Literature Review On Biological Neural Network

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Chapter-2: Literature Review In this chapter, we discuss a brief introduction of neural network and biometrics . Traditionally, the term neural network had been used to refer to a network or circuit of biological neurons. Neural networks are inspired by our brains. 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: 2.1 Biological Neural Network generally, a biological neural network is consists of a set or sets of chemically linked or functionally linked neurons. The human brain owns about 1014 synapses and 1011 neurons. A neuron consisting of a soma (cell body),dendrites (receive signal) and axons (send signal). A synapses…show more content…
Setting the weights. Activation function. 2.3.1 Network Architecture There are several types of architecture of ANN. However, the two most commonly used ANN are discussed below: 1. Feed-forward networks Feed-forward ANNs allow signals to travel one-direction only, from the input to the output. There is no feedback (loops) i.e. output of any layer does not affect that the same layer. Feed-forward ANNs usually straight forward the networks that connect inputs with outputs. They are widely used in the pattern recognition. There are two types of Feed-forward neural networks, Single-layer and Multi-layer Feed-forward neural network . Single-layer Feed-forward Network: This type of network consisting of two layers, the input layer and the output layer. The input layer neurons receives the input signals and the output layer neurons receives the output signals. The synaptic links carrying the weights connect each input neuron to the output neuron but not the opposite(See Figure (2-3)). Figure( 2-3): Single layer neural network…show more content…
(See Figure (2-5)) . Figure( 2-5): Supervised Learning Rule [1] 2.Unsupervised learning Network works to calculate the output without a previous expectation, Where we offer network only inputs and it is find target And working on a self-organizing data Where it competes neurons to get a signal and the neuron Winner we get it on the output and this is called "self-regulation of the network neurons" . (See Figure (2-6)). Figure(2-6):Unsupervised Learning Rule [1] 2.3.3. Transfer Function In behavior of an ANN depends in each of the weights and the input-output function (transfer function) that is selected for the units. This function usually located into one of three types[10]: Linear (or slope): The output activity is commensurate to the total weighted output (see Figure (2-7)). F(X)=X ; for all x Figure( 2-7): Linear activation function Threshold: The output is appoint at one of two levels, depending on whether the total input is less or greater than than some threshold
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