A glimpse into the double-edged nature of Brain Organoid Reservoir Computing, with the pros/cons of this biological computing approach
From a young age, I was captivated by the mysteries of science and the promise of technology, wondering how they could shape our understanding of the world. I was fortunate to receive STEM education early on in a specialized school, where my creativity and curiosity set me apart from my peers.
For the past 42 years, I have had the privilege of turning my hobbies into a fulfilling career that never felt like work. My exciting journey has allowed me to bridge the gap between the corporate world and academia, leading to exciting collaborations for cutting-edge fusion of biology, technology, and bio-digital transformation.
Recently, a group of passionate PhD students contacted me after discovering my article on Brain Organoid Reservoir Computing Bringing Hope for Cognitive Disorders like NeuroHIV. Eager to explore the integration of human brain (cerebral) organoids with electronic circuits, they asked me about sharing my literature reviews on neurocomputing. It was an absolute pleasure for me to share my ongoing research in the field.
When their supervisor invited me to present my findings in a guest lecture, I saw it as an opportunity to inspire these bright minds and connect them with leading researchers in the field for enhanced collaboration that could drive innovation.
Given the technical depth of the lecture, I decided to simplify the content for my readers interested in emerging biotechnologies, offering a glimpse of hope for addressing complex challenges in the near future with pros and cons. In this short story, I want to reveal the potential impact of organoid intelligence and its implications for the future of neurocomputing.
Summary of Key Points at a High Level
While human brains are slower than machines at simple tasks, they excel at processing complex information, especially in situations of uncertainty. This ability has evolved over millions of years, whereas machines have existed for only about a century, primarily as concepts in the human mind.
Our biological brains possess the unique capability of handling sequential and parallel processing, which gives them a significant advantage in decision-making from large and incomplete datasets.
In contrast, even advanced supercomputers struggle with this complexity. For instance, a 2013 experiment demonstrated that a Fijutsi supercomputer required 40 minutes to model just one second of human brain activity, underscoring the brain’s remarkable processing power. Each human brain, with billions of neurons and countless connections, has an estimated storage capacity of about 2,500 terabytes.
The emerging field of “organoid intelligence” (OI) aims to leverage this biological potential by using brain organoids — 3D cultures of human brain cells that better replicate the structure and function of actual brain tissue than traditional 2D cell cultures.
There are significant differences between biological learning and machine learning. For example, a larval zebrafish can navigate its environment using only 0.1 microwatts of energy, while a human brain requires about 20 watts.
In comparison, advanced machine learning systems may consume up to a million watts for training. This highlights humans’ energy efficiency, achieving complex tasks with far less power.
Moreover, humans learn from far fewer examples than machines. While humans can grasp simple concepts with just ten training samples, machines often require millions. This efficiency gap illustrates AI’s struggle with real-time learning and adapting to dynamic environments.
However, the landscape is changing rapidly, thanks to the promise of organoid intelligence. As discussed in my previous article, OI focuses on using brain organoids for computing tasks, offering potential alternatives to traditional silicon-based computers with advantages in speed, efficiency, and energy consumption.
The field is still in its infancy, requiring further research to realize its full potential. One exciting aspect of OI is its potential applications, which could lead to new biological computing technologies and a deeper understanding of cognition, learning, and memory.
Additionally, OI may pave the way for innovative brain-computer interfaces and neuromimetic AI algorithms capable of overcoming current AI limitations.
Researchers are actively exploring innovative ways to connect these brain organoids with computers and electronic sensors to enhance their ability to learn and respond to sensory inputs. This endeavor could bring groundbreaking advancements in technology and medicine.
Though using brain organoids in computing is still evolving, it holds significant promise for the future, potentially transforming our understanding and interaction with biological and artificial intelligence.
What are the Key Future Use Cases of BORC?
Brain Organoid Reservoir Computing (BORC) represents a groundbreaking fusion of biological computing and advanced brain-like models, paving the way for several intriguing and captivating healthcare applications.
At its core, reservoir computing uses a recurrent neural network architecture that features a fixed, randomly generated reservoir of neurons. The dynamics of this reservoir are initialized randomly and remain constant, with only the output weights trained.
This approach has captured the attention of scientists and technologists for its simplicity, efficiency, and effectiveness in processing temporal data. I want to summarize four key areas where BORC could make a significant impact in the future.
