Open In App

Difference between ANN and BNN

Improve
Improve
Improve
Like Article
Like
Save Article
Save
Share
Report issue
Report

Do you ever think of what it’s like to build anything like a brain, how these things work, or what they do? Let us look at how nodes communicate with neurons and what are some differences between artificial and biological neural networks.

1. Artificial Neural Network: Artificial Neural Network (ANN) is a type of neural network that is based on a Feed-Forward strategy. It is called this because they pass information through the nodes continuously till it reaches the output node. This is also known as the simplest type of neural network. Some advantages of ANN :

  • Ability to learn irrespective of the type of data (Linear or Non-Linear).
  • ANN is highly volatile and serves best in financial time series forecasting.

Some disadvantages of ANN :

  • The simplest architecture makes it difficult to explain the behavior of the network.
  • This network is dependent on hardware.

2. Biological Neural Network: Biological Neural Network (BNN) is a structure that consists of Synapse, dendrites, cell body, and axon. In this neural network, the processing is carried out by neurons. Dendrites receive signals from other neurons, Soma sums all the incoming signals and axon transmits the signals to other cells. 

Some advantages of BNN :  

  • The synapses are the input processing element.
  • It is able to process highly complex parallel inputs.

Some disadvantages of BNN :

  • There is no controlling mechanism.
  • Speed of processing is slow being it is complex.

Differences between ANN and BNN :

Biological Neural Networks (BNNs) and Artificial Neural Networks (ANNs) are both composed of similar basic components, but there are some differences between them.

Neurons: In both BNNs and ANNs, neurons are the basic building blocks that process and transmit information. However, BNN neurons are more complex and diverse than ANNs. In BNNs, neurons have multiple dendrites that receive input from multiple sources, and the axons transmit signals to other neurons, while in ANNs, neurons are simplified and usually only have a single output.

Synapses: In both BNNs and ANNs, synapses are the points of connection between neurons, where information is transmitted. However, in ANNs, the connections between neurons are usually fixed, and the strength of the connections is determined by a set of weights, while in BNNs, the connections between neurons are more flexible, and the strength of the connections can be modified by a variety of factors, including learning and experience.

Neural Pathways: In both BNNs and ANNs, neural pathways are the connections between neurons that allow information to be transmitted throughout the network. However, in BNNs, neural pathways are highly complex and diverse, and the connections between neurons can be modified by experience and learning. In ANNs, neural pathways are usually simpler and predetermined by the architecture of the network.

Parameters ANN BNN
Structure 

input

weight

output

hidden layer

dendrites

synapse

axon

cell body

Learning very precise structures and formatted data they can tolerate ambiguity
Processor

complex

high speed

one or a few

simple

low speed

large number

Memory 

separate from a processor

localized

non-content addressable

integrated into processor 

distributed

content-addressable

Computing

centralized

sequential

stored programs

distributed

parallel

self-learning

Reliability very vulnerable robust
Expertise

numerical and symbolic

manipulations

perceptual 

problems

Operating Environment

well-defined

well-constrained

poorly defined

un-constrained

Fault Tolerance the potential of fault tolerance performance degraded even on partial damage

Overall, while BNNs and ANNs share many basic components, there are significant differences in their complexity, flexibility, and adaptability. BNNs are highly complex and adaptable systems that can process information in parallel, and their plasticity allows them to learn and adapt over time. In contrast, ANNs are simpler systems that are designed to perform specific tasks, and their connections are usually fixed, with the network architecture determined by the designer.


Last Updated : 30 Mar, 2023
Like Article
Save Article
Previous
Next
Share your thoughts in the comments
Similar Reads