10.11. Control questions and self-study tasks
(Translation by Rafał Opiał, rafal.opial@op.pl)
1. What is the difference between self learning and self organization?
2. Kohonen Networks very often are called: tools that let „take a look into multidimensional spaces
of data”. Can you give reasons for such designation?
3. One of possible applications of Kohonen Networks is using them as a „novelty detector”. A
network in such application should signal a fact that here occurred a set of input signals that had
never occurred before (neither in an identical form nor any similar). Automatic signaling of such
situation may have essential meaning e.g. for detecting a theft of a credit card or a cell phone –
thief usually use it in a different way than an owner. What do you think – in what way a Kohonen
Network signalizes that it encountered a signal having character of „novelty”?
4. Study a course of a self organization process after a change of sub area of an input signal space
from which come random input signals and which is filled with „grid” created by a network.
Program lets you choose one of three shapes of a sub area: square, triangle or a cross. Analyze
consequences of this choice. If you are advanced in programming, you may work a little on a
program and add some another shapes from yourself.
5. Study what impact has a coefficient of learning of a neuron, named Alpha0 in the program.
Changes of this coefficient cause acceleration of learning (when it is increased) or „calming” of a
process when it is decreased. The coefficient is step by step automatically decreasing (see below),
which effects in that a learning process – fast and dynamic at the beginning, becomes more stable
as time elapses. You can also change this, and then analyze observed results.
6. Study what impact on a self organization process have changes of a coefficient of learning of
neurons being neighbors of a winner neuron. The coefficient is called Alpha1 in the program. The
higher the value of this coefficient the more visible the effect of „pulling” neighbors along the
winner neuron. Analyze and describe results of changes of the coefficient value (as well as ratio
Alpha0/Alpha1) and their impact on the network behavior.
7. Study what impact have different values of neighborhood range on a network's behavior and the
self organization process. This number states how many neurons count into a neighborhood, i.e.
how many neurons undergo forced teaching when a „winner neuron” is self learning. This number
should depend on a network size, and exactly this is the way it is set by default in this program.
However I suggest, as an exercise, careful examination of its impact on a network behavior. It is
worth note that bigger numbers of neighborhood range visibly slow down the learning process.
8. Study what impact on a self organization process have changing of the EpsAlpha coefficient which
controls the decreasing of coefficients Alpha0 and Alpha1 in every iteration. Note that the smaller
the coefficient is the faster will be decrease of learning coefficients and the faster the learning
process will stabilize. You can – if you want – set a value 1 to this coefficient, then the learning
coefficients will not decrease, or you can even set a value slightly bigger than 1 which will result in
more „brutal” learning in every next step (see what will be happening!).
9. Study what impact on a network behavior and a learning process will have changing the range of a
neighborhood. You can change coefficient EpsNeighb which controls the process of narrowing (in
consecutive iterations) a range of neighborhood. Try comparing effects of its changing to effects
you've observed regarding the EpsAlpha coefficient.
10. Study what effects cause attempts to "over teach" a network, which at the beginning learns to fill
e.g. cross shaped subarea of an input signal space, then changed to rectangular or a triangular.
Remember that in experiments regarding "over teaching", after a sub area change you need to
increase values of Alpha0 and Alpha1 coefficients and a range of a neighborhood.
11. Advanced exercise: Modify the program so that it will be capable of modeling tasks where
Kohonen Network deals with a phenomenon of highly irregular probability of occurrence of points
coming from different regions of an input signal space. Conduct a self learning and notice that, in
a taught network, much more neurons will specialize in recognizing signals coming from regions
more often represented in a teaching set. Compare the effect with a structure of a map of a
cerebral cortex presented in Fig. 10.40. What conclusions can you draw from this exercise?
Advanced exorcise: Write a program simulating behavior of a robot depicted in chapter 10.9.
Conduct attempts to simulate gaining skills of associating the sensory stimulates describing a state of
environment with behaviors favorable for a robot (causing beneficial changes in its environment).
Study what kind of representation of a knowledge about a surroundings (simulated environment of a
robot) will be achieved and used by a self organizing neural network being its „brain”. By changing
the environmental conditions (which you stimulate, so you can impose any possible laws in it), study,
which of the conditions robot can „discover” in its self organizing network and which turn out to be
too difficult?