Neurocomputing and Artificial Neural Networks: For BCA / MCA / BTech Students – Easy & Exam-Oriented Notes

Part 1: Neurocomputing and Neuroscience

Neurocomputing means studying the brain and copying its working style to build smart machines.

Neuroscience means study of the human brain and nervous system.

This chapter connects biology (brain) with computer science (artificial neural networks).

The Human Brain

What is the Human Brain?

• The brain is the control centre of the body.

• It controls:

  • Thinking

  • Memory

  • Movement

  • Emotions

  • Decision making

📌 Brain contains billions of nerve cells.

Why Brain is Important in Computing?

Scientists study brain because:

• Brain learns from experience

• Brain recognizes patterns

• Brain makes decisions quickly

• Brain works even with incomplete data

College Example

• You recognize your teacher even if lighting is low.

• Brain fills missing information.

Mobile Example

• Face unlock in phone works like brain pattern recognition.

Structure of the Brain (Simple View)

• Brain has many neurons (nerve cells).

• Neurons connect with each other.

• These connections form a large network.

Neuron → Neuron → Neuron
   |         |        |
 Connected in a network

Biological Neurons

What is a Biological Neuron?

A neuron is a basic unit of the brain.

It has 3 main parts:

Part	Function
Dendrites	Receive signals
Cell body	Process signal
Axon	Send signal

How Neuron Works (Step-by-Step)

1. Dendrites receive a signal.

2. Cell body checks signal.

3. If the signal is strong → neuron sends output.

4. Axon passes a signal to next neuron.

Daily Life Example

• Friend calls you → you think → you reply.

• Input → process → output.

Exam Definition (Memorize This)

A biological neuron is a nerve cell that receives, processes, and transmits information in the brain.

Neural Processing

Neural processing means how neurons work together to think and learn.

• Many neurons connect.

• They pass signals to each other.

• Strong connections mean strong learning.

Example

• If you practice math daily → brain builds strong connections.

• If you stop practice → connection becomes weak.

Biological Neural Network

When many neurons connect together, they form a biological neural network.

📌 This network allows:

• Learning

• Memory

• Decision making

College Example

• When you solve previous year papers → brain builds better network for exams.

Remember This (Exam Tip)

Neuron = Single unit
Neural Network = Many neurons connected

Part 2: Artificial Neural Networks (ANN)

Introduction

Artificial Neural Network (ANN) is a computer model inspired by the human brain.

Scientists copy brain structure to make machines intelligent.

Historical Notes

• 1943: First simple neuron model developed.

• 1950s: Perceptron model introduced.

• 1980s: Backpropagation became popular.

• Today: Used in AI, Machine Learning, Deep Learning.

📌 Remember timeline for exam short questions.

Artificial Neuron Model

Artificial neuron is a simple math model.

It has 3 parts:

Part	Meaning
Input	Data given to system
Weight	Importance of input
Output	Final result

How Artificial Neuron Works

1. Take input values.

2. Multiply by weight.

3. Add them.

4. Produce output.

Simple Example

• Shopping app suggests product.

• It checks your clicks (input).

• It gives importance (weight).

• It shows suggestion (output).

Exam Definition

Artificial neuron is a mathematical model that receives inputs, processes them, and produces output similar to biological neurons.

Knowledge Representation

Knowledge representation means how network stores information.

ANN stores knowledge in:

• Weights

• Connections

📌 When network learns, weights change.

Example

• YouTube improves recommendations as you watch more videos.

• It adjusts internal values.

Comparison: Biological vs Artificial Neural Network

Feature	Biological NN	Artificial NN
Structure	Real neurons	Mathematical model
Speed	Slower	Faster
Learning	Natural	Programmed
Energy	Very efficient	Needs electricity

Exam Tip

Always write at least 3 differences in exams.

Applications of ANN

ANN is used in:

• Face recognition

• Speech recognition

• Medical diagnosis

• Stock prediction

• Recommendation systems

Mobile Example

• Google Photos detects faces.

• Instagram suggests reels.

College Example

• Online exam software checks cheating patterns.

Part 3: Learning Process in ANN

Learning means improving performance by changing weights.

Supervised Learning

The teacher gives:

• Input

• Correct output

Network learns from mistakes.

Example

• The teacher gives a question + answer.

• You compare and correct mistakes.

App Example

• An email spam filter trained with labeled spam emails.

📌 Important for exams.

Unsupervised Learning

No correct answer given.

Network finds pattern itself.

Example

• Students form groups based on similar interests.

• No teacher assigns them.

Shopping Example

• Amazon groups customers with similar buying habits.

Error Correction Learning

Network:

1. Produces output.

2. Compares with correct output.

3. Calculates error.

4. Reduces error next time.

Example

• You solve mock test.

• Check answer key.

• Improve next time.

Competitive Learning

Neurons compete.
Only strongest neuron wins.

Example

• In class, only one student answers fastest.

• Others stay silent.

Used in:

• Clustering

• Pattern grouping

Adaptation Learning

System adjusts based on environment.

Example

• Phone brightness adjusts automatically.

• You adjust study time near exams.

Statistical Nature of Learning Process

Statistical means based on:

• Data

• Probability

• Patterns

ANN does not memorize like humans.
It finds patterns from data.

Example

• Weather app predicts rain using past data.

• It calculates chance (probability).

Important Exam Questions

Short Questions

1. Define biological neuron.

2. What is artificial neural network?

3. Define supervised learning.

4. Give two applications of ANN.

5. Difference between biological and artificial neuron.

Long Questions

1. Explain structure of biological neuron with diagram.

2. Compare biological and artificial neural networks.

3. Explain learning methods in ANN.

4. Describe supervised and unsupervised learning with examples.

Quick Revision Table

Topic	Key Point
Brain	Made of neurons
Neuron	Input → Process → Output
ANN	Brain-inspired model
Supervised	Teacher gives answer
Unsupervised	No answer given
Error Correction	Reduce mistake
Competitive	One winner
Adaptation	Adjust to environment

Key Points to Remember

• Brain inspired ANN.

• Neuron is basic unit.

• Weights store knowledge.

• Learning changes weights.

• ANN used in AI applications.

Final Summary (Fast Revision)

• Human brain contains neurons.

• Neurons connect to form biological neural network.

• Scientists copied this idea to build Artificial Neural Networks.

• ANN has inputs, weights, and outputs.

• Learning methods include supervised, unsupervised, error correction, competitive, and adaptation learning.

• ANN works using data and patterns.

• Used in face recognition, recommendation systems, and medical field.

If you revise this structure 2–3 times, you can easily write strong answers in exams.