44 citations · 111 across the 5 of their papers we have counts for
4 papers · 1 filter
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…
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…