Jinghui Geng

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.

SpikeLab is a text-to-analysis framework for neural spike data that ensures correct and reproducible results using agentic systems, outperforming other models in a controlled benchmark.

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 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 study compares synaptic effects of NRXN1 deletions in ASD and SCZ iPSC models, finding distinct synaptic and network impairments in ASD.

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.

The paper uses high-density electrophysiology to study epileptiform activity in human hippocampal slices from epilepsy patients, revealing unexpected network oscillations in dentate gyrus slices.

The paper introduces Piphys, an open-source platform for neural recording that enables remote control and scalability through cloud-based software and low-cost hardware.