Following spinal cord injuries a subpopulation of glial cells undergo rapid changes that enables a transient cell states with regenerative potential. To understand the regulatory logic behind this, deep Learning models are trained for each temporal pseudobulk within a multiomic single cell dataset of the murine spinal cord injury. The models are then subsequently interpreted to elucidate drivers of regeneration.