The human brain, which serves as the "command center" of humanity, has about 200 billion cells and is interconnected by trillions of nanometer-sized synapses.
Currently, artificial neural networks powered by artificial intelligence (AI) hardware require about:8 million wattsenergy, while the human brain only needs about20 watts
Through neuroplasticity and neurogenesis, the brain is also able to efficiently process and Xi noisy data with minimal training costs, thus avoiding the high energy consumption requirements of high-precision computing methods.
Inspired by the structural functions of the human brain, researchers from Indiana University Bloomington, the University of Florida, the Children's Hospital Medical Center of Cincinnati, and the University of Cincinnati co-invented itA hybrid machine-organoid computing system – brainoware
The system includes traditional computing hardware and brain organoids that can perform tasks such as speech recognition and nonlinear equations**. In addition, the system is flexible to vary and reorganize in response to electrical stimulationIt is expected to meet the challenges of current AI hardware in terms of time and energy consumption, as well as heat generation
The study** has been published in Nature Electronics, a sub-journal of Nature, under the title "Brain Organoid Reservoir Computing for Artificial Intelligence".
* The author mentions,Brain organoids are only one part of this system, and more complex artificial neural networks are yet to be demonstrated
In a concurrent News & Perspectives article, Johns Hopkins University associate professor Lena Smirnova and colleagues wrote, "As the complexity of these organoid systems continues to increase, the academic study of biocomputational systems involving human neural tissue faces numerous neuroethical issues that are becoming increasingly important." While it may take decades to create a universal biocomputing system, this research is expected to provide fundamental insights into mechanisms such as Xi, neurodevelopment, and neurodegenerative diseases.
Speech recognition is possible
Much of the recent success of AI has been driven by the development of artificial neural networks (ANNs) that use silicon computing chips to process large data sets. However, training an artificial neural network on current AI computing hardware is energy-intensive, time-consuming, and physically separated from the data processing unit, i.e., existsVon Neumann bottleneck
The structure, function, and efficiency of the human brain provided inspiration for the development of AI hardware, which naturally avoids the von Neumann bottleneck by fusing data storage and processing in a biological neural network (BNNS).
Inspired by BNNS, scientists have tried to develop highly efficient and low-cost neuromorphic chips, such as using memristors. However, current neuromorphic chips can only partially mimic brain function, and it is important to increase its processing power
In response, the study introduces an AI hardware that utilizes the ability of the human brain organoid neural network (ONNS) to perform reservoir computation and unsupervised Xi that is embedded in organoids. This approach enables the processing of spatiotemporal information and enables unsupervised Xi through the neuroplasticity of organoids.
Figure Brainoware AI computation with unsupervised Xi (The).
Compared to current two-dimensional in vitro neuronal cultures and neuromorphic chips, Brainoware can provide more insights into AI computing because organoids can provide the complexity, connectivity, neuroplasticity, and neurogenesis of BNNS, as well as low energy consumption and fast learning Xi.
Thanks to the high plasticity and adaptability of organoids, Brainoware is able to flexibly change and reorganize in response to electrical stimulation, highlighting its ability to perform adaptive reserve calculations
It is proved that the method can show the physical reserve properties such as nonlinear dynamics, declining memory and spatial information processing, and can also be carried outSpeech recognitionwithNonlinear equations**。In addition, the study demonstrates that this approach can Xi learn from the training data by reshaping the functional connectivity of onns.
Figure Speech Recognition (The).
However, there are several limitations and challenges with the current brainoware approach.
One technical challenge is organoid generation and maintenance. Despite the successful establishment of various protocols, current organoids still have problems such as high heterogeneity, low production efficiency, necrosis and hypoxia, and various activities. In addition, it is crucial to properly maintain and support organoids to tap their computing power.
While current Brainoware hardware is less power-intensive, it requires additional peripherals, which are still quite power-hungry. Due to the development of the electronics industry and system integration, it should be possible in the future to achieve very low-energy integration using customized systems that maintain and interface organoids.
Brainoware uses flat, rigid MEA electrodes to interface with organoids, which can only stimulate a small number of neurons on the surface of the recording organ. Therefore, it is necessary to develop methods to comprehensively interface organoids with AI hardware and software.
Another technical challenge is data management and analytics. Encoding and decoding spatiotemporal information from Brainoware still needs to be optimized, which can be achieved by improving the efficiency of data interpretation, extraction, and processing from multiple ** and patterns. In addition, this new type of AI hardware is likely to generate large amounts of data, which may require the development of new algorithms and methods for data analysis and visualization.
The application prospect is broad
The above study on brainoware is just one attempt by scientists in the direction of organoids.
As one of the research focuses,Organoids refer to a kind of micro-organs with a three-dimensional structure that can be cultivated in vitro environment, with a complex structure similar to that of real organs, and can partially simulate the physiological functions of real organs
In 2009, the Hans Clevers team at the Hubrecht Institute in the Netherlands successfully cultured adult stem cells into small intestinal crypts and villous structures, marking the beginning of organoid technology.
Organoids are promising for organ transplantation and drug screening, and at the same time, organoids offer an opportunity to create cellular models of human disease, which can be studied in the laboratory to better understand the cause of the disease and determine possible methods. The power of organoids in this regard was first applied to the genetic form of microcephaly, in which patient cells are used to make cerebral organoids, which are smaller and show abnormalities in early neurons.
In 2021, a team of researchers from the Austrian Academy of Sciences in Vienna successfully cultivated the world's first in vitro self-organizing cardiac organoid model using human pluripotent stem cells, which can spontaneously form cavities and beat autonomously without stent support. At the same time, this cardiac organoid can autonomously mobilize cardiac fibroblasts to migrate and repair damage after injury.
Figure |The beating heart organoid (**The Mendjan Lab).
Earlier this month, an article published in the journal Nature Methods showed that scientists at the Institute of Molecular Biotechnology of the Austrian Academy of Sciences had successfully developed an organoid model of the dopamine system. This model reveals in detail the complex functions of the dopamine system and its potential impact on Parkinson's disease. Excitingly, this organoid model can be used to improve cells for Parkinson's disease**.
Around the same time, in a study published in the scientific journal Cell Reports, scientists from Stanford University School of Medicine and other institutions used three-dimensional organ tissue models of organoids to screen for genes that cause the growth of many different types of cancer, and identified promising potential targets in oral cancer and esophageal squamous cell carcinoma.
At present, organoid culture technology is going through the stage of rapid technological development and the emergence of a large number of scientific research resultsApplication prospectsExpansive, but at the same time facing a number of important challenges: including how to effectively use stem cells from human embryos to establish stable and durable in vitro models;How to simulate the human microenvironment more realistically;and how to achieve mass production of products with scientific research attributes and successfully transform them into clinical products.
In the future, we look forward to the continuous development of organoid technology, which will bring more opportunities and breakthroughs in the fields of medicine, biology, drug discovery, and AI.