Our research focuses on brain-inspired computational intelligence, with the aim of developing intelligent systems capable of autonomous learning and adaptation in complex environments by simulating and drawing inspiration from the structure and function of the brain. 

Specifically, the research interests include: 

(1) Modeling Brain-like Structures and Functions: Investigating the simulation of neural and synaptic networks in the brain, with a particular focus on spatiotemporal dynamic modeling of biological neurons, spiking neural networks (SNNs), and synaptic plasticity mechanisms. 

(2) Design of Bio-Inspired Computational Models: Enhancing the performance, efficiency, and robustness of artificial and spiking neural networks by emulating the brain's mechanisms for learning and memory. 

(3) Cross-Domain Applications: Exploring the application of brain-inspired spiking computational models in areas such as computer vision, natural language processing, generative tasks, and multimodal tasks. 

(4) Neuromorphic Hardware Design: Investigating co-design approaches for algorithms and hardware to realize brain-inspired systems with low power consumption, high performance, and low latency.

Brain-Inspired Computing (BIC)