Stefan Köhler

This study investigates how the hippocampus contributes to explicit and implicit statistical learning in TLE patients, finding impairments in explicit measures but intact performance in implicit measures.

This study uses closed-loop auditory stimulation to enhance NREM sleep oscillations in older adults and links fast spindle activity to individual memory improvements.

This paper uses diffusion MRI and computational methods to map macro- and microstructural features of the human hippocampus, revealing distinct subfield characteristics and potential fiber pathway orientations.

The paper examines pRF size along the AP axis of the hippocampus and finds it correlates with scene sensitivity, similar to surrounding MTL regions.

HippUnfold is an automated BIDS-App for hippocampal unfolding and subfield segmentation using deep learning and topological constraints, enabling individual-specific morphometric analysis.

The paper shows that activity in perirhinal and entorhinal cortex best predicts perceived visual similarities between category exemplars compared to other ventral visual stream regions.

The paper uses 3D histological data and computational unfolding to identify hippocampal subfields through clustering of laminar and morphological features, revealing subfield boundaries and longitudinal differences.

The paper investigates domain-level organization of object representations in the human medial temporal lobes using fMRI and RSA, finding evidence for animacy and real-world size effects in perirhinal, parahippocampal, and hippocampal regions.

This paper introduces a computational method to unfold the hippocampus, enabling 2D mapping of subfields and revealing structural regularities validated by histology.