Search Results for author:
Found 5 papers, 3 papers with code
Machine learning for neural decoding
Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods.
Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity
We curate four datasets of neural spiking activity from cognitive, sensory, and motor areas to promote models that apply to the wide variety of activity seen across these areas.
Targeted Neural Dynamical Modeling
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
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.
Capturing cross-session neural population variability through self-supervised identification of consistent neuron ensembles
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.
