Our work centers on understanding how the brain represents information and intention in order to develop high-performance, robust, and practical assistive devices for people with disabilities and neurological disorders.
Most recent work
- Applying deep learning techniques to uncover neural population dynamics: “Inferring single-trial neural population dynamics using sequential auto-encoders” (preprint).
- A brain-machine interface that enabled high performance communication by people with paralysis:
[Pandarinath*, Nuyujukian* et al., eLife 2017]
Related press coverage: [Scientific American] [PBS News Hour] [IEEE Spectrum] [Emory News] [Digital Trends] [Stanford Medicine]
Note to prospective graduate students:
All students joining the lab would first need to be accepted by one of the relevant graduate programs at Emory or Georgia Tech (Biomedical Engineering, Neuroscience, Electrical Engineering, Computer Science, Bioengineering, etc). It is not possible to join the lab until a student is first accepted to a graduate program.
[2017-06] A preprint of our manuscript on applying deep learning techniques to uncover neural dynamics is now available on BioRxiv: “Inferring single-trial neural population dynamics using sequential auto-encoders”.
[2017-05] Thrilled to participated in the first New Directions in Motor Control workshop at Emory. Gave a talk entitled “Deep learning methods to precisely estimate motor cortical population state and its dynamics” #ATLmotorcontrol
[2017-03] Yahia Ali, an undergraduate in the lab, was selected for a prestigious President’s Undergraduate Research Award from Georgia Tech. Congrats Yahia!
[2017-01] Our paper “High performance communication by people with tetraplegia using an intracortical brain-machine interface” was accepted at the journal eLife.
[2017-01] Our abstract “Precise estimates of single-trial neural population state in motor cortex via deep learning methods” was accepted at Computational and Systems Neuroscience (Cosyne) 2017.
[2017-01] Our paper “Signal-independent noise in intracortical brain-computer interfaces causes movement time properties inconsistent with Fitts’ law” was accepted at the Journal of Neural Engineering.
[2016-11] Our paper “Feedback control policies employed by people using intracortical brain-machine interfaces” was accepted at the Journal of Neural Engineering.
[2016-08] The preprint of our paper, “LFADS – Latent Factor Analysis via Dynamical Systems,” is now available.
[2016-06] Gave an invited talk at the International BCI Society, “Using dynamical models of motor cortical activity to improve BCIs.”
[2016-06] David Sussillo gave an invited talk at the Grossman Center for the Statistics of the Mind at Columbia University, on our joint work “Inferring latent dynamics from single trial neural population activity using variational temporal autoencoders.”