Scientists Fuse Brain-Like Tissue with Electronics to Make Computer | Explained
Scientists have achieved a groundbreaking advancement in the field of neuromorphic computing by fusing brain-like tissue with electronics to create an organoid neural network. This innovative system, developed by researchers from Indiana University, the University of Cincinnati, Cincinnati Children’s Hospital Medical Centre, and the University of Florida, marks a significant leap forward in the intersection of tissue engineering, electrophysiology, and neural computation.
By directly incorporating brain tissue into a computer, the scientists have taken neuromorphic computing to a whole new level. This practice involves modeling computers after the human brain to mimic its capabilities. In their study, published on December 11, the team used brain organoids – three-dimensional clusters of brain cells – to construct an organoid neural network capable of recognizing voices and solving complex mathematical problems.
The development of this system comes amidst the exponential growth of artificial intelligence (AI) and artificial neural networks. While AI has revolutionized various fields by processing large datasets, conventional computers struggle with the memory and processing separation required. The hardware of these neural networks has separate memory and data processing units, leading to increased time and energy demands as information needs to be constantly transferred between the two.
In an attempt to create more efficient neuromorphic chips, researchers have incorporated short-term memory units. However, these chips only partially mimic brain functions and lack processing capability, energy efficiency, and the ability to account for real-life uncertainty.
To address these limitations, scientists are now exploring the use of live brain cells to create biological neural networks. The brain organoids used in this study were derived from human pluripotent stem cells, which can differentiate into various types of cells in the body. The organoids consisted of neuron progenitor cells, early-stage neurons, mature neurons, and astrocytes.
The researchers connected the brain organoid to an array of microelectrodes to form an organoid neural network. This network, known as a reservoir computer, consisted of three layers: input, reservoir, and output. The input signals were converted into mathematical entities and routed to the reservoir for processing before being transmitted to the output layer.
The scientists named their system ‘Brainoware’ and demonstrated its capabilities by predicting a Henon map, a mathematical function that draws a curve on a graph. Additionally, Brainoware successfully recognized Japanese vowels after ‘learning’ from audio clips. Although it was slightly less accurate than artificial neural networks with short-term memory units, Brainoware required significantly less training.
While the study highlights the potential of organoid intelligence, the researchers acknowledge certain limitations. Maintaining a biological neural network requires technical expertise and infrastructure. Organoids also exhibit a heterogeneous mix of cell types, making uniformity a challenge. However, efforts are underway to overcome these limitations and optimize the performance of organoid neural networks.
The introduction of brain-like tissue into electronic systems raises ethical concerns as well. Questions arise regarding the dignity of the organoids and their consciousness. This groundbreaking research has the potential to provide foundational insights into learning mechanisms, neural development, and the cognitive implications of neurodegenerative diseases.
In conclusion, the fusion of brain-like tissue with electronics represents a significant milestone in the field of neuromorphic computing. By leveraging brain organoids, scientists have pioneered the development of an ‘organoid neural network’ that exhibits promising capabilities for recognizing voices and solving complex mathematical problems. Although challenges remain, this study lays the foundation for future advancements in biocomputing and our understanding of the brain’s intricacies.
