1. Artificial Neural Networks
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2. Overview
1. Introduction
2. Biological inspiration
3. Artificial neurons and neural networks
4. Learning processes
5. Learning with artificial neural networks
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3. Introduction
• What is an (artificial) neural network
 A set of nodes (units, neurons, processing elements)
 Each node has input and output
 Each node performs a simple computation by its node function
 Weighted connections between nodes called Synaptic Weight.
 Connectivity gives the structure/architecture of the net
 What can be computed by a NN is primarily determined by the
connections and their weights
 A very much simplified version of networks of neurons in animal
nerve systems
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4. STRUCTURE OF NEURON
Each neuron has a body, an axon, and many dendrites
Can be in one of the two states: firing and rest.
Neuron fires if the total incoming stimulus exceeds
the threshold
Synapse: thin gap between axon of one neuron and
dendrite of another.
Signal exchange
Synaptic strength/efficiency
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5. Structure of Neuron
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6. COMPARISON
ANN
Bio NN
Nodes Cell body
input signal from other
output neurons
firing frequency
node function
firing mechanism
Synapses
Connections
synaptic strength
connection strength
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7. Neural Network Architecture
Single Layer Feed forward Network
Multi Layer Feed forward Network
Recurrent Network
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8. Single Layer Feed forward Network
Input Layer of source node that projects on to an output layer of nuerons(computation nodes) but not
vice-versa.
This is refers as a single layer network because processing or computation takes place only on output
layer.
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9. Multi Layer Feed Forward Network
One or More Hidden Layer are there.
Whose Computation nodes are called Hidden neurons or
Hidden nodes.
Function of Hidden neurons is to intermean b/w the external
input and network output.
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10. Recurrent Network
At least one feedback
loop.
Consist of a single layer of
neurons.
Each neuron feeding its
output signal back as
inputs of others neurons
except itself.
May or may not have
Hidden neurons.
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11. Biological inspiration
Animals are able to react adaptively to changes in their
external and internal environment, and they use their nervous
system to perform these behaviours.
An appropriate model/simulation of the nervous system
should be able to produce similar responses and behaviours in
artificial systems.
The nervous system is build by relatively simple units, the
neurons, so copying their behavior and functionality should be
the solution.
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12. Biological inspiration
Dendrites
Soma (cell body)
Axon
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13. Biological inspiration
dendrites
axon
synapses
The information transmission happens at the synapses.
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14. Biological inspiration
The spikes travelling along the axon of the pre-synaptic
neuron trigger the release of neurotransmitter substances
at the synapse.
The neurotransmitters cause excitation or inhibition in the
dendrite of the post-synaptic neuron.
The integration of the excitatory and inhibitory signals
may produce spikes in the post-synaptic neuron.
The contribution of the signals depends on the strength of
the synaptic connection.
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15. Artificial neural networks
Output
Inputs
An artificial neural network is composed of many artificial
neurons that are linked together according to a specific
network architecture. The objective of the neural network
is to transform the inputs into meaningful outputs.
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16. Artificial neural networks
Tasks to be solved by artificial neural networks:
• controlling the movements of a robot based on self-
perception and other information (e.g., visual
information);
• deciding the category of potential food items (e.g.,
edible or non-edible) in an artificial world;
• recognizing a visual object (e.g., a familiar face);
• predicting where a moving object goes, when a robot
wants to catch it.
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17. ANN Business Applications
Evaluation of personnel and job candidates
Resource allocation
Data mining
Foreign exchange rate
Stock, bond, and commodities selection and trading
Signature validation
Tax fraud
Loan applications evaluation
Solvency prediction
New product analysis
Airline fare management
Prediction
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