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May 8, 2024 · We introduce a novel neural decoder model built upon GP models. The core idea is that two GPs generate neural data and their associated labels using a set of ...
May 14, 2024 · The core idea is that two GPs generate neural data and their associated labels using a set of low- dimensional latent variables. Under this ...
May 9, 2024 · This research introduces a novel neural decoder model built upon Gaussian Process (GP) models. The key idea is that two GPs generate neural ...
May 13, 2024 · Latent Variable Double Gaussian Process Model for Decoding Complex Neural Data - Faculty members Drs. Scott Sponheim and Alik Widge are ...
Publication: Latent Variable Double Gaussian Process Model for ...
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Latent Variable Double Gaussian Process Model for Decoding Complex Neural Data. Navid Ziaei Joshua J. Stim Melanie D. Goodman-Keiser Scott Sponheim Alik S.
This research proposes a novel non-parametric modeling approach, leveraging the Gaussian process (GP), to characterize high-dimensional data by mapping it to a ...
Jul 19, 2024 · This model consists of Poisson spiking observations and two gaussian processes, one governing the temporal evolution of latent variables and ...
Missing: Double Complex
Latent Variable Double Gaussian Process Model for Decoding Complex Neural Data ... Processes (GP), show promising results in the analysis of complex data.
This dissertation attempts to meet this need, extending and applying the modern tools of latent variable modeling to problems in neural data analysis. It is ...
A novel latent variable model that extracts smoothly evolving latent subspaces that are shared between or independent to distinct data modalities.
Missing: Complex | Show results with:Complex