The Next Frontier of Computation: An Exhaustive Analysis of the Benefits and Applications of Wetware Computers and Organoid Intelligence

Full report

On this page

Introduction to the Biological Computational Paradigm

Since the dawn of the digital computing era, engineers have sought to emulate the functionality of the human brain, a pursuit most evidently manifested in the development of artificial intelligence (AI) and artificial neural networks (ANNs)1. However, the modern proliferation of large language models (LLMs) and deep learning architectures has exposed the fundamental physical, thermodynamic, and architectural limitations of traditional silicon-based infrastructure. The sheer scale of data processing required today demands energy expenditures that are rapidly becoming unsustainable; for instance, language generation alone can consume up to 600 billion Joules per day globally2. As semiconductor manufacturing approaches the physical limits of Moore’s Law, a radical paradigm shift is required. In response to these cascading hardware bottlenecks, a highly interdisciplinary synthesis of bioengineering, stem cell biology, machine learning, and neuroethics has birthed a revolutionary computing paradigm: wetware computing, encapsulated within the scientific discipline of Organoid Intelligence (OI)3. This approach defines biocomputing as any form of computation executed on a system combining computer technology with biological components—specifically, living human brain-based organoid cultures functioning as a physical substrate for learning and data processing6. By integrating three-dimensional, high-density neural tissues with electronic interfaces such as microelectrode arrays (MEAs), researchers have successfully created bio-hybrid systems capable of receiving complex spatiotemporal inputs, processing data through innate biological neuroplasticity, and generating measurable, actionable outputs5. The benefits of wetware computers represent a categorical departure from incremental improvements in silicon microprocessors. These biological systems offer unparalleled thermodynamic efficiency, the eradication of the von Neumann bottleneck, highly efficient few-shot learning, and immunity to the catastrophic forgetting that plagues digital neural networks. Furthermore, because these processors are cultivated from human genetic material, they serve a dual purpose as unprecedented in vitro models for neurological disease research, pharmacology, and personalized medicine1. This exhaustive report delineates the structural, computational, and medical advantages of wetware computing, while critically examining the complex bioethical frameworks required to govern the integration of living human tissue into computational hardware.

The Biological Hardware: Substrate Preparation and Architecture

The foundational advantage of wetware computing lies in the immense structural complexity and self-organizing capabilities of its biological hardware. Unlike silicon chips, which must be painstakingly etched in cleanrooms, bioprocessors are grown, leveraging millions of years of evolutionary refinement to achieve computational density.

Cellular Reprogramming and Organoid Cultivation

The genesis of a wetware computer begins with cellular reprogramming, a Nobel Prize-winning technology that reverts adult somatic cells—typically human skin cells—into induced pluripotent stem cells (iPSCs)8. These iPSCs possess the embryonic-like capacity to differentiate into any cell type in the human body. Through highly controlled chemical signaling and specific environmental conditions, these cells are coaxed into becoming Neural Stem Cells (NSCs)9. By providing the precise biochemical cocktail of soluble growth factors—including Glial cell line-derived neurotrophic factor (GDNF) and Brain-derived neurotrophic factor (BDNF)—scientists guide these NSCs to differentiate across the primary germ layers (endoderm, mesoderm, and ectoderm)9. Rather than growing in flat, two-dimensional monolayers, which fail to replicate in vivo neural dynamics, the cells are cultivated in suspended microfluidic environments that encourage the formation of three-dimensional spherical structures5. Over weeks to months, these structures self-organize into highly dense, mature brain organoids. They develop complex architectures mirroring the human cortex, featuring ventricular and subventricular zones, and generate diverse cellular populations including mature neurons, neural progenitor cells, and critical support cells such as astrocytes and glial cells3. The resulting organoids exhibit spontaneous electrophysiological activity, myelination, and the formation of active synapses capable of transmitting action potentials5.

Substrate Adhesion and Homeostatic Maintenance

To function as computational hardware, these organoids must be physically integrated with digital sensors. Because biological neurons naturally prefer adhering to an extracellular matrix (ECM), the surface of the hardware—typically a glass or complementary metal-oxide-semiconductor (CMOS) multi-electrode array—must be meticulously engineered13. Engineers apply primary coatings of synthetic polymers such as poly-D-lysine (PDL), poly-ethyleneimine (PEI), or poly-L-ornithine (PLO), followed by secondary biological polymer coatings like laminin, fibronectin, collagen, or Matrigel13. This complex synthetic-biological interface ensures the neurons establish robust structural and electrical connections with the hardware. Sustaining the computational viability of the tissue requires advanced microfluidic systems that strictly regulate the organoid's environment. These systems utilize microscopic tubes, pumps, and valves to deliver vital sugars, buffering chemicals for pH stabilization, osmolytes for intracellular balance, and dissolved oxygen, while simultaneously removing cellular waste3. This artificial vasculature overcomes the passive diffusion limits of avascular tissue, enabling the reliable cultivation of 3-5 millimeter organoids for continuous operational lifespans exceeding 100 days, and in some highly controlled laboratory settings, over a full year2.

