diff --git a/som.cabal b/som.cabal
--- a/som.cabal
+++ b/som.cabal
@@ -1,5 +1,5 @@
 Name:              som
-Version:           7.4.0
+Version:           7.4.1
 Stability:         experimental
 Synopsis:          Self-Organising Maps.
 Description:       A Kohonen Self-organising Map (SOM) maps input patterns 
@@ -33,7 +33,7 @@
 source-repository this
   type:     git
   location: https://github.com/mhwombat/som.git
-  tag:      7.4.0
+  tag:      7.4.1
 
 
 library
diff --git a/src/Data/Datamining/Clustering/SSOMInternal.hs b/src/Data/Datamining/Clustering/SSOMInternal.hs
--- a/src/Data/Datamining/Clustering/SSOMInternal.hs
+++ b/src/Data/Datamining/Clustering/SSOMInternal.hs
@@ -38,24 +38,24 @@
   rate :: f -> LearningRate f -> LearningRate f
 
 -- | A typical learning function for classifiers.
---   @'Exponential' r0 rf tf@ returns a gaussian function. At time zero,
---   the learning rate is @r0@. Over time the learning rate tapers off,
---   until at time @tf@, the learning rate is @rf@. Normally the
---   parameters should be chosen such that:
+--   @'Exponential' r0 d@ returns a function to calculate the
+--   learning rate. At time zero, the learning rate is @r0@. Over time
+--   the learning rate decays exponentially. Normally the parameters
+--   should be chosen such that:
 --
---   * 0 < rf << r0 < 1
+--   * 0 < r0 < 1
 --
---   * 0 < tf
+--   * 0 < d
 --
 --   where << means "is much smaller than" (not the Haskell @<<@
 --   operator!)
-data Exponential a = Exponential a a a
+data Exponential a = Exponential a a
   deriving (Eq, Show, Generic)
 
 instance (Floating a, Fractional a, Num a)
     => LearningFunction (Exponential a) where
   type LearningRate (Exponential a) = a
-  rate (Exponential r0 rf tf) t = r0 * ((rf/r0)**(t/tf))
+  rate (Exponential r0 d) t = r0 * exp (-d*t)
 
 -- | A Simplified Self-Organising Map (SSOM).
 data SSOM f t k p = SSOM