From Human Neurones To Artificial Neurones

Published Date: 02 Nov 2017

INTRODUCTION:

Nueral networks are a new method of programming computers .They are exceptionally good at performing pattern recognition and other tasks that are very difficult to program using conventional techniques.

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..

bio neurone

HISTORICAL BACKGROUND:

Neural network simulations appear to be a recent development. However, this field was established before the advent of computers, and has survived at least one major setback and several eras.

Many important advances have been boosted by the use of inexpensive computer emulations. Following an initial period of enthusiasm, the field survived a period of frustration and disrepute.

The first artificial neuron was produced in 1943 by the neurophysiologist Warren McCulloch and the logician Walter Pits. But the technology available at that time did not allow them to do too much.

WHY USE NUERAL NETWORKS???

Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an "expert" in the category of information it has been given to analyse. This expert can then be used to provide projections given new situations of interest and answer "what if" questions.

Other advantages include:

Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience.

Self-Organisation: An ANN can create its own organisation or representation of the information it receives during learning time.

Real Time Operation: ANN computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability.

Fault Tolerance via Redundant Information Coding: Partial destruction of a network leads to the corresponding degradation of performance.

From Human Neurones to Artificial Neurones:

We conduct these neural networks by first trying to deduce the essential features of neurones and their interconnections. We then typically program a computer to simulate these features. However because our knowledge of neurones is incomplete and our computing power is limited, our models are necessarily gross idealisations of real networks of neurones.

http://www.doc.ic.ac.uk/%7End/surprise_96/journal/vol4/cs11/report.artn.jpg

Architecture of neural networks:

Feed-forward networks

Feed-forward ANNs (figure 1) allow signals to travel one way only; from input to output. There is no feedback (loops) i.e. the output of any layer does not affect that same layer. Feed-forward ANNs tend to be straight forward networks that associate inputs with outputs. They are extensively used in pattern recognition. This type of organisation is also referred to as bottom-up or top-down

http://www.doc.ic.ac.uk/%7End/surprise_96/journal/vol4/cs11/report.multil.jpg

Feedback networks

Feedback networks (figure 1) can have signals travelling in both directions by introducing loops in the network. Feedback networks are very powerful and can get extremely complicated. Feedback networks are dynamic; their 'state' is changing continuously until they reach an equilibrium point. They remain at the equilibrium point until the input changes and a new equilibrium needs to be found. Feedback architectures are also referred to as interactive or recurrent, although the latter term is often used to denote feedback connections in single-layer

http://ars.els-cdn.com/content/image/1-s2.0-S0896627309005479-gr6.jpg

Network layers:

The commonest type of artificial neural network consists of three groups, or layers, of units: a layer of "input" units is connected to a layer of "hidden" units, which is connected to a layer of "output" units. (see Figure 4.1)

http://www.doc.ic.ac.uk/%7End/surprise_96/journal/vol4/cs11/report.bullet.gifThe activity of the input units represents the raw information that is fed into the network.

http://www.doc.ic.ac.uk/%7End/surprise_96/journal/vol4/cs11/report.bullet.gifThe activity of each hidden unit is determined by the activities of the input units and the weights on the connections between the input and the hidden units.

http://www.doc.ic.ac.uk/%7End/surprise_96/journal/vol4/cs11/report.bullet.gifThe behaviour of the output units depends on the activity of the hidden units and the weights between the hidden and output units

Applications of neural networks:

6.1 Neural Networks in Practice

Given this description of neural networks and how they work, what real world applications are they suited for? Neural networks have broad applicability to real world business problems. In fact, they have already been successfully applied in many industries.

Since neural networks are best at identifying patterns or trends in data, they are well suited for prediction or forecasting needs including:

http://www.doc.ic.ac.uk/%7End/surprise_96/journal/vol4/cs11/report.bullet.gifsales forecasting

http://www.doc.ic.ac.uk/%7End/surprise_96/journal/vol4/cs11/report.bullet.gifindustrial process control

http://www.doc.ic.ac.uk/%7End/surprise_96/journal/vol4/cs11/report.bullet.gifcustomer research

http://www.doc.ic.ac.uk/%7End/surprise_96/journal/vol4/cs11/report.bullet.gifdata validation

http://www.doc.ic.ac.uk/%7End/surprise_96/journal/vol4/cs11/report.bullet.gifrisk management

http://www.doc.ic.ac.uk/%7End/surprise_96/journal/vol4/cs11/report.bullet.giftarget marketing

The Back-Propagation Algorithm:

In order to train a neural network to perform some task, we must adjust the weights of each unit in such a way that the error between the desired output and the actual output is reduced. This process requires that the neural network compute the error derivative of the weights (EW). In other words, it must calculate how the error changes as each weight is increased or decreased slightly. The back propagation algorithm is the most widely used method for determining the EW.

The back-propagation algorithm is easiest to understand if all the units in the network are linear. The algorithm computes each EW by first computing the EA, the rate at which the error changes as the activity level of a unit is changed. For output units, the EA is simply the difference between the actual and the desired output. To compute the EA for a hidden unit in the layer just before the output layer, we first identify all the weights between that hidden unit and the output units to which it is connected. We then multiply those weights by the EAs of those output units and add the products. This sum equals the EA for the chosen hidden unit. After calculating all the EAs in the hidden layer just before the output layer, we can compute in like fashion the EAs for other layers, moving from layer to layer in a direction opposite to the way activities propagate through the network. This is what gives back propagation its name. Once the EA has been computed for a unit, it is straight forward to compute the EW for each incoming connection of the unit. The EW is the product of the EA and the activity through the incoming connection.

Electronic noses:

ANNs are used experimentally to implement electronic noses. Electronic noses have several potential applications in telemedicine. Telemedicine is the practice of medicine over long distances via a communication link. The electronic nose would identify odours in the remote surgical environment. These identified odours would then be electronically transmitted to another site where an door generation system would recreate them. Because the sense of smell can be an important sense to the surgeon, telesmell would enhance telepresent surgery.

Conclusion:

The computing world has a lot to gain fron neural networks. Their ability to learn by example makes them very flexible and powerful. Furthermore there is no need to devise an algorithm in order to perform a specific task; i.e. there is no need to understand the internal mechanisms of that task. They are also very well suited for real time systems because of their fast responseand computational times which are due to their parallel architecture.

Neural networks also contribute to other areas of research such as neurology and psychology. They are regularly used to model parts of living organisms and to investigate the internal mechanisms of the brain.

Perhaps the most exciting aspect of neural networks is the possibility that some day 'consious' networks might be produced. There is a number of scientists arguing that conciousness is a 'mechanical' property and that 'consious' neural networks are a realistic possibility.

Finally, I would like to state that even though neural networks have a huge potential we will only get the best of them when they are intergrated with computing, AI, fuzzy logic and related subjects.

 http://www.doc.ic.ac.uk/%7End/surprise_96/journal/vol4/cs11/report.neural2.jpg

.

http://www.doc.ic.ac.uk/%7End/surprise_96/journal/vol4/cs11/report.simple_neuron.jpg

N.S.S PRATAP

RAJA MAHENDRA COLLEGE

OF

ENGINEERING

BTECH 3RD