Thermodynamic and Architectural Superiority

The most immediately quantifiable benefits of wetware computers stem from their resolution of systemic inefficiencies inherent to classical digital architectures. Biological neural networks inherently solve the two greatest challenges facing modern supercomputing: exponential power consumption and data transfer latency.

The Eradication of the von Neumann Bottleneck

Standard digital computers, from basic laptops to the most advanced AI data centers, operate on the von Neumann architecture, which strictly separates the central processing unit (CPU) from random access memory (RAM). Computation requires data to be continuously shuttled back and forth across a physical memory bus. This constant movement of electrons, known as the von Neumann bottleneck, introduces significant latency and accounts for a massive proportion of the system's total energy expenditure14. Wetware computers naturally eradicate this bottleneck. In biological neural networks, memory and processing are not physically separated; they are intrinsically co-located within the synaptic connections between neurons9. When an organoid processes an electrical input, the strength of its connections (the synaptic weights) adjusts in real-time through neuroplasticity9. Information is therefore stored exactly where it is processed, entirely eliminating the energy-expensive data shuffling required by silicon chips9.

Extreme Energy Efficiency and Storage Density

The human brain is universally acknowledged as the most efficient computational engine in existence. Housing approximately 86 to 100 billion neurons and up to a quadrillion synaptic connections, the brain processes complex, multimodal data streams continuously on a power budget of merely 20 watts8. To replicate this level of parallel processing capability using conventional silicon-based supercomputers would require an estimated 10 megawatts of power—an inefficiency factor of several orders of magnitude17. This thermodynamic superiority translates directly into biocomputing hardware. Existing commercial bioprocessors utilizing live human neurons consume roughly one million times less energy than the silicon chips utilized in modern cloud infrastructure9. This efficiency is rooted in the physics of action potentials. Biological systems do not force electrons through resistive logic gates; instead, they rely on carefully optimized electrochemical ion gradients (sodium and potassium) and sparse coding, where only a minimal necessary fraction of the network fires at any given time5. Furthermore, the three-dimensional architecture of the biological brain allows for unprecedented data storage density. Theoretical estimates place the storage capacity of the human brain at 2,500 terabytes (TB)8. Even scaled down to the microscopic level, an organoid measuring roughly 500 micrometers and containing 100,000 cells (one three-millionth the size of the human brain) provides the theoretical equivalent of 800 megabytes of highly dense, associative memory storage5. Due to this extreme efficiency, researchers hypothesize that a fully scaled biocomputing network could meet the entire data storage capacity needs of the United States utilizing only 1,600 kilowatts of energy8.

Advanced Computational Dynamics: Reservoir Computing and Plasticity

Beyond energy efficiency, wetware computers excel at forms of computation that heavily burden traditional artificial algorithms. Biological neural networks thrive in environments characterized by noise, heterogeneous data, and incomplete information, operating through mechanisms that AI engineers are only beginning to emulate3.

Biological Reservoir Computing

The primary theoretical framework enabling practical wetware computation is "reservoir computing" (RC). In standard deep learning, backpropagation algorithms must iteratively calculate and update the mathematical weights of every single node in a multi-layered network—a computationally exhaustive process21. Reservoir computing bypasses this by utilizing a "black box" physical reservoir with fixed or self-organizing internal dynamics. In wetware RC, the human brain organoid acts as an "adaptive living reservoir"12. When spatiotemporal electrical signals are delivered into the organoid via an input matrix ([Figure omitted from source export]), the biological tissue maps these lower-dimensional signals into a highly complex, higher-dimensional computational space12. The natural oscillatory dynamics of the biological neurons, which resonate with features of the input signals, act as a massive nonlinear dynamical system22. The inherent biological "noise" of the living cells, which might disrupt a standard digital circuit, is actually highly beneficial in this context, pushing the system to operate at the "edge of chaos"—the optimal regime for complex computation and fading memory (short-term retention of continuous inputs)15. Instead of attempting to map the exact internal synaptic changes of the organoid, engineers utilize supervised learning to train only a simple, linear "readout" layer ([Figure omitted from source export])21. This digital readout layer interprets the biological state and maps it to the desired classification or output14. Tools like the conn2res Python toolbox have been developed specifically to interface empirical connectomes and biological dynamics with artificial networks, allowing researchers to study how physical brain architecture shapes computational capacity21.

