Advancing Organoid
Intelligence
Pioneering research at the intersection of biology and computation, developing next-generation organoid intelligence systems and biological computing interfaces for sustainable AI applications.
Research Areas
Our multidisciplinary research spans computational biology, neuroscience, and bioengineering to unlock the potential of organoid intelligence.
Biological Reservoir Computing
Harnessing the computational power of biological neural networks in organoids for reservoir computing applications, enabling energy-efficient processing of temporal data.
Key Publications
Reservoir computing with biological neural networks: organoid intelligence for temporal pattern recognition
Energy-efficient computing through biological reservoir systems in brain organoids
Biological Neural Networks
Understanding and leveraging the intrinsic computational capabilities of organoid neural networks for next-generation AI and hybrid computing systems.
Key Publications
Self-organizing neural networks in brain organoids: implications for artificial intelligence
Synaptic plasticity in organoid neural networks: a pathway to biological learning
Organoid Computer Interface
Developing bidirectional interfaces between organoid systems and digital computers, enabling hybrid biological-silicon computing architectures.
Key Publications
Bidirectional organoid-silicon interfaces for hybrid computing systems
Real-time signal processing in organoid computer interfaces
OrganoidOS Platform
Our proprietary operating system for organoid intelligence, providing standardized protocols and interfaces for biological computing applications.
Key Publications
OrganoidOS: a standardized platform for organoid intelligence applications
Scalable organoid computing architectures using OrganoidOS
Neuromorphic Computing
Implementing brain-inspired computing paradigms using organoid systems, bridging biological and artificial neural processing.
Key Publications
Neuromorphic computation in brain organoids: spike-based processing for AI applications
Low-power neuromorphic systems using organoid neural networks
Organoid Development & Culture
Advanced techniques for growing, maintaining, and optimizing brain organoids for computational applications and long-term stability.
Key Publications
Optimized culture protocols for computational brain organoids
Long-term maintenance of organoid neural networks for computing applications
Featured Research
Breakthrough discoveries in organoid intelligence and biological computing
Distinct microRNA signatures define sporadic PSP-RS and PD in patient-derived midbrain organoids
Progressive supranuclear Palsy–Richardson syndrome (PSP-RS) is a rare, rapidly progressive tauopathy often misdiagnosed as Parkinson's disease due to overlapping clinical features and the lack of reliable molecular biomarkers.
Generation of Individualized, Standardized, and Electrically Synchronized Human Midbrain Organoids
Organoids allow to model healthy and diseased human tissues and have applications in developmental biology, drug discovery, and cell therapy. Traditionally cultured in immersion/suspension, organoids face issues like lack of standardization.
High-performance neuromorphic computing architecture of brain
Artificial intelligence can outperform humans in specific tasks but consumes substantial energy. How the human brain can work at just 20 watts with complex cognitive intelligence? Here we decode the fundamental information strategy unit.
Human Cerebral Organoids Maintain Integrity and Viability after Transport through Mail
Human cerebral organoids are stem-cell derived three-dimensional (3D) tissue cultures used to advance our understanding of human neurodevelopment processes and neurological disorders.
Two roads diverged: Pathways toward harnessing intelligence in neural cell cultures
Interest in using in vitro neural cell cultures embodied within structured information landscapes has rapidly grown. Whether for biomedical, basic science, or information processing and intelligence applications, these systems hold significant potential.
HuiduRep: A Robust Self-Supervised Framework for Learning Neural Representations from Extracellular Spikes
Extracellular recordings are brief voltage fluctuations recorded near neurons, widely used in neuroscience as the basis for decoding brain activity at single-neuron resolution. Spike sorting, which assigns each spike to its source neuron, is a critical step.
Research Impact
Our research is shaping the future of sustainable AI and biological computing, with applications spanning healthcare, environmental monitoring, and next-generation AI systems.
Latest Breakthrough
Successfully demonstrated real-time learning in organoid neural networks, opening new possibilities for adaptive biological computing systems.
Join Our Research Community
Stay updated with the latest breakthroughs in organoid intelligence and biological computing. Access research papers, technical reports, and collaboration opportunities.