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Unit 4: Data Mining Classification

way to implement a solution” motivated by the simplicity of their design and because of their notes
universality, only shadowed by the traditional design obtained by studying the physics of the
problem. At present, artificial neural networks are emerging as the technology of choice for many
applications, such as pattern recognition, prediction, system identification, and control.
the Biological Model

Artificial neural networks emerged after the introduction of simplified neurons by McCulloch
and Pitts in 1943 (McCulloch & Pitts, 1943). These neurons were presented as models of biological
neurons and as conceptual components for circuits that could perform computational tasks. The
basic model of the neuron is founded upon the functionality of a biological neuron. “Neurons are
the basic signaling units of the nervous system” and “each neuron is a discrete cell whose several
processes arise from its cell body”.

The neuron has four main regions to its structure. The cell body, or soma, has two offshoots
from it, the dendrites, and the axon, which end in presynaptic terminals. The cell body is the
heart of the cell, containing the nucleus and maintaining protein synthesis. A neuron may have
many dendrites, which branch out in a treelike structure, and receive signals from other neurons.
A neuron usually only has one axon which grows out from a part of the cell body called the
axon hillock. The axon conducts electric signals generated at the axon hillock down its length.
These electric signals are called action potentials. The other end of the axon may split into several
branches, which end in a presynaptic terminal. Action potentials are the electric signals that
neurons use to convey information to the brain. All these signals are identical. Therefore, the
brain determines what type of information is being received based on the path that the signal
took. The brain analyzes the patterns of signals being sent and from that information it can
interpret the type of information being received. Myelin is the fatty tissue that surrounds and
insulates the axon. Often short axons do not need this insulation. There are uninsulated parts
of the axon. These areas are called Nodes of Ranvier. At these nodes, the signal traveling down
the axon is regenerated. This ensures that the signal traveling down the axon travels fast and
remains constant (i.e. very short propagation delay and no weakening of the signal). The synapse
is the area of contact between two neurons. The neurons do not actually physically touch. They
are separated by the synaptic cleft, and electric signals are sent through chemical 13 interaction.
The neuron sending the signal is called the presynaptic cell and the neuron receiving the signal
is called the postsynaptic cell. The signals are generated by the membrane potential, which is
based on the differences in concentration of sodium and potassium ions inside and outside the
cell membrane. Neurons can be classified by their number of processes (or appendages), or by
their function. If they are classified by the number of processes, they fall into three categories.
Unipolar neurons have a single process (dendrites and axon are located on the same stem), and
are most common in invertebrates. In bipolar neurons, the dendrite and axon are the neuron’s
two separate processes. Bipolar neurons have a subclass called pseudo-bipolar neurons, which
are used to send sensory information to the spinal cord. Finally, multipolar neurons are most

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