Artificial neural network: Here’s everything you need to know about black box of AI

Neural circuits are groups of neurons that are connected by synapses – a structure that allows neurons to pass chemical and electrical signals to other neurons. Neural networks, within the brain and in human intelligence, have been the inspiration behind creating artificial neural networks in artificial intelligence.

What is an artificial neural network?

An artificial neural network is a system of algorithms that works in a similar way. It is one of the subsets of machine learning under artificial intelligence. The models are based off of biological neurons in the brain forming a neural network.

A sample illustration of an artificial neural network.

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The artificial neurons form the basis of artificial neural networks. Similarly to biological neural networks, which have neurons and synapses, ANNs have nodes and connections between nodes. As the ANN analyzes large amounts of data, it forms new connections and develops the capability to solve difficult problems or perform challenging tasks.

Past history of ANNs

In 1958, Frank Rosenblatt, an American psychologist, created one of the first prototype models of an artificial neural network, building on the work of researchers Warren McCullock and Walter Pitts, who published their concept of an artificial brain cell as a logic gate with binary outputs in 1943. The name of Rosenblatt’s creation was the Perceptron. The machine was considered one of the first to “perceive an original idea.” It was created to replicate how the human brain processes information and learned to identify various items.

Dr. Rosenblatt worked on the “perceptron” – what he described as the first machine “capable of having an original idea.”

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“Stories about the creation of machines having human qualities have long been a fascinating province in the realm of science fiction,” Rosenblatt wrote in 1958. “Yet we are about to witness the birth of such a machine – a machine capable of perceiving, recognizing, and identifying its surroundings without any human training or control.”

Although the Perceptron was not able to recognize complex patterns, it was an innovative step forward in artificial neural network research.

How do artificial neural networks learn and work?

ANNs are inspired by neural networks in animals and are able to “learn” and improve in order to solve problems, such as those related to pattern recognition. The artificial neuron is a mathematical function that acts in some ways as a simulation of biological neurons.

The artificial neurons receive input and then use the information to create the output or data. Biological neurons similarly have input and output signals. However, ANN uses mathematical equations to connect all of the artificial neurons to create the artificial neural network.

The system of artificial neural networks is still an area within artificial intelligence that needs to be further studied and examined. The biological inspiration behind ANNs is extremely complex and distinctive.

Researchers are also taking a look at different ways to incorporate biological components of communication within the artificial neural network. Recently, scientists have developed a realistic artificial neuron that can communicate in diverse ways, both chemically and through electric pulses, more closely mimicking a biological neural network. The unprecedented study was published on November 7, 2022, in the journal Nature Electronics.