What is an Artificial Neural Network?
The phrase neural network or artificial neural network describes a branch of artificial intelligence (AI) that is inspired by biology and is based on the human brain. “An artificial neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain.”1 Similar to how neurons in a human brain are linked to one another, neurons in artificial neural networks are linked to one another at different layers of the networks. 2
In short, neural networks mimic how the human brain operates, enabling computer programs in AI, machine learning, and deep learning to identify patterns and address common issues. 2,3
Billions of neurons, or nerve cells, make up the human brain, and axons connect them to a million other cells. Dendrites accept input from sensory organs and stimuli from the outside environment. These inputs produce electric impulses and quickly pass through the neural network. A neuron can either forward the message to another neuron for handling or stop doing so.4 The artificial neural network is created to behave just like interconnected brain cells to replicate the neuronal network that comprises the human brain so that computers will have the option to understand concepts and make decisions in a human-like manner. 5
The structure of artificial neural networks is based on the concept of nodes, which contain input, hidden, and output layers. There are connections between each node or artificial neuron, and each one has a threshold and weight that go along with it. Changes in weight values allow artificial neural networks to learn.5,3
What Makes Neural Networks so Crucial?
Computers can make intelligent decisions with minimal human intervention, thanks to neural networks. This is due to the fact that they have the ability to learn and model complex, nonlinear relationships between input and output data.1
The numerical value of artificial neural networks also allows them to carry out multiple tasks at once.1
Without formal training, neural networks can understand unstructured data and make broad observations. 1
Data is stored throughout the network, not just in databases. Therefore, the network continues to function even if a few pieces of data are lost in one location.1
The Uses of Neural Networks
Neural networks are helping people today survive the changes brought about by the new eras in the financial, aerospace, and automotive industries. As you have an understanding of the fundamentals of neural networks and deep learning, you can further learn and understand how they are advancing various industries. In a variety of fields, they can perform tasks that are easy to perform for humans, but difficult to accomplish by machines. Facial recognition systems, stock market forecasting, military, picture compression, medicinal chemistry and drug research, signature verification and handwriting analysis, healthcare, and security are just a few of the fields that fall under this category.1,6
For more information on coding, related technologies, and the miracles that code can accomplish, explore BYJU’s FutureSchool blog.
- What is a Neural Network? Explanation and Examples. (n.d.). Retrieved October 11, 2022, from https://www.techtarget.com/searchenterpriseai/definition/neural-network
- (PDF) Artificial Neural Network Systems. (n.d.). Retrieved October 11, 2022, from https://www.researchgate.net/publication/350486076_Artificial_Neural_Network_Systems
- Explained: Neural networks | MIT News | Massachusetts Institute of Technology. (n.d.). Retrieved October 11, 2022, from https://news.mit.edu/2017/explained-neural-networks-deep-learning-0414
- The remarkable, yet not extraordinary, human brain as a scaled-up primate brain and its associated cost | PNAS. (n.d.). Retrieved October 11, 2022, from https://www.pnas.org/doi/10.1073/pnas.1201895109
- Artificial Neural Network: Understanding the Basic Concepts without Mathematics – PMC. (n.d.). Retrieved October 11, 2022, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6428006/
- Neural Networks – Applications. (n.d.). Retrieved October 11, 2022, from https://cs.stanford.edu/people/eroberts/courses/soco/projects/neural-networks/Applications/index.html