Arnau Marin‐Llobet

This study uses flexible electrodes to track and model representational drift in mouse visual cortex, enabling a stable decoder for brain-machine interfaces.

This paper introduces growth-adaptive spring electronics and a spike processing pipeline to enable long-term, same-neuron mapping in developing rat brains, revealing neuron-specific developmental decorrelation.

Agentic Lab integrates AI agents with physical lab operations to improve reproducibility in cell and organoid research by autonomously generating protocols and analyzing experimental data.

DeviceAgent is an AI agent that integrates LLMs and VLMs to automate bioelectronics research tasks, demonstrating capabilities in design, fabrication, defect inspection, and electrophysiological analysis.

This paper introduces BCI-Agent, an AI framework that classifies neuronal cell types from electrophysiological data without fine-tuning, validated in rodent and human datasets.

BehaveAgent automates cross-species behavior analysis from video input using multimodal AI without manual intervention or retraining, enabling zero-shot reasoning and end-to-end analysis.

The paper introduces STAgent, an AI tool that automates spatial transcriptomics analysis, revealing in vivo maturation of human stem cell-derived pancreatic cells.

SpikeAgent automates spike sorting with a multimodal LLM-based AI agent, achieving curation consistency equal to or exceeding human experts and drastically reducing expertise barriers.

This paper introduces AutoSort, a deep learning method for real-time decoding of neural activity from the same cells over months, showing improved efficiency and accuracy compared to existing methods.