---
title: "ANN vs human brain"
subject: "Artificial Intelligence"
chapter: 6
key: "semester_6/artificial_intelligence:ann-vs-human-brain"
source: https://www.bctengineer.com/q/semester_6/artificial_intelligence/ann-vs-human-brain
site: BCT Engineering
---

# ANN vs human brain

Artificial Intelligence · Chapter 6: Machine Learning

- Asked in: 2069 Bhadra, 4 marks; 2071 Bhadra, 3+5 marks (asked 2 times)
- Video: [From Natural Brain to Artificial Neural Networks](https://www.youtube.com/watch?v=e0OXSqhbyuQ) — Human Mind Power
- Answer checked by Claude Opus 5.5 (high effort) on 2026-10-14
- Page: https://www.bctengineer.com/q/semester_6/artificial_intelligence/ann-vs-human-brain
- Whole chapter: https://www.bctengineer.com/md/semester_6/artificial_intelligence/chapter-6

## Question

What is Artificial Neural Network (ANN)? Compare ANN with human brain with its functioning principle.

## Short answer

An ANN is a computing model of interconnected artificial neurons that learns by adjusting weights. The human brain uses ~10^11 slow biological neurons with electro-chemical signals and massive parallelism; ANNs use far fewer fast artificial nodes with numerical signals, simulated on serial computers. Both are parallel, distributed, learn from examples, and store knowledge in connection strengths.

## Answer

An **Artificial Neural Network (ANN)** is a computing model inspired by the biological brain. It consists of many simple processing units (artificial neurons) connected by weighted links; it learns from examples by adjusting the weights, and can perform classification, prediction and pattern recognition.

#### Working principle of an artificial neuron
```text
 x1 --w1--\
 x2 --w2---->[ Σ wi·xi + b ]--> f(net) --> y
 x3 --w3--/
```
Inputs are multiplied by weights, summed with a bias, and passed through an activation function (step, sigmoid, tanh). Learning changes the weights.

#### Working principle of the biological neuron
Dendrites receive signals from other neurons through synapses; the cell body (soma) sums them; if the total exceeds a threshold, the neuron fires a pulse along the axon to other neurons. Synaptic strengths change with experience, which is biological learning.

#### Comparison
| Point | Human brain | ANN |
|---|---|---|
| Basic unit | Biological neuron (about 10¹¹ neurons) | Artificial node (tens to millions) |
| Connections | About 10¹⁴ synapses, very dense | Limited, layered connections |
| Speed | Slow (milliseconds) per neuron | Fast (nanoseconds) per operation |
| Processing | Massively parallel, asynchronous | Mostly simulated on serial computers, synchronous |
| Signals | Electro-chemical pulses | Numbers |
| Learning | Changing synaptic strengths, continuous, unsupervised and supervised | Weight update by algorithms (e.g. back-propagation) |
| Fault tolerance | High; neurons die daily without failure | Moderate; graceful degradation |
| Knowledge storage | Distributed in synapses | Distributed in weights |
| Power use | About 20 watts | High for large networks |
| Capability | General intelligence, reasoning, creativity | Narrow, task-specific |

Similarities: both are parallel and distributed, learn from examples, store knowledge in connection strengths and tolerate noise.

## Check yourself

The answer's key points as multiple-choice questions, with the correct option and why.

1. How does an artificial neuron produce its output?
   A. It multiplies inputs by weights, sums with a bias and passes the result through an activation function
   B. It adds the inputs and outputs the largest one
   C. It multiplies the weights together and outputs the product
   D. It stores inputs and outputs them unchanged
   Answer: A. Net = sum(wi*xi) + b, then an activation function f gives y. (Key point: Artificial neuron working)

2. In the biological neuron, when does the cell fire a pulse along the axon?
   A. Whenever any single dendrite receives a signal
   B. When the summed signals received through dendrites exceed a threshold
   C. Only when the axon is fully charged
   D. When all synapses have equal strength
   Answer: B. The soma sums incoming signals and fires if the total exceeds a threshold. (Key point: Biological neuron)

3. Which comparison of brain and ANN is correct?
   A. The brain neurons are faster than ANN operations and process serially
   B. Both have the same speed and number of units
   C. The brain has slow neurons (milliseconds) but massively parallel processing; ANNs are fast per operation but mostly simulated serially
   D. ANNs are asynchronous and massively parallel while the brain is synchronous
   Answer: C. Brain: slow but parallel, asynchronous. ANN: nanosecond operations on mostly serial computers. (Key point: Speed and processing)

4. How does the brain learn compared with an ANN?
   A. The brain updates weights by back-propagation exactly
   B. The brain never changes connections after birth
   C. An ANN learns by growing new neurons physically
   D. The brain changes synaptic strengths; an ANN updates weights using algorithms such as back-propagation
   Answer: D. Both store knowledge in connection strengths, but the ANN uses algorithmic weight updates. (Key point: Learning)

## Related in Artificial Intelligence

- [Neural network; supervised vs unsupervised](https://www.bctengineer.com/question.md?key=semester_6%2Fartificial_intelligence%3Ann-supervised-unsupervised) — Artificial Intelligence, chapter 6
- [Role of perceptron in neural network](https://www.bctengineer.com/question.md?key=semester_6%2Fartificial_intelligence%3Aperceptron-role) — Artificial Intelligence, chapter 6, asked 2×
- [Perceptron; neural network as OR gate](https://www.bctengineer.com/question.md?key=semester_6%2Fartificial_intelligence%3Aperceptron-or-gate) — Artificial Intelligence, chapter 6, asked 2×
- [Perceptron; neural network as XOR gate](https://www.bctengineer.com/question.md?key=semester_6%2Fartificial_intelligence%3Aperceptron-xor-gate) — Artificial Intelligence, chapter 6, asked 2×
- [Neural network; back-propagation](https://www.bctengineer.com/question.md?key=semester_6%2Fartificial_intelligence%3Aneural-network-bp-algorithm) — Artificial Intelligence, chapter 6
- [Neural network, back-propagation, perceptron](https://www.bctengineer.com/question.md?key=semester_6%2Fartificial_intelligence%3Ann-bp-perceptron) — Artificial Intelligence, chapter 6

## Related in other subjects

- [ANN and back propagation](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Aann-backpropagation) — Data Mining, chapter 3
- [Short note: Neural network, perceptron rule](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Aneural-network-perceptron-note) — Data Mining, chapter 3
- [Natural vs artificial convection; heating curve](https://www.bctengineer.com/question.md?key=semester_5%2Felectric_machine_design%3Anatural-artificial-convection) — Electric Machine Design, chapter 2
- [Working principle of a (vector) network analyzer](https://www.bctengineer.com/question.md?key=semester_7%2Frf_and_microwave_engineering%3Anetwork-analyzer-working) — RF and Microwave Engineering, chapter 8, asked 2×