[projects] little updates

projects/README

@@ -11,14 +11,12 @@ no statistics, but kmeans
 
 project_fano_slope
 OK, difficult
-Add t-test
 
 project_fano_test
 OK -
 
 project_fano_time
-OK, difficult
+OK, medium-difficult
-Add t-test
 
 project_ficurves
 OK, medium

projects/project.mk

@@ -1,14 +1,10 @@
 BASENAME=$(subst project_,,$(notdir $(CURDIR)))
 
-latex:
-	pdflatex $(BASENAME).tex
-	pdflatex $(BASENAME).tex
-
 
 pdf: $(BASENAME).pdf
 
 $(BASENAME).pdf : $(BASENAME).tex ../header.tex ../instructions.tex
-	pdflatex -interaction=scrollmode $< | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex -interaction=scrollmode $< || true
+	pdflatex -interaction=scrollmode $< | tee /dev/stderr | fgrep -q "Rerun to get" && pdflatex -interaction=scrollmode $< || true
 
 
 watch :
@@ -19,7 +15,11 @@ clean:
 	rm -rf *.log *.aux *.out auto
 	rm -f `basename *.tex .tex`.pdf
 	rm -f *.zip
+	pdflatex $(BASENAME).tex
+
+latex:
+	pdflatex $(BASENAME).tex
 
-zip: latex
+zip: pdf
 	rm -f zip $(BASENAME).zip
 	zip $(BASENAME).zip *.pdf *.m data/* $(ZIPFILES)

projects/project_fano_slope/fano_slope.tex

@@ -1,6 +1,6 @@
 \documentclass[a4paper,12pt,pdftex]{exam}
 
-\newcommand{\ptitle}{Stimulus discrimination}
+\newcommand{\ptitle}{Stimulus discrimination: gain}
 \input{../header.tex}
 \firstpagefooter{Supervisor: Jan Benda}{phone: 29 74573}%
 {email: jan.benda@uni-tuebingen.de}
@@ -95,10 +95,11 @@ spikes = lifboltzmanspikes(trials, input, tmax, gain);
     the neuron? Plot them for the four different values of the gain
     used in (a).
 
-    \part Think about a measure based on the spike-count histograms
+    \part \label{discrmeasure} Think about a measure based on the
-    that quantifies how well the two stimuli can be distinguished
+    spike-count histograms that quantifies how well the two stimuli
-    based on the spike counts. Plot the dependence of this measure as
+    can be distinguished based on the spike counts. Plot the
-    a function of the gain of the neuron.
+    dependence of this measure as a function of the gain of the
+    neuron.
 %
     For which gains can the two stimuli perfectly discriminated?
 
@@ -110,6 +111,11 @@ spikes = lifboltzmanspikes(trials, input, tmax, gain);
     results in the best discrimination performance. How can you
     quantify ``best discrimination'' performance?
 
+    \part Another way to quantify the discriminability of the spike
+    counts in response to the two stimuli is to apply an appropriate
+    statistical test and check for significant differences. How does
+    this compare to your findings from (\ref{discrmeasure})?
+
  \end{parts}
 
 \end{questions}

projects/project_fano_time/fano_time.tex

@@ -1,6 +1,6 @@
 \documentclass[a4paper,12pt,pdftex]{exam}
 
-\newcommand{\ptitle}{Stimulus discrimination}
+\newcommand{\ptitle}{Stimulus discrimination: time}
 \input{../header.tex}
 \firstpagefooter{Supervisor: Jan Benda}{phone: 29 74573}%
 {email: jan.benda@uni-tuebingen.de}
@@ -87,10 +87,11 @@ input = 15.0; % I_2
     observation time $T$? Plot them for four different values of $T$
     (use values of 10\,ms, 100\,ms, 300\,ms and 1\,s).
 
-    \part Think about a measure based on the spike-count histograms
+    \part \label{discrmeasure} Think about a measure based on the
-    that quantifies how well the two stimuli can be distinguished
+    spike-count histograms that quantifies how well the two stimuli
-    based on the spike counts. Plot the dependence of this measure as
+    can be distinguished based on the spike counts. Plot the
-    a function of the observation time $T$.
+    dependence of this measure as a function of the observation time
+    $T$.
 
     For which observation times can the two stimuli perfectly
     discriminated?
@@ -103,6 +104,11 @@ input = 15.0; % I_2
     results in the best discrimination performance. How can you
     quantify ``best discrimination'' performance?
 
+    \part Another way to quantify the discriminability of the spike
+    counts in response to the two stimuli is to apply an appropriate
+    statistical test and check for significant differences. How does
+    this compare to your findings from (\ref{discrmeasure})?
+
  \end{parts}
 
 \end{questions}

projects/project_lif/lif.tex

@@ -96,7 +96,7 @@ time = [0.0:dt:tmax]; % t_i
 
     Write a function that implements this leaky integrate-and-fire
     neuron by expanding the function for the passive neuron
-    appropriate. The function returns a vector of spike times.
+    appropriately. The function returns a vector of spike times.
 
     Illustrate how this model works by appropriate plot(s) and
     input(s) $E(t)$, e.g. constant inputs lower and higher than the
@@ -115,8 +115,8 @@ time = [0.0:dt:tmax]; % t_i
       r = \frac{n-1}{t_n - t_1}
     \end{equation}
     What do you observe? Does the firing rate encode the frequency of
-    the stimulus? Look at the spike trains in response the sine waves
+    the stimulus? Look at the spike trains in response to the sine
-    to figure out what is going on.
+    waves to figure out what is going on.
   \end{parts}
 
 \end{questions}

projects/project_noiseficurves/noiseficurves.tex

@@ -63,8 +63,8 @@ spikes = lifspikes(trials, current, tmax, Dnoise);
     of this neuron?
 
     \part Compute the $f$-$I$ curves of neurons with various noise
-    strengths \texttt{Dnoise}. Use for example $D_{noise} = 1e-3$,
+    strengths \texttt{Dnoise}. Use for example $D_{noise} = 10^{-3}$,
-    $1e-2$, and $1e-1$.
+    $10^{-2}$, and $10^{-1}$.
 
     How does the intrinsic noise influence the response curve?
 
@@ -76,6 +76,8 @@ spikes = lifspikes(trials, current, tmax, Dnoise);
     responses of the four different neurons to the same input, or by
     the same resulting mean firing rate.
 
+    How do the responses differ?
+
     \part Let's now use as an input to the neuron a 1\,s long sine
     wave $I(t) = I_0 + A \sin(2\pi f t)$ with offset current $I_0$,
     amplitude $A$, and frequency $f$. Set $I_0=5$, $A=4$, and

projects/project_pca_natural_images/pca_natural_images.tex

@@ -32,9 +32,8 @@ In you zip file you find a natural image called {\tt natimg.jpg}.
 \begin{thebibliography}{1}
 \bibitem{BG} Buchsbaum, G., \& Gottschalk, A. (1983). Trichromacy,
   opponent colours coding and optimum colour information transmission
-  in the retina. Proceedings of the Royal Society of London. Series B,
+  in the retina. Proceedings of the Royal Society of London B. Royal
-  Containing Papers of a Biological Character. Royal Society (Great
+  Society (Great Britain), 220(1218), 89–113.
-  Britain), 220(1218), 89–113.
 \end{thebibliography}