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26 Artificial Neural Networks

differences to other approaches are discussed in the next sections.

3.2 Network Characteristics

Meanwhile, a large variety of neural network types have emerged. In
the following we present a (certainly incomplete) taxonomic ordering and
point out several distinguishable axes:

Supervised versus Unsupervised and Reinforcement Learning: In super-
vised learning paradigm, the training input signal is given with a
pairing output signal from a supervisor or teacher knowing the cor-
rect answer. Unsupervised networks (e.g. competitive learning, vec-
tor quantization, SOM, see below) draw information from redundan-
cies in the input data distribution.
An intermediate form is the reinforcement learning. Here the sys-
tem receives a “reward” or “quality” signal, indicating whether the
network output was more or less successful. A major problem is
the meaningful credit assignment to the responsible network parts.
The structural problem is extended by the temporal credit assignment
problem if the quality signal is delayed and a sequence of decisions
contributed to the overall result.

Feed-forward versus Recurrent Networks: In feed-forward networks the
information flow is unidirectional from the input to the output layer.
In contrast, recurrent networks also connect neuron outputs back as
additional feedback inputs. This enables a network intern dynamic,
controlled by the given input and the learned network characteris-
tics.

A typical application is the associative memory, which can iteratively
recall incomplete or noisy images. Here the recurrent network dy-
namics is built such, that it leads to a settling of the network. These
relaxation endpoints are fix-points of the network dynamic. Hop-
field (1984) formulated this as an energy minimization process and
introduced the statistical methods known e.g. in the theory of mag-
netism. The goal of learning is to place the set of point attractors at
the desired location. As shown later, the PSOM approach will uti-