Page 186 - DCAP208_Management Support Systems

Unit 11: Neural Networks

problems. The initial success in neural network applications has inspired renewed interest from Notes
industry and business.

11.1.2 Biological and Artificial Neural Networks

The human brain is composed of special cells called neurons. These cells do not die when a
human is injured (all other cells reproduce to replace themselves and then die). This phenomenon
may explain why we retain information. Information storage spans sets of neurons. The estimated
number of neurons in a human brain is 50 to 150 billion, of which there are more than 100
different kinds. Neurons are partitioned into groups called networks. Each network contains
several thousand highly interconnected neurons. Thus, the brain can be viewed as a collection of
neural networks. The ability to learn and react to changes in our environment requires intelligence.
The brain and the central nervous system control thinking and intelligent behavior. People who
suffer brain damage have difficulty learning and reacting to changing environments. Even so,
undamaged parts of the brain can often compensate with new learning.
A portion of a network composed of two cells is shown in Figure 11.1. The cell itself includes a
nucleus (the central processing portion of the neuron). To the left of cell 1, the dendrites provide
input signals to the cell. To the right, the axon sends output signals to cell 2 via the axon
terminals. These axon terminals merge with the dendrites of cell 2. Signals can be transmitted
unchanged, or they can be altered by synapses. A synapse is able to increase or decrease the
strength of the connection from neuron to neuron and cause excitation or inhibition of a
subsequent neuron. This is where information is stored.
An ANN model emulates a biological neural network. Neural computing actually uses a very
limited set of concepts from biological neural systems. It is more of an analogy to the human
brain than an accurate model of it. Neural concepts are usually implemented as software
simulations of the massively parallel processes that involve processing elements (also called
artificial neurons, or neurodes) interconnected in a network architecture. The artificial neuron
receives inputs analogous to the electrochemical impulses the dendrites of biological neurons
receive from other neurons. The output of the artificial neuron corresponds to signals sent out
from a biological neuron over its axon. These artificial signals can be changed by weights in a
manner similar to the physical changes that occur in the synapses (see Figure 11.2).

Figure 11.1: Portion of a Network: Two Interconnected Biological Cells

Source: http://www70.homepage.villanova.edu/matthew.liberatore/Mgt2206/turban_online_ch06.pdf

LOVELY PROFESSIONAL UNIVERSITY 179