An exclusive interview with Dr. Omowuyi Omoniyi Olajide, pioneering the fusion of brain organoids and robotics to redefine intelligence.
Q: Can we meet you?
A: I am Omowuyi Omoniyi Olajide, born and raised in Nigeria in a family that valued scholarship, faith, and service. I grew up in a modest neighborhood where power outages were common and resources scarce, yet my parents instilled in me the belief that discipline, curiosity, and persistence could overcome any limitation.
As a child, I spent evenings repairing broken appliances, stripping wire from discarded electronics, and teaching myself circuitry from borrowed textbooks. When something broke in the house, I treated it like a lab assignment, sketching block diagrams and experimenting until it worked again. That habit, asking why a system failed and what lay beneath the surface shaped my lifelong passion with understanding the root of every problem.
In school, I joined science clubs, built makeshift experiments, and thrived on turning constraints into creativity. I pursued rigorous studies in mathematics, physics, and computer science, eventually leading to my current research in brain-inspired computing, semiconductor electronics, and robotics. These are fields that combine precision, adaptability, and innovation to serve humanity.
Q: What inspired you to combine brain organoids and robotics? How did the brain-on-a-chip idea begin?
A: Growing up, I was captivated by the human brain, the most complex system of the human body. My undergraduate study in electrical engineering at Ahmadu Bello University deepened that fascination. I learned to design amplifiers and processors that mimic neural activity, but I soon realized that traditional hardware could never truly capture the brain’s fluid adaptability.
During my master’s in computer engineering, I explored how biological systems manage noise and errors, much like communication networks correct transmission faults. This synergy between biology and engineering became the foundation for my PhD in bioengineering at UC San Diego, where I collaborated with Prof. Alysson Muotri’s stem cell lab. There, I encountered brain organoids, which are tiny, living clusters of human brain cells that fire and communicate like a miniature brain.
One day, while observing organoids pulsing with activity under a microscope, a thought struck me: What if we gave them a body? What if they could learn through interaction? That question sparked my journey toward integrating organoids with robotics, creating what I now call the brain-on-a-chip system, a living computational interface bridging biology and machines.
This innovation has profound potential: from brain prosthetics and personalized medicine to space exploration and adaptive robotics, all powered by living intelligence.
That question sparked my journey toward integrating organoids with robotics, creating what I now call the brain-on-a-chip system, a living computational interface bridging biology and machines.
Q: How does your system translate brain organoid activity into robotic movement?
A: It’s a closed-loop system that converts biological signals into precise mechanical motion within milliseconds. At its core lies a custom-designed multi-electrode array (MEA) chip, a 1024-channel semiconductor system-on-chip on which the brain organoid grows directly.
As the neurons mature, they connect with the chip’s electrodes and can generate electrical impulses. These signals are captured, filtered, and translated into motor commands using algorithms I developed. When specific patterns of neural firing occur, the robot responds by moving, avoiding obstacles, or performing tasks.
It’s essentially a living processor, organic intelligence guiding engineered motion.
Q: How do you know these organoids are learning rather than just reacting?
A: We measure genuine learning through controlled experiments. In early trials, untrained organoids performed avoidance tasks with only 8% accuracy. After 20 repetitions, performance improved to nearly 50%, demonstrating adaptation through Hebbian plasticity, the biological rule that “neurons that fire together wire together.”
Learning is confirmed by silencing neurons with tetrodotoxin (TTX); when the spikes cease, learning also ceases. Once regular activity resumed, there was a significant improvement. Calcium imaging and electrophysiological analyses revealed that long-term potentiation synapses strengthened over time.
In short, these organoids are not pre-programmed; they self-evolve through experience, learning much like living brains do.
Q: How do you measure the intelligence of these mini-brains?
A: I measure the intelligence of brain organoids through a multifaceted, quantitative framework that assesses their cognitive capabilities, drawing from neuroscience and AI benchmarks, while ensuring ethical, non-invasive protocols using iPSC-derived tissues. Core metrics include learning efficiency, where the organoids are interfaced with the chip to train on pattern recognition tasks, and quantifying adaptation via error rate reduction as well as synaptic plasticity measured by long-term potentiation (LTP) changes in neural firing rates. Memory retention is evaluated by recall accuracy in stimulus-response paradigms, tracking neural oscillations (theta/gamma waves) via electrophysiology to mimic human episodic memory, with success rates hitting 70-90% in advanced assembloids. Problem-solving and reasoning are tested in biohybrid setups, where organoids control virtual or robotic environments, scoring on adaptability metrics such as obstacle navigation efficiency or multi-step decision-making, benchmarked against AI models (e.g., organoids achieve 10 times the energy efficiency in equivalent tasks). Morphological and developmental proxies, such as network complexity (measured by neuron density and connectivity via imaging) and integrated information theory for consciousness-like integration, provide foundational indicators of intelligence, analyzed with AI tools for automated quantification. This holistic approach evolves iteratively, validating results as a bridge between biological and artificial intelligence, with real-time optical or MEA readouts ensuring precise and reproducible insights.
