H. Schweiger

PULSE, a foundation model trained on neural recordings, reveals how disease variants affect network development and how circuits encode information in brain organoids.

HIPPIE is a deep learning model that classifies neurons from electrophysiological recordings by combining self-supervised pretraining and supervised fine-tuning, showing better performance than unsupervised methods.

The paper presents a cloud-based pipeline for analyzing and visualizing neuronal electrophysiology data from HD-MEAs, demonstrating its utility in simplifying data analysis and understanding neural activity.

This paper develops a cloud-connected stem cell platform to enhance scientific identity in underrepresented students, showing improved outcomes in competence and research readiness.

This paper develops a 3D-printed, servo-actuated microvalve system for automated organoid culture, validated in mouse and human models with comparable viability and gene expression to manual methods.

This study uses mouse forebrain organoids to show how variations in cellular composition influence the self-organization of neural circuits, revealing intrinsic principles of forebrain circuit assembly.

This study demonstrates goal-directed learning in cortical organoids using a closed-loop electrophysiology framework and artificial reinforcement learning.

This study shows that structured firing sequences emerge in human and murine brain organoids and neonatal brain slices, suggesting innate rather than experience-dependent neural coding.

This study shows that endometriosis patient-derived stromal cells have a defective DNA damage response, leading to reduced senescence and altered DDR activation under replication stress.

This paper introduces a sealed recirculatory system for organoid culture that eliminates the need for CO2 incubators and enables continuous live imaging and metabolic viability.

This paper introduces an automated brain organoid platform with IoT integration for continuous monitoring and control, showing increased neural activity and stable firing rates during automated media exchange.

This study shows that the identity of MGE-derived cortical interneurons can be dynamically influenced by their environment, with human 3D models promoting Pvalb expression over Sst.