Guanghao Sun
Papers
1
Total Citations
4
H-Index
1
About
Guanghao Sun is a computational neuroscience researcher whose work sits at the intersection of neural engineering and machine learning, with a particular focus on advancing brain-machine interface (BMI) technology. His most recognized contribution explores the complex neural dynamics underlying motor control, specifically investigating how interconnections between the dorsal premotor cortex (PMd) and primary motor cortex (M1) can be leveraged to improve movement decoding. In his 2019 paper, "Decoding Velocity from Spikes Using a New Architecture of Recurrent Neural Network," Sun proposed a novel recurrent neural network architecture that explicitly accounts for the intricate intercortical relationships between PMd and M1 — a dimension that conventional BMI decoders had largely overlooked. Rather than treating these regions as independent signal sources, his approach models their functional connectivity to extract richer information from neural spike data for velocity decoding. This work, which has garnered citations within the BMI research community, represents a meaningful step toward more biologically informed and accurate neural decoders. Sun's research holds promise for improving assistive technologies designed to restore motor function in individuals with paralysis or neurological disorders.
Research Focus
Key Achievements
Top Papers
- 1