Few-Shot Learning and Neuroplasticity

Because the living reservoir adapts itself through unsupervised biological mechanisms, the system achieves remarkable reductions in training overhead. Traditional AI is notoriously data-hungry; the AlphaGo system required training data from 160,000 games to optimize its network8. Biological systems, however, exhibit "few-shot learning," recognizing complex patterns from a minimal number of samples—often as few as ten8. Furthermore, standard ANNs suffer from "catastrophic forgetting," where learning a new task overwrites the weights associated with previous tasks24. Biological neurons counteract this through structural neuroplasticity. When exposed to stimuli, the organoid physically reshapes its hardware—growing new dendrites, reinforcing functional synapses (long-term potentiation), and pruning unused connections15. This dynamic biological memory allows the system to engage in continuous, on-chip learning without destroying prior computational frameworks5. To quantify these complex, multi-scale physical and functional changes, researchers deploy advanced unsupervised machine learning techniques, including principal component analysis (PCA), independent component analysis (ICA), autoencoders, and space-time dependent causal modeling5.

Milestone Implementations: Moving from Theory to Hardware

The transition of Organoid Intelligence from theoretical concept to applied engineering has accelerated dramatically, highlighted by distinct platforms that have proven the viability of brain-computer integration.

Brainoware: Solving Chaos and Speech Recognition

Developed by a team led by Feng Guo at Indiana University, the "Brainoware" system provided a critical proof-of-concept for the computational superiority of organoids. The team connected human brain organoids to a MaxOne high-density microelectrode array (HD-MEA) containing 1,020 active electrodes grouped into 32 distinct regions11. This spatial resolution allowed for precise delivery of encoded data and the recording of highly complex responses within milliseconds11. Subjected to real-world AI benchmarking, Brainoware was tested on speech recognition tasks. Researchers fed the system 240 digital audio clips of eight individuals speaking Japanese vowels. The audio was converted into electrical pulses, prompting the organoid to generate distinct neural activity patterns26. After merely two days of training, the digital readout layer decoded the biological activity to identify the speaker with 78% accuracy14. Moreover, Brainoware outperformed traditional ANNs in predicting the Hénon map, a complex nonlinear mathematical model exhibiting chaotic dynamics25. The most profound finding was the data efficiency: Brainoware achieved high accuracy after only four training epochs, whereas an artificial neural network utilizing Long Short-Term Memory (LSTM) required fifty epochs. This represents a greater than 90% reduction in AI training time, derived not from a faster clock speed, but from a fundamentally superior biological learning mechanism3.

FinalSpark’s Neuroplatform: The Biocomputing Cloud

While Brainoware proved computational validity in a localized setting, the Swiss startup FinalSpark, founded by Dr. Martin Kutter and Dr. Fred Jordan, established the world's first scalable, commercially accessible biocomputing infrastructure9. Launched in May 2024 in Vevey, Switzerland, the "Neuroplatform" allows researchers globally to conduct 24/7 remote experiments on living biological processors over the internet, fundamentally democratizing access to wetware hardware2. The Neuroplatform's architectural specifications are rigorous. It houses 16 distinct forebrain organoids across four MEAs, totaling approximately 160,000 living human neurons9. The system is managed by Intan RHS 32 controllers operating at a 30 kHz sampling frequency with 16-bit resolution, ensuring electrical recording accuracy down to 0.15 microvolts2. The platform’s closed-loop software executes an ultra-fast hardware cycle, buffering data every 33 microseconds and deploying sophisticated bi-phasic electrical currents to stimulate the tissue2. FinalSpark tracks the "Center of Activity" (CA) of the organoids, utilizing Python APIs and Jupyter Notebooks to allow remote researchers to shape neural behavior28. The system even features UV light triggers for the molecular uncaging of neurotransmitters like dopamine and serotonin, chemically rewarding the network to optimize learning pathways20. Utilizing this setup, researchers from institutions like the University of Exeter have trained the organoids to perform temporal sequence processing and even read Braille characters by translating spatial text into electrical pulses, achieving an 83% accuracy rate9. Over three years, this platform has collected over 18 terabytes of electrophysiological data across 1,000 organoids, establishing the foundational datasets required for the next generation of biocomputing algorithms2.

