Neuroscience & AI: A Symbiotic Relationship

While artificial human intelligence is not yet created, AI systems continue to astonish us with advancements such as handwriting recognition , disease detection , and even impressive solutions in sports . Advanced tech solutions bode well for major industries today, but there’s one area that turned things around and had a tangible impact on AI development — neuroscience.?

Neuroscience has played a significant part in AI research. After all, AI is intended to mimic the way the human brain works and makes decisions, so scientists from all around the world go the extra mile to create neural networks that could reflect brain structure. Let’s see closer why this issue is on the top of today’s AI agenda!?

How Does Neuroscience Improve AI-Powered Technologies?

Did you know that neural networks used to build AI devices have been inspired by our brain structure? The neural networks are similar, if not identical, to our biological neurons. AI neurons, just like neurons in the human brain, link and activate as soon as they get the correct input, disseminating the information throughout the system. And yes, they both learn when fed with the right information: training data for AI models and real-world information for humans.?

When you think about it, AI and neurology are both dealing with the same issue: experts in both areas strive to constantly explore the depths and myriad capabilities of the human brain. Several areas of artificial intelligence and neurology overlap . While they serve different ends, it isn’t a big leap to claim that AI and neuroscience are inextricably intertwined. Indeed, neuroscience learnings serve as a bedrock for data professionals when developing AI-powered systems. Let’s take a closer look at some examples, shall we??

?— Transfer Learning

To cope effectively with unfamiliar situations, ML models must be able to draw on prior knowledge to make sound judgments. Humans are already competent at this: they can drive a car, operate a laptop, or chair a meeting even when presented with an unusual vehicle, operating system, or social scenario.

Researchers are currently taking the initial steps toward understanding how the very same process may be accomplished in artificial systems. A progressive network , for example, is a novel type of network design that may apply information learned in one video game to learn another one. The same architecture has also been demonstrated to transfer information from a simulated robotic arm to a human arm, significantly lowering training time. Surprisingly, these networks resemble models of sequential task learning in humans. Such enticing connections imply that future AI research has a lot to learn from neuroscience. Isn't that exciting??

?— Mimicking Human Intelligence

Despite their biological inspiration and performance achievements, AI systems differ from human intelligence in crucial ways. For a machine to learn or think like a person, it would need to be able to:

• Explain and understand problems;
• Learning-to-learn for acquiring knowledge;
• Generate predictions, recommendations, and recognize patterns in data;
• Generalize knowledge to new tasks and situations.?

In a study conducted by KAIST , researchers have developed a computational and neural mechanism for human meta reinforcement learning. As humans, we can adapt to complexity and uncertainty when making decisions. That’s why, currently, scientists create models that can make judgments and solve issues just like people. Does this mean we are soon to see more human-like machines??

— Reflecting Brain Structure

We borrowed inspiration from the structure of a human brain (that contains about 86 billion neurons!) to design the neural networks we know today. Neural networks learn from a large amount of data. Each network link is assigned to a weight that determines the relevance of neurons. Weights are adjusted during the training phase to strengthen or decrease the link between neurons. These processes can reflect parts of the brain , showing how it organizes and accesses spatial information. So, who knows, maybe machines will be able to do jobs and solve problems better than humans in the future, rivaling or perhaps exceeding human intelligence.?

Now, It’s AI Turn to Advance Neuroscience!

As we see, neuroscience can be used to help us dig deeper into our brains and use this knowledge for the creation of artificial neural networks. But how exactly does AI fuel the development of neuroscience?

— Uncovering Peculiarities of How Our Brain Works

AI can actually help us understand some of the unsolved mysteries of the human brain. Based on virtual brains, scientists can research how our thoughts take shape , how the brain processes language , and even investigate aging processes . All this data can help us a lot more than just satisfy our curiosities.

The information provided by neural activity patterns, which are similar to brain activity patterns, can help us better detect mental disorders and empower people with disabilities to increase their mobility . Don't expect a virtual brain to compete with the highly complex nature of the human brain right away, but it's definitely a good place to start!?

— Reinventing the Golden Age of Neuroscience

When it comes to investigating our human thoughts, complexity is a major concern. Neuroscientists are uncovering the abstruseness of how billions of brain neurons interact using machine learning to examine data by revealing patterns of the brain activity.

Scientists can construct better models of the brain for educating neurology students in medical institutions with richer, AI-generated information about its structure and functions. Furthermore, pattern recognition skills developed by ML systems can help scientists test novel theories more rapidly and publish their findings for medical improvements. Many features of the human brain are still largely unexplored. If AI can identify these hidden facts using its vast array of talents and abilities, neuroscience can make significant steps towards its excellence.?

Neurologists will be able to break the unknown codes using big data and deep learning algorithms to gain a better understanding of human behavior. Furthermore, these findings may be utilized in a variety of sectors, including occupational health and safety, as well as forensics.

The Tip of the Iceberg

Even though AI systems aren’t (yet) developed enough to handle all the issues that humans have, researchers are not giving up so easily. Their discoveries aim to train AI to solve problems just like we do, and there are many prospects for more powerful solutions.

As a result, the progress we witness in both fields is not just critical for each other, but is also valuable for numerous fields and industries that rely on AI. I’m sure it’s going to happen soon because of how essential it is that AI and neuroscience nurture each other in all possible ways. Sounds exciting? Let me know your opinion on the topic below!??