Introduction
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Artificial neural network can be defined as a model of reasoning based on the human brain. The brain consists of a densely interconnected set of nerve cells, or basic information-processing units, called neurons. The human brain incorporates nearly 10 billion neurons and 60 trillion connections(synapses).
Although each neuron has a very simple structure, an army of such elements constitutes a tremendous processing power. A neuron consists of a cell body called soma, a number of fibers called dendrites, and a single long fiber called the axon. While dendrites branch into a network around the soma, the axon stretches out to the dendrites and somas of other neurons.
Biological neural network
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Dynamic Connections of Neurons and Artificial Neural Networks
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Signals travel between neurons through complicated chemical reactions. When chemical substances are released from synapses, they change the electricity in the cell body. Once the electricity reaches a certain point, it sends an electrical pulse called an action potential down the axon. This pulse spreads and reaches synapses, which can either increase or decrease their electricity. The most interesting thing is that the brain can change and form new connections between neurons, even entire groups of neurons can move from one place to another. These changes are what allow us to learn and remember things.
The most interesting finding is that neural network exhibits plasticity. In response to the stimulation pattern, neurons also can form new connections with other neurons. Even entire collections of neurons may sometimes migrate from one place to another. These mechanisms form the basis for learning in the brain.
Based on the characteristic of plasticity, connections between neurons leading to the ‘right answer’ are strengthened while those leading to the ‘wrong answer’ weaken. As a result, neural networks have the ability to learn through experience.
Although a present-day artificial neural network (ANN) resembles the human brain much as a paper plane resembles a supersonic jet, it is a big step forward. ANNs are capable of ‘learning’, that is, they use experience to improve their performance. When exposed to a sufficient number of samples, ANNs can generalize to others they have not yet encountered.
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How artificial neural networks model the brain
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An artificial neural network consists of a number of very simple and highly interconnected processors, also called neurons, which are analogous to the biological neurons in the brain. The neurons are connected by weighted links passing signals from one neuron to another. Each neuron receives a number of input signals through its connections; however, it never produces more than a single output signal. The output signal is transmitted through the neuron’s outgoing connection (corresponding to the biological axon). The outgoing connection, in turn, splits into a number of branches that transmit the same signal (the signal is not divided among these branches in any way). The outgoing branches terminate at the incoming connections of other neurons in the network.
Structure of artificial neural networks
The following table shows analogy between biological and artificial neural networks
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