COURSE NAME : Introduction to Computing CS101

Student ID: bc120200536@vu.edu.pkStudent Name: Nimra Tariq

Study Program: BS Mass Communication

INTRODUCTION OF TOPIC:

An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological

nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large

number of highly interconnected processing elements (neurones) working in unison to solve specific problems.

ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data

classification, through a learning process. Learning in biological systems involves adjustments to the synaptic

connections that exist between the neurones. This is true of ANNs as well.

The term neural network was traditionally used to refer to a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes.

Artificial neural networks are composed of interconnecting artificial neurons (programming constructs that mimic the properties of biological neurons). Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system.

Historically, computers evolved from the von Neumann model, which is based on sequential processing and execution of explicit instructions. On the other hand, the origins of neural networks are based on efforts to model information processing in biological systems, which may rely largely on parallel processing as well as implicit instructions based on recognition of patterns of 'sensory' input from external sources. In other words, at its very heart a neural network is a complex statistical processor (as opposed to being tasked to sequentially process and execute).

Neural coding is concerned with how sensory and other information is represented in the brain by neurons. The main goal of studying neural coding is to characterize the relationship between the stimulus and the individual or ensemble neuronal responses and the relationship among electrical activity of the neurons in the ensemble.

Advantages: It is capable of handling large number of features. It is capable of detecting complex relationships. It can solve numerous complex and miscellaneous tasks. It is easy to theorize. It identifies all possible connections. It handles large amount of academic study. It needs less formal arithmetical training. It is speedy and fast calculation.

Disadvantages: Computing one input path needs calculating the network. It cannot resolve all machine learning difficulties. It is difficult to implement trial and error for choosing many neurons. It is an experimental nature of model improvement. It has greater computational load . It has problematic to interpret. It has low training time.

Today’s Computers and Technology can learn and achieve a lot from neural networks. Neural networks capability to learn from examples makes them great and influential. Moreover it is not essential to create an algorithm to complete a particular job. Have no need to know the inner parameters of the task. Furthermore neural networks are well-matched for systems due to their fast reaction and calculating times as to their consistent structure.

They also add great values to other fields of medical science like neurology and psychology; Neural networks used to design different parts of organisms and to inspect the inner structures of the brain.

Possibly the great inspiring feature which also seems to be promising is, in the future conscious networks might come. Many experts and scientists claim that, consciousness is a mechanical property and it seems possible to create conscious neural networks.

In conclusion, nevertheless neural networks have a massive potential but human kind can only take the great benefits, as soon as these networks are combined with computing.