Research Coordination Network: Cognitive Functions in the Learning of Symbolic Signals & Systems

这个研究协调网络(RCN)的目标是促进对生物系统如何学习复杂的符号信号的理解，并创造具有类似能力的人工系统。通过定义一个共同的框架来描述这些信号及其在空间和时间上的变化，RCN将开发适用于包括语言、音乐、动作、感知和导航在内的广泛领域的方法和工具。RCN将建立在神经形态工程研究及其生物启发的低功率计算平台、传感器和信号处理开发的基础上。使用这些工具，RCN将专注于高级认知功能，以创建复杂的、受生物启发的系统，这些系统通过参与任务进行学习。该网络将把神经科学、认知科学、应用数学、计算机科学和工程学结合在一起，重点是机器学习和人工智能。网络成员将参加一年一度的为期三周的实践研讨会，该研讨会将开发和测试新工具和想法，鼓励新的协作，并教育学生独特的跨学科技能。RCN将促进参与研究人员之间的互动和合作项目，采用广泛的范式，具体处理三个方面：神经可塑性在学习符号系统中的作用；感觉-运动任务学习的基本适应机制；以及向现实世界应用的过渡，如自动语音和动态场景理解、神经形态硬件实现、认知计算算法和数据库获取。这些不同项目的具体例子包括评估学习和专业知识的脑过程模型；基于生理或抽象事件的算法，处理来自神经形态硬件的输入；以及开发用于信号解释和行动执行的软件和神经形态硬件。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The objective of this Research Coordination Network (RCN) is to advance understanding of how biological systems learn complex symbolic signals, and create artificial systems with similar capabilities. By defining a common framework to describe these signals, and their variability across space and time, the RCN will develop methods and tools applicable to a wide range of domains, including language, music, action, perception, and navigation. The RCN will build upon research in Neuromorphic Engineering and its development of bio-inspired, low-power computing platforms, sensors, and signal processing. Using these tools, the RCN will focus on high-level cognitive functions, to create complex, bio-inspired systems that learn through engagement in tasks. The network will bring together neuroscience, cognitive science, applied mathematics, computer science, and engineering, with emphasis on machine learning and artificial intelligence. Network members will participate in a yearly three-week, hands-on workshop, that will develop and test new tools and ideas, stimulate new collaborations, and educate students on unique interdisciplinary skills. The RCN will facilitate interactions and collaborative projects among participating researchers employing a wide range of paradigms that specifically deal with three thrusts: the role of neural plasticity for learning symbolic systems; the adaptive mechanisms underlying the learning of sensory-motor tasks; and transitioning to real-world applications such as automatic speech and dynamic scene understanding, neuromorphic hardware implementations, cognitive computational algorithms, and databases acquisition. Specific examples of such diverse projects include brain process models that assess learning and expertise; algorithms, based on physiological or abstract events, that process input from neuromorphic hardware; and development of software and neuromorphic hardware for signal interpretation and action execution.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.