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Can AI help us understand the human brain better?

February 08, 2024

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Can AI help us understand the human brain better?

Exploring the intricacies of the human brain has captivated scientists and philosophers for centuries. Today, artificial intelligence (AI) stands at the forefront of this exploration, offering tools and insights that could revolutionize our understanding. How might AI illuminate the labyrinthine workings of our neural networks? Let's dive into the possibilities and challenges that come with marrying AI with brain research.

  • 🧠 Brain Decoding: AI has the potential to decode the brain's complex communication systems. Neural networks, a subset of AI inspired by our brain's architecture, can analyze patterns in brain data like fMRI scans (We wrote about how neural networks work several weeks ago here). They might help us understand how certain thoughts or conditions are represented in the brain. For instance, imagine an AI that can accurately map which brain areas light up when we think about words like "apple" or "joy". By doing so, we unlock a new level of understanding not just of language, but of intricate thought processes.

  • 🤖 Synergy and Simulation: AI may help create simulations of brain activity, offering a non-invasive window into the living mind. These simulations can allow researchers to experiment with changes in neural connections and observe potential outcomes. It's like having a sandbox version of the brain to play with—adjust a parameter here, simulate years of development, and witness the potential evolution of cognitive processes or the progression of degenerative diseases under controlled conditions.

  • đź”® Predictive Powers: AI could be a crystal ball, predicting the onset of neurological conditions. Through deep learning, AI systems can be trained on existing patient data to spot early warning signs of disorders like Alzheimer's far sooner than current methods allow. This could lead communities to intervene earlier, potentially improving outcomes and giving patients better quality of life.

  • 🤝 Patient-Tailored Treatment: Tailoring treatments to the individual could become the norm with AI's help. By understanding a patient's unique brain structure and activity, AI can contribute to designing personalized therapies. Picture a world in which every treatment plan for mental health conditions is custom-crafted with the aid of AI, optimizing the choice of medication, therapy, and lifestyle changes for the best possible recovery.

  • 🚧 Roadblocks and Realism: AI's integration into brain science isn't without hurdles. Ethical concerns regarding data privacy and the potential misuse of neurotechnology are prominent. There's also the risk of over-reliance on AI, which could lead researchers to downplay the value of human intuition and traditional investigative methods. Plus, the 'black box' nature of AI poses a challenge—how do we trust decisions made by systems we don't fully understand? Balancing innovation with these concerns will be key to ethical progress.

Artificial intelligence, though in its embryonic stages regarding brain research, holds incalculable promise. It positions itself not as a tool of replacement but as a catalyst to amplify our current understanding of the human brain. As we stand on the cusp of this new frontier, it's essential to ask the right questions, establish ethical guidelines, and cautiously navigate toward a future of unprecedented brain insights. Given the advances that are already underway, how likely is it that these AI-driven revelations about our brain will become an everyday reality?

In the real world…

  • Researchers at the University of Wisconsin-Madison have developed the first 3D-printed functional human brain tissue that can grow cell networks in the same way a human brain does.

  • Neuralink is developing a device that would be implanted in a person's brain to record and stimulate brain activity. In January 2024, Musk claimed Neuralink's first human patient had already received a brain implant.

  • BrainSightAI uses AI and machine learning to map the human brain and help medical and research experts, by assisting with the early detection of neurological disorders.

  • A team at Northwestern University has developed a new type of transistor, called a moirĂ© synaptic transistor, which allows AI systems to move potentially match the efficiency of the neurons within a human brain.

What do the experts say?

"Neuroscientists now face orders of magnitude higher complexity as they have moved on to studying populations of neurons in living animals and people. The data, even from just 100 neurons, is dizzyingly large. It varies dynamically with no obvious rhyme and reason. And it’s rarely clear which parts of it are truly relevant to the brain function being studied. These factors have made it much harder to come up with models, conceptual or mathematical, to describe the neural activity."

— Bahar Gholipour, from AI is helping scientists explain our brain in Cold Spring Harbor Laboratory

"[R]esearchers from the University of Texas at Austin used a neural network to monitor brain signals from participants in an MRI scanner. Using just data from the MRI, the [artificial neural network] could produce a rough summary of a story that the test subject was listening to, a description of a film they were watching, or the gist of a sentence they were imagining."

"While AI can outperform humans in tasks from creative thinking to hiring staff, it takes a long time for AI to learn to do this. Humans are able to learn from a single instance of a new experience, while AI needs to be exposed to examples hundreds, if not thousands of times. And, very importantly, when humans learn something new it doesn't interfere with what we already know, whereas this is the case for AI."

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