This project aims to build a brain-wide reference atlas of electrophysiological signatures by leveraging the IBL’s large-scale, standardized datasets. By characterizing spike shapes, spike trains, local field potentials, and their relationships across brain areas, we seek to map neural signals to anatomical location and genetic class. This atlas will support both offline analyses and real-time applications, such as improving probe targeting accuracy during recordings, and will serve as a foundation for adapting processing algorithms like spike sorting to region-specific signal characteristics.

To support this vision, we are developing predictive models, visualization tools, and integration with existing IBL infrastructure including the trajectory planner and potentially SpikeGLX. The atlas will be openly accessible and designed for community contributions, with plans to incorporate data from external partners like the Allen Institute. Alongside this, we are creating tools to improve histological alignment, automate region boundary detection, and refine brain parcellations by linking gene expression data to electrophysiological patterns. Together, these technical developments will enhance accuracy, reproducibility, and accessibility in large-scale electrophysiology.