What is a Neural Network?

• Definition A neural network is a computational method

inspired by studies of the brain and nervous systems in biological organisms.

Processing of information by neural networks is characteristically done in parallel rather than in series (or sequentially) as in earlier binary computers.

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• Characterization - It consists of interconnected processing elements

called nodes or neurons that work together to produce an output function. The output of a neural network relies on the cooperation of the individual neurons within the network to operate.

- Connections between nodes are called synapses

Neuron structureA simple neuronAn artificial neuron is a device with many inputs and one output. The neuron has two modes of operation; the training mode and the using mode. In the training mode, the neuron can be trained to fire (or not), for particular input patterns. In the using mode, when a taught input pattern is detected at the input, its associated output becomes the current output. If the input pattern does not belong in the taught list of input patterns, the firing rule is used to determine whether to fire or not.

Firing rulesThe firing rule is an important concept in neural networks and accounts for their high flexibility. A firing rule determines how one calculates whether a neuron should fire for any input pattern.

Structure of a neuron

History of neural network

• Neural network simulations appear to be a recent development. However, this field was established before the advent of computers, and has survived at least one major setback and several eras.

• The first artificial neuron was produced in 1943 by the neurophysiologist Warren McCulloch and the logician Walter Pits. They developed models of neural networks based on their understanding of neurology. These models made several assumptions about how neurons worked.

• But the technology available at that time did not allow them to do too much.

Neural networks versus conventional computers

* Conventional computers use an algorithmic approach.

i.e. the computer follows a set of instructions in order to solve a problem.

* Conventional computers is based on sequential processing and execution of explicit instructions.

*Neural networks are more readily adaptable to fuzzy logic computing tasks than computers.

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* Neural network are complex statistical processors , based on parallel processing and implicit instructions.

* Neural network learn by example , they cannot be programmed to perform a specific task .

* Simulating the behavior of a brain on traditional computer hardware is necessarily slow and inefficient

* Neural networks process information in a similar way the human brain does.

What is the best approach?

• Neural networks and conventional algorithmic computers are not in competition but complement each other.

• a large number of tasks, require systems that use a combination of the two approaches (normally a conventional computer is used to supervise the neural network) in order to perform at maximum efficiency.

Why use neural networks?

• can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques .

• A trained neural network can be used to provide projections given new situations of interest and answer "what if" questions.

Applications

• Neural networks in medicine recognizing diseases using scans , Neural

networks learn by example so the details of how to recognize the disease are not needed.

• Neural network in business such as accounting or financial analysis and other

business purposes including resource allocation , scheduling and database mining.

• System control( vehicle control).• Game playing(chess).

Advantages

1. Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience.

2. Self-Organization: An ANN can create its own organization or representation of the information it receives during learning time.

Advantages

3. Fault Tolerance : a unique property of a neural network is that it can still perform its overall function even if some of the neurons are not functioning. In other words it tolerate error or failure.

Neural networks do not perform miracles. But if used sensibly

they can produce some amazing results.