Digital twin brain: bridging biological intelligence and artificial intelligence

A series of recent developments in neuroscience and AI technology inspired by the structure of the human brain have opened up new possibilities for us to solve the mystery of intelligence. Now, a research team led by Professor Jiang Tianzi from the Institute of Automation, Chinese Academy of Sciences, has outlined the key components and features of an innovative platform called "Digital Twin Brain." The platform promises to bridge the gap between biological intelligence and artificial intelligence and provide novel solutions for both ends.

This research was published in the journal "Intelligent Computing" on September 22.

One of the major similarities between biological intelligence and artificial intelligence is that both belong to network structures. Since the brain is composed of biological networks, researchers hope to use artificial networks to build corresponding digital models or brain "twins" to input knowledge about biological intelligence into the models. The ultimate goal of this move is to "promote the development of general artificial intelligence and promote precision mental health care." And there is no doubt that the realization of this ambitious goal cannot be achieved without the joint efforts of scientists from various disciplines around the world.

Using digital twin brains, researchers can explore the working mechanism of the human brain by simulating/adjusting the brain to perform various cognitive tasks in different states. For example, they could model how the brain functions normally at rest and what problems might occur as a result of disease, or devise new ways to regulate brain activity and guide it out of unhealthy states.

Although it sounds like science fiction, digital twin brains do have a solid biological theoretical basis. It integrates three core elements: a brain map that serves as a structural scaffold and biological constraint mechanism, a multi-level neural model trained on biological data to simulate brain function, and a series of tools used to evaluate and update the current "twin" copy. application.

These three core elements are expected to continue to develop and interact through closed loops. Dynamic brain mapping can improve neural models to produce more realistic functional simulations. In the past, “twins” composed of such models have been validated in an ever-expanding range of practical application scenarios, including disease biomarker discovery and drug testing. These applications will provide continuous feedback, thereby enhancing the brain map to complete the entire operational loop.

The biological brain has a complex structure and dynamic system, so it is necessary to establish an extremely detailed brain map, including maps of different scales, multiple modes, and even from different species, in order to master the construction logic of digital twins. By comprehensively collecting relevant maps, researchers can deeply explore all aspects of the brain, as well as the connections and interactions between different regions in the brain, and ultimately solve the mystery of the principles of brain organization.

On the other hand, the brain map also represents a constraint, that is, the neural model must be based on the map to achieve "biological rationality", which also brings technical challenges.

Jiang Tianzi’s team believes that the brain network map will become an important part of the development of digital twin brains. In 2016, researchers from the Institute of Automation of the Chinese Academy of Sciences announced that the macromap included 246 brain regions and was moving toward an "extensive and detailed mapping" of brain structure and connectivity.

At the same time, given that existing brain simulation platforms often lack anatomical foundations, the authors believe that it will be crucial to design "a set of open source, efficient, flexible, user-friendly and atlas-constrained brain simulation platforms" . The platform must be powerful enough to support multi-scale and multi-modal modeling. Of course, there are still many outstanding issues to be solved, such as how to effectively weave complex biological knowledge into digital twin copies, how to design better simulation models, and how to integrate digital twin brains into actual scenarios.

In short, such a digital twin brain represents the integration of neuroscience and artificial intelligence. By integrating complex brain maps, dynamic neural models, and a host of applications, this platform promises to revolutionize our understanding of biological intelligence and artificial intelligence. With the joint efforts of scientists around the world, digital twin brains are expected to promote the development of general artificial intelligence, revolutionize precision psychological medicine, and ultimately help us thoroughly grasp human thoughts, plan the development of intelligent technology, and seek transformative treatments for brain diseases. Paving the way in the direction.

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