Feb 21, 2023 · This technique has become increasingly popular for extracting a minimal number of latent features to explain high-dimensional data in non-human primates (NHPs).
This technique has become increasingly popular for extracting a minimal number of latent features to explain high-dimensional data in non-human primates (NHPs).
Here, we demonstrate these methods in both NHP and human data. In NHP subjects (n=2), we reduced the number of features to an average of 26.86% and 14.86% of ...
Factor analysis on fMRI data shows that FA-based classifiers can maintain the performance fidelity observed with PCA-based decoders and allow researchers to ...
Estimating Intrinsic Manifold Dimensionality to Classify Task-Related Information in Human and Non-Human Primate Data. BioCAS 2022: 650-654. [+][–]. Coauthor ...
How do human brains represent tasks of varying structure? The lateral prefrontal cortex (lPFC) flexibly represents task information.
To estimate the ID of data-representations in deep networks, we leverage a recently developed global ID-estimator ('TwoNN') that is based on computing the ratio.
Estimating Intrinsic Manifold Dimensionality to Classify Task-Related Information in Human and Non-Human Primate Data. Conference Paper. Oct 2022. Zachary ...
Mar 28, 2024 · We employ a task-driven modeling approach to investigate the neural code of proprioceptive neurons in cuneate nucleus (CN) and somatosensory cortex area 2 (S1).
The objective of this study was to evaluate the efficacy of several representative algorithms for estimating the dimensionality of linearly and nonlinearly ...
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