CRCNS:Proto-object based perceptual organization in three dimensions

CRCNS：基于原型对象的三维感知组织

• 批准号: 9336905
• 负责人: Ralph Etienne-Cummings
• 金额: $ 37.44万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9336905
• 关键词: Address; Agreement; Area; Attention; Biological; Biological Sciences; Biotechnology; Brain; CCL7 gene; Cells; Code; Cognition; Cognitive; Collaborations; Communication Research; Computer Simulation; Development; Dimensions; Educational workshop; Elements; Engineering; Faculty; Feedback; Fire - disasters; Goals; Grouping; Human; Image; Income; Individual; Interdisciplinary Study; Lead; Link; Mathematics; Mentors; Minority-Serving Institution; Modeling; Neural Network Simulation; Neurons; Participant; Pattern; Perception; Population; Primates; Recording of previous events; Recurrence; Research; Research Ethics; Research Project Grants; Retinal; Role; Rotation; STEM research; Science; Sensory; Shapes; Signal Transduction; Site; Speed; Structure; Student recruitment; Students; System; Technology; Testing; Theoretical Studies; Time; Training; Visual; Visual system structure; Work; awake; base; cognitive process; community college; experimental study; graduate student; high school; insight; member; models and simulation; neuronal circuitry; nonhuman primate; object recognition; perceptual organization; programs; relating to nervous system; response; selective attention; simulation; skills; student mentoring; success; tool; two-dimensional; undergraduate student; visual information

The visual brain infers a three-dimensional world from twodimensional images and organizes the visual information in terms of objects in three-dimensional space, representing even objects that are partially occluded and appear fragmented in the retinal image. This organization is the basis for attentive selection, action planning and object recognition. A combination of experimental and theoretical studies together with model implementations in neuromorphic hardware will be used to elucidate the interface between visual feature representations and attentive cognitive processes. Previous findings on the neural coding of figure-ground structure can be understood in terms of grouping mechanisms that structure the incoming sensory information as proto-objects (objects as defined by the system at this stage). The grouping mechanisms also provide handles for top-down mechanisms to address and select object-related information. The proposed work will explain how neuronal circuitry organizes spatially disconnected visual features into perceptual objects. How is this implemented neurally to lead to a coherent representation? Detailed computational models of the underlying circuitry will be developed, both as standard numerical simulations and in fast, neuromorphic hardware, and then tested by multiple single-cell recordings in awake non-human primates. Specifically, while prior studies examined spike time correlations indiscriminately in all neurons, our recent studies differentiated neurons according to their role in the grouping circuits. The grouping hypothesis predicts elevated synchrony only in pairs of neurons that belong to the same grouping circuit, but not in other pairs. These model predictions were confirmed in a recent study which showed that spike-spike correlation functions are in qualitative agreement with the idea that perceptual grouping is implemented by feedback from populations of dedicated grouping cells. Quantitative understanding requires the development of explicit spiking models, which is one of the main foci of this proposal. Models will be implemented on neuromorphic spiking hardware since the complexity of the cortical circuitry makes realistic model simulations on CPU/GPU system impossible. Predictions of integrate-and-fire type models of this circuitry will be compared with rate and synchrony observed in our recordings and deviates used to fine-tune the models. We will pursue the educational and broader impacts aims on five fronts. 1) Students will be crosstrained and mentored in biological, mathematical and engineering sciences, which will lead to graduates with unique skill sets. 2) We will contribute to the development of the nascent neuromorphic engineering field, providing new research problems that can benefit from the crosstraining and collaboration. We plan to participate in the NSF sponsored Telluride Neuromorphic Cognition Engineering and Capo Caccia Neuromorphic Cognitive Engineering Workshops for this purpose. 3)We will provide an opportunity for undergraduate students to participate in the research as part of our Site REU (managed by one of the PIs). They are trained in communications, research ethics and project management, which are crucial for success in todays biotechnology and bioscience work and market place. 4) We currently host students from local high-schools who conduct STEM research practicum rotations in our labs. This project will provide a perfect venue for the rotators to get exposed and mentored on multi-disciplinary research problems. We will use a tiered mentoring structure, where undergraduates mentor K-12 rotators, graduate students mentor undergraduates, and faculty members mentor all participants. 5) Our student recruitment plans will build on our current partnerships with MARC, LSAMP, McNair, SWE, SHPE and other similar programs and minority-serving institutions and local community colleges, to help develop a pipeline of qualified, diverse individuals who will contribute to the workforce in the area of STEM.

视觉脑从二维世界推断出三维世界 图像和组织的视觉信息在三维物体方面， 空间，表示即使是部分被遮挡的物体，在视网膜中也会出现碎片 形象这种组织是仔细选择、行动规划和物体识别的基础。一 实验和理论研究的结合，以及模型的实施， 神经形态硬件将被用来阐明视觉特征表征之间的接口 和专注的认知过程。先前关于图形-背景结构的神经编码的研究结果可以 将输入的感觉信息结构化为 proto-objects（原型对象）：系统在此阶段定义的对象。分组机制还提供 用于自顶向下机制的句柄，用于处理和选择对象相关信息。拟议工作 将解释神经元回路如何将空间上断开的视觉特征组织成感知的 对象这是如何实现神经导致一个连贯的表示？详细计算 将开发底层电路的模型，作为标准数值模拟和快速， 神经形态硬件，然后在清醒的非人类中通过多个单细胞记录进行测试 灵长类动物具体来说，虽然之前的研究不加区别地检查了所有人的尖峰时间相关性， 神经元，我们最近的研究根据它们在分组回路中的作用区分神经元。的 分组假说预测只有在属于同一神经元的神经元对中才能提高同步性。 分组电路，但不是在其他对。这些模型预测在最近的一项研究中得到了证实， 表明，尖峰-尖峰相关函数与感知的想法定性一致， 通过来自专用分组小区群的反馈来实现分组。定量 理解需要发展明确的尖峰模型，这是本研究的主要焦点之一。 提议模型将在神经形态尖峰硬件上实现，因为 皮质电路使得在CPU/GPU系统上进行逼真的模型模拟是不可能的。预测 集成和消防型模型的这一电路将比较率和同步观察我们的 记录和偏差用于微调模型。 我们将在五个方面追求教育和更广泛的影响目标。1)学生们将被交叉训练 并在生物，数学和工程科学方面进行指导，这将导致 拥有独特技能的毕业生。2)我们将为新生神经形态的发展做出贡献。 工程领域，提供新的研究问题，可以受益于交叉应变和 协作我们计划参加NSF赞助的碲化物神经形态认知 工程和卡波卡恰神经形态认知工程研讨会为此目的。3）我们 将为本科生提供一个参与研究的机会，作为我们网站的一部分 REU（由其中一个PI管理）。他们接受通信，研究道德和项目培训 管理，这是在今天的生物技术和生物科学工作和市场的成功至关重要 地方4)我们目前接待来自当地高中的学生，他们进行STEM研究实习 在我们的实验室里旋转。这个项目将提供一个完美的场地，为旋转得到曝光， 指导多学科研究问题。我们将采用分层指导结构， 本科生指导K-12旋转器，研究生指导本科生，以及教职员工 指导所有参与者。5)我们的学生招聘计划将建立在我们目前的合作伙伴关系， MARC、LSAMP、McNair、SWE、SHPE和其他类似方案以及为少数群体服务的机构， 当地社区学院，以帮助开发合格的，多样化的个人谁将有助于管道 to the workforce劳动力in the area区of STEM干.