CAREER:Deciphering the Neural Code From Perception To Cognition

人类对物体的识别具有高度选择性、鲁棒性和快速性。我们的识别机制的一个显著特性是，即使经过旋转、平移、缩放、遮挡和杂乱等变换，也有可能选择性地识别物体。人类大脑是如何完成识别的还不太清楚。对于感觉信息从受体到皮层初始阶段的处理，我们所知道的要比感知数据随后转化为认知的过程更多。与此同时，在过去的几十年里，在建立更精确、更复杂的计算机和设备来捕获感官信息方面取得了重大进展，但在开发算法和硬件来自动解释感官数据方面进展缓慢。在美国国家科学基金会的支持下，加布里埃尔·克里曼博士正在进行一项研究，其目的是阐明大脑皮层将输入信息转化为认知功能和行为的计算步骤和算法。这项研究集中在认知经验的一个特定方面，即视觉处理背后的神经元机制和回路。虽然视觉只是认知的许多方面之一，但从研究视觉皮层中吸取的经验教训最终也可以帮助描述皮层功能的其他方面，并为研究认知的其他具有挑战性的方面铺平道路。为了研究视觉认知，克雷曼博士利用了一个难得的机会，直接从癫痫患者的大脑中以高空间和时间分辨率刺激和记录电活动。该研究调查了视觉认知与传入的感觉处理分离的任务，以分离涉及识别的认知操作。关于生物神经回路功能的发现将应用于开发生物物理启发的鲁棒机器视觉算法。视觉识别对于大多数日常任务都是必不可少的，包括导航、阅读、识别物体、面孔和情绪。通过进一步加深我们对感知信息向认知转化的理解，该研究有助于实现两个广泛的目标：(1)通过开发脑机接口，帮助缓解涉及认知障碍的挑战性条件；(2)应用神经元回路知识开发从感官数据中提取认知信息的计算算法。建立一个快速、健壮、可靠的人工视觉系统将对许多科学和工程领域产生深远的影响，包括模式识别、监视和安全、自动导航、临床图像分析等。这些科学和工程上的进步可以反过来转化为工业伙伴关系感兴趣的重要现实应用。为了实现这些目标，克雷曼博士研究了能够解决视觉识别挑战的最佳系统——人类大脑。理解视觉系统依赖于许多技能，从计算机科学到工程学、物理学、神经科学到心理学。该项目很好地培养了一代多学科学生，他们可以建立在基础科学知识的基础上，并将这些知识应用于具有挑战性的生物学问题。

Human beings can recognize objects in a highly selective, robust and fast manner. One of the remarkable properties of our recognition machinery is the possibility to selectively recognize objects even after transformations such as rotation, translation, scaling, occlusion and clutter. How the human brain accomplishes recognition is not well understood. More is known about processing of sensory information from receptors to the initial stages in cortex than about the subsequent transformation of perceptual data into cognition. In parallel, over the last decades, major progress has been made in building ever more accurate and sophisticated computers and devices to capture sensory information but progress has been slower in terms of developing algorithms and hardware to automatically interpret the sensory data. With support from the National Science Foundation, Dr. Gabriel Krieman is undertaking research whose aim is to elucidate the computational steps and algorithms implemented by the cerebral cortex to transform incoming inputs into cognitive functions and behavior. The research focuses on one particular aspect of cognitive experience, the neuronal mechanisms and circuits that underlie visual processing. While vision is only one of many aspects of cognition, lessons learnt from studying visual cortex can also eventually help describe other aspects of cortical function and can pave the way for research on other challenging aspects of cognition. To investigate visual cognition, Dr. Kreiman takes advantage of a rare opportunity to both stimulate and record electrical activity at high spatial and temporal resolution directly from the human brain in epilepsy patients. The study investigates tasks where visual cognition is dissociated from the incoming sensory processing in order to isolate the cognitive operations involved in recognition. The discoveries about the function of biological neural circuits will be applied to develop biophysically-inspired robust machine vision algorithms. Visual recognition is essential for most everyday tasks including navigating, reading, and identifying objects, faces and emotions. By furthering our understanding of the transformation of perceptual information into cognition, the study is contributing to two broad goals: (1) Helping to alleviate the challenging conditions that involve cognitive disorders through the development of brain-machine interfaces; and (2) Applying knowledge about neuronal circuits to develop computational algorithms to extract cognitive information from sensory data. Building a fast, robust and reliable artificial vision system would have profound repercussions in many areas of science and engineering including pattern recognition, surveillance and security, automatic navigation, clinical image analysis and others. These scientific and engineering advances could in turn translate into important real-world applications of interest for industrial partnerships. Dr. Kreiman pursues these goals by studying the best possible system that can solve visual recognition challenges, the human brain. Understanding the visual system relies on many skills ranging from computer science to engineering to physics to neuroscience to psychology. The project serves well to train a generation of multidisciplinary students who can build on the fundamental science knowledge and apply this knowledge to challenging biological problems.