Author(s)
Mohammad Reza Rezaei
Published 3 Projects
Bioinformatics Gaussian Mixture Model Mixture Model Mixture Merging Algorithm Real Time Filter
Sydney S Cash
Published 5 Projects
Neuroscience Bioinformatics Neurology Bioengineering Neuromodulation
Ali Yousefi
Published 7 Projects
Neuroscience Bioinformatics Neurology Gaussian Mixture Model Neuromodulation
Content
Video Abstract (AI generated) (01:38) Paper PreprintThe Bayesian state-space neural encoder-decoder modeling framework is an established solution to reveal how changes in brain dynamics encode physiological covariates like movement or cognition. Although the framework is increasingly being applied to progress the field of neuroscience, its application to modeling high-dimensional neural data continues to be a challenge. Here, we propose a novel solution that avoids the complexity of encoder models that characterize high-dimensional data as a function of the underlying state processes. We build a discriminative model to estimate state processes as a function of current and previous observations of neural activity. We then develop the filter and parameter estimation solutions for this new class of state-space modeling framework called the direct decoder model. We applied the model to decode movement trajectories of a rat in a W-shaped maze from the ensemble spiking activity of place cells and achieve comparable performance to modern decoding solutions, without needing an encoding step in the model development. We further demonstrate how a dynamical auto-encoder can be built using the direct decoder model; where the underlying state process links the high-dimensional neural activity to the behavioral readout. We applied the dynamical auto-encoder model in estimating the intention to verbally communicate of an epileptic participant and their companions. The result shows that the dynamical auto-encoder can optimally estimate the low-dimensional dynamical manifold which represents the relationship between the brain and behavior.
More Projects
Loren Frank
13 views • 2 years ago
Global Immunotalks
390 views • 3 years ago
Laurel Yohe
2 views • 2 years ago
Global Immunotalks
130 views • 3 years ago
Jignesh H. Parmar
0 views • 2 years ago
Winston A. Haynes
0 views • 2 years ago
Noam Mazor
0 views • 2 years ago
Global Immunotalks
182 views • 3 years ago
Cem Yuksel
345 views • 3 years ago
Oscar Gonzalez-Recio
3 views • 2 years ago
Please pick a style:
Uri T Eden. (2021, Nov 8).Bayesian Decoder Models with a Discriminative Observation Process[Video]. Scitok. https://scitok.com/project/p/764de270
Reza Rezaei Mohammad. "Bayesian Decoder Models with a Discriminative Observation Process" Scitok, uploaded by T Eden Uri, 8 Nov, 2021, https://scitok.com/project/p764de270
Uri T Eden. "Bayesian Decoder Models with a Discriminative Observation Process" Scitok. (Nov 8, 2021). https://scitok.com/project/p/764de270
Uri T Eden (Nov 8, 2021). Bayesian Decoder Models with a Discriminative Observation Process Scitok. https://scitok.com/project/p/764de270
Uri T Eden. Bayesian Decoder Models with a Discriminative Observation Process[video]. 2021 Nov 8. https://scitok.com/project/p/764de270
@online{al2006link, title={ Bayesian Decoder Models with a Discriminative Observation Process }, author={ T Eden, Uri }, organization={Scitok}, month={ Nov }, day={ 8 }, year={ 2021 }, url = {https://scitok.com/project/p/764de270}, }