Scalability and the Virtual Wire Method

A major hurdle in biocomputing is scaling beyond the physical size limits of a single organoid. Because massive biological brains require complex vascular systems to prevent necrotic cores, researchers are developing methods to interconnect multiple small organoids into distributed biological networks. The Virtual Wire Method (VWM) establishes reliable, structured communication between physically separated neural cultures2. By utilizing parity-based error correction and confidence-based bit toggling, researchers have demonstrated that multiple organoids can exchange structured information with aggregate word accuracy exceeding 52%, well above chance levels2. This foundational framework paves the way for massively parallel multi-network in vitro computational architectures. Furthermore, researchers are exploring the integration of genetically enhanced organoids. Drawing on Floyd Romesberg's development of a six-letter genetic alphabet, future wetware systems may feature synthetic biological cells explicitly designed for computation, transcending the default limitations of natural DNA25.

Transforming Healthcare: Disease Modeling and Pharmacology

Because wetware computers are constructed from human cellular material, their utility extends profoundly into the medical sciences. The integration of high-density computational monitoring with living human tissue creates the ultimate "intelligence-in-a-dish," offering unparalleled insights into human pathology and pharmacology1.

Precision Disease Modeling

Historically, neurological research has relied on murine (mouse) models to study diseases like Alzheimer’s, Parkinson’s, and autism. However, due to significant evolutionary divergence, therapies that cure neurological defects in mice frequently fail in human clinical trials10. Wetware computers eliminate this species gap. By taking skin cell samples from a patient suffering from a neurodegenerative disease, scientists can reprogram those cells into iPSCs and cultivate a customized, patient-specific biocomputer8. This organoid inherently possesses the genetic and cellular architecture of the patient's disease. When this diseased organoid is tasked with computational operations (such as memory retention or pattern recognition) via the MEA, scientists can quantitatively measure how the disease degrades cognitive function at a circuit level3. For example, researchers can directly observe how the accumulation of pathological proteins disrupts the fading memory properties of a biological reservoir, bridging the gap between molecular pathology and cognitive decline16.

High-Throughput Toxicology and Personalized Medicine

Wetware systems serve as dynamic, living assays for pharmacological screening. Utilizing AI-driven data analytics, high-content microscopy, and deep learning algorithms, researchers can monitor the real-time structural and functional responses of an organoid as novel drug candidates are introduced into its microfluidic environment3. This enables high-throughput toxicity testing, allowing pharmaceutical companies to rapidly identify compounds that impair synaptic plasticity or induce neurotoxicity before they enter costly human trials8. Moreover, patient-derived biocomputers enable a new frontier of personalized medicine. Clinicians can test a variety of neuro-active drugs on a patient's customized organoid, predicting the exact efficacy and adverse side-effect profile specific to that individual's unique genetic background3. Consequently, organoid intelligence provides a robust, human-relevant platform that has the potential to substantially reduce, and eventually eliminate, the scientific and ethical reliance on animal testing in preclinical trials1.

The Embedded Ethics Framework: Navigating the Moral Frontier

The unprecedented act of engineering human biological cognition carries immense philosophical, legal, and ethical weight. Recognizing the disruptive potential of this technology, a diverse coalition of neuroscientists, engineers, and ethicists convened at Johns Hopkins University in February 2022 to draft the "Baltimore Declaration"7. This foundational manifesto formally established Organoid Intelligence as an interdisciplinary field, mandating that technological progress must remain permanently tethered to robust bioethical oversight7.

The Question of Sentience and Moral Status

The most acute philosophical dilemma in biocomputing revolves around the emergence of consciousness. Because brain organoids are designed explicitly to replicate the complex structural and functional dynamics of human learning, it forces a confrontation with the biological thresholds of subjective experience5. In bioethics, if an entity possesses the capacity to experience pain (sentience) or holds subjective awareness (consciousness), it acquires a moral status, granting it certain rights and protections against harm30. At their current scale, the microscopic organoids used in platforms like Brainoware and the Neuroplatform lack the global sensory integration and top-down architectural complexity required for human-level consciousness31. As Thomas Hartung, a pioneer in the field, noted regarding the spontaneous electrical activity of early organoids: "They have nothing to think about without input and output"1. However, as biocomputers are increasingly integrated into closed-loop systems—receiving constant environmental feedback and being chemically "rewarded" with dopamine—the boundaries of rudimentary sentience become highly ambiguous1. Public perception regarding these issues is complex. Experimental bioethics surveys, such as those conducted by Ota et al., reveal that the moral permissibility of using brain organoids drops significantly if the public believes the organoids have the capacity for visual or painful experiences31. Furthermore, qualitative data suggests that public moral valuation heavily depends on visualizing what the organoid looks like and its functional capabilities31.

