Search Results for author: Lee E. Miller

Found 5 papers, 3 papers with code

Capturing cross-session neural population variability through self-supervised identification of consistent neuron ensembles

no code implementations19 May 2022 Justin Jude, Matthew G. Perich, Lee E. Miller, Matthias H. Hennig

Classification of consistent versus unfamiliar neurons across sessions and accounting for deviations in the order of consistent recording neurons in recording datasets over sessions of recordings may then maintain decoding performance.

Targeted Neural Dynamical Modeling

2 code implementations NeurIPS 2021 Cole Hurwitz, Akash Srivastava, Kai Xu, Justin Jude, Matthew G. Perich, Lee E. Miller, Matthias H. Hennig

These approaches, however, are limited in their ability to capture the underlying neural dynamics (e. g. linear) and in their ability to relate the learned dynamics back to the observed behaviour (e. g. no time lag).

Adversarial Domain Adaptation for Stable Brain-Machine Interfaces

no code implementations ICLR 2019 Ali Farshchian, Juan A. Gallego, Joseph P. Cohen, Yoshua Bengio, Lee E. Miller, Sara A. Solla

However, implementation of an Adversarial Domain Adaptation Network trained to match the empirical probability distribution of the residuals of the reconstructed neural signals outperforms the two methods based on latent variables, while requiring remarkably few data points to solve the domain adaptation problem.

Domain Adaptation Time Series Analysis

Machine learning for neural decoding

1 code implementation2 Aug 2017 Joshua I. Glaser, Ari S. Benjamin, Raeed H. Chowdhury, Matthew G. Perich, Lee E. Miller, Konrad P. Kording

Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods.

BIG-bench Machine Learning Hippocampus

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