Research Projects and Datasets

Our laboratory is deeply engaged in cutting-edge fields such as brain-inspired computing, neuromorphic computing, and spiking neural networks. We are committed to unraveling the mysteries of the brain’s information processing and constructing computational paradigms that are closer to biological intelligence.

Based on in-depth analysis of neurodynamics, we break through the limitations of traditional artificial neural networks, develop low-power consumption and highly parallel spiking neural networks, and explore innovative applications of spike coding mechanisms in real-time perception and autonomous learning.

Our team integrates neuroscience, computer science, and microelectronics technology, and has achieved a series of results in areas such as brain-inspired cognitive models and dynamic environment adaptation algorithms. These efforts provide core technical support for the new generation of artificial intelligence, edge computing, and brain-computer interfaces, driving the evolution of intelligent systems toward higher efficiency, robustness, and human-like characteristics.

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An integrative data-driven model simulating C. elegans brain, body and environment interactions

computational neurosciencemachine learningai4science

We are developing a data-driven model to simulate the interactions between the C. elegans brain, body, and environment. The model is based on the principles of neuroscience and computational neuroscience, and is used to study the behavior of the C. elegans. The important innovation of BAAIWorm Tianbao lies in that it not only focuses on the modeling of the nervous system, but also takes the body and the environment into consideration, forming a closed - loop system. By simulating the behavior of nematodes, it explores how the neural structure affects intelligent behavior. This work not only provides a new platform for the study of biological intelligence, but also lays the foundation for the further development of the embodied intelligence theory and its application in the field of artificial intelligence. Padraig Gleeson from University College London (member of the OpenWorm team and one of the reviewers of this paper) evaluated BAAIWorm as follows: "This is a remarkable achievement. It integrates the physiological and anatomical information of Caenorhabditis elegans into a computational model. It shows a lot of progress at different levels, and all the achievements are integrated with each other, forming a clear picture. I think this is an important advance in our modeling of Caenorhabditis elegans and understanding the 'brain - body - environment' interaction."

Collaborators: BAAI