Donor Rights and Distributive Justice

Beyond the consciousness debate, the physical origin of the cells raises complex legal questions. When human donors provide the skin cells that are subsequently reprogrammed into a functioning biocomputer, do their property and privacy rights extend to the computational system? The embedded ethics framework interrogates whether a donor has the right to dictate novel uses of their derived specimens beyond the initial medical consent form30. Furthermore, there is a debate regarding the moral obligation of researchers to inform a donor if the biocomputer inadvertently reveals a genetic predisposition to a neurological disease during computational stress testing30. The commercialization of OI also poses questions of distributive justice. Patient-derived organoids used for proprietary pharmacological development create tension between public health benefits and private corporate profit29. If biocomputing infrastructure is strictly monopolized, it could limit equitable access to the personalized medical therapies derived from it. To prevent these outcomes, the Baltimore Declaration enforces an "embedded ethics" methodology, ensuring that teams of ethicists, legal scholars, and public representatives actively participate in laboratory research, iteratively updating regulatory guidelines as the capabilities of wetware expand4.

Conclusion

Wetware computers and the broader discipline of Organoid Intelligence represent a profound inflection point in both the history of computation and the biological sciences. By harnessing the evolutionary perfection of human neural tissue, biocomputing effectively bypasses the thermodynamic limits, energy crises, and architectural bottlenecks that currently threaten to stall the advancement of digital artificial intelligence. The intrinsic abilities of biological neural networks to execute highly efficient few-shot learning, eradicate the von Neumann bottleneck through synaptic memory co-location, and compute massive nonlinear dynamics at the absolute edge of chaos establish a computing paradigm that synthetic logic gates may never truly replicate. The successful implementation of commercial biocomputing infrastructure, demonstrated by platforms such as Brainoware and FinalSpark's Neuroplatform, proves that living biological reservoirs can slash AI training times by over 90% while consuming a million times less energy. Concurrently, because this hardware is forged from human genetics, these systems provide unmatched, circuit-level insight into neurodegenerative diseases, paving the way for high-throughput toxicity screening and the realization of truly personalized, patient-specific medicine. However, the realization of wetware's ultimate potential is entirely contingent on solving complex bioengineering constraints—particularly tissue vascularization for scaled networks—and strict adherence to the embedded ethics framework outlined in the Baltimore Declaration. As researchers continue to push the boundaries of biological data processing and synthetic genetics, they must navigate the profound moral thresholds of in vitro sentience and donor rights. Managed responsibly, wetware computing stands poised not merely as a sustainable successor to silicon, but as an entirely new tier of intelligence, bridging the gap between engineered machinery and the essence of living cognition.

Works cited

1. Organoid Intelligence (OI) – The Ultimate Functionality of a Brain Microphysiological System \- SciSpace, https://scispace.com/pdf/organoid-intelligence-oi-the-ultimate-functionality-of-a-2bfin4wp.pdf

2. (PDF) Open and remotely accessible Neuroplatform for research in wetware computing, https://www.researchgate.net/publication/380315842\_Open\_and\_remotely\_accessible\_Neuroplatform\_for\_research\_in\_wetware\_computing

3. Organoid Intelligence (OI): A New Frontier in Bio-Inspired Computing | Sino Biological, https://www.sinobiological.com/resource/organoid-review/organoid-intelligence

4. Organoid intelligence: a new biocomputing frontier, https://www.frontiersin.org/journals/science/article-hubs/organoid-intelligence-a-new-biocomputing-frontier

5. Organoid intelligence (OI): the new frontier in biocomputing and intelligence-in-a-dish, https://www.frontiersin.org/journals/science/articles/10.3389/fsci.2023.1017235/full

6. What is biocomputing? \- FinalSpark, https://finalspark.com/what-is-biocomputing/

7. Organoid Intelligence: Toward Biohybrid Architectures Beyond Silicon \- ELEKS, https://eleks.com/research/organoid-intelligence/

