Optogenetics has transformed modern neuroscience by enabling millisecond-scale control of genetically targeted neuronal populations through light. Although experimental precision has improved significantly, the field still faces important limitations, including optical scattering in deep tissue, heterogeneity in opsin expression, and the challenge of interpreting high-dimensional neural data. Artificial intelligence (AI) and bioinformatics have emerged as powerful complementary tools to address these barriers. AI methods improve light-delivery optimization, automate signal extraction from imaging and electrophysiological data, and support predictive modeling of circuit dynamics. Meanwhile, bioinformatics contributes essential frameworks for analyzing the genetic, proteomic, and transcriptomic foundations of neuronal function, offering a molecular perspective that enhances optogenetic experimental design. This review summarizes the foundational principles of optogenetics, outlines its current challenges, and presents how AI and bioinformatics can collectively accelerate neurological research. We highlight applications in epilepsy, Parkinson’s disease, Alzheimer’s disease, and network-level studies, emphasizing how computational tools strengthen the interpretative and translational potential of optogenetic technologies. The combined integration of optogenetics, AI, and bioinformatics represents a promising direction for understanding and manipulating neuronal systems in both health and disease. 

Acceder al documento: https://sciencehoodpublishing.org/article/reframing-optogenetics-through-bioinformatics-towards-multimodal-neurological-analysis