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The term neural network was traditionally used to refer to a network or circuit of biological
neurons.
The modern usage of the term often refers to artificial neural networks, which are composed
of artificial neurons or nodes. Thus the term has two distinct usages . Biological neural
networks are made up of real biological neurons that are connected or functionally related in
the peripheral nervous system or the central nervous system. In the field of neuroscience,
they are often identified as groups of neurons that perform a specific physiological function in
laboratory analysis. . Artificial neural networks are composed of interconnecting artificial
neurons programming constructs that mimic the properties of biological neurons. Artificial
neural networks may either be used to gain an understanding of biological neural networks,
or for solving artificial intelligence problems without necessarily creating a model of a real
biological system. The real, biological nervous system is highly complex artificial neural
network algorithms attempt to abstract this complexity and focus on what may hypothetically
matter most from an information processing point of view.
. . What is a Neural Network
An Artificial Neural Network ANN is an information processing paradigm that is inspired by
the way biological nervous systems, such as the brain, process information. The key element
of this paradigm is the novel structure of the information processing system. It is composed
of a large number of highly interconnected processing elements neurones working in unison
to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a
specific application, such as pattern recognition or data classification, through a learning
process. Learning in biological systems involves adjustments to the synaptic connections
that exist between the neurones. This is true of ANNs as well.
. Why use neural networks
. Adaptive learning An ability to learn how to do tasks based on the data given for training or
initial experience. . SelfOrganisation An ANN can create its own organisation or
representation of the information it receives during learning time. . Real Time Operation ANN
computations may be carried out in parallel, and special hardware devices are being
designed and manufactured which take advantage of this capability. . Fault Tolerance via
Redundant Information Coding Partial destruction of a network leads to the corresponding
degradation of performance. However, some network capabilities may be retained even with
major network damage.
. Neural networks versus conventional computers
Neural networks take a different approach to problem solving than that of conventional
computers. Conventional computers use an algorithmic approach i.e. the computer follows a
set of instructions in order to solve a problem. Unless the specific steps that the computer
They cannot be programmed to perform a specific task. Neurons are electrically charged or
quotpolarizedquot by membrane transport proteins that pump ions across their membranes.
and so on. Ions of like charge repel each other.needs to follow are known the computer
cannot solve the problem. it responds by releasing ions into the space outside the cell.
Emotion Markup Language An Emotion Markup Language EML or EmotionML is defined by
the WC Emotion Incubator Group EmoXG as a generalpurpose emotion annotation and
representation language. in a wave. Recording these voltages over time gives us the EEG.
the difference in push. and when many ions are pushed out of many neurons at the same
time. between any two electrodes can be measured by a voltmeter. The disadvantage is that
because the network finds out how to solve the problem by itself. Neural networks learn by
example. its operation can be unpredictable. who push their neighbors. That restricts the
problem solving capability of conventional computers to problems that we already
understand and know how to solve. they can push or pull electrons on the metal on the
electrodes. But computers would be so much more useful if they could do things that we dont
exactly know how to do. When the wave of ions reaches the electrodes on the scalp. When a
neuron receives a signal from its neighbor via an action potential.
ElectroencephalographyEEG The brains electrical charge is maintained by billions of
neurons. The examples must be selected carefully otherwise useful time is wasted or even
worse the network might be functioning incorrectly. Neural networks process information in a
similar way the human brain does. Since metal conducts the push and pull of electrons
easily. or voltage. This process is known as volume conduction. which should be usable in a
large variety of technological contexts where . they can push their neighbors. The network is
composed of a large number of highly interconnected processing elementsneurones working
in parallel to solve a specific problem.
g. Emotions are a basic part of human communication and should therefore be taken into
account.g. using emotional models for timecritical decision enforcement. in emotional Chat
systems or emphatic voice boxes. The modeling of human emotions in computer processing
can help to build more efficient systems.emotions need to be represented. This involves
specification. e. analysis and display of emotion related states. e. Emotionoriented computing
or quotaffective computingquot . Emotion and intelligence are strongly interconnected. To
enhance systems processing efficiency. Representing the emotional states of a user or the
emotional states to be simulated by a user interface requires a suitable representation
format. A standard for an emotion markup language would be useful for the following
purposes y y To enhance computermediated or humanmachine communication.
Emotionoriented computing or quotaffective computingquot is gaining importance as
interactive technological systems become more sophisticated.