Synthetic neural networks for neuromorphic applications

用于神经形态应用的合成神经网络

• 批准号: RGPIN-2020-03937
• 负责人: GomesdaRocha, Claudia
• 金额: $ 2.04万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716729
• 关键词: Synthetic; neural; networks; neuromorphic; applications

The human brain contains billions of neurons that exchange signals through synapses. We have unique intellectual abilities that outstrip the fastest supercomputers; e.g. high-level pattern recognition, energy-efficiency, and the ultimate skill of learning from experience. Such attributes have inspired the creation of so-called neuromorphic (brain-like) devices, highly connected electronic circuits that attempt to mimic the architectures present in the brain. Neurons and synapses form the primitive building blocks in biological neural systems. One of the biggest challenges in neural-inspired technologies is to find suitable building blocks that can emulate brain synapses in the synthetic realm. Moreover, we need to know how to integrate and to control these building blocks in order to meet the basic requirements of neuromorphic computing. These involve decentralized communication between the blocks, co-location of memory and processing, and multistate/analog memory response that supports learning and adaptation. My goal is to create a research program that will unveil new material concepts and building blocks for the development of cutting-edge neuromorphic devices. I will investigate how cognitive features emerge from nanoscale materials in which their electric conductivity is not static but changes with the amount of current/voltage set in their terminals. This is typically seen in memristive systems, nonvolatile memory materials whose resistance behaves as a dynamical quantity. I have structured an innovative computational platform that will model the memristive characteristics of self-assembled networks of nanoscale cognitive materials seen as promising candidates for neuromorphics. Examples of such a network are spaghetti-like structures made by randomly dispersed nanowires in which complex memristive phenomena take place in their wire-wire contact points. I target the theoretical description of emergent resistive mechanisms controlling the propagation and memorization of electrical signals throughout their disordered frame. Virtual circuit models of cognitive network materials integrated with electronic control systems will be built to simulate typical brain-functions, e.g. data recognition, memorization, and fault-tolerant processing. We will reveal optimal materials properties, circuit designs, and training protocols that will be tested in the laboratory of long-term collaborators that envision the fabrication of a proof-of-concept neuromorphic device inspired by the outcomes of our simulations. This program will enable young scientists to engage in a truly interdisciplinary environment connecting numerous fields, e.g. nanotechnology, computer science, and neuroscience; it will also place a Canadian institution (and Canada) as the knowledge exchange hub of a disruptive brain-inspired technology that will greatly impact artificial intelligence, a sector expected to be one of the leading economic drivers world-wide in the next decades.

人脑包含数十亿个神经元，它们通过突触交换信号。我们拥有超越最快超级计算机的独特智力；例如高级模式识别、能源效率以及从经验中学习的终极技能。这些属性激发了所谓的神经形态（类脑）设备的创建，这些设备是高度连接的电子电路，试图模仿大脑中存在的架构。神经元和突触形成生物神经系统的原始构件。神经启发技术的最大挑战之一是找到可以在合成领域模拟大脑突触的合适构建模块。此外，我们需要知道如何集成和控制这些构建块，以满足神经形态计算的基本要求。这些涉及块之间的分散通信、内存和处理的共置以及支持学习和适应的多状态/模拟内存响应。 我的目标是创建一个研究计划，揭示新的材料概念和用于开发尖端神经形态设备的构建模块。我将研究认知特征如何从纳米级材料中显现出来，其中纳米级材料的电导率不是静态的，而是随着其端子中设置的电流/电压的量而变化。这通常出现在忆阻系统、非易失性存储材料中，其电阻表现为动态量。我构建了一个创新的计算平台，该平台将模拟纳米级认知材料自组装网络的忆阻特性，纳米级认知材料被视为神经形态学的有希望的候选者。这种网络的例子是由随机分散的纳米线制成的意大利面条状结构，其中线与线的接触点发生复杂的忆阻现象。我的目标是对控制电信号在整个无序框架中的传播和记忆的新兴电阻机制的理论描述。将建立与电子控制系统集成的认知网络材料的虚拟电路模型来模拟典型的大脑功能，例如数据识别、记忆和容错处理。我们将揭示最佳的材料特性、电路设计和培训协议，这些协议将在长期合作者的实验室中进行测试，这些合作者设想在我们的模拟结果的启发下制造概念验证的神经形态设备。该计划将使年轻科学家能够参与连接众多领域的真正跨学科环境，例如纳米技术、计算机科学和神经科学；它还将把一个加拿大机构（和加拿大）作为颠覆性脑启发技术的知识交流中心，该技术将极大地影响人工智能，预计人工智能领域将在未来几十年成为全球主要经济驱动力之一。