In order to discuss this topic we need to know all of the terms:
Biomimetics applied: Kingfisher bird (left) and Shinkansen 500 Series (right)
Biomimetics or biomimicry is the emulation of the models, systems, and elements of nature for the purpose of solving complex human problems.
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks.
Biological neural network (left) and artificial neural network (right)
Biological neural network (BNN) is a structure that consists of synapse, dendrites, cell body, and axon.
Artificial neural network (ANN) is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a BNN.
The BNN is composed of several synaptically coupled processing units called neurons. These neurons either take in input or the outcome of other neurons. The last layer, where the findings may be displayed to the outside world, is where the produced output from the individual neurons propagates its impact on the whole network.
When the network is being trained, each synapses is assigned a processing value and weight. The amount of neurons in the network, their connections between them (i.e., topology), and the weights assigned to each synapse all have a significant impact on the network's performance and potency.
Similarly, the ANN is also made up of a variety of processing components, called (artificial) neurons, that are connected by weighted paths to create networks. Each element's output is calculated by applying a non-linear function to its weighted inputs. Networks that combine these processing components may perform arbitrary complicated non-linear operations, such as tasks involving classification, prediction, or optimization.
These artificial neural networks can recover important data from a noisy environment and learn from experiences and examples, just like the human brain.
The main differences between BNN and ANN:
| Basis for comparison | BNN | ANN |
|---|---|---|
| Processing | Parallel and distributed | Sequential and centralised |
| Speed (in processing information) | Slow | Fast |
| Size | Large | Small |
| Allocation for storage to a new process | Easy as it is added just by adjusting the interconnection strengths | Strictly irreplaceable as the old location is saved for the previous process |
| Control mechanism | Activites are centrally controlled (not monitored by a control unit) | Activites are monitored by a control unit |
| Fault tolerance | Implicitly fault tolerant | Intolerant to the failure |
In conclusion the ANN is the outcome of the implementation of the BNN approach. Although there are some similarities between the two, the differences are apparent.
Bibliography:
https://www.youtube.com/watch?v=iMtXqTmfta0
https://en.wikipedia.org/wiki/Biomimetics
https://en.wikipedia.org/wiki/Artificial_neural_network
https://www.geeksforgeeks.org/difference-between-ann-and-bnn/
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