Multiscale Modeling of the Neural Subcircuits in the Outer-Plexiform Layer of the Retina

视网膜是中枢神经系统的一部分，是研究大脑信息处理的理想区域。它是可访问的，有据可查的，并由临床，实验和理论科学的研究人员研究。视网膜的图像处理开始于外丛状层，在那里双极细胞、水平细胞和光感受器细胞相互作用。本研究的目标是对外丛状层的子回路进行详细的数学建模，在两个空间尺度上捕捉时空动态：单个突触的尺度和感受野的尺度。电生理、解剖和分子生物学数据的可用性为开发复杂的系统多尺度模型提供了难得的机会。数学模型将用于发现各种电路元件的功能影响，就像实验家对选择性药理学试剂所做的那样，只是并非所有所需的试剂都实际存在。主要目的是探索两个相互竞争的假设来解释在视网膜外丛状层观察到的突触反馈效应。具体来说，该项目将调查锥体感光器突触的反馈效应是由电或化学机制驱动的，还是两者兼而有之。该项目对视网膜电路的新见解将影响健康和医学领域的研究，包括生物医学工程、图像处理、视觉心理物理学和药理学。该项目所产生的建模和计算技术将为应用数学和工程领域的其他问题提供有效和创新的方法。

The retina is part of the central nervous system and an ideal region to study information processing in the brain. It is accessible, well documented, and studied by researchers spanning the clinical, experimental, and theoretical sciences. Image processing in the retina begins in the outer plexiform layer, where bipolar, horizontal, and photoreceptor cells interact. The goal of this research is to mathematically model, in detail, the subcircuits of the outer plexiform layer, capturing spatio-temporal dynamics on two spatial scales: the scale of an individual synapse and the scale of the receptive field. The availability of electrophysiological, anatomical, and molecular biological data provides a rare opportunity to develop a complex multiscale model for the system. Mathematical modeling will be used to discover the functional impact of various circuitry elements, much the way an experimentalist does with selective pharmacological agents, except that not all the agents that are needed actually exist. A primary objective is to explore two competing hypotheses for explaining synaptic feedback effects that are observed experimentally in the outer plexiform layer of the retina. Specifically, the project will investigate if feedback effects in the cone photoreceptor's synapse are driven by electrical or chemical mechanisms, or both. New insights into the understanding of retinal circuitry from this project will impact research in health and medicine, including biomedical engineering, image processing, visual psychophysics, and pharmacology. The modeling and computational techniques resulting from this project will provide efficient and innovative approaches for other problem areas in applied mathematics and engineering.