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Features of Biological Neural Networks 1) Robustness and Fault Tolerance. 2) Flexibility. 3) Ability to deal with variety of Data situations. 4) Collective Computation. Neural Networks Basics

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Page 1: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Features of Biological Neural Networks

1) Robustness and Fault Tolerance.

2) Flexibility.

3) Ability to deal with variety of Data situations.

4) Collective Computation.

Neural Networks Basics

Page 2: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Biological Neural Netwroks.

Page 3: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Page 4: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Parts of Biological Neuron

1) Cell Body 2) Dendrites

3) Axon Hillock

4) Axon

5) Synapse

6) Nucleous

Page 5: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Page 6: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Page 7: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Comparison of Computer and Biological Neural Networks.

1) Speed

2) Processing

3) Size and Complexity

4) Storage

5) Fault Tolerance

6) Controll Mechanism

Page 8: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Benefits of Artificial Neural Networks 1) Non Linearity 2) Input/ Output Map 3) Adaptivity 4) Evidential Response 5) Contextual Information 6) Fault Tolearnce 7) VLSI Implementability 8) Uniformity in Analysis and Design 9) Neurobiological Analogy

Define ANN

Page 9: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Artificial Neural Network ( Terminology)

1)Processing Unit ( Activation values and Activation functions) 2) Interconnections ( defined by weight) 3) Operations

1) Activation Dynamics: Activation states : Activation State Space. 2) Output States: Output State Space. 4) Weights 1) Set of all weights : Weight Space. 2) Adjustment of Weights: Learning. 3) Updation of Weights: Learning Algorithm. 5) Update: Output can be updated synchronously or asynchronosly.

Page 10: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

s is called activation function.

Page 11: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Activation function used in MP Model.

Graph for Activation function for MP Model.

We cannot readjust the weights.

Page 12: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Rosenblatt’s Perceptron Model.

Page 13: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

b is desired/ target output, s is actual output

Page 14: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Widrow’s Adaline Model

b is desired/ target output, s is actual output.

Page 15: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Page 16: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Page 17: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Page 18: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Types of Activation Functions

Page 19: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Page 20: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Page 21: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Page 22: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Page 23: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Page 24: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Page 25: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Page 26: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Page 27: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Page 28: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Neural Network Architecture

Page 29: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Multi Layer Feed Forward Network

Page 30: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Recurrent Neural Network

Page 31: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Given Logic Gates ( Truth Tables): Given Circuits: Realize it.

Given Circuit: Find Truth Tables, Find Logic Function using K Map.

Given Logical Function: Find Truth Table and Circuits, using Basic Circuits.

Problems

Page 32: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Learning

Page 33: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Page 34: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

This is called Index of Performance, leads to Widrow Hoff Rule, Delta Rule.

Incremental Change

Page 35: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

New Weights

Page 36: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Consider

The first parameter, is input vector, second parameter is target output

Page 37: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Page 38: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Page 39: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Page 40: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Hebb’s Learning

Page 41: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Page 42: Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective

Yagnanarayana Page 15- Page 31

Simon Haykin Page 23- Page 37

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