.mat} files, illustrate the
data, and estimate the orientation angle of the bar from single
trial data by the two different methods.
\part Compare, illustrate and discuss the performance of the two
decoding methods by using all of the recorded responses (all
\texttt{population*.mat} files). How exactly is the orientation of
the bar encoded? How robust is the estimate of the orientation
from trial to trial?
\end{parts}
\end{questions}
\end{document}
gains and angles of the 6 neurons:
gain=10.7 phase=5
gain=18.0 phase=38
gain=11.3 phase=71
gain=14.1 phase=108
gain=19.0 phase=138
gain=16.4 phase=174