GOOD NEWS
Brain cells learn faster than machine learning, new research reveals
Cortical Labs
Melbourne, Australia - 12 August 2025 - Researchers have demonstrated that brain cells learn faster and carry out complex networking more effectively than machine learning by comparing how both a Synthetic Biological Intelligence (SBI) system known as ‘DishBrain’ and state-of-the-art RL (reinforcement learning) algorithms react to certain stimuli.
The study, ‘Dynamic Network Plasticity and Sample Efficiency in Biological Neural Cultures: A Comparative Study with Deep Reinforcement Learning’, is the first known of its kind.
The research was led by Cortical Labs, the Melbourne-based startup which created the world’s first commercial biological computer, the CL1. The CL1, through which the research was conducted, fuses lab-cultivated neurons from human stem cells with hard silicon to create a more advanced and sustainable form of AI, known as “Synthetic Biological Intelligence” (SBI).
The research investigated the complex network dynamics of in vitro neural systems using DishBrain, which integrates live neural cultures with high-density multi-electrode arrays in real-time, closed-loop game environments. By embedding spiking activity into lower-dimensional spaces, the study distinguished between ‘Rest’ and ‘Gameplay’ conditions, revealing underlying patterns crucial for real-time monitoring and manipulation.
The analysis highlights dynamic changes in connectivity during Gameplay, underscoring the highly sample-efficient plasticity of these networks in response to stimuli. To explore whether this was meaningful in a broader context, researchers compared the learning efficiency of these biological systems with state-of-the-art deep RL algorithms such as DQN, A2C, and PPO in a Pong simulation.
In doing so, the researchers were able to introduce a meaningful comparison between biological neural systems and deep RL, concluding that when samples are limited to a real-world time course, even these very simple biological cultures outperformed deep RL algorithms across various game performance characteristics, implying a higher sample efficiency.
The research was led by Cortical Labs, in conjunction with the Turner Institute for Brain and Mental Health, Monash University, Clayton, Australia; IITB-Monash Research Academy, Mumbai, India; and the Wellcome Centre for Human Neuroimaging, University College London, United Kingdom.
Brett Kagan, Chief Scientific Officer at Cortical Labs, commented: “While substantial advances have been made across the field of AI in recent years, we believe actual intelligence isn’t artificial. We believe actual intelligence is biological. In this research, we set out to investigate whether elementary biological learning systems achieve performance levels that can compete with state-of-the-art deep RL algorithms. The results so far have been very encouraging. Understanding how neural activity is linked to information processing, intelligence and eventually behaviour is a core goal of neuroscience research - this paper is an important and exciting step in that journey.
“This breakthrough was a critical proofpoint that led to the eventual creation of the CL1, the world’s first biological computer, to access these properties. However, this is the beginning of the journey, not the end. Through further research into Bioengineered Intelligence (BI) we believe we can unlock capabilities that far surpass anything demonstrated to date.”
Based on the original breakthrough, and the launch of the CL1, Cortical Labs has launched a second paper - ‘Two Roads Diverged: Pathways Towards Harnessing Intelligence in Neural Cell Cultures’ - proposing a novel approach to generating intelligent devices called Bioengineered Intelligence (BI).
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. Currently, coordinated efforts have established the field of Organoid Intelligence (OI) as one pathway.
However, specifically engineering neural circuits could be leveraged to give rise to another pathway, which the paper proposes to be Bioengineered Intelligence (BI). The research paper examines the opportunities and prevailing challenges of OI and BI, proposing a framework for conceptualising these different approaches using in vitro neural cell cultures for information processing and intelligence.
In doing so, BI is formalised as a distinct innovative pathway that can progress in parallel with OI. Ultimately, it is proposed that while significant steps forward could be achieved with either pathway, the juxtaposition of results from each method will maximise progress in the most exciting, yet ethically sustainable, direction.
