Real Time Generation of Three Dimensional Patterns for Multiphoton Stimulation - Pozzi, Paolo; Mapelli, Jonathan (Frontiers in Cellular Neuroscience, 2021)
The advent of optogenetics has revolutionized experimental research in the field of Neuroscience and the possibility to selectively stimulate neurons in 3D volumes has opened new routes in the understanding of brain dynamics and functions. The combination of multiphoton excitation and optogenetic methods allows to identify and excite specific neuronal targets by means of the generation of cloud of excitation points. The most widely employed approach to produce the points cloud is through a spatial light modulation (SLM) which works with a refresh rate of tens of Hz. However, the computational time requested to calculate 3D patterns ranges between a few seconds and a few minutes, strongly limiting the overall performance of the system. The maximum speed of SLM can in fact be employed either with high quality patterns embedded into pre-calculated sequences or with low quality patterns for real time update. Here, we propose the implementation of a recently developed compressed sensing Gerchberg-Saxton algorithm on a consumer graphical processor unit allowing the generation of high quality patterns at video rate. This, would in turn dramatically reduce dead times in the experimental sessions, and could enable applications previously impossible, such as the control of neuronal network activity driven by the feedback from single neurons functional signals detected through calcium or voltage imaging or the real time compensation of motion artifacts.