Artificial neural networks are the backbone of deep learning algorithms, the cutting edge of AI. Moreover, a neural network assists machines in acting like humans. This article gives an overview of artificial neural networks. But, first, let us discuss artificial neural networks' history, significance, structure, and benefits.

Evolution of neural networks

• A 1943 paper by neurophysiologist Warren McCulloch and mathematician Walter Pitts laid the groundwork for artificial neural networks.
• Nathanial Rochester of IBM's research laboratories led the first attempt to simulate a neural network in the 1950s. That initial attempt was unsuccessful. However, subsequent attempts were successful. During this time, traditional computing flourished, and the focus shifted away from neural research.
• In 1956, the Dartmouth Summer Research Project on Artificial Intelligence accelerated the development of artificial intelligence and neural networks.
• In 1959, Stanford's Bernard Widrow and Marcian Hoff developed the ADALINE and MADALINE models. MADALINE was the first neural network to solve a real-world problem.
• By 1985, the American Institute of Physics had established what has grown into an annual conference titled Neural Networks for Computing. 
• By 1987, the Institute of Electrical and Electronics Engineers (IEEE) had attracted over 1,800 attendees to its first International Conference on Neural Networks.
• In 1989, Bernard Widrow told the Neural Networks for Defense audience that they were in World War IV, "World War III never happened," with global trade and manufacturing as the battlefields.

Why neural networks?

A computer or a human can use neural networks' remarkable ability to extract meaningful data from imprecise data to detect trends and patterns. A trained neural network can be made an "expert" in the data under investigation and used to make projections. The majority of business applications and commercial enterprises use these technologies. Other applications include speech-to-text transcription, data analysis, check processing, weather prediction, and signal processing.

The structure of ANN

An ANN is composed of artificial neurons analogous to the neurons found in the human brain. Like synapses in a biological brain, each connection can signal other neurons. An artificial neuron receives a signal, processes it, and signals other neurons. The "signal" at each link is a number, and each neuron's output is a nonlinear function of its inputs. Edges are the connections. The weight of neurons and edges changes as learning progresses. The weight affects the signal strength at a link. A neuron may have a threshold that only sends a signal if the aggregate signal crosses it. Signals travel from the input layer to the output layer, possibly multiple times.

Benefits of neural networks

• Self-organization: During the learning process, an ANN can generate its representation of the information it receives.
• Real-Time Operation: AReal-time ANN calculations are possible with some unique (hardware) devices.
• Adaptive learning: Adaptive learning refers to the ability to learn how to solve problems based on the data provided in the training set.
• Redundant Information Coding Through Fault Tolerance: The performance of a network goes down when a part of it is damaged. Furthermore, some networks will be able to retain data even if the network is severely damaged.

Conclusion

Neural networks contribute to other fields of study, such as psychology and neurology. It is used in neurology to study the brain's internal mechanisms and model the parts of living organisms. The most intriguing aspect of neural networks is the possibility of developing 'conscious' networks in the future. According to some scientists, consciousness is a "mechanical property", and conscious neural networks are feasible and realistic. We can maximize the potential of neural networks by collaborating with fuzzy logic, computing, AI, and machine learning.