1 — Neurological Disorder Research
Brain organoids — miniature models of brain tissue — enable researchers to mimic neural development and study complex conditions such as autism, dementia, and epilepsy. By examining how neural networks form and function in organoids derived from patients, researchers aim to uncover new treatments targeting synaptic connections and neurodevelopmental pathways.
2 — Mental Health and Behavioral Science
BORC systems could revolutionize the study of psychiatric conditions by replicating brain environments in controlled lab settings. This advancement may lead to the identification of more effective treatments for mental health conditions like depression, bipolar disorder, and schizophrenia, where understanding neural circuitry is essential for progress.
3 — Biocomputing and Drug Testing
These organoids are experimental platforms for predicting human brain responses to new medications. With the potential to replace animal models in drug development, BORC could make testing more ethical and accurately reflect human biology, significantly improving the drug discovery process. This is a key enabler of Medicine 3.0.
4 — Brain-Machine Interfaces
Last but not least, BORC aims to interface with AI and machine learning systems, enhancing communication with prosthetics and other assistive technologies. This innovation could revolutionize neurorehabilitation for patients recovering from brain injuries, neuroinflammation, and cardiovascular events, such as strokes. This means that we might prevent cognitive decline and impairment which I covered in a previous article.
Conclusions and Key Takeaways
By leveraging the power of BORC, these use cases and applications promise to advance our understanding of the brain and offer exciting possibilities for improving healthcare outcomes and patient quality of life.
The field is still in its early stages, and scaling up these systems remains challenging. Moreover, ethical concerns about using organoids, especially as they become more advanced, are being actively discussed.
Notably, a recently appointed and respected Nobel Laurette and computer scientist, Dr. Geoffrey Hinton, voiced serious concerns and apprehensions about uncontrolled AI systems, warning of potential existential crises.
Like Dr. Hinton’s thoughts and sentiments, I articulated my growing concerns in an article in 2021 titled Artificial Intelligence Does Not Concern Me, but Artificial Super-Intelligence Frightens Me.
However, the potential of these super-intelligence systems for transforming neuroscience and healthcare is immense, especially in advanced diagnostics and personalized medicine.
These advancements represent the critical enablers of Medicine 3.0, moving us away from the inefficiencies of Medicine 2.0, which often fails to meet the demands of modern society.
I want to highlight the hybrid neuromorphic system, now known as Brainoware. It combines human brain organoids with electronic circuits and represents a breakthrough in the intersection of neuroscience and AI.
Developed by researchers at Indiana University, this biocomputer leverages brain organoids — tiny 3D brain-like cell cultures derived from stem cells — to perform computational tasks by interacting with a multielectrode array.
These arrays allow the organoids to receive input and send output through electrical signals, which are then processed using machine learning algorithms which can be processed by sophisticated AI systems implemented and hosted on supercomputers with quantum capabilities.
This type of innovative system might offer exciting possibilities. For instance, a recent experiment demonstrated speech recognition capabilities by identifying speakers from a pool with 78% accuracy.
Moreover, the biological nature of the brain organoids introduces advantages like low energy consumption, fast adaptation, and unsupervised learning, mimicking the brain’s neuroplasticity.
Therefore, I am closely following the work of Dr Feng Guo, whose papers were cited by 7,700 scientists. PhD students I supervise are now reading and digesting his remarkable papers on this exciting topic in Nature Electronics and other leading journals like Frontiers in Science.
Biotechnology and neurocomputing researchers see potential applications in understanding neurological disorders like Alzheimer’s and replacing animal models in brain research. However, challenges remain in maintaining the stability and scalability of the organoids for more complex computing tasks.
It is still in the early days for these biotechnology marvels, but emerging studies highlight how future AI systems could merge biological and electronic elements to achieve higher levels of adaptability and efficiency.
If we can manage ethical concerns, the journey ahead is filled with exciting and delightful possibilities. So, I invite you to explore these remarkable advancements with me and many aspiring scientists and technologists contributing to this field.
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A Quick Update on My Recent Book Projects
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I also published a new version of the Substack Mastery for busy people and explained the reasons in a new story.
How I Will Help Freelance Writers Save $600 by Condensing My Bestseller 5 Times for Them
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