8. Powering up the next generation of biocomputers with brain organoids \- Frontiers, https://www.frontiersin.org/journals/science/article-hubs/organoid-intelligence-a-new-biocomputing-frontier/explainer

9. Inside FinalSpark's Neuroplatform: How 160,000 Living Neurons Compute Using One Million Times Less Energy Than Your Data Centre | The Small Business Cybersecurity Guy, https://thesmallbusinesscybersecurityguy.co.uk/blog/finalspark-neuroplatform-living-neurons-biocomputing-deep-dive-2025/

10. Brain in a dish – the potential of organoid intelligence and biological computing, https://www.team-consulting.com/us/insights/brain-in-a-dish-the-potential-of-organoid-intelligence-and-biological-computing/

11. Brain Organoid Reservoir Computing on MaxOne HD-MEA \- MaxWell Biosystems, https://www.mxwbio.com/community-voice/feng-guo

12. Brain organoid reservoir computing for artificial intelligence \- Kinam Park, http://kinampark.com/T-Polymers/files/All%20References/Cai%202023%2C%20Brain%20organoid%20reservoir%20computing%20artificial%20intelligence.pdf

13. Starting a synthetic biological intelligence lab from scratch \- PMC \- NIH, https://pmc.ncbi.nlm.nih.gov/articles/PMC12142634/

14. Brainoware: Integrating Human Brain Tissue with Electronics \- \- Legal Reader, https://www.legalreader.com/brainoware-integrating-human-brain-tissue-with-electronics/

15. Brain Organoid Computing for Artificial Intelligence \- bioRxiv, https://www.biorxiv.org/content/10.1101/2023.02.28.530502v1.full-text

16. Reservoir computing in simulated neuronal cultures: Effect of network structure \- AIP Publishing, https://pubs.aip.org/aip/cha/article/36/2/023126/3380262/Reservoir-computing-in-simulated-neuronal-cultures

17. FinalSpark: Home, https://finalspark.com/

18. FinalSpark Launches the First Remote Research Platform Using Human Neurons for Biocomputing | Business Wire \- Via TT, https://via.tt.se/pressmeddelande/3471752/finalspark-launches-the-first-remote-research-platform-using-human-neurons-for-biocomputing?publisherId=259167

19. First Organoid Intelligence (OI) workshop to form an OI community, https://pure.johnshopkins.edu/en/publications/first-organoid-intelligence-oi-workshop-to-form-an-oi-community/

20. Scientists want to build 'living' computers—powered by live brain cells | National Geographic, https://www.nationalgeographic.com/science/article/brain-cells-organoids-computers-ai-energy

21. Connectome-based reservoir computing with the conn2res toolbox \- PMC \- NIH, https://pmc.ncbi.nlm.nih.gov/articles/PMC10803782/

22. (PDF) Brain-inspired interpretable reservoir computing with resonant recurrent neural networks \- ResearchGate, https://www.researchgate.net/publication/392918232\_Brain-inspired\_interpretable\_reservoir\_computing\_with\_resonant\_recurrent\_neural\_networks

23. Neural-Inspired Multi-Agent Molecular Communication Networks for Collective Intelligence, https://arxiv.org/html/2601.18018v1

24. Human Brain Inspired Artificial Intelligence Neural Networks, https://journal.hep.com.cn/JIN/EN/10.31083/JIN26684

25. Speculaltions on How Genetically Enhanced Organoids Could Outperform Silicon \- Medium, https://medium.com/@Gbgrow/speculaltions-on-how-genetically-enhanced-organoids-could-outperform-silicon-53e7ee01ba94

26. Computer Made From Human Brain Cells Can Perform Voice Recognition \- Futurism, https://futurism.com/neoscope/computer-human-brain-cells-perform-voice-recognition

27. Innovative Brain Cell Chips Unlock New Horizons in Speech Recognition, https://neurosciencenews.com/brain-cell-chips-speech-recognition-25352/

28. Open and remotely accessible Neuroplatform for research in wetware computing \- Frontiers, https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2024.1376042/full

29. Brain organoids and organoid intelligence from ethical, legal, and social points of view \- PMC, https://pmc.ncbi.nlm.nih.gov/articles/PMC10796793/

30. First Organoid Intelligence (OI) workshop to form an OI community \- PMC, https://pmc.ncbi.nlm.nih.gov/articles/PMC10013972/

31. Views on the distribution of consciousness influence ethical judgements toward brain organoids as biological computers: an exploratory study \- Oxford Academic, https://academic.oup.com/nc/article/doi/10.1093/nc/niag023/8728409