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Introduction

Neural Networks

The Artificial Neural Networks (ANN) began with Warren McCulloch and Walter Pitts (1943) who created a computational model for neural networks based on algorithms called threshold logic

Computational model that mimes the functional of human brain to perform various tasks faster than traditional system. ANN is an efficient information processing system which resembles the characteristics of biological neural network

Warren McCulloch & Walter Pitts

Biological Neurons

Dendrites : Responsible for receiving information from other neurons and bring it to the cell body(soma)
Soma : Responsible for processing of information, they have received from dendrites.
Axon : It is just like a cable through which send information from the cell body to other neurons.
Synapses : It is the connection between the axon and other neuron dendrites.

The input signals is received by dendrites, and processing generally to the cell body(soma). Incoming signals can be either excitatory which means they tend to make the neuron fire (generate an electrical impulse).

Most neurons receive many input signals throughout their dendritic trees. A single neuron may have more than one set of dendrites and may receive many thousands of input signals. To decide whether a neuron is excited to fire an impulse depends on the sum of all of the excitatory and inhibitory signals it receives. The processing of this information takes place in soma which is neuron cell body. If the neuron does end up firing, the nerve impulse, or action potential, is conducted down the axon.

Towards its end, the axon splits up into many branches known as axon terminals (or nerve terminals), which makes connections on target cells.

The junctions that allow signal transmission between the axons terminals and dendrites are called synapses. The process of transmission is by diffusion of chemicals called neuro transmitters across the synaptic cleft

Artificial Neural Networks Model

The artificial neuron model has N input, denoted as x₁, x₂, ...x_n. Each line connecting these inputs to the neuron is assigned a weight, which are denoted as w₁, w₂, .., w_n respectively. Weights in the artificial model correspond to the synaptic connections in biological neurons. The threshold in artificial neuron is usually represented by Θ and the activation corresponding to the graded potential is given by the formula:

y_{i n} = x_{1} . w_{1} + x_{2} . w_{2} + x_{3} . w_{3} \dots x_{n} . w_{n}

i.e., Net input

y_{i n} = \sum_{i}^{n} x_{i} . w_{i} + b

The output can be calculated by applying the activation function over the net input.

Y = F (y_{i n})

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