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Ximeng Mao

2 accepted papers

2025

Generalizable, real-time neural decoding with hybrid state-space models

NeurIPS 2025poster

Real-time decoding of neural activity is central to neuroscience and neurotechnology applications, from closed-loop experiments to brain-computer interfaces, where models are subject to strict latency constraints. Traditional methods, including simple recurrent neural networks, are fast and lightwei…

Cited by 0SourceScholar
2023

A Unified, Scalable Framework for Neural Population Decoding

NeurIPS 2023poster

Our ability to use deep learning approaches to decipher neural activity would likely benefit from greater scale, in terms of both the model size and the datasets. However, the integration of many neural recordings into one unified model is challenging, as each recording contains the activity of diff…

Cited by 41SourcePDFScholar