The similarities and differences between neural networks and the human brain
Unlocking the Mystery: Exploring the Parallels Between Neural Networks and the Human Brain
How similar are Neural Networks to our Brains?
Have you ever come across an unusual animal or plant and tried to figure out what it might be? You might not realise it, but the process that your mind goes through in trying to identify the organism is the same principle that drives the operation of artificial neural networks. Your brain runs an image across thousands to millions of ‘reference images’ stored in it. It then executes quick checks with the animal species information it has accumulated over the years. This process uses your biological neural network to reprocess past experiences, making it possible to deal with the unfamiliar situation at hand.
The Evolution of Artificial Neural Networks
The science of artificial intelligence, which was gaining popularity in the past, pondered the idea of creating machines capable of learning, adapting, and making decisions much like the human brain. Hence, artificial neurons were developed, inspired by the brain’s biological neurons. These artificial neurons could be interconnected in intricate ways to create artificial neural networks (ANN) capable of creating more complex outputs.
The concept of artificial neural networks dates as far back as 1943, with major milestones like Frank Rosenblatt’s Perceptron in the 1950s. The Perceptron was an artificial neural network that could “learn” based on data examples, essentially emulating the functionality of biological neural networks.
However, the Perceptron showed weaknesses in dealing with certain problems, especially non-linear functions. Years later, deep learning with hidden layers of neurons was introduced to resolve these challenges. In fact, around 2006, the combination of powerful GPU processors, big data, and cloud computing renewed interest in the potential of artificial neural networks. Today, they are key components of voice assistants, image and facial recognition technology, online translation services, and search engines.
Comparing Neural Networks and the Human Brain
The human brain is undoubtedly the most complex and potent information processor known to man. Similarly, artificial neural networks seek to replicate the efficacy of the brain in processing information. We can witness the success of this in AI systems that have matched, and even surpassed, the human brain in tasks like object recognition and language translation.
Neurons are a common aspect in both human brains and artificial neural networks. However, they function differently in the two cases. Known components of the human neuron include dendrites for receiving information and axons for outputting information, forming the cell body. In the artificial neuron, however, input and output are taken directly from the neuron.
Despite these differences, artificial neural networks still seek to replicate the brain’s capability to process large volumes of information in complex ways. Remember, the human brain functions using 100 billion neurons and has about 100 trillion synapses, which are the junctions between two neurons. It’s a level of complexity artificial neural networks aspire to achieve.
Want to know more about the similarities and differences between the human brain and neural networks? Check out this article on Fast Data Science!
Today, technology has advanced to the point where we have artificial neural networks that can perform functions with a reasonable semblance to the workings of a human brain. However, achieving the complexity and efficacy of the human brain remains a challenge at the forefront of AI and neural network research. As we continue to understand more about the functioning of our brains and deepen our understanding of AI, it’s only a matter of when, not if, we will achieve this and who knows what other incredible milestones!