"Our goal was to go beyond anecdotal demonstrations of biological learning and provide rigorous, quantitative evidence that living neural networks exhibit rapid and adaptive reorganization in response to stimuli—capabilities that remain out of reach for even the most advanced deep reinforcement learning systems,” added Cortical Labs’ Forough Habibollahi. “While artificial agents often require millions of training steps to show improvement, these neural cultures adapt much faster, reorganizing their activity in response to feedback. By analyzing how their electrical signals evolved over time, we found clear patterns of learning and dynamic connectivity changes that mirror key principles of real brain function, demonstrating the potential of biological systems as fast, efficient learners."
Cortical Labs’ Moein Khajehnejad added: “By converting high-dimensional spiking activity into interpretable, low-dimensional representations, we were able to uncover the internal plasticity and network reconfiguration patterns that accompany learning in biological neural cultures. These were not just statistical differences; they were real, functional reorganizations that paralleled improvements in task performance over time.
“What makes this study truly groundbreaking is that it’s the first to establish a head-to-head benchmark between synthetic biological systems and deep RL under equivalent sampling constraints. When opportunities to learn are limited, a condition closer to how animals and humans actually learn, these biological systems not only adapt faster but do so more efficiently and robustly. That’s an exciting and humbling result for the fields of AI and neuroscience alike.”
Support for Cortical Labs:
“This study strengthens the case for Bioengineered Intelligence as a powerful, adaptive substrate for computation. Bioengineered Intelligence could reshape how we think about machines - and minds. This work hints at living systems that can outlearn machines.” - Adeel Razi, Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia.
Professor Mirella Dottori, Head of Stem Cell and Neural Modelling Lab, School of Medical, Indigenous and Health Sciences, University of Wollongong, added: “Cortical Labs’ research studies are paving the way forward in an emerging, and exciting new frontier for neuroscience, whereby in vitro neural models are being developed and used to tackle some of the most complex aspects of brain function - learning and memory - both major constituents of intelligence. The CL1 technology sets up a much-needed platform for neuroscience research in understanding brain function; however, the innovation is that it can provide a measure of ‘intelligence’ whereby neuronal functionality is determined in an interactive, dynamic approach. This is a significant step for the field. Of further significance, this technology can be applied in the longer term to study how neuronal networks and function differ in neurocognitive diseases and disorders.”
Hideaki Yamamoto, Associate Professor at the Research Institute of Electrical Communication, Tohoku University, commented: "These synthetic biological systems will certainly provide a new approach to understanding the physical substrate of brain computation. Furthermore, they may open a new class of computing, especially in tasks that the brain excels at. The CL1 will be a strong platform for putting this vision into action. When I first met the team three years ago, they had just started discussing the idea of building their own MEA system. That they have developed the CL1 and brought it to commercialisation in such a short time is deeply impressive."
ENDS
Notes to editors
ARTICLE TITLE: Dynamic Network Plasticity and Sample Efficiency in Biological Neural Cultures: A Comparative Study with Deep Reinforcement Learning
JOURNAL: Cyborg and Bionic System: A Science Partner Journal
DOI: https://doi.org/10.34133/cbsystems.0336
METHOD OF RESEARCH: Experimental study
SUBJECT OF RESEARCH: Learning, synthetic biology, intelligence
ARTICLE PUBLICATION DATE: 4th August, 2025
ARTICLE TITLE:
Two Roads Diverged: Pathways Towards Harnessing Intelligence in Neural Cell Cultures
JOURNAL: Cell Biomaterials
DOI: https://doi.org/10.1016/j.celbio.2025.100156
METHOD OF RESEARCH: Hypothesis and Theory
SUBJECT OF RESEARCH: Intelligence, biomaterials, synthetic biology
ARTICLE PUBLICATION DATE: 8th August, 2025
ARTICLE TITLE: The CL1 as a platform technology to leverage biological neural system functions
JOURNAL: Nature Review Biomaterials
DOI: https://doi.org/10.1038/s44222-025-00340-3
METHOD OF RESEARCH: New Technology
SUBJECT OF RESEARCH: Biotechnology, Intelligence, New Technology
ARTICLE PUBLICATION DATE: 7th July, 2025
About Cortical Labs
Cortical Labs is an Australian biological computing startup that merges live human neurons with computing systems to revolutionise computing. Cortical Labs combines synthetic biology with computing devices to develop a class of AI, known as “Synthetic Biological Intelligence” (SBI). Cortical Labs grows clusters of lab-cultivated neurons from human stem cells, which are then hooked to hard silicon to create the CL1, a biological computer that runs a software known as a Biological Intelligence Operating System (biOS).
Method of Research
Experimental study
Subject of Research
Cells
Article Title
Dynamic Network Plasticity and Sample Efficiency in Biological Neural Cultures: A Comparative Study with Deep Reinforcement Learning
Article Publication Date
13-Aug-2025
Head-to-head against AI, pharmacy students won
A study showed that when compared with students, ChatGPT 3.5 was less likely to correctly answer questions on therapeutics exams focused on clinical applications and cases.
University of Arizona Health Sciences
image:
Brian Erstad, PharmD, is the interim dean and a professor at the R. Ken Coit College of Pharmacy.
view moreCredit: Photo by Kris Hanning, U of A Office of Research and Partnerships
TUCSON, Ariz. — Students pursuing a Doctor of Pharmacy degree routinely take – and pass – rigorous exams to prove competency in several areas. Can ChatGPT accurately answer the same questions? A new study by University of Arizona R. Ken Coit College of Pharmacy researchers said no, it can’t.
Researchers found that ChatGPT 3.5, a form of artificial intelligence, fared worse than PharmD students in answering questions on therapeutics examinations that ensure students have the knowledge, skills, and critical thinking abilities to provide safe, effective and patient-centered care.
ChatGPT was less likely to correctly answer application-based questions (44%) compared with questions focused on recall of facts (80%). It also was less likely to answer case-based questions correctly (45%) compared with questions that weren’t focused on patient cases (74%). Overall, ChatGPT answered only 51% of the questions correctly.
The results provide additional insights into the uses and limitations of the technology and may also prove valuable in the development of pharmacy exam questions. The study findings appear in Currents in Pharmacy Teaching and Learning.
“AI has many potential uses in health care and education, and it’s not going away,” said Christopher Edwards, PharmD, an associate clinical professor of pharmacy practice and science. “One of the things we were hoping to answer with the study was if students wanted to use AI on an exam, how would they perform? I wanted to have data to show the students and tell them they can do well in the exams by studying hard and they don’t necessarily need these tools.”
A secondary goal was to find out what kinds of questions AI would struggle with. Coit College of Pharmacy Interim Dean Brian Erstad, PharmD, wasn’t surprised that ChatGPT did better with straightforward multiple choice and true-false questions and was less successful with application-based questions.
“The kinds of places where evidence is limited and judgment is required, which is often in a clinical setting, was where we found the technology somewhat lacking,” he said. “Ironically those are the kinds of questions clinicians are always facing.”
Edwards, Erstad, and Bernadette Cornelison, PharmD, an associate professor of pharmacy practice and science, evaluated answers to 210 questions from six exams in two pharmacotherapeutics courses that are part of the university’s Coit College of Pharmacy PharmD program.
The questions came from a first-year PharmD course focused on disorders related to nonprescription medications for heartburn, diarrhea, atopic dermatitis, cold and allergies. The other class was a second-year course that covered cardiology, neurology and critical care topics.
To compare the exam performances of pharmacy students and ChatGPT, they calculated mean composite scores as a measure of the ability to correctly answer questions. For ChatGPT, they added individual scores for each exam and divided by the number of exams. To figure out the mean composite score for the students, they divided the sum of the mean class performance on each exam by the number of exams. The mean composite score for six exams for ChatGPT was 53 compared to 82 for pharmacy students.
Educators, clinicians and others continue to debate the value of AI large language models, such as ChatGPT, in academic medicine. While such models will continue to play a range of roles in health care, pharmacy practice and other areas, many are concerned that relying too much on the technology could hamper the development of needed reasoning and critical thinking skills in students.
Both Erstad and Edwards acknowledged that in time, newer and more advanced technology may change these results.
Journal
Currents in Pharmacy Teaching and Learning
Method of Research
Data/statistical analysis
Subject of Research
People
Article Title
Comparison of a generative large language model to pharmacy student performance on therapeutics examinations
Article Publication Date
11-Aug-2025
