The intrinsic attractor manifold and population dynamics of a canonical cognitive circuit across waking and sleep
Neural circuits construct distributed representations of key variables—external stimuli or
internal constructs of quantities relevant for survival, such as an estimate of one's location in …
internal constructs of quantities relevant for survival, such as an estimate of one's location in …
Probing variability in a cognitive map using manifold inference from neural dynamics
Hippocampal neurons fire selectively in local behavioral contexts such as the position in an
environment or phase of a task,–and are thought to form a cognitive map of task-relevant …
environment or phase of a task,–and are thought to form a cognitive map of task-relevant …
A deep learning approach for facial emotions recognition using principal component analysis and neural network techniques
In this work, advanced facial emotions are recognized using Neural network‐based (NN)
PCA methodology. The earlier models are cannot detect facial emotions with moving …
PCA methodology. The earlier models are cannot detect facial emotions with moving …
Common population codes produce extremely nonlinear neural manifolds
A De, R Chaudhuri - … of the National Academy of Sciences, 2023 - National Acad Sciences
Populations of neurons represent sensory, motor, and cognitive variables via patterns of
activity distributed across the population. The size of the population used to encode a …
activity distributed across the population. The size of the population used to encode a …
Fast generalization rates for distance metric learning: Improved theoretical analysis for smooth strongly convex distance metric learning
Distance metric learning (DML) aims to find a suitable measure to compute a distance
between instances. Facilitated by side information, the learned metric can often improve the …
between instances. Facilitated by side information, the learned metric can often improve the …
Learning nonlinear level sets for dimensionality reduction in function approximation
Abstract We developed a Nonlinear Level-set Learning (NLL) method for dimensionality
reduction in high-dimensional function approximation with small data. This work is motivated …
reduction in high-dimensional function approximation with small data. This work is motivated …
Learning multiple local metrics: Global consideration helps
Learning distance metric between objects provides a better measurement for their relative
comparisons. Due to the complex properties inside or between heterogeneous objects …
comparisons. Due to the complex properties inside or between heterogeneous objects …
3-D morphology prediction of progressive spinal deformities from probabilistic modeling of discriminant manifolds
S Kadoury, W Mandel, M Roy-Beaudry… - IEEE transactions on …, 2017 - ieeexplore.ieee.org
We introduce a novel approach for predicting the progression of adolescent idiopathic
scoliosis from 3-D spine models reconstructed from biplanar X-ray images. Recent progress …
scoliosis from 3-D spine models reconstructed from biplanar X-ray images. Recent progress …
Image-guided tethering spine surgery with outcome prediction using spatio-temporal dynamic networks
W Mandel, R Oulbacha, M Roy-Beaudry… - … on Medical Imaging, 2020 - ieeexplore.ieee.org
Recent fusionless surgical techniques for corrective spine surgery such as Anterior Vertebral
Body Growth Modulation (AVBGM) allow to treat mild to severe spinal deformations by …
Body Growth Modulation (AVBGM) allow to treat mild to severe spinal deformations by …
Learning identity-preserving transformations on data manifolds
Many machine learning techniques incorporate identity-preserving transformations into their
models to generalize their performance to previously unseen data. These transformations …
models to generalize their performance to previously unseen data. These transformations …