6 papers
On the Nonasymptotic Scaling Guarantee of Hyperparameter Estimation in Inhomogeneous, Weakly-Dependent Complex Network Dynamical Systems
Yi Yu, Yubo Hou, Yinchong Wang +3
Hierarchical Bayesian models are increasingly used in large, inhomogeneous complex network dynamical systems by modeling parameters as draws from a hyperparameter-governed distribu…
Dark Signals in the Brain: Augment Brain Network Dynamics to the Complex-valued Field
Jiangnan Zhang, Chengyuan Qian, Wenlian Lu +3
Recordings of brain activity, such as functional MRI (fMRI), provide low-dimensional, indirect observations of neural dynamics evolving in high-dimensional, unobservable spaces. Em…
EEG-fused Digital Twin Brain for Autonomous Driving in Virtual Scenarios
Yubo Hou, Zhengxin Zhang, Ziyi Wang +3
Current methodologies typically integrate biophysical brain models with functional magnetic resonance imaging(fMRI) data - while offering millimeter-scale spatial resolution (0.5-2…
Stochastic Forward-Forward Learning through Representational Dimensionality Compression
Zhichao Zhu, Yang Qi, Hengyuan Ma +2
The Forward-Forward (FF) learning algorithm provides a bottom-up alternative to backpropagation (BP) for training neural networks, relying on a layer-wise "goodness" function with…
Optimal signal transmission and timescale diversity in a model of human brain operating near criticality
Yang Qi, Jiexiang Wang, Weiyang Ding +4
Cortical neurons exhibit a hierarchy of timescales across brain regions in response to input stimuli, which is thought to be crucial for information processing of different tempora…
Uncertainty Quantification in Working Memory via Moment Neural Networks
Hengyuan Ma, Wenlian Lu, Jianfeng Feng
Humans possess a finely tuned sense of uncertainty that helps anticipate potential errors, vital for adaptive behavior and survival. However, the underlying neural mechanisms remai…