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.

视觉大脑从二维中渗透了三维世界 图像并根据三维对象来组织视觉信息 空间，代表部分被遮住并在视网膜中碎片的物体 图像。该组织是专心选择，行动计划和对象识别的基础。一个 实验和理论研究以及模型实施的结合 神经形态硬件将用于阐明视觉特征表示之间的接口 和细心的认知过程。关于图形结构的神经编码的先前发现可以 从构造传入的感官信息的机制分组机制来理解 原始对象（该阶段系统定义的对象）。分组机制也提供 为自上而下的机制解决方案，以解决和选择与对象相关的信息。拟议的工作 将解释神经元电路如何将空间断开的视觉特征组织到感知中 对象。这是如何神经实施的，以导致连贯的代表？详细的计算 无论是标准的数值模拟还是快速，都将开发基础电路的模型 神经形态硬件，然后在醒着的非人类中通过多个单细胞录音进行测试 灵长类动物。具体而言，尽管先前的研究在所有人中都不明显地检查了峰值时间相关性 神经元，我们最近的研究根据神经元在分组电路中的作用区分了神经元。这 分组假设仅在属于相同的神经元对中预测同步升高 分组电路，但不在其他对中。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是定性的一致性 分组是通过专用分组单元组的反馈来实现的。定量 理解需要开发明确的尖峰模型，这是其中的主要重点之一 提议。模型将在神经形态尖峰硬件上实现，因为 皮层电路使CPU/GPU系统上的现实模型模拟不可能。预测 将将此电路的集成与火力类型模型与我们在我们的中观察到的速率和同步进行比较 记录和偏差用于微调模型。 我们将追求对五个战线的教育和更广泛的影响。 1）学生将被跨学历 并在生物，数学和工程科学领域进行指导，这将导致 具有独特技能的毕业生。 2）我们将有助于新生神经形态的发展 工程领域，提供新的研究问题，可以从交叉培训和 合作。我们计划参加NSF赞助的牙脲神经形态认知 为此，工程和Capo Caccia神经形态认知工程研讨会。 3）我们 作为我们网站的一部分，将为本科生提供参与研究的机会 REU（由PI之一管理）。他们接受了沟通，研究道德和项目的培训 管理，这对于今天的生物技术和生物科学工作和市场至关重要 地方。 4）我们目前接待来自当地高中生的学生，他们进行STEM研究实践 我们实验室的旋转。该项目将为旋转器提供公开和 对多学科研究问题进行了指导。我们将使用分层的指导结构 大学生K-12旋转者，研究生导师本科生和教职员工的本科生 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与 Marc，Lsamp，McNair，Swe，SHPE和其他类似计划以及少数派服务机构以及 当地社区大学，以帮助开发一条合格的，多样化的人的渠道 到STEM地区的劳动力。