44 citations · 111 across the 5 of their papers we have counts for
6 papers
Capturing cross-session neural population variability through self-supervised identification of consistent neuron ensembles
Justin Jude, Matthew G. Perich, Lee E. Miller +1
Decoding stimuli or behaviour from recorded neural activity is a common approach to interrogate brain function in research, and an essential part of brain-computer and brain-machin…
Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation
Justin Jude, Matthew G Perich, Lee E Miller +1
Neural population activity relating to behaviour is assumed to be inherently low-dimensional despite the observed high dimensionality of data recorded using multi-electrode arrays.…
Targeted Neural Dynamical Modeling
Cole Hurwitz, Akash Srivastava, Kai Xu +4
Latent dynamics models have emerged as powerful tools for modeling and interpreting neural population activity. Recently, there has been a focus on incorporating simultaneously mea…
Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity
Felix Pei, Joel Ye, David Zoltowski +13
Advances in neural recording present increasing opportunities to study neural activity in unprecedented detail. Latent variable models (LVMs) are promising tools for analyzing this…
Adversarial Domain Adaptation for Stable Brain-Machine Interfaces
Ali Farshchian, Juan A. Gallego, Joseph P. Cohen +3
Brain-Machine Interfaces (BMIs) have recently emerged as a clinically viable option to restore voluntary movements after paralysis. These devices are based on the ability to extrac…
Machine learning for neural decoding
Joshua I. Glaser, Ari S. Benjamin, Raeed H. Chowdhury +3
Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods. Modern machine learning tools, which are versatile and e…