Abstract
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.
Highlights
The recent advances in the field of photonics (Pozzi et al, 2015) combined with methods of molecular (Gandolfi et al, 2017) and genetic manipulation of the samples (Boyden et al, 2005; Mutoh et al, 2012), have provided novel tools to investigate neural functions
The near-simultaneous stimulation of multiple cells heterogeneously distributed in three dimensions can be achieved by time multiplexing with high-speed, inertia-free scanners (Wang et al, 2011), but the only known method for truly simultaneous stimulation is the use of spatial light modulators (SLM) (Packer et al, 2012)
In order to prove the improvement in performance provided by CS-Weighted Gerchberg-Saxton (WGS) is detectable and significant in experimental scenarios, we provided verification of the results of Figure 3 on the setup described in the methods section
Summary
The recent advances in the field of photonics (Pozzi et al, 2015) combined with methods of molecular (Gandolfi et al, 2017) and genetic manipulation of the samples (Boyden et al, 2005; Mutoh et al, 2012), have provided novel tools to investigate neural functions. In order to stimulate areas wider than the diffraction limit, the technique can be combined with either temporal focusing (Pégard et al, 2017), or spiral or raster scanning (Packer et al, 2012, 2013) While this method is widely used in optogenetics, it has a variety of applications extending beyond the field of neuroscience and including optical trapping (Grier and Roichman, 2006), high throughput spectroscopy (Nikolenko et al, 2008; Gandolfi et al, 2014; Pozzi et al, 2015), and adaptive optics (Pozzi et al, 2020)
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