Difference between Artificial Neural Network and Biological Neural Network

Artificial Neural Network and Biological Neural Network both are forms of neural networks. The primary distinction between these networks is that in an artificial neural network, the system's unique functional memory is placed independently with the CPU. On the other hand, the distributed memory in the biological neural network is located within the neural interlinks.

The biological processes of the human nervous system serve as the foundation for neural computers (brain). Neural computing entails substantial parallel processing and self-learning computers similar to a brain, which is made feasible by the brain's neural network. A neural network is just a collection of processing pieces that are interconnected in a web-like fashion and can provide some outcomes after receiving input.

In this article, you will learn the difference between Artificial Neural Network and Biological Neural Network. But before discussing the differences, you must know about Artificial Neural Network and Biological Neural Network with their advantages and disadvantages.

What is Artificial Neural Network?

The artificial neural network is the mathematical model which is mainly inspired by the biological neuron system in the human brain. The neural network is made up of a large number of processing components that are linked together by weighted paths to form networks. The result of every element is computed by applying a non-linear function of its weighted inputs. When these processing components are combined into networks, they can perform arbitrarily complicated non-linear functions like classification, prediction, or optimization.

Like the human brain, these artificial neural networks learn from experiences, generalize from examples, and may extract important information from noisy input. They can operate in parallel at a faster rate and are fault resilient.

Advantages and Disadvantages of Artificial Neural Network

There are various advantages and disadvantages of artificial neural networks. Some advantages and disadvantages of artificial neural networks are as follows:

Advantages

1. Large volumes of data may be utilized to train and generalize artificial neural networks. They may be trained using vast datasets, allowing them to make patterns-based predictions and judgments.
2. ANNs may be improved and employed efficiently on hardware accelerators or dedicated AI processors like GPUs and AI accelerators for quick and parallel processing.
3. Another advantage of ANN is that they continue to function even in the presence of noise or errors in data. As a result, they are appropriate in scenarios involving noisy, partial, or distorted data.
4. They are non-linear in nature as well. It enables them to represent complex data relationships and patterns. They can also be customized to handle various sorts of data and perform various activities.
5. They are capable of extracting features from data. It removes the need for manual feature editing. They can also be taught to handle many jobs at once. As a result, they may be utilized in advanced AI applications.

Disadvantages

1. Artificial neural networks may grow overly complex due to their architecture and the massive information used to train them. They can memorize the training data. It may result in a poor generalization of new data.
2. Artificial neural networks require suitable hardware components like central processors or dedicated AI accelerators, vast storage spaces, and massive random access memory.
3. Their working principles and even outcomes can be difficult to grasp because of the complexities of ANNs. Some people may find it difficult to comprehend their decision-making processes.
4. No explicit rule determines the structure of ANN. The proper network structure is obtained by trial and error.
5. They are also susceptible to adversarial instances or slight changes in input data. These modifications may cause the artificial neural network to make wrong decisions and produce irrelevant results.

What is Biological Neural Network?

The biological neural network is also composed of several processing pieces known as neurons that are linked together via synapses. These neurons accept either external input or the results of other neurons. The generated output from the individual neurons propagates its effect on the entire network to the last layer, where the results can be displayed to the outside world.

Every synapse has a processing value and weight recognized during network training. The performance and potency of the network fully depend on the neuron numbers in the network, how they are connected to each other (i.e., topology), and the weights assigned to every synapse.

Advantages and Disadvantages of Biological Neural Network

There are various advantages and disadvantages of the biological neural network. Some advantages and disadvantages of the biological neural network are as follows:

Advantages

1. It can handle extremely complex parallel inputs.
2. The input processing element is the synapses.

Disadvantages

1. As it is complex, the processing speed is slow.
2. There is no controlling mechanism in this network.

Key differences between Artificial Neural Network and Biological Neural Network

There are various key differences between Artificial Neural Network and Biological Neural Network. Some main differences between the Artificial Neural Network and Biological Neural Network are as follows:

1. ANN is the mathematical model which is mainly inspired by the biological neuron system in the human brain. In contrast, the biological neural network is also composed of several processing pieces known as neurons that are linked together via synapses.
2. An artificial neural network's processing was sequential and centralized. In contrast, a biological neural network processes information in parallel and distributive.
3. The artificial neural network is of a much smaller size than the biological neural network. In contrast, the biological neural network is large in size.
4. The biological neural network is fault tolerant. In contrast, the artificial neural network is not fault tolerant.
5. The processing speed of an artificial neural network is in the nanosecond range, which is faster than the biological neural network, where the cycle time associated with a neural event triggered by an external input is in the millisecond range.
6. BNN may perform more difficult issues than artificial neural networks.
7. The operating environment of the artificial neural network is well-defined and well-constrained. In contrast, the operating environment of the biological neural network is poorly defined and unconstrained.
8. The reliability of the artificial neural network is very vulnerable. In contrast, the reliability of the biological neural network is robust.

Head-to-head comparison between Artificial Neural Network and Biological Neural Network

Here, you will learn head-to-head comparisons between Artificial Neural Network and Biological Neural Network. The main differences between Artificial Neural Network and Biological Neural Network are as follows:

Conclusion

ANN and BNN both are forms of neural networks. The ANN is the outcome of the implementation of the BNN method. The main goal of developing the ANN is to create an expert system based on artificial intelligence (AI).