diff --git a/projects/project_fano_slope/fano_slope.tex b/projects/project_fano_slope/fano_slope.tex
index 7d2fe2d..ebb30c0 100644
--- a/projects/project_fano_slope/fano_slope.tex
+++ b/projects/project_fano_slope/fano_slope.tex
@@ -103,13 +103,12 @@ spikes = lifboltzmanspikes( trials, input, tmax, Dnoise, imax, ithresh, slope );
 
     For which slopes can the two stimuli be well discriminated?
 
-    \underline{Hint:} A possible readout is to set a threshold $n_{thresh}$ for
-    the observed spike count.  Any response smaller than the threshold
-    assumes that the stimulus was $I_1$, any response larger than the
-    threshold assumes that the stimulus was $I_2$. What is the
-    probability that the stimulus was indeed $I_1$ or $I_2$,
-    respectively? Find the threshold $n_{thresh}$ that
-    results in the best discrimination performance.
+    \underline{Hint:} A possible readout is to set a threshold
+    $n_{thresh}$ for the observed spike count.  Any response smaller
+    than the threshold assumes that the stimulus was $I_1$, any
+    response larger than the threshold assumes that the stimulus was
+    $I_2$. Find the threshold $n_{thresh}$ that results in the best
+    discrimination performance.
 
     \part Also plot the Fano factor as a function of the slope. How is it related to the discriminability?
 
diff --git a/projects/project_fano_time/fano_time.tex b/projects/project_fano_time/fano_time.tex
index 2c893b1..761561d 100644
--- a/projects/project_fano_time/fano_time.tex
+++ b/projects/project_fano_time/fano_time.tex
@@ -97,12 +97,11 @@ input = 75.0; % I_2
 
     For which observation times can the two stimuli perfectly discriminated?
 
-    \underline{Hint:} A possible readout is to set a threshold $n_{thresh}$ for
-    the observed spike count.  Any response smaller than the threshold
-    assumes that the stimulus was $I_1$, any response larger than the
-    threshold assumes that the stimulus was $I_2$. What is the
-    probability that the stimulus was indeed $I_1$ or $I_2$,
-    respectively? For a given $W$ find the threshold $n_{thresh}$ that
+    \underline{Hint:} A possible readout is to set a threshold
+    $n_{thresh}$ for the observed spike count.  Any response smaller
+    than the threshold assumes that the stimulus was $I_1$, any
+    response larger than the threshold assumes that the stimulus was
+    $I_2$. For a given $W$ find the threshold $n_{thresh}$ that
     results in the best discrimination performance.
 
     \part Also plot the Fano factor as a function of $W$. How is it related to the discriminability?
diff --git a/projects/project_isipdffit/isipdffit.tex b/projects/project_isipdffit/isipdffit.tex
index fc48361..7ffc3a1 100644
--- a/projects/project_isipdffit/isipdffit.tex
+++ b/projects/project_isipdffit/isipdffit.tex
@@ -119,7 +119,7 @@ spikes = pifouspikes( trials, input, tmax, Dnoise, outau );
     interspike intervals. How well do they describe the real
     distributions for the different conditions?
 
-    \part Also fit eq.~(\ref{pcn}) to the data. Here you need to apply a non-linear fit algorithm.
+    \part Also fit eq.~(\ref{pcn}) to the data using maximum (log)-likelihood. 
 
     How well does this function describe the data?