Jürgen Jost

The paper introduces a computational model of the two dentate gyrus blades, showing how they support pattern separation and integration through distinct architectures and mechanisms.

The paper uses persistent homology to analyze brain functional connectivity changes in healthy aging and ASD, identifying clinically relevant regions with node persistence.

This paper investigates how symmetric learning rules affect predictive neural representations and generalization in spatial navigation using a neural network model of hippocampal subfields.

This paper improves fMRI-based intelligence prediction by using semi-blind machine learning with educational level as supplementary data, achieving better accuracy even with small sample sizes.

This study applies discrete Ricci curvatures to functional connectivity networks of young and older adults, finding age-related differences in brain regions associated with cognitive domains and behavioral scores.

This study applies graph Ricci curvatures to compare functional connectivity networks in ASD and typically developing individuals, finding region-specific differences associated with cognitive impairments and potential therapeutic targets.

The paper introduces a machine learning algorithm that predicts intelligence from task-based fMRI data with high accuracy and faster scan times than resting state fMRI.