Brain microstructural and functional MRI: developments and application to a rat model of Alzheimer's disease

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Neurons in the human amygdala and hippocampus are classically thought to encode a person’s identity invariant to visual features. However, it remains largely unknown how visual information from higher visual cortical areas is translated into such a semanti ...

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

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Deep learning models (DLM) are efficient replacements for computationally intensive optimization techniques. Musculoskeletal models (MSM) typically involve resource-intensive optimization processes for determining joint and muscle forces. Consequently, DLM ...

Recent studies have reported social cognitive deficits, particularly in emotional processing, in Parkinson's disease (PD). However, a comprehensive characterization of these deficits and their underlying neural correlates remains elusive. Therefore, this s ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

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Brain microstructural and functional MRI: developments and application to a rat model of Alzheimer's disease

Chat with Graph Search

Feature-based encoding of face identity by single neurons in the human amygdala and hippocampus

Understanding generalization and robustness in modern deep learning

Foundations of a New Learning Paradigm in AI Grounded in the Principles of Evolutionary Developmental Biology

Glenohumeral joint force prediction with deep learning

Mental State Recognition Deficits Linked to Brain Changes in Parkinson's Disease Without Dementia

Robust machine learning for neuroscientific inference

Improving the accuracy and interpretability of neural networks for wind power forecasting

Posterior-Based Analysis of Spatio-Temporal Features for Sign Language Assessment

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (2/6)

Probing the latent hierarchical structure of data via diffusion models

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (3/6)

Emergent Rate-Based Dynamics in Duplicate-Free Populations of Spiking Neurons

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (4/6)

Fast High-Resolution Metabolite Mapping in the rat Brain Using 1H-FID-MRSI at 14.1 T

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (5/6)

Advances on Real Time M/EEG Neural Feature Extraction

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (6/6)

Residual-based attention in physics-informed neural networks

A Riemannian framework for incorporating white matter bundle prior in orientation distribution function based tractography algorithms

Topics in statistical physics of high-dimensional machine learning