AccelNet: Accelerating Research on Neuromorphic Perception, Action, and Cognition

AccelNet：加速神经形态感知、行动和认知的研究

• 批准号: 2020624
• 负责人: Cornelia Fermuller
• 金额: $ 175.41万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2020624&HistoricalAwards=false
• 关键词: AccelNet; Accelerating; Research; Neuromorphic; Perception

Artificial intelligence is becoming ubiquitous in modern life. To build systems under the current paradigm, large amounts of energy are required for computing and sensing. This causes environmental problems, pollution, and challenges for small-sized systems, as well as privacy issues. The field of neuromorphic science and technology offers an alternative by seeking to understand principles of biological brains and build on their basis artificial systems using low-power hardware and software solutions. While its advantages have been demonstrated, further advances are necessary and will require common computational tools and principled experimental approaches. This AccelNet project, NeuroPacNet, links international experts in neuromorphic engineering with computational neuroscientists, roboticists, control theorists, and researchers of perception from seven global networks to set the foundations for building systems that can robustly process real-world signals in time and adapt to changes. This network of networks will facilitate the development of new methods and approaches for intelligent system design and prepare the next generation of leaders in neuromorphic science and technology. As different industries adopt neuromorphic hardware, society will have access to new applications, such as in computing on cell phones, neuroprostheses, intelligent hearing aids, and smart sensory systems with predictive capabilities.NeuroPacNet will advance computational research on modeling the integration of perception, action, and cognition. The network of network will coordinate across those research thrusts and develop new approaches grounded in theoretical neuroscience for sensorimotor control, motor learning, event-based computations, and learning in spiking neural networks. NeuroPacNet will also include robotics research in the areas of drone navigation and human activity understanding for humanoids and will address social and ethical issues in humanoid robotics. The network of networks will use innovative hardware design and mixed signals computational systems to address computation for emerging and unconventional technologies. International collaboration and knowledge exchange will include an immersive research exchange program providing scholarships to students and postdoctoral researchers, an annual workshop to discuss common issues and concerns in a stimulating environment and to engage in hands-on projects, meetings to define challenges, opportunities, and actions to accelerate progress, and competitions with two challenges to be solved by teams of researchers and students. An interactive project website will become a portal for archived webinar talks, tools, and data.The Accelerating Research through International Network-to-Network Collaborations (AccelNet) program is designed to accelerate the process of scientific discovery and prepare the next generation of U.S. researchers for multiteam international collaborations. The AccelNet program supports strategic linkages among U.S. research networks and complementary networks abroad that will leverage research and educational resources to tackle grand scientific challenges that require significant coordinated international efforts. Co-funding for this project is provided by the Directorate for Social, Behavioral, and Economic Sciences.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.

人工智能在现代生活中变得无处不在。 为了在当前范式下构建系统，需要大量的能量来进行计算和传感。这会导致环境问题、污染和小型系统的挑战，以及隐私问题。神经形态科学和技术领域通过寻求理解生物大脑的原理并在其基础上使用低功耗硬件和软件解决方案构建人工系统提供了另一种选择。虽然它的优点已经得到证明，进一步的进步是必要的，将需要共同的计算工具和原则性的实验方法。NeuroPacNet项目将神经形态工程领域的国际专家与来自七个全球网络的计算神经科学家、机器人专家、控制理论家和感知研究人员联系起来，为构建能够及时稳健地处理现实世界信号并适应变化的系统奠定基础。这个网络的网络将促进智能系统设计的新方法和方法的发展，并为神经形态科学和技术的下一代领导者做好准备。随着不同行业采用神经形态硬件，社会将有机会获得新的应用，如在手机上的计算，神经假体，智能助听器，和具有预测能力的智能传感系统。NeuroPacNet将推进对感知，动作和认知的集成建模的计算研究。网络的网络将协调这些研究重点，并开发基于理论神经科学的新方法，用于感觉运动控制，运动学习，基于事件的计算和尖峰神经网络的学习。 NeuroPacNet还将包括无人机导航和人形机器人人类活动理解领域的机器人研究，并将解决人形机器人的社会和伦理问题。网络的网络将使用创新的硬件设计和混合信号计算系统来解决新兴和非传统技术的计算问题。 国际合作和知识交流将包括一个沉浸式的研究交流计划，为学生和博士后研究人员提供奖学金，每年举办一次研讨会，在一个刺激的环境中讨论共同的问题和关注点，并参与实践项目，会议定义挑战，机遇和行动，以加速进展，以及由研究人员和学生团队解决两个挑战的比赛。 一个互动的项目网站将成为存档的网络研讨会演讲，工具和数据的门户网站。通过国际网络到网络合作加速研究（CNET）计划旨在加速科学发现的过程，并为下一代美国研究人员做好准备多团队国际合作。 该计划支持美国研究网络和海外互补网络之间的战略联系，这些网络将利用研究和教育资源来应对需要重大协调国际努力的重大科学挑战。该项目的共同资助由社会、行为和经济科学理事会提供。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Performance on stochastic figure-ground perception varies with individual differences in speech-in-noise recognition and working memory capacity.

• DOI: 10.1121/10.0016756
• 发表时间: 2023-01
• 期刊: The Journal of the Acoustical Society of America
• 作者: Michael A. Johns;Regina C. Calloway;Ian Phillips;Valerie Karuzis;Kelsey Dutta;E. Smith;S. Shamma

RetinoSim: an Event-based Data Synthesis Tool for Neuromorphic Vision Architecture Exploration

RetinoSim：用于神经形态视觉架构探索的基于事件的数据合成工具

• DOI: 10.1145/3546790.3546805
• 发表时间: 2022
• 期刊: Proceedings of the International Conference on Neuromorphic Systems
• 作者: Sengupta, Jonah;Liu, Susan;Andreou, Andreas
• 通讯作者: Andreou, Andreas

Gluing Neural Networks Symbolically Through Hyperdimensional Computing

通过超维计算以符号方式粘合神经网络

• 发表时间: 2022
• 期刊: Proceedings of International Joint Conference on Neural Networks
• 作者: Sutor, peter;Yuan, Dehao;Summer-Stay, Douglas;Fermuller, Cornelia;Aloimonos, Yiannis
• 通讯作者: Aloimonos, Yiannis

Investigating the cortical tracking of speech and music with sung speech

研究歌唱语音对语音和音乐的皮层追踪

• DOI: 10.21437/interspeech.2023-1949
• 发表时间: 2023
• 期刊: ISCA
• 作者: Cantisani, Giorgia;Chalehchaleh, Amirhossein;Di Liberto, Giovanni;Shamma, Shihab
• 通讯作者: Shamma, Shihab

Dynamic Analysis of Higher-Order Coordination in Neuronal Assemblies via De-Sparsified Orthogonal Matching Pursuit

通过去稀疏正交匹配追踪对神经元组件中的高阶协调进行动态分析

• 发表时间: 2021
• 期刊: Advances in Neural Information Processing Systems 34 (NeurIPS 2021
• 作者: Mukherjee, Shoutik;Babadi, Behtash
• 通讯作者: Babadi, Behtash