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final fixes

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Jan Benda 2017-01-24 09:40:00 +01:00
parent 3af1c4110a
commit d10d67b039
2 changed files with 32 additions and 22 deletions
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30
projects/disclaimer.tex
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@ -8,21 +8,21 @@
\vspace{1ex}
The {\bf code} and the {\bf presentation} should be uploaded to
ILIAS at latest on Wednesday, February 8th, 23:59h. We will
ILIAS at latest on Thursday, February 9th, 12:59h. We will
store all presentations on one computer to allow fast
transitions between talks. The presentations start on
Thursday 9:00h. Please hand in your presentation as a pdf file. Bundle
everything (the pdf, the code, and the data) into a {\em
single} zip-file.
transitions between talks. The presentations start on Thursday,
February 9th at 1:00h c.t.. Please hand in your presentation as
a pdf file. Bundle everything (the pdf, the code, and the data)
into a {\em single} zip-file.
\vspace{1ex}
The {\bf code} should be exectuable without any further
adjustments from our side. A single {\em main} script should
The {\bf code} should be executable without any further
adjustments from our side. A single \texttt{main.m} script should
coordinate the analysis by calling functions and sub-scripts and
should produce the {\em same} figures that you use in your
slides. The code should be properly commented and comprehensible
by a third persons (use proper and consistent variable and
by a third person (use proper and consistent variable and
function names).
\vspace{1ex}
@ -34,11 +34,11 @@
The {\bf presentation} should be {\em at most} 10min long and be
held in English. In the presentation you should (i) briefly
describe the problem, (ii) explain how you solved it
algorithmically (don't show your entire code), and (iii) present
figures showing your results. All data-related figures you show
in the presentation should be produced by your program. It is
always a good idea to illustrate the problem with basic plots of
the raw-data.
describe the problem, (ii) present figures introducing, showing,
and discussing your results, and (iii) explain how you solved
the problem algorithmically (don't show your entire code). All
data-related figures you show in the presentation should be
produced by your program. It is always a good idea to illustrate
the problem with basic plots of the raw-data. Make sure the axis
labels are large enough!
}}

24
projects/project_populationvector/populationvector.tex
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@ -36,10 +36,10 @@
respond to bars in dependence on their orientation.
How is the orientation of a bar encoded by the activity of a
population of orientation sensisitive neurons?
population of orientation sensitive neurons?
In an electrophysiological experiment, 6 neurons have been recorded
simultaneously. First, the tuning of these neurons was characteried
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,
@ -76,14 +76,24 @@
gain factor that sets the maximum firing rate.
\part How can the orientation angle of the presented bar be read
out from the population activity of the 6 neurons? One is the so
called ``population vector''. Think of another (simpler) method.
out from one trial of the population activity of the 6 neurons?
One 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.
Load one of the \texttt{population*.mat} files, illustrate the data,
and estimate the orientation angle of the bar by two different methods.
Think of another (simpler) method how the orientation of the bar
may be approximately read out from the population.
Load one of the \texttt{population*.mat} files, illustrate the
data, and estimate the orientation angle of the bar by the two
different methods.
\part Compare, illustrate and discuss the performance of your two
decoding methods.
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}