Brain-machine interfaces (BMIs) have the potential to enhance quality of life by restoring functions lost to neurological disease. However, current minimally-invasive BMIs (e.g. cortical surface electrodes) do not provide enough information for complex control. BMIs that yield high information content (e.g. implanted electrode arrays) damage brain tissue, limiting their lifespan. This project will develop a next-generation BMI that uses light instead of electricity, is less invasive and longer-lasting than implanted electrode arrays, and is capable of high information-content recording. This proposal will enhance teaching materials and summer programs with strong emphasis on recruitment of UMR students (and those who did not have real lab research experience). This could positively influence more students and attract them to multidisciplinary sciences or STEM programs in general.
The project will virally express calcium-sensitive bioluminescent proteins in neurons, so light is emitted with neural activity. To distinguish different neurons, multiple colors of such sensors are stochastically expressed, so each neuron contains different amounts of each color of the bioluminescent sensor, yielding a unique spectral signature. To monitor neural activity, the wavelength and intensity of emitted light is detected as a function of time. These data are processed to yield information on the activity of a large ensemble of individual neurons. This approach could provide a path to a long-term, robust, and high-fidelity BMI.
