Ioannis Delis

The paper compares neural coupling in motor units during index finger flexion and precision pinch tasks, finding stronger nonlinear coupling during pinch.

This study uses a novel muscle network analysis framework to assess post-stroke motor impairment and treatment responsiveness, identifying biomarkers and shifts in muscle coordination associated with recovery.

This study examines how prior information affects perceptual decision-making in psychosis, finding that individuals with psychosis show reduced sensory gain and increased pre-evidence biases compared to healthy controls.

The paper uses PCA and FA to show a single low-dimensional motor unit component explains most force oscillations during repetitive isometric tasks.

This study quantifies hierarchical muscle interactions' contributions to motor function using Partial Information Decomposition, revealing networks linked to balance and motor variability.

This paper introduces a framework to analyze muscle synergies by categorizing their task relevance, revealing how muscle interactions contribute to task performance in different ways.

This study trains a minimalist cerebellar cortex model to learn time-series data, revealing that dense granule cell activity and task-specific pattern separation features underlie temporal learning.

This paper introduces a network information theoretic framework to extract muscle synergies, capturing spatial, temporal, and spatiotemporal interactions in reaching movements.

The paper evaluates a space-by-time decomposition method to assess modularity in muscle activity during whole-body reaching tasks, finding it effective for data approximation and task discrimination.