CRCNS: Collaborative Research: Naturalistic computation and signaling by neural populations in the primate retina

CRCNS：协作研究：灵长类视网膜神经群的自然计算和信号传导

• 批准号: 1430239
• 负责人: Liam Paninski
• 金额: $ 41.46万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1430239&HistoricalAwards=false
• 关键词: CRCNS; Collaborative; Research; Naturalistic; computation

Vision begins in the retina, where light is converted into electrical signals, processed to extract and compress visual information, and transmitted through the optic nerve to the brain. Despite decades of research, a full understanding of these transformations remains incomplete. In particular, most studies have documented specific properties of the responses of single retinal cells in isolation, using specialized artificial visual stimuli. The research performed under this grant aims to develop a full, unified computational model of retinal processing, including spatial and temporal filtering, nonlinear transformations, and adaptation to local luminance and contrast, in complete populations of neurons. The model will be tested by comparing its predictions to data from large-scale multi-electrode recordings of primate retinal ganglion cells (RGCs), verifying that it can mimic known retinal responses, and critically, testing its ability to explain responses to natural visual images, including the effects of fixational and saccadic eye movements. The resulting model will provide a compact encapsulation of the "neural code" of the retina, which will serve as a substrate for understanding all subsequent visual processing in the brain. In addition, the model will provide an essential component in the development of high-acuity retinal prostheses for people blinded by diseases of photoreceptor degeneration. Finally, the model will offer a useful tool for the development and testing of new display technologies.The research has two main aims: (1) Develop and test a model of nonlinear subunits in RGC populations-- No current model captures the effects of nonlinear computations in a complete sensory neural circuit. The researchers will develop a model incorporating nonlinear subunits that captures the stimulus encoding properties of complete populations of RGCs at the resolution of photoreceptors, and will quantify the implications of these nonlinearities for encoding naturally-occurring visual stimuli. The researchers will develop methods to reliably fit the model to RGC responses to targeted stimuli that stringently constrain model structure, and verify model predictions in closed-loop experiments. (2) Incorporate adaptation; test model with targeted and naturalistic stimuli-- RGC responses adapt to luminance and stimulus contrast. No current model of the RGC population response incorporates adaptation with subunit nonlinearities, natural scenes, and eye movements. The researchers will incorporate adaptation in the model, fit the adaptive model using stochastic stimuli with varying mean and contrast, and test the model using stimuli that produce adaptation within and across subunits.

视觉开始于视网膜，在那里光被转换成电信号，被处理以提取和压缩视觉信息，并通过视神经传输到大脑。 尽管进行了数十年的研究，但对这些转变的全面理解仍然不完整。 特别是，大多数研究都记录了使用专门的人工视觉刺激的孤立单个视网膜细胞的反应的特定特性。 在该资助下进行的研究旨在开发一个完整的，统一的视网膜处理计算模型，包括空间和时间滤波，非线性变换，以及在完整的神经元群体中适应局部亮度和对比度。 该模型将通过将其预测与灵长类动物视网膜神经节细胞（RGC）的大规模多电极记录数据进行比较来进行测试，验证它可以模拟已知的视网膜反应，并严格测试其解释自然视觉图像反应的能力，包括固定和扫视眼球运动的影响。 由此产生的模型将提供视网膜“神经代码”的紧凑封装，这将作为理解大脑中所有后续视觉处理的基础。 此外，该模型将为因光感受器变性疾病而失明的人提供高视力视网膜假体的开发。 本研究的主要目的有两个：（1）建立和测试RGC群体中非线性亚基的模型--目前还没有一个模型能在一个完整的感觉神经回路中捕捉到非线性计算的影响。 研究人员将开发一种模型，该模型包含非线性亚基，该亚基以光感受器的分辨率捕获完整RGC群体的刺激编码特性，并将量化这些非线性对编码自然发生的视觉刺激的影响。研究人员将开发方法来可靠地将模型拟合到RGC对严格约束模型结构的目标刺激的反应，并在闭环实验中验证模型预测。 (2)结合适应;测试模型与有针对性的和自然的刺激- RGC反应适应亮度和刺激对比度。 目前没有RGC群体反应的模型将适应与亚基非线性，自然场景和眼球运动。研究人员将在模型中加入适应性，使用具有不同均值和对比度的随机刺激来拟合适应性模型，并使用在亚基内和跨亚基产生适应性的刺激来测试模型。