Almir Aljović

SingleBehavior Lab (SBL) models behavior across experimental contexts using spatiotemporal embeddings and lightweight adapters, enabling behavior sequencing and task-specific refinement with few-shot learning.

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.

This paper introduces FlexiSoft, a flexible and robust electronic platform for brain-computer interfaces that enables long-term neural recording and curved implantation.

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

The paper shows that TCA cycle dysfunction contributes to axonal energy deficiency in neuroinflammatory lesions and that restoring TCA enzymes can alleviate this deficit.

This study introduces ALMA, a deep-learning-based tool for automated limb motion analysis in mice, demonstrating its effectiveness in tracking locomotor deficits and detecting abnormalities in neurological disorders.