Pozzi Paolo, Mapelli Jonathan
Department of Beiomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy.
Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Modena, Italy.
Front Cell Neurosci. 2021 Feb 24;15:609505. doi: 10.3389/fncel.2021.609505. eCollection 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 . 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.
光遗传学的出现彻底改变了神经科学领域的实验研究，在三维空间中选择性刺激神经元的可能性为理解大脑动态和功能开辟了新途径。多光子激发与光遗传学方法的结合，通过产生激发点云来识别和激发特定的神经元靶点。产生点云最广泛使用的方法是通过空间光调制（SLM），其刷新率为数十赫兹。然而，计算三维图案所需的计算时间在几秒到几分钟之间，这严重限制了系统的整体性能。实际上，SLM的最大速度要么用于嵌入预计算序列中的高质量图案，要么用于实时更新的低质量图案。在此，我们提出在消费级图形处理器单元上实现最近开发的压缩感知格奇伯格 - 萨克斯顿算法，从而能够以视频速率生成高质量图案。这反过来将极大地减少实验过程中的停滞时间，并能够实现以前不可能的应用，例如通过钙成像或电压成像检测到的单神经元功能信号的反馈驱动神经网络活动的控制，或运动伪影的实时补偿。
