Artificial Neural Networks Architecture

 What is Artificial Neural Networks Architecture ?

Artificial neural networks are a technology based on studies of the brain and nervous system as depicted in Fig.

These networks emulate a biological neural network but they use a reduced set of concepts from biological neural systems. Specifically, ANN models simulate the electrical activity of the brain and nervous system. Processing elements (also known as either a neurode or perceptron) are connected to other processing elements. Typically the neurodes are arranged in a layer or vector, with the output of one layer serving as the input to the next layer and possibly other layers. A neurode may be connected to all or a subset of the neurodes in the subsequent layer, with these connections simulating the synaptic connections of the brain. Weighted data signals entering a neurode simulate the electrical excitation of a nerve cell and consequently the transference of information within the network or brain. The input values to a processing element, in, are multiplied by a connection weight, wn,m, that simulates the strengthening of neural pathways in the brain. It is through the adjustment of the connection strengths or weights that learning is emulated in ANNs.

There are different biological neurons in different parts of the brain, however, their basic structure is the same . Dendrites transmit input signals to the neuron and an axon transmits the output signal over the synapses towards the dendrites of other neurons. Although highly simplified, ANN models suffice for useful modeling and analyses which enable better understanding of processes in the brain.