Q: How did your background in electrical and computer engineering influence this biological research?
A: My engineering background taught me to see biology as a system, a network of feedback loops, information flow, and dynamic equilibrium. Concepts such as signal processing, control theory, and hardware optimization provided me with tools to introduce quantitative precision into biological experimentation.
In essence, I approach the cell like a circuit, and the brain like a computational architecture. This perspective helps transform biology from observation into engineering, something that can be designed, optimized, and understood systematically.
Q: Can your system be scaled—could multiple organoids collaborate to control complex machines?
A: Absolutely. Multiple organoids can be linked through neural bridges and shared electrode networks, forming distributed bio-networks capable of parallel computation. Essentially, living neural clusters for advanced robotic control.
Q: How might this transform the way we test drugs for Alzheimer’s or Parkinson’s?
A: It’s revolutionary. Instead of testing on animals, we can now study human disease in human neural tissue. Patient-derived brain organoids replicate specific pathologies, enabling direct observation of how drugs affect living neural circuits.
In our lab, we’ve seen drugs like levodopa restore dopamine signaling in Parkinson’s organoids and anti-amyloid therapies dramatically boost synaptic function in Alzheimer’s models. This approach offers 95% predictive accuracy for clinical outcomes, which is far superior to that of animal models, and drastically accelerates the drug development process.
Q: Could patient-derived organoids make personalized medicine a reality?
A: Yes, and it’s already happening. With a simple skin or blood sample, organoids can be grown that mirror a patient’s unique neurobiology, allowing for direct testing of drugs on them. In a short period, doctors can identify which treatments will work best, eliminating guesswork and trial-and-error.
It’s precision medicine powered by your own biology.
Q: What ethical considerations arise when using living human neurons to control machines?
A: Ethics is at the core of my work. These organoids are created from adult stem cells (iPSCs) obtained under strict consent. No fetal or embryonic tissue is involved. The key questions I continually assess are whether the highest ethical standards, transparency, respect for human dignity, and regulatory oversight are maintained.
Q: How do you see biological and artificial intelligence evolving together?
A: I believe they’re on the path to convergence, not competition. Biological intelligence offers adaptability and creativity, while artificial intelligence offers scale and precision. Together, they form a biohybrid superintelligence, systems that think, learn, and evolve more efficiently than either alone.
By 2040, we could see distributed networks of bio-silicon processors. These will be living computers operating at room temperature, capable of climate modeling, medical discovery, and real-time reasoning far beyond today’s AI.
Q: How did your experiences in Nigeria shape your passion for innovation?
A: Growing up in Nigeria, the frequent, inadequate power supply that often left me studying and experimenting in the dark for extended periods provoked my passion and motivated me to explore innovative solutions in bioinspired energy systems. The limited presence of innovative research leading to groundbreaking discoveries in science and technology inspired my resolve to engage deeply in advanced, methodical research in critical fields that shape the underlying technology of our day. I also grew up in a home that valued discipline and emphasized persistence. I view failure as a learning opportunity, and it has shaped my methodical approach to experimentation in engineering, research, and development.
Q: What’s your ultimate vision for this technology?
A: My ultimate vision for this technology is to forge a revolutionary breakthrough in biocomputing, where lab-grown neural networks derived from stem cells become the cornerstone of next-generation systems that surpass traditional silicon-based architectures in efficiency, adaptability, and problem-solving prowess, unlocking solutions to humanity's grandest challenges in health and discovery. This technology will evolve into powerful tools for accelerating drug discovery, enabling rapid and precise simulations of neurological diseases to identify cures that have eluded us for decades. This will transform the landscape of medicine by predicting treatment outcomes with unprecedented accuracy and speed. Beyond that, the profound fusion of biological computation with artificial systems will catalyze breakthroughs in information processing, allowing us to tackle complex global issues through energy-efficient, self-learning platforms that mimic the brain's innate ingenuity.
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