Chinese Scientists Unveil Brain-Inspired Neural Circuit Evolution

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Chinese Scientists Unveil Brain-Inspired Neural Circuit Evolution

Chinese scientists have recently unveiled an innovative strategy for brain-inspired neural circuit evolution, presenting exciting possibilities for developing spiking neural networks that closely resemble biological systems in terms of efficiency and plausibility. The study, led by Zeng Yi from the Institute of Automation under the Chinese Academy of Sciences (CAS), proposes a groundbreaking approach to advancing general brain-inspired cognitive intelligence.

The human brain consists of various neural circuits that contribute to different cognitive functions, working together harmoniously to enhance human perception, learning, and decision-making. By exploring the dynamic characteristics of these biological neural circuits from a computational perspective, researchers aim to leverage this knowledge to enhance artificial intelligence systems.

Through the application of evolved neural circuits, the team of scientists successfully constructs spiking neural networks designed for image classification and reinforcement learning tasks. Leveraging the brain-inspired Neural circuit Evolution strategy (NeuEvo) together with diverse neural circuit types, the evolved spiking neural network demonstrates significantly enhanced capabilities in perception and reinforcement learning tasks, according to the study.

Incorporating on-policy and off-policy deep reinforcement learning algorithms, NeuEvo achieves performance levels comparable to artificial neural networks, offering promising results for future applications. These findings were recently published in Proceedings of the National Academy of Sciences (PNAS), a well-respected scientific journal in the United States.

This breakthrough research opens up new avenues for developing artificial intelligence systems that imitate the brain’s intricate neural circuits more closely. By leveraging the knowledge gained from studying the dynamic characteristics of biological neural circuits, Chinese scientists are harnessing this computational perspective to enhance the capabilities of AI systems. The evolved spiking neural networks display remarkable improvements in perception and reinforcement learning tasks, paving the way for more efficient and biologically plausible AI models.

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The implications of this study stretch beyond artificial intelligence systems. By better understanding the adaptive synergy among different neural circuits in the brain, scientists can gain insights into human cognition. This knowledge can be applied in various fields, including psychology, neuroscience, and even healthcare, potentially leading to advancements in treatments for cognitive disorders and brain injuries.

The Chinese scientists’ research sheds light on the immense potential of brain-inspired neural circuit evolution and its applications in the field of artificial intelligence. As the study demonstrates the remarkable improvements achieved by incorporating evolved neural circuits into spiking neural networks, it paves the way for more biologically plausible and efficient AI systems. With further advancements and collaborations within the scientific community, we can expect to witness groundbreaking developments in the field of brain-inspired cognitive intelligence.

Frequently Asked Questions (FAQs) Related to the Above News

What is brain-inspired neural circuit evolution?

Brain-inspired neural circuit evolution is a strategy that aims to develop artificial neural networks that closely resemble the structure and function of biological neural circuits found in the human brain. It involves studying the dynamic characteristics of these circuits from a computational perspective and applying that knowledge to enhance artificial intelligence systems.

Who conducted the research on brain-inspired neural circuit evolution?

The research on brain-inspired neural circuit evolution was conducted by Chinese scientists, specifically led by Zeng Yi from the Institute of Automation under the Chinese Academy of Sciences (CAS).

What are the potential applications of brain-inspired neural circuit evolution?

The potential applications of brain-inspired neural circuit evolution extend beyond artificial intelligence systems. By better understanding the adaptive synergy among different neural circuits in the brain, scientists can gain insights into human cognition and apply that knowledge in fields such as psychology, neuroscience, and healthcare. This research has the potential to contribute to advancements in treatments for cognitive disorders and brain injuries.

What were the key findings of the research?

The research found that by applying evolved neural circuits, the scientists were able to construct spiking neural networks with enhanced capabilities in perception and reinforcement learning tasks. These evolved neural circuits improved performance levels comparable to artificial neural networks. The study's findings were published in the Proceedings of the National Academy of Sciences (PNAS), a respected scientific journal in the United States.

How does brain-inspired neural circuit evolution contribute to the development of AI systems?

Brain-inspired neural circuit evolution contributes to the development of AI systems by allowing for the creation of artificial neural networks that closely mimic the structure and function of biological neural circuits. By replicating the efficiency and plausibility of these circuits, AI systems can potentially achieve enhanced cognitive intelligence, perception, and decision-making capabilities.

What are the future possibilities for brain-inspired cognitive intelligence?

The research on brain-inspired neural circuit evolution opens up new possibilities for the development of artificial intelligence systems that closely imitate the intricate neural circuits of the brain. With further advancements and collaborations within the scientific community, we can expect groundbreaking developments in the field of brain-inspired cognitive intelligence, leading to more efficient and biologically plausible AI models.

Please note that the FAQs provided on this page are based on the news article published. While we strive to provide accurate and up-to-date information, it is always recommended to consult relevant authorities or professionals before making any decisions or taking action based on the FAQs or the news article.

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