Difference between ANN and BNN

Do you ever think of what it’s like to build anything like a brain, how these things work, or what do 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 which 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 complex.

Differences between ANN and BNN :

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

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