1. 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

2. Biological Neural Netwroks.

4. Parts of Biological Neuron 1) Cell Body 2) Dendrites 3) Axon Hillock 4) Axon 5) Synapse 6) Nucleous

7. Comparison of Computer and Biological Neural Networks. 1) Speed 2) Processing 3) Size and Complexity 4) Storage 5) Fault Tolerance 6) Controll Mechanism

8. 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

9. 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.

10. s is called activation function.

11. Activation function used in MP Model. Graph for Activation function for MP Model. We cannot readjust the weights.

12. Rosenblatt’s Perceptron Model.

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

14. Widrow’s Adaline Model b is desired/ target output, s is actual output.

18. Types of Activation Functions

28. Neural Network Architecture

29. Multi Layer Feed Forward Network

30. Recurrent Neural Network

31. 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

32. Learning

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

35. New Weights

36. Consider The first parameter, is input vector, second parameter is target output

40. Hebb’s Learning

42. Yagnanarayana Page 15- Page 31 Simon Haykin Page 23- Page 37 More to Follow