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Neural Networks: A Beginner's Understanding

Updated on October 26, 2011

Consider the human brain. It consists of billions of interconnected neurons with each neuron being able to transfer information to many other neurons in the brain.

A neuron can be in either a state of firing or in a state of rest. A neuron is fired, or activated, when it receives and combines signals from many other neurons and then produces an output which it sends to other neurons. A neuron inputs the signals through connecting structures called dendrites. The neuron combines the signals and produces a signal which is output along a path called an axon. Synapses are the pathways between the axons and dendrites through which the information must pass.

Neurons fire at different rates and the function of their physical states is to convey information to other neurons. This simple transfer of information is chemical in nature and the amount of signal transferred depends on the amount of chemicals released in the brain region at the time. When the brain learns, the efficiency of the chemical transfer of information from the output of one neuron to the inputs of many neurons is modified.

This transfer process between neurons occurs in parallel which enables the brain to carry out its many functions and to do many of them at the same time. For example, the brain can take in sensory information, interpret this input and send out signals to the muscles to produce a movement while at the same time as seeing, hearing and carrying out a conversation.

Thus, the brain can be thought of as an information-processing system which can do many things at the same time. An attempt to understand how the brain functions has been made by emphasising the computational ability of the brain and creating simplified models of how neurons in the brain function. By modeling how the brain works through computation it is possible to explain some of the brains' functions, such as perception, imagery and motor action, in ways which mirror the brain's ability to carry out these functions.

photo: Aigars Mahinovs (flickr)
photo: Aigars Mahinovs (flickr)


What is Connectionism?

Connectionism (or parallel distributed processing) describes the use of computational models which consist of neuron-like units which work together to process information interactively and in parallel. These computational models (neural networks) aim to model the way the human brain processes information and hence to model human behaviour. Neural networks operate by mapping various inputs on to particular outputs and by storing these associations between inputs and outputs in the networks

The Components of a Connectionist Architecture

The brain is considered to be a network, consisting of simple units which are connected to each other. A unit is analogous to a neuron in the brain and operates in a similar manner. Each unit translates inputs into outputs which corresponds to a neuron in a state of either firing or resting. The processing of information in the network takes place through the interaction of these units, with each unit sending excitatory or inhibitory signals to many other units.

This simple transfer of information is mathematical in nature, rather than chemical, with the sending of numbers along the connections between units in the network. These numbers indicate whether a unit is to be active (fire) or inactive (resting). To indicate the activity level each unit is associated with an activation value. The activity level of a unit at any given point in time is its current output. The activity of the units convey information and determine the temporary physical state of the network, in the same way that neurons determine the physical state of the brain through the patterns of their neural activity.

In artificial neural networks the simple, interconnected units tend to be situated into three basic layers consisting of input units, output units and hidden units. Input units receive external inputs such as sensory information and information from other networks. Output units send information out of the network which will affect other networks. Each input unit is connected to many hidden units and each hidden unit is connected to many output units.

photo: fdecomite (flickr)
photo: fdecomite (flickr)

Each unit receives inputs from many other units. These inputs added together become the net input to the unit. The combined input is modified by a transfer function. This transfer function can be a threshold function which will only pass on information, if the combined activity level reaches a certain level. If this the combined input exceeds a threshold value, the unit will produce an output. However, if the sum of all the inputs is less than the threshold, then no output will be produced.

Every connection between two units has a weight or strength which regulates the transfer of information between the two units. The weight determines how much of the output gets through to the unit. This weight modifies the output of a unit to become the input of the next unit. Weights correspond to the efficiency of the synapses in the brain and modify the signals from neurons positively or negatively ie. by making them excitatory or inhibitory signals. An excitatory signal is one which attempts to activate (fire) the unit which receives it while an inhibitory signal is one which attempts to keep the unit from firing. The greater the numerical value of the weight the greater the effect of the outputting unit in its attempt to excite or inhibit the receiving units.

The pattern of the weights between the input, hidden and output units convey information, represent aspects of knowledge and determine the more permanent physical state of the network. Information is stored in the weights and the patterns of connectivity among the units determine what the network represents. All knowledge then is contained implicitly in the multitude of weights on the connections between units.

The combination of weights of each connection in the network represents then the total physical state of the network. These weights can be modified with experience and so a unit can change what it represents through the modification of the strength of existing connections. Thus a network learns when the connection weights between the units are modified which corresponds to the brain learning through the modification of the chemical information through the synapses.

Learning is accomplished through a learning rule which indicates how the connection weights must be adapted or changed in response to inputs. This learning rule indicates whether the value of weights in a network are to be increased or decreased and such modifications will mean a change in the total physical state of a network.

photo: Esbjorn Jorsater (flickr)
photo: Esbjorn Jorsater (flickr)


This article has attempted to describe the fundamental components of a connectionist architecture.

But what are they good for?

Neural networks models can solve the problems of goal-directed learning, of storing motor programs and of producing novel movements. An essential property of neural networks which make them adept for modeling human performance is the networks' ability to learn from experience and adapt to the environment. This means that the network is able to discover solutions to problems, where the solutions are unknown or where there are several solutions to a problem. For example, learning a motor skill.

However, learning occurs very slowly because the weights are gradually changed and fine tuned by a period of training - neural networks learn from being shown examples. Neural networks need to be shown many examples to be able to solve difficult problems. This gradual nature of neural network learning mirrors much human learning which is slow and gradual because expertise takes time and difficult problems require many learning trials.

In another hub I will discuss some of the principle features of neural networks which make them adept at modeling human information-processing


Artificial Neural Networks For Beginners

Free Online Course from MIT: Introduction to Neural Networks. This course explores the organization of synaptic connectivity as the basis of neural computation and learning.


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