Marino Krstulović

This paper introduces a fully automated homecage-based novel object recognition test for assessing long-term object memory in mice and demonstrates its robustness and reproducibility across facilities.

The paper develops variational log-Gaussian point-process methods to efficiently calculate spatial statistics for grid cells in large environments using Gaussian processes.

This study shows that grid cells and place cells in rats have time shifts proportional to their spatial scales, enabling the representation of multiple time horizons for navigation.

The paper introduces a digital platform for remote, unsupervised assessment of spatial working memory and image recognition, showing practice effects and challenges in real-world cognitive testing.

This study demonstrates that sheep can serve as a viable model for longitudinal electrophysiological studies due to their stable recordings and naturalistic behavior.