Centering cognitive neuroscience on task demands and generalization
Cognitive neuroscience seeks generalizable theories explaining the relationship between
behavioral, physiological and mental states. In pursuit of such theories, we propose a …
behavioral, physiological and mental states. In pursuit of such theories, we propose a …
Integrating media content analysis, reception analysis, and media effects studies
R Schmälzle, R Huskey - Frontiers in Neuroscience, 2023 - frontiersin.org
Every day, the world of media is at our fingertips, whether it is watching movies, listening to
the radio, or browsing online media. On average, people spend over 8 h per day consuming …
the radio, or browsing online media. On average, people spend over 8 h per day consuming …
Predicting multiple observations in complex systems through low-dimensional embeddings
Forecasting all components in complex systems is an open and challenging task, possibly
due to high dimensionality and undesirable predictors. We bridge this gap by proposing a …
due to high dimensionality and undesirable predictors. We bridge this gap by proposing a …
Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness
Disorders of consciousness are complex conditions characterised by persistent loss of
responsiveness due to brain injury. They present diagnostic challenges and limited options …
responsiveness due to brain injury. They present diagnostic challenges and limited options …
The human claustrum tracks slow waves during sleep
Slow waves are a distinguishing feature of non-rapid-eye-movement (NREM) sleep, an
evolutionarily conserved process critical for brain function. Non-human studies suggest that …
evolutionarily conserved process critical for brain function. Non-human studies suggest that …
A heat diffusion perspective on geodesic preserving dimensionality reduction
Diffusion-based manifold learning methods have proven useful in representation learning
and dimensionality reduction of modern high dimensional, high throughput, noisy datasets …
and dimensionality reduction of modern high dimensional, high throughput, noisy datasets …
MUNPE: Multi-view uncorrelated neighborhood preserving embedding for unsupervised feature extraction
KR Venugopal - Knowledge-Based Systems, 2024 - Elsevier
In order to identify the shared subspace between two views, in Canonical Correlation
Analysis (CCA), a popular multi-view dimension reduction technique tries to maximize …
Analysis (CCA), a popular multi-view dimension reduction technique tries to maximize …
S2MVTC: a Simple yet Efficient Scalable Multi-View Tensor Clustering
Anchor-based large-scale multi-view clustering has attracted considerable attention for its
effectiveness in handling massive datasets. However current methods mainly seek the …
effectiveness in handling massive datasets. However current methods mainly seek the …
Learning shared neural manifolds from multi-subject FMRI data
Functional magnetic resonance imaging (fMRI) data is collected in millions of noisy,
redundant dimensions. To understand how different brains process the same stimulus, we …
redundant dimensions. To understand how different brains process the same stimulus, we …
Manifold Learning Uncovers Nonlinear Interactions Between the Adolescent Brain and Environment That Predict Emotional and Behavioral Problems
Background To progress adolescent mental health research beyond our present
achievements—a complex account of brain and environmental risk factors without …
achievements—a complex account of brain and environmental risk factors without …