1 citations · 2 across the 6 of their papers we have counts for
6 papers
Inferring brain plasticity rule under long-term stimulation with structured recurrent dynamics
Zhichao Liang, Jingzhe Lin, Xinyi Li +2
Understanding how long-term stimulation reshapes neural circuits requires uncovering the rules of brain plasticity. While short-term synaptic modifications have been extensively ch…
Artificial intelligence as a surrogate brain: Bridging neural dynamical models and data
Yinuo Zhang, Demao Liu, Zhichao Liang +6
Recent breakthroughs in artificial intelligence (AI) are reshaping the way we construct computational counterparts of the brain, giving rise to a new class of ``surrogate brains''.…
Uncovering cognitive taskonomy through transfer learning in masked autoencoder-based fMRI reconstruction
Youzhi Qu, Junfeng Xia, Xinyao Jian +5
Data reconstruction is a widely used pre-training task to learn the generalized features for many downstream tasks. Although reconstruction tasks have been applied to neural signal…
Suppressing seizure via optimal electrical stimulation to the hub of epileptic brain network
Zhichao Liang, Guanyi Zhao, Yinuo Zhang +4
The electrical stimulation to the seizure onset zone (SOZ) serves as an efficient approach to seizure suppression. Recently, seizure dynamics have gained widespread attendance in i…
Controlling network-coupled neural dynamics with nonlinear network control theory
Zhongye Xia, Weibin Li, Zhichao Liang +2
This paper addresses the problem of controlling the temporal dynamics of complex nonlinear network-coupled dynamical systems, specifically in terms of neurodynamics. Based on the L…
Reverse engineering the brain input: Network control theory to identify cognitive task-related control nodes
Zhichao Liang, Yinuo Zhang, Jushen Wu +1
The human brain receives complex inputs when performing cognitive tasks, which range from external inputs via the senses to internal inputs from other brain regions. However, the e…