Researchers From John Hopkins Use Brain Cells To Develop Biological Computers

Although human brains are inferior to computers when processing simple information like mathematics, they excel when processing complicated information because they are better equipped to deal with limited and ambiguous information. The brain excels above computers when deciding on big, extremely varied, and incomplete information.

At first glance, biological learning and machine learning/AI by an intelligent agent create internal representations of the environment to enhance task performance. Yet, due to fundamental differences in the implementation processes and the aims, the efficiencies of biological and machine learning are very different. To start, compared to computer problem-solving, biological understanding requires far less energy. To add insult to injury, biological knowledge solves issues with fewer observations.

The idea of connecting the human brain with artificial systems appeared fifty years ago. Long-term potentiation was first seen in studies of learning and memory conducted on simple animals like the lamprey long before human cell cultures and brain organoids became available. Hence, research into brain-machine interaction was undertaken to create two-way connections between the brain and mechanical equipment.

Scientists at Johns Hopkins University believe that developing a “biocomputer” run by human brain cells is feasible during lifetimes. This will vastly increase the capabilities of current computers and give rise to entirely new fields of study. Researchers at Johns Hopkins have been developing brain organoids, tiny spheres with neurons and other properties that show promise for maintaining fundamental cognitive processes like learning and memory. Scientists have been able to study the kidneys, lungs, and other organs for over two decades without resorting to human or animal experimentation by using microscopic organoids, lab-made tissue imitating fully mature organs.

When asked how long it would be until organoid intelligence could power a system as sophisticated as a mouse, researchers speculated it might be decades. Lena Smirnova, an assistant professor of environmental health and engineering at Johns Hopkins and a study co-leader, has suggested that organoid intelligence might transform drug testing research for neurodevelopmental diseases and neurodegeneration. He believes that in the future, biocomputers will have increased computing speed, processing power, data efficiency, and storage capacities because of the advanced manufacturing of brain organoids and the training they will undergo with artificial intelligence.

Making use of recent discoveries on the chemical underpinnings of biological learning

Enhancing the learning and OI potential of organoid systems will require further research into synaptic plasticity’s molecular biology. Organoid neurons may now have their growth adjusted to promote the expression of genes necessary for human learning. They have to make receptors for the neurotransmitters that control communication between nerve cells. Differentiation-related changes in the expression of NMDA receptor subunits have already been documented (unpublished observation). Gene expression and the switching of the key receptor subunits have been comprehensively studied to provide a framework for estimating long-term organoid maturation and age. To achieve their full potential, organoids will need to express IEGs. IEGs are quickly transcribed in mature neurons during information processing because they influence synaptic pathways critical to memory consolidation.

Developments in healthcare are brought about by OI-driven research and innovation

Research in OI will not only pave the way for the groundbreaking use of human brain organoids in computing and learning, but it will also make it possible to investigate the wide range of neurodevelopmental and neurodegenerative variations among stem cell donors. One possible top research goal is into Alzheimer’s disease and related dementias. There has been a dramatic increase in some conditions, such as autism, which raises serious concerns. One needs human-based preclinical models to understand better and treat neurodegenerative disorders, and adapting OI research models would provide the first such model. While the bioengineering of these models from stem cells mirrors the phases of brain development, OI is also useful for studying neurodevelopmental problems.

To better understand the pathophysiology, risk factors, and therapies for various illnesses, comparing organoids grown from iPSCs from patients with those produced from healthy controls may be useful. Hence, utilizing an OI strategy with these cell lines has great promise for illuminating and characterizing the root causes of neurodegenerative, neurodevelopmental, and mental health conditions. This has a wide range of potential uses, such as reducing the potential for damage from (pediatric) medications, finding toxicants and illegal substances that have lasting impacts on cognitive capacities, and improving the effectiveness of lead drug candidates that act on specific pharmacological targets.

Just like OI’s other scientific and biotechnological facets, this is a completely unknown ground. With more research into organoid systems comes the potential for new ethical issues and perspectives. Thus, it is essential to outline the ethical concerns from the outset of this study in a way that includes all anticipated difficulties and regularly reflects on progress and new learning. For OI to advance in a socially and ethically responsible manner, an “embedded ethics” approach is necessary, in which teams comprised of ethicists, researchers, and members of the public work together to identify, discuss, and analyze ethical issues and then feed these back to inform future research and work.

In conclusion, scientists are working towards a biological computing revolution that would have far-reaching effects due to its potential to solve many problems plaguing traditional computing and artificial intelligence. OI-based biocomputing systems are expected to improve data and energy efficiency while facilitating speedier decision-making (even when dealing with enormous, incomplete, and heterogeneous datasets). The creation of “intelligence-in-a-dish” could also aid in discovering novel therapeutic approaches to address major global unmet needs by providing previously unavailable opportunities to study the biological basis of human cognition, learning, and memory, as well as various disorders associated with cognitive deficits.

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