1. ARTIFICIAL NEURAL networks.
Presented by:
Mudit Misra
Deveshri Srivastava
Richa Sharma
Neerja Gupta

2. WHAT IS A NEURAL NETWORK ?
An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as brain, process information.
It comprises of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems.

3. WHY USE NEURAL NETWORKS ?
Adaptive Learning
Self Organization
Real Time Operation
Fault Tolerance via Redundant Information Coding

4. NEURAL NETWORKS VERSUS CONVENTIONAL COMPUTERS
Neural Networks do not need a pre-defined algorithm to execute an instruction.
The data used by Neural Networks need not be very precise.
The information in a Neural Network is processed by constantly changing patterns of activity.

5. The biological inspiration
Neuron consists of a branching input structure- dendrites, a cell body, and a branching output structure-axon.
Each neuron can propagate electrochemical signals.
Dendrites
Soma (cell body)
Axon

6. HOW THE HUMAN BRAIN LEARNS
dendrites
axon
synapses
The information transmission takes place at the synapses.
Neuron , when activated fires an electrochemical signal along the axon, which travels through synapses to the other neuron.
The strength of the synaptic connections is responsible for the learning process.

7. from human neurons to artificial neurons
Artificial Neurons receive and provide information in the form of spikes.
The below model is known as the McCullough-Pitts model. x1 x2 x3 …
xn-1 xn
Output
Inputs

8. THE BASIC ARTIFICAL MODEL
The artificial neuron receives one or more input signals, sums these, and produces an output .
Activation of neuron= weighted sum of inputs – threshold.
The output is produced after passing the sum through a non-linear function known as an activation or transfer function.

9. HOW SHOULD NEURONS BE CONNECTED TOGETHER ?
If a network is of any use, there must be an input and an output.
There are hidden neurons also that play an internal role in the network.
The input , hidden and output neurons need to be connected together.

10. TYPES OF NEURAL NETWORK
Feed forward Network .
Recurrent Network.

11. APPLYING A NEURAL NETWORK TO SOLVE A PROBLEM
A solution to the problem is defined by the way the network works and the way they are trained.
They are used to infer some unknown information from known information.
The condition to get a solution is that there must be a relationship between the proposed known input and unknown output.

12. TYPES OF TRAINING
Supervised learning
Unsupervised learning

13. IMPLEMENTATION AND FUTURE TECHNOLOGY
Neural networks mimics the brain, they have shown much promise in so-called sensory processing task.
Neural networks can perform as well as humans.
Neural network have the ability to learn from a set of examples and generalize this knowledge to new situation, they are excellent for work requiring adaptive control system.
They can sustain damage and still function properly.
Neural networks are currently a hot research area in medicine.