The brain is the most complex organ in the human body. Composed of intricate overlapping networks existing on multiple spatiotemporal and philosophical scales, a comprehensive and unified understanding of the brain does not exist. The genomics revolution has enabled new approaches in scientific innovation through novel tool development. These genetically encodable tools may offer an improved and translatable approach in understanding the brain’s complexity. Here, I present methods for engineering viral vectors which genetically encode chromatic photoproteins to transduce neurons in the mammalian cortex such that each neuron may have a unique spectral signature. The photoproteins generate luminescence in response to Ca2+ oscillations, a primary messenger in neuronal signaling. Optically separated luminescent signals serve as a platform for feature generation where machine learning ensembles are able to correlate signal features and predict responses to external stimuli. With analysis suggesting increased color heterogeneity improves predictor accuracy, I engineered novel photoproteins with enhance kinetic and chromatic properties. With continued optimization of indicator, detector, and algorithmic design, this approach could serve as a translatable platform for a human brain-machine interface.