Conference on Cognitive Computational Neuroscience (CCN): September 2018, Philadelphia, PA

该项目将为认知计算神经科学会议(CCN)提供为期三年的支持。这次会议为神经学家提供了一年一度的科学会议，他们的目标是开发通过计算定义的大脑信息处理模型，解释大脑活动和行为的丰富测量。从历史上看，不同的学科实现了这些目标的子集：认知科学在认知层面开发了计算模型；计算神经科学在较低水平开发了神经生物学上可信的计算模型；认知神经科学将过程映射到大脑区域；人工智能开发了合成系统。CCN的独特之处在于它专注于这些领域之间的交叉。除了推进研究，CCN还寻求促进人工智能中越来越多的生物启发硬件和软件的商业使用，并成为广泛影响教育和社会的工具。CCN的一个特别重点是通过发言机会提高来自代表性不足人群的妇女和科学家的能见度。这项奖励将部分资助用于这一目的的旅费赠款。会议还将包括实践教程，这些教程的材料将传播到各种大学课程。该奖项将支持教程和讲座的视频录制。这些录音将在网站上公布，以增加会议对广大社区和无法出席者的更广泛影响。神经科学的一个中心目标是了解大量神经元是如何引发复杂行为的。今天，各个领域的进步为取得根本的概念突破提供了切实的可能性。现代神经记录技术现在提供了以前所未有的分辨率和规模观察神经活动的机会。与此同时，认知科学的研究在识别可能作为人类认知基础的计算原理方面变得越来越复杂，机器学习和人工智能在构建模型以自主解决复杂认知任务方面取得了长足进步。然而，这些不同学科之间的互动仍然很少。这次新的会议可能会刺激统一的框架，充分实现这些个人进步的跨学科潜力。具体地说，CCN的目标是创建和培育一个社区，该社区将开发具有几个关键功能的大脑信息处理模型。这些模型应该(1)完全通过计算定义并在计算机模拟中实现；(2)在神经生物学上是可信的；(3)解释大脑活动的测量(并随着时空分辨率和尺度的提高而继续这样做)；(4)解释自然刺激和任务背景下的行为；以及(5)执行智能的壮举，如识别、内部建模和表示环境、决策、规划、动作和运动控制。这样的模式目前还不存在，如果没有极大的跨学科参与，就不太可能出现。这一奖项反映了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。

This project will provide three-years of support for the Conference on Cognitive Computational Neuroscience (CCN). This conference provides an annual scientific meeting for neuroscientists whose goal is to develop computationally defined models of brain information processing that explain rich measurements of brain activity and behavior. Historically, different disciplines have met subsets of these goals: Cognitive science has developed computational models at the cognitive level; computational neuroscience has developed neurobiologically plausible computational models at lower levels; cognitive neuroscience has mapped processes onto brain regions; and artificial intelligence has developed synthetic systems. CCN is unique in its focus on the intersection between these fields. In addition to advancing research, CCN seeks to contribute to the growing commercial use of biologically inspired hardware and software in Artificial Intelligence as well as being a vehicle for broadly impacting education and society. One particular focus of CCN is increasing the visibility of women and scientists from underrepresented populations via speaking opportunities. This award will partially support travel grants for this purpose. The conference will also include hands-on tutorials, and materials from these will propagate to various university curricula. The award will support video recordings of the tutorials and talks. These recordings will be made publicly available on the website to increase the broader impact of the conference to the wider community and those unable to attend. A central goal of neuroscience is to understand how vast populations of neurons give rise to complex behavior. Today, advances in various domains offer tangible possibilities to make fundamental conceptual breakthroughs. Modern neural recording technologies now provide opportunities to observe neural activity at unprecedented resolution and scale. At the same time, research in cognitive science has become increasingly sophisticated in identifying computational principles that may serve as the basis for human cognition, and machine learning and artificial intelligence have made great strides in building models to autonomously solve complex cognitive tasks. However, interactions among these distinct disciplines remain rare. This new conference may stimulate unifying frameworks that fully realize the cross-disciplinary potential of these individual advances. Concretely, the goal of CCN is to create and foster a community that will develop models of brain information processing with several key features. These models should (1) be fully computationally defined and implemented in computer simulations; (2) be neurobiologically plausible; (3) explain measurements of brain activity (and continue to do so as spatiotemporal resolution and scale improve); (4) explain behavior in the context of naturalistic stimuli and tasks; and (5) perform feats of intelligence such as recognition, internal modelling and representation of the environment, decision-making, planning, action, and motor control. Such models currently do not exist and are unlikely to emerge without greatly improved cross-disciplinary engagement.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.