Neural Network

Neural Network (NN) is an abstract computer model of the human brain. The human brain has an estimated 1011 tiny units called neurons. These neurons are interconnected with an estimated 1015 links. Although more research needs to be done, the neural network of the brain is considered to be the fundamental functional source of intelligence, which includes perception, cognition, and learning for humans as well as other living creatures.

Similar to the brain, a neural network is composed of artificial neurons (or units) and interconnections. When we view such a network as a graph, neurons can be represented as nodes (or vertices), and interconnections as edges.
Although the term "neural networks" (NNs) is most commonly used, other names include artificial neural networks (ANNs) to distinguish from the natural brain neural networks-neural nets, PDP(Parallel Distributed Processing) models (since computations can typically be performed in both parallel and distributed processing), connectionist models, and adaptive systems.

A Neuron

The basic element of the brain is a natural neuron; similarly, the basic element of every neural network is an artificial neuron, or simply neuron. That is, a neuron is the basic building block for all types of neural networks.

Description of a neuron
A neuron is an abstract model of a natural neuron, as illustrated in Figure nn.01. As we can see in these figures, we have inputs x₁, x₂, ..., x_m coming into the neuron. These inputs are the stimulation levels of a natural neuron. Each input x_i is multiplied by its corresponding weight w_i then the product x_iw_i is fed into the body of the neuron.

Figure nn.01
(a) A neuron model that retains the image of a natural neuron.
(b) A further abstraction of Fig. (a)


The weights represent the biological synaptic strengths in a natural neuron. The neuron adds up all the products for i = 1, m. The weighted sum of the products is usually denoted as net in the neural network literature, so we will use this notation. That is, the neuron evaluates net = x₁w₁ + x₂w₂ + ... + x_mw_m.
In mathematical terms, given two vectors x = (x₁,x₂, ...,x_m) and w = (w₁, w₂, ..., w_m), net is the dot (or scalar) product of the two vectors, x.w = x₁w₁ + x₂w₂ + ... +x_mw_m.
Finally, the neuron computes its output y as a certain function of net, i.e., y = ƒ(net). This function is called the activation (or sometimes transfer) function. We can think of a neuron as a sort of black box, receiving input vector x then producing a scalar output y. The same output value y can be sent out through multiple edges emerging from the neuron.

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