Collaborative Research: Conference on Cognitive Computational Neuroscience (CCN)

认知计算神经科学（CCN）是神经科学家的年度科学会议，研究复杂行为背后的神经计算。目标是开发计算定义的大脑信息处理模型，以解释大脑活动和行为的丰富测量。这些模型最终将不得不在自然条件下完成诸如感知、内部建模和环境记忆、决策、计划、行动和运动控制等智能壮举。从历史上看，不同的学科已经满足了这些目标的子集。认知科学在认知层面发展了计算模型来解释复杂行为的各个方面。计算神经科学已经发展出神经生物学上合理的计算模型来解释神经元对感觉刺激和某些低维决策、记忆和控制过程的反应。认知神经科学已经将广泛的认知过程映射到大脑区域。人工智能已经开发出能够执行智能壮举的模型。社区现在必须把拼图的碎片放在一起，而CCN的独特之处在于它专注于这些领域之间的交叉点。CCN不仅被设想为推进研究的引擎，而且是对教育和社会产生更广泛影响的工具。正如最近大型企业收购由神经科学家创立的人工智能初创公司的趋势所证明的那样，电子和软件开发的生物灵感是一个日益增长的趋势，具有重大的经济意义。在早期阶段，CCN更广泛的影响重点将是通过演讲机会和旅行奖励，提高来自代表性不足人群的妇女和科学家的知名度。此外，指导委员会和咨询委员会中女性部分的妇女代表人数超过了有关领域的典型代表人数，但在资格方面没有妥协。会议将包括实践教程，其中的材料将传播到各种大学课程中。神经科学的一个中心目标是了解大量神经元是如何产生复杂行为的。今天，各个领域的进步为实现基本概念突破提供了切实的可能性。从实验的角度来看，神经记录技术，如高分辨率功能磁共振成像、密集记录阵列、脑磁图（MEG）和钙成像，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巨大进展。然而，这些不同学科之间的相互作用仍然很少。这次新的会议可能会激发统一的框架，充分认识到这些个别进展的跨学科潜力。更具体地说，CCN的目标是创建和培养一个社区，该社区将开发具有几个关键特征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)在神经生物学上合理；(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的行为；(5)执行诸如识别、内部建模和环境表示、决策、计划、行动和运动控制等智能壮举。这样的模型目前还不存在，如果没有跨学科合作的极大改善，也不太可能出现。

Cognitive Computational Neuroscience (CCN) is an annual scientific meeting for neuroscientists characterizing the neural computations that underlie complex behavior. The goal is to develop computationally defined models of brain information processing that explain rich measurements of brain activity and behavior. Such models will ultimately have to perform feats of intelligence such as perception, internal modelling and memory of the environment, decision-making, planning, action, and motor control under naturalistic conditions. Historically, different disciplines have met subsets of these goals. Cognitive science has developed computational models at the cognitive level to explain aspects of complex behavior. Computational neuroscience has developed neurobiologically plausible computational models to explain neuronal responses to sensory stimuli and certain low-dimensional decision, memory, and control processes. Cognitive neuroscience has mapped a broad range of cognitive processes onto brain regions. Artificial intelligence has developed models that perform feats of intelligence. The community must now put the pieces of the puzzle together, and CCN is unique in its focus on the intersection between these fields. CCN is envisioned not only as an engine for advancing research, but as a vehicle for making broader impacts on education and society. As evidenced by the recent trend of major corporate acquisitions of AI startups founded by neuroscientists, biological inspiration for electronics and software development is a growing trend with significant economic implications. In its early stages, the broader impact focus of CCN will be on increasing the visibility of women and scientists from underrepresented populations via speaking opportunities and travel awards. In addition, representation on women on the female fractions on the steering and advisory committees exceed those typical in relevant fields, without compromise in qualifications. Conferences will include hands-on tutorials, and materials from these will propagate to various university curricula.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. From an experimental point of view, neural recording technologies, such as high-resolution fMRI, dense recording arrays, magnetoencephalography (MEG), and calcium imaging, 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. In more concrete terms, 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 for 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.