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Comparing Immune and Comparing Immune and Neural NetworksNeural NetworksLeandro Nunes de Castrolnunes@dca.fee.unicamp.br

http://www.dca.fee.unicamp.br/~lnunes

CNPq Profix: 540396/01-1

Computer and Electrical Engineering School

State University of Campinas

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Contents

• Background and Motivation

• The Immune System and Network Theory

• A Brief Outline of Neural Networks

• Similarities and Differences

• Discussion

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Background and Motivation

• The nervous and immune system can be viewed as composed of networks

• Immune networks as a new computational intelligence approach

• New avenues for research in both fields

• Development of hybrid structures

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The Immune System and Network Theory

• Basic components

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• The Immune Network Theory

The Immune System and Network Theory

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• Immune network models:– Continuous

– Discrete

• s = NA NS + INC DUC – s: stimulation

– NA: network activation

– NS: network suppression

– INC: influx of new cells

– DUC: death of unstimulated cells

The Immune System and Network Theory

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Neural Networks

• Characterized by:– Neuron model– Network architecture

• Feedforward• Recurrent

– Learning approaches• Supervised• Unsupervised• Reinforcement

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Immune and Neural Networks

• Immune– Cells or molecules

composed of attribute strings in a given shape-space

– Connection links

– Stimulation level

• Neural– Cells composed of

an integrator and an activation function

– Connection links

– Linear combination of the inputs and the weight vector

– “Feedforward” signal propagation

Basic Components

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Immune and Neural Networks

• Immune– Response: reproduction

and/or alteration in its shape

– Cells and molecules distributed according to the universe of antigens

• Neural– Response: output

stimulus (real value or spike)

– Neurons have pre-specified positions in the network

Basic Components

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Immune and Neural Networks

• Immune– Indicate with which

elements a cell interacts

– Its strength corresponds to the degree of interaction

– Stimulatory or suppressive interactions

– Not directly updated

• Neural– Indicate with which

neurons a unit interacts

– Weights the degree of input stimulation

– Excitatory or inhibitory interactions

– Tuned so as to perform better in the environment

Connections

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Immune and Neural Networks

• Immune– “Free” structures that

usually follow the spatial distribution of the input data set

– Adaptation based on the stimulation level. It allows for the reproduction and adaptation of attributes

• Neural– Usually, pre-defined

structures, even if constructive or pruning techniques are used

– Adaptation based on the activation. It allows for the variation in connection strengths (attributes)

Structure and Adaptability

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Discussion

• Quite different approaches– Components– Structure– Adaptation

• Similar applications:– Pattern recognition and classification,

prediction, robotics, data analysis, function optimization

• Hybrid algorithms