Chongxi Lai

This paper develops a low-latency system for assigning single-neuron identities from neural signals, enabling real-time applications in brain-machine interfaces.

The paper shows that learning in the hippocampus produces orthogonalized neural representations resembling a state machine, captured by HMMs and biologically plausible networks, but not by gradient-based models.

This paper demonstrates that rats can navigate virtual environments by voluntarily activating hippocampal representations of remote locations using a brain-machine interface.

Covariance-based features plus unsupervised density-peak clustering sort spikes from up to 1000 channels in real time on one workstation, while tolerating noise and probe drift.