US-Belgium workshop: Atomic Switch Networks for Neuromorphic Reservoir Computing; Late Fall-2015/Early Spring 2016; University of Ghent-Belgium.

NSF CNIC提案#1444214US-比利时研讨会：用于神经形态储存库计算的原子开关网络第1部分：人脑在复杂且往往嘈杂和容易出错的环境中在图像、运动跟踪、声音识别和决策等多项任务中的表现优于数字计算机。数字计算机本质上不适合执行自主控制(导航、机器人)、模式识别(语音、视觉)或预测(天气、金融市场)等任务。另一方面，一种受生物启发的计算方法被称为水库计算(RC)，它展示了高效执行复杂任务的潜力。为了进行RC，一种名为原子开关网络(ASN)的新开发的硬件平台使用纳米技术创建了数十亿个合成突触，其连接方式类似于人脑中新皮质的方式。一个有效的RC-ASN系统的实施需要比利时根特大学RC方法领域公认的世界领导者和开发ASN设备的加州大学洛杉矶分校团队的合作专业知识。加州大学洛杉矶分校提出了一个参与者驱动的研讨会，包括受邀的讲座、硬件和软件的实践教程以及分组讨论，目的是加速这种新形式的计算机系统的实现。该研讨会将为5名美国学生和早期职业研究人员提供国际研究机会，同时也促进团队建设技能、学生驱动的合作和文化交流。通过结合纳米科学、神经科学和机器学习的概念，该提案寻求利用各方的集体专业知识来推进这一下一代认知技术。这项研究的成功成果也将使大脑倡议受益，这是美国的一个优先研究领域。第二部分：原子开关网络(ASN)是一类独特的生物灵感计算体系结构，旨在通过功能纳米级材料的集体相互作用来产生复杂的动态系统。这些自组织设备保留了其组成的阻性开关元件的固有记忆能力，同时产生了一类通常与生物认知相关的紧急行为。它们对以分布式方式处理和存储的输入信息进行非线性转换的能力，产生了可用作计算平台基础的动态时空活动模式。最近对ASN的运行动态进行建模、模拟和测量的努力指向水库计算(RC)的硬件实现，RC是一个新兴领域，研究复杂生物启发系统的计算能力，以解决数据不断变化、不完整或容易出错的问题，这表明有必要与机器学习领域的专家建立合作关系。拟议研讨会参与者的综合专业知识将侧重于对如何最好地利用ASN设备来克服RC范例中实时信号处理的当前操作限制(如速度、网络密度和可扩展性)的关键评估。除了讲课和讨论部分之外，来自美国和欧盟的参与者提供的教程研讨会将用于传播/演示(1)ASN的建模/仿真、(2)ASN的物理实现以及(3)其他硬件系统(忆阻器、光电子学等)的物理实现的当前状态。目标成果包括确定国家科学基金会现有核心计划内近期合作和后续资金的具体领域。这一新的合作将提供一个巨大的机会来探索世界领先的RC研究产生的最佳情况，并为基于硬件的RC提供一个潜在的开创性平台，为实时信息处理和计算中的新方法做出贡献。

NSF CNIC proposal #1444214US-Belgium Workshop: Atomic Switch Networks for Neuromorphic Reservoir ComputingPart 1:The human brain outperforms digital computers in a number of tasks such as image, motion tracking and sound recognition and decision making in complex and often noisy and error prone environments. Digital computers are by their nature poorly-suited for tasks such as autonomous control (navigation, robotics), pattern recognition (speech, vision) or prediction (weather, financial markets). A biologically inspired approach to computing, called Reservoir Computing (RC), on the other hand, has demonstrated the potential to perform complex tasks efficiently. To perform RC, a newly developed hardware platform called the Atomic Switch Network (ASN) uses nanotechnology to create billions of synthetic synapses wired up in a fashion similar to that of the neocortex in the human brain. The implementation of a functioning RC-ASN system requires the collaborative expertise from recognized world leaders in RC methods at Ghent University, Belgium and the UCLA team who have developed the ASN device. UCLA has proposed a participant-driven workshop involving invited lectures, hands-on tutorials with hardware and software and breakout discussions with the goal to accelerate realization of this new form of computers system. This workshop will provide international research opportunities to 5 US students and early career researchers, while also promoting team-building skills, student-driven collaboration, and cultural exchange. By combining concepts from nanoscience, neuroscience, and machine learning, this proposal seeks to leverage the collective expertise of all parties to advance this next-generation cognitive technology. The successful outcomes of this research will also benefit the BRAIN Initiative, which is a priority research area of the U.S. Part 2:Atomic Switch Networks (ASN) are a unique class of biologically inspired computing architectures designed to produce a complex, dynamical system through the collective interactions of functional nanoscale materials. These self-organized devices retain the intrinsic memory capacity of their component resistive switching elements while generating a class of emergent behaviors commonly associated with biological cognition. Their capacity for non-linear transformation of input information, which is processed and stored in a distributed fashion, generates patterns of dynamic spatiotemporal activity that can be used as the basis for a computational platform. Recent efforts to model, simulate, and measure the operational dynamics of ASNs toward hardware implementation of reservoir computing (RC), a burgeoning field that investigates the computational aptitude of complex biologically inspired systems to address problems in which data is constantly changing, incomplete, or subject to errors, indicate the necessity to establish a collaboration with experts in the field of machine learning. The combined expertise of proposed workshop participants will focus on a critical assessment of how to best utilize ASN devices to overcome current operational limits on real-time signal processing in the RC paradigm such as speed, network density, and scalability. Beyond lectures and discussion sections, tutorial workshops delivered by participants from the US and EU will be utilized to disseminate/demonstrate the current status of (1) modeling/simulation of ASNs, (2) physical implementations of ASNs, and (3) physical implementations of other hardware systems (memristors, optoelectronics, etc.). Targeted outcomes include identification of specific areas for near-term collaboration and follow-on funding within existing Core programs at the NSF. This new collaboration will provide a tremendous opportunity to explore the best-case scenario resulting from the world's leading RC research with a potentially groundbreaking platform for hardware-based RC to contribute to novel approaches in real-time